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公开(公告)号：US20230281171A1
公开(公告)日：2023-09-07
申请号：US17653792
申请日：2022-03-07
Applicant: ADOBE INC.
Inventor： Prantik Bhowmick , Piyush Gupta , Vinayak Fakira Jadhav , Wissam Zeidan , Narasimha Bharadwaj , Vasanthi Holtcamp
IPC: G06F16/21 , G06F16/248 , G06F16/25
CPC classification number: G06F16/212 , G06F16/248 , G06F16/258
Abstract: Systems and methods for enterprise applications supported by common metadata repository are described. One or more aspects of the systems and methods include storing a plurality of entity schemas in a metadata repository, wherein each of the plurality of entity schemas corresponds to a different entity service from a plurality of entity services that interact with an application; storing a plurality of extension schemas in the metadata repository, wherein each of the plurality of extension schemas corresponds to a different extension service from a plurality of extension services utilized by the application; receiving, at the metadata repository from an extension service of the plurality of extension services, an entity schema request indicating an entity schema corresponding to an entity service of the plurality of entity services; and providing, from the metadata repository to the extension service, the entity schema in response to the entity schema request.
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公开(公告)号：US11606343B2
公开(公告)日：2023-03-14
申请号：US16402930
申请日：2019-05-03
Applicant: Adobe Inc.
Inventor： Piyush Gupta , Sourabh Goel , Mansukh Patidar
IPC: H04L9/40 , H04L67/02 , G06F40/109
Abstract: Systems and methods are disclosed for securely identifying a computing device via a web browser utilizing a customized digital font. In particular, in one or more embodiments, the disclosed systems and methods generate a customized digital font and install the customized digital font on a computing device. Moreover, the disclosed systems and methods utilize the customized digital font to identify the computing device. In particular, one or more embodiments include systems and methods that identify an element of a webpage rendered by the computing device utilizing the customized digital font and identify the client device based on the rendered element of the webpage.
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公开(公告)号：US20230021797A1
公开(公告)日：2023-01-26
申请号：US17383051
申请日：2021-07-22
Applicant: Adobe Inc.
Inventor： Piyush Gupta , Binit Kumar Sinha , Eunyee Koh , Fan Du , Gaurav Makkar , Silky Kedawat , Subrahmanya Kumar Giliyaru , Vasanthi Holtcamp , Nikhil Belsare
IPC: G06F16/33 , G06F40/40 , G06F16/338
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.
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公开(公告)号：US11295491B2
公开(公告)日：2022-04-05
申请号：US16850677
申请日：2020-04-16
Applicant: Adobe Inc.
Inventor： Nupur Kumari , Piyush Gupta , Akash Rupela , Siddarth R , Balaji Krishnamurthy
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.
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公开(公告)号：US11107115B2
公开(公告)日：2021-08-31
申请号：US16057743
申请日：2018-08-07
Applicant: Adobe Inc.
Inventor： Pankhri Singhai , Sundeep Parsa , Piyush Gupta , Nupur Kumari , Nikaash Puri , Mayank Singh , Eshita Shah , Balaji Krishnamurthy , Akash Rupela
IPC: G06Q30/00 , G06Q30/02 , G06N20/00 , G05B19/418
Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.
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Patent Agency Ranking
公开(公告)号：US10609434B2
公开(公告)日：2020-03-31
申请号：US16057729
申请日：2018-08-07
Applicant: Adobe Inc.
Inventor： Pankhri Singhai , Sundeep Parsa , Piyush Gupta , Nikaash Puri , Eshita Shah , Balaji Krishnamurthy , Nupur Kumari , Mayank Singh , Akash Rupela
IPC: H04N21/25 , H04N21/2668 , H04N21/258 , H04N21/475 , G06N20/00 , H04N21/81 , G06Q30/02
Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.
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公开(公告)号：US20200092593A1
公开(公告)日：2020-03-19
申请号：US16694612
申请日：2019-11-25
Applicant: Adobe Inc.
Inventor： Pankhri Singhai , Sundeep Parsa , Piyush Gupta , Nikaash Puri , Eshita Shah , Balaji Krishnamurthy , Nupur Kumari , Mayank Singh , Akash Rupela
IPC: H04N21/25 , H04N21/258 , G06Q30/02 , H04N21/475 , H04N21/81 , G06N20/00 , H04N21/2668
Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.
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公开(公告)号：US20190147369A1
公开(公告)日：2019-05-16
申请号：US15812991
申请日：2017-11-14
Applicant: Adobe Inc.
Inventor： Piyush Gupta , Sukriti Verma , Pratiksha Agarwal , Nikaash Puri , Balaji Krishnamurthy
Abstract: Rule determination for black-box machine-learning models (BBMLMs) is described. These rules are determined by an interpretation system to describe operation of a BBMLM to associate inputs to the BBMLM with observed outputs of the BBMLM and without knowledge of the logic used in operation by the BBMLM to make these associations. To determine these rules, the interpretation system initially generates a proxy black-box model to imitate the behavior of the BBMLM based solely on data indicative of the inputs and observed outputs—since the logic actually used is not available to the system. The interpretation system generates rules describing the operation of the BBMLM by combining conditions—identified based on output of the proxy black-box model—using a genetic algorithm. These rules are output as if-then statements configured with an if-portion formed as a list of the conditions and a then-portion having an indication of the associated observed output.
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公开(公告)号：US20190114673A1
公开(公告)日：2019-04-18
申请号：US15787369
申请日：2017-10-18
Applicant: Adobe Inc.
Inventor： Piyush Gupta , Nikaash Puri , Balaji Krishnamurthy
Abstract: Digital experience targeting techniques are disclosed which serve digital experiences that have a high probability of conversion with regard to a given user visit profile. In some examples, a method may include predicting a probability of each digital experience in a campaign being served based on a user visit profile and an indication that a user exhibiting the user visit profile is going to convert, predicting a probability of each digital experience in the campaign being served based on the user visit profile and an indication that the user exhibiting the user visit profile is not going to convert, and deriving, for the user visit profile, a probability of conversion for each digital experience in the campaign. The probability of conversion for each digital experience in the campaign for the user visit profile may be derived using a Bayesian framework.
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公开(公告)号：US11861636B2
公开(公告)日：2024-01-02
申请号：US16910357
申请日：2020-06-24
Applicant: ADOBE INC.
Inventor： Pankhri Singhai , Piyush Gupta , Balaji Krishnamurthy , Jayakumar Subramanian , Nikaash Puri
IPC: G06Q30/02 , G06Q30/0204 , G06N20/00 , G06Q30/0201 , G06Q10/0633
CPC classification number: G06Q30/0205 , G06N20/00 , G06Q10/0633 , G06Q30/0201
Abstract: Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained. A journey can include journey paths indicating workflows through which audience members can traverse. The journey data can include audience member attributes (e.g., demographics) and labels indicating journey paths traversed by audience members. A set of audience segments are determined that describe a set of audience members traversing a particular journey path. The set of audience segments can be determined using the journey data to train a segmentation model and, thereafter, analyzing the segmentation model to identify patterns that indicate audience segments associated with the particular journey path. An indication of the set of audience segments that describe the set of audience members traversing the particular journey path can be provided for display.
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