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公开(公告)号：US11763325B2
公开(公告)日：2023-09-19
申请号：US17097508
申请日：2020-11-13
Applicant: Adobe Inc.
Inventor： Ayush Chauhan , Vineet Malik , Sourav Suman , Siddharth Jain , Gaurav Sinha , Aayush Makharia
IPC: G06Q30/0201 , G06F16/901 , G06N20/00 , G06N7/01
CPC classification number: G06Q30/0201 , G06F16/9024 , G06N7/01 , G06N20/00
Abstract: Introduced here are approaches to determining causal relationships in mixed datasets containing data related to continuous variables and discrete variables. To accomplish this, a marketing insight and intelligence platform may employ a multi-phase approach in which dependency is established before the data related to continuous variables is discretized. Such an approach ensures that information regarding dependence is not lost through discretization.
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公开(公告)号：US20230031050A1
公开(公告)日：2023-02-02
申请号：US17960585
申请日：2022-10-05
Applicant: Adobe Inc.
Inventor： Ayush Chauhan , Shiv Kumar Saini , Parth Gupta , Archiki Prasad , Amireddy Prashanth Reddy , Ritwick Chaudhry
Abstract: A system implements a key value memory network including a key matrix with key vectors learned from training static feature data and time-series feature data, a value matrix with value vectors representing time-series trends, and an input layer to receive, for a target entity, input data comprising a concatenation of static feature data of the target entity, time-specific feature data, and time-series feature data for the target entity. The key value memory network also includes an entity-embedding layer to generate an input vector from the input data, a key-addressing layer to generate a weight vector indicating similarities between the key vectors and the input vector, a value-reading layer to compute a context vector from the weight and value vectors, and an output layer to generate predicted time-series data for a target metric of the target entity by applying a continuous activation function to the context vector and the input vector.
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公开(公告)号：US20220156759A1
公开(公告)日：2022-05-19
申请号：US17097508
申请日：2020-11-13
Applicant: Adobe Inc.
Inventor： Ayush Chauhan , Vineet Malik , Sourav Suman , Siddharth Jain , Gaurav Sinha , Aayush Makharia
IPC: G06Q30/02 , G06N7/00 , G06N20/00 , G06F16/901
Abstract: Introduced here are approaches to determining causal relationships in mixed datasets containing data related to continuous variables and discrete variables. To accomplish this, a marketing insight and intelligence platform may employ a multi-phase approach in which dependency is established before the data related to continuous variables is discretized. Such an approach ensures that information regarding dependence is not lost through discretization.
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公开(公告)号：US12008589B2
公开(公告)日：2024-06-11
申请号：US18362833
申请日：2023-07-31
Applicant: Adobe Inc.
Inventor： Ayush Chauhan , Vineet Malik , Sourav Suman , Siddharth Jain , Gaurav Sinha , Aayush Makharia
IPC: G06Q30/0201 , G06F16/901 , G06N7/01 , G06N20/00
CPC classification number: G06Q30/0201 , G06F16/9024 , G06N7/01 , G06N20/00
Abstract: Introduced here are approaches to determining causal relationships in mixed datasets containing data related to continuous variables and discrete variables. To accomplish this, a marketing insight and intelligence platform may employ a multi-phase approach in which dependency is established before the data related to continuous variables is discretized. Such an approach ensures that information regarding dependence is not lost through discretization.
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公开(公告)号：US11907531B2
公开(公告)日：2024-02-20
申请号：US17851443
申请日：2022-06-28
Applicant: Adobe Inc.
Inventor： Raunak Shah , Koyel Mukherjee , Khushi , Kavya Barnwal , Karanpreet Singh , Harsh Kesarwani , Ayush Chauhan
IPC: G06F3/06
CPC classification number: G06F3/0604 , G06F3/067 , G06F3/0644
Abstract: Some techniques described herein relate to determining how to optimally store datasets in a multi-tiered storage device with compression. In one example, a method includes assigning, to a data partition of a dataset, a priority based on access patterns of the data partition. Compression data is accessed describing results of compressing a data sample associated with the data partition using multiple compression schemes. Based both on the priority of the data partition and the compression data, a storage tier is determined for storing the data partition in the multi-tiered storage device. Further, based both on the priority of the data partition and the compression data, a compression scheme is determined for compressing the data partition for storage in the multi-tiered storage device. The data partition is compressed using the compression scheme to produce a compressed data partition, and the compressed data partition is stored in the storage tier.
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Patent Agency Ranking
公开(公告)号：US20210350175A1
公开(公告)日：2021-11-11
申请号：US16868942
申请日：2020-05-07
Applicant: Adobe Inc.
Inventor： Ayush Chauhan , Shiv Kumar Saini , Parth Gupta , Archiki Prasad , Amireddy Prashanth Reddy , Ritwick Chaudhry
Abstract: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.
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公开(公告)号：US12086646B2
公开(公告)日：2024-09-10
申请号：US17674578
申请日：2022-02-17
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Shiv Kumar Saini , Sapthotharan Krishnan Nair , Saarthak Sandip Marathe , Manupriya Gupta , Brahmbhatt Paresh Anand , Ayush Chauhan
CPC classification number: G06F9/5055 , H04L47/826 , H04L67/10
Abstract: In implementations of systems for cloud-based resource allocation using meters, a computing device implements a resource system to receive resource data describing an amount of cloud-based resources reserved for consumption by client devices during a period of time and a total amount of cloud-based resources consumed by the client devices during the period of time. The resource system determines a consumption distribution using each meter included in a set of meters. Each of the consumption distributions allocates a portion of the total amount of the cloud-based resources consumed to each client device of the client devices. A particular meter used to determine a particular consumption distribution is selected based on a Kendall Tau coefficient of the particular consumption distribution. An amount of cloud-based resources to allocate for a future period of time is estimated using the particular meter and an approximate Shapley value.
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公开(公告)号：US20240061830A1
公开(公告)日：2024-02-22
申请号：US18492551
申请日：2023-10-23
Applicant: Adobe Inc.
Inventor： Pulkit Goel , Naman Poddar , Gaurav Sinha , Ayush Chauhan , Aurghya Maiti
IPC: G06F16/23
CPC classification number: G06F16/2365
Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.
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公开(公告)号：US11797515B2
公开(公告)日：2023-10-24
申请号：US16813424
申请日：2020-03-09
Applicant: Adobe Inc.
Inventor： Pulkit Goel , Naman Poddar , Gaurav Sinha , Ayush Chauhan , Aurghya Maiti
IPC: G06F16/23
CPC classification number: G06F16/2365
Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.
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公开(公告)号：US11501107B2
公开(公告)日：2022-11-15
申请号：US16868942
申请日：2020-05-07
Applicant: Adobe Inc.
Inventor： Ayush Chauhan , Shiv Kumar Saini , Parth Gupta , Archiki Prasad , Amireddy Prashanth Reddy , Ritwick Chaudhry
Abstract: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.
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