公开(公告)号：US11954309B2

公开(公告)日：2024-04-09

申请号：US16866261

申请日：2020-05-04

Applicant: Adobe Inc.

Inventor： Amit Doda ,  Gaurav Sinha ,  Kai Yeung Lau ,  Akangsha Sunil Bedmutha ,  Shiv Kumar Saini ,  Ritwik Sinha ,  Vaidyanathan Venkatraman ,  Niranjan Shivanand Kumbi ,  Omar Rahman ,  Atanu R. Sinha

IPC: G06F17/18 ,  G06F3/0481 ,  G06F3/04842 ,  G06F11/07 ,  G06F18/21 ,  G06F18/2113 ,  G06F18/241 ,  G06F18/2431 ,  G06N20/00

CPC classification number: G06F3/04842 ,  G06F3/0481 ,  G06F11/079 ,  G06F17/18 ,  G06F18/2113 ,  G06F18/2163 ,  G06F18/241 ,  G06F18/2431 ,  G06N20/00

Abstract: In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time.

公开(公告)号：US11551194B2

公开(公告)日：2023-01-10

申请号：US17449124

申请日：2021-09-28

Applicant: Adobe Inc.

Inventor： Shiv Kumar Saini ,  Ritwick Chaudhry ,  Harvineet Singh ,  Bhavya Bahl ,  Sriya Sainath ,  Savya Sindhu Gupta

IPC: G06Q30/00 ,  G06Q20/12 ,  G06F16/23 ,  G06N20/00 ,  G06Q30/06 ,  G06N7/00 ,  G06Q30/02

Abstract: Techniques for exchanging data segments between data aggregators and data consumers. In an embodiment, a value of an arbitrary data segment selected by a data consumer is computed. In particular, an individual user value is calculated for each user represented in the data segment, wherein the individual user value is a weighted sum (or other function) of the one or more features of the data segment attributable to that user, plus an additive gaussian noise. The overall value of the data segment is the sum of the individual user values. An offer price for the data segment can then be calculated using the overall value. Once a request is received from the consumer to purchase the data segment at the offer price, the data segment can be exchanged between the aggregator and consumer. Thus, a data marketplace or platform for the exchange of data segments is enabled.

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

IPC: G06K9/62 ,  G06F7/544 ,  G11C16/14 ,  G06Q10/10 ,  G06N3/063

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.

公开(公告)号：US10943497B2

公开(公告)日：2021-03-09

申请号：US15964869

申请日：2018-04-27

Applicant: ADOBE INC.

Inventor： Shiv Kumar Saini ,  Ritwick Chaudhry ,  Pradeep Dogga ,  Harvineet Singh

IPC: G09B7/00 ,  G06N7/00 ,  G09B5/06 ,  G06N20/00

Abstract: Techniques are described for jointly modeling knowledge tracing and hint-taking propensity. During a read phase, a co-learning model accepts as inputs an identification of a question and the current knowledge state for a learner, and the model predicts probabilities that the learner will answer the question correctly and that the learner will use a learning aid (e.g., accept a hint). The predictions are used to personalize an e-learning plan, for example, to provide a personalized assessment. By using these predictions to personalize a learner's experience, for example, by offering hints at optimal times, the co-learning system increases efficiencies in learning and improves learning outcomes. Once a learner has interacted with a question, the interaction is encoded and provided to the co-learning model to update the learner's knowledge state during an update phase.

公开(公告)号：US20190272553A1

公开(公告)日：2019-09-05

申请号：US15909723

申请日：2018-03-01

Applicant: Adobe Inc.

Inventor： Shiv Kumar Saini ,  Vishwa Vinay ,  Vaibhav Nagar ,  Aishwarya Mittal

IPC: G06Q30/02 ,  G06N7/00 ,  G06N3/04 ,  G06F17/30

Abstract: This disclosure involves predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks. For example, a method includes a processing device performing operations including accessing input data for an entity and transforming the input data into a dense vector entity representation representing the entity. Transforming the input data includes applying, to the input data, a neural network including simultaneously trained propensity models. Each propensity model predicts a different task based on the input data. Transforming the input data also includes extracting the dense vector entity representation from a common layer of the neural network to which the propensity models are connected. The operations performed by the processing device include computing a predicted behavior by applying a predictive model to the dense vector entity representation and transmitting the predicted behavior to a computing device that customizes a presentation of electronic content at a remote user device.

公开(公告)号：US10404777B2

公开(公告)日：2019-09-03

申请号：US14886453

申请日：2015-10-19

Applicant: Adobe Inc.

Inventor： Shiv Kumar Saini ,  Ritwik Sinha ,  Michael Rimer ,  Anandhavelu N

IPC: H04L12/26 ,  H04L29/08 ,  G06Q30/02 ,  G06F11/34 ,  H04L12/24

Abstract: The present disclosure is directed toward systems and methods for identifying contributing factors associated with a multi-variable metric anomaly. One or more embodiments described herein identify one or more contributing factors that led to an anomaly in a multi-variable metric by calculating linearizing weights such that the total deviation in the multi-variable metric can be written as a weighted sum of deviations for dimension elements associated with the multi-variable metric.

公开(公告)号：US10395272B2

公开(公告)日：2019-08-27

申请号：US14942109

申请日：2015-11-16

Applicant: Adobe Inc.

Inventor： Meghanath Macha Yadagiri ,  Shiv Kumar Saini ,  Ritwik Sinha

IPC: G06Q30/02 ,  G06Q30/00

Abstract: Techniques for analyzing marketing channels are described. Users are exposed to the marketing channels. User responses (e.g., purchases and no-purchases) to the exposures are tracked. Upon a request from a marketer to analyze an attribution of a marketing channel, the user responses are analyzed. The attribution represents the credit that the marketing channel should get for influencing the users exposed thereto into exhibiting a particular user response (e.g., a purchase). The analysis involves multiple steps. In a first step, a non-parametric estimation is used to generate a value function at a user-level. In a second step, a coalitional game approach is used to estimate the attribution based on the value function. A response is provided to the marketer with data about the attribution.

公开(公告)号：US12112349B2

公开(公告)日：2024-10-08

申请号：US15238208

申请日：2016-08-16

Applicant: ADOBE INC.

Inventor： Natwar Modani ,  Iftikhar Ahamath Burhanuddin ,  Gaurush Hiranandani ,  Shiv Kumar Saini

IPC: G06Q30/0242

CPC classification number: G06Q30/0244

Abstract: Methods and systems are provided herein for summarizing a set of anomalies corresponding to a group of metrics of interest to a monitoring system user. Initially, a set of anomalies corresponding to a group of metrics is identified as having values that are outside of a predetermined range. A correlation value is determined for at least a portion of pairs of anomalies in the set of anomalies. For each anomaly in the set of anomalies, an informativeness value is computed that indicates how informative each anomaly in the set of anomalies is to the monitoring system user. The correlation values and the informativeness values are then used to identify at least one key anomaly and a plurality of non-key anomalies from the set of anomalies. A summary is generated of the identified at least one key anomaly to provide information to the monitoring system user about the set of anomalies for a particular time period.

公开(公告)号：US20240232775A9

公开(公告)日：2024-07-11

申请号：US17969643

申请日：2022-10-19

Applicant: Adobe Inc.

Inventor： Atanu R. Sinha ,  Shiv Kumar Saini ,  Prithvi Bhutani ,  Nikhil Sheoran ,  Kevin Cobourn ,  Jeff D. Chasin ,  Fan Du ,  Eric Matisoff

IPC: G06Q10/0639 ,  G06F3/0484

CPC classification number: G06Q10/06393 ,  G06F3/0484

Abstract: In some examples, an environment evaluation system accesses interaction data recording interactions by users with an online platform hosted by a host system and computes, based on the interaction data, interface experience metrics. The interface experience metrics includes an individual experience metric for each user and a transition experience metric for each transition in the interactions by the users with the online platform. The environment evaluation system identifies a user with the individual experience metric below a pre-determined threshold, identifies a transition performed by the user that has a transition experience metric below a second threshold, and analyzes the transition to determine users who have performed the transition. The environment evaluation system updates the host system with the individual experience metrics and the transition metrics, based on which the host system can perform modifications of interface elements of the online platform to improve the experience.

公开(公告)号：US11886964B2

公开(公告)日：2024-01-30

申请号：US17322108

申请日：2021-05-17

Applicant: Adobe Inc.

Inventor： Atanu R. Sinha ,  Xiang Chen ,  Sungchul Kim ,  Omar Rahman ,  Jean Bernard Hishamunda ,  Goutham Srivatsav Arra ,  Shiv Kumar Saini

IPC: G06N20/00 ,  G06F3/0484 ,  H04L67/50

CPC classification number: G06N20/00 ,  G06F3/0484 ,  H04L67/535

Abstract: Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.