Adobe, Inc.

Atanu Ranjan Sinha

Deepali Jain

Nikhil Sheoran

Dhruv Singal

Deepali Gupta

Sungchul Kim

Iftikhar Ahamath Burhanuddin

Sopan Khosla

Eunyee Koh

Shiv Kumar Saini

Gaurush Hiranandani

Kumar Ayush

Pranav Ravindra Maneriker

Harvineet Singh

Prakhar Gupta

Omar Rahman

Chinnaobireddy Varsha

Sai Varun Reddy Maram

Saurabh Mahapatra

Ryan A Rossi

Fan Du

Ritwik Sinha

Branislav Kveton

Hung Hai Bui

Sunav Choudhary

Xiang Chen

Aurghya Maiti

Sapthotharan Krishnan Nair

Manoj Kilaru

Charles Li Chen

Somak Aditya

Reshmi Naduparambil Sasidharan

Nayan Raju Vysyaraju

Varun Srivastava

Nisheeth Golakiya

William Brandon George

Wei Zhang

Sumit Shekhar

Deepak Pai

Sonali Surange

Kushal Chawla

Tong Yu

Paridhi Maheshwari

Niranjan Shivanand Kumbi

Shunan Guo

Gaurav Sinha

Vasanthi Holtcamp

Uttaran Bhattacharya

Jane Elizabeth Hoffswell

Said Kobeissi

Meghanath Macha Yadagiri

Rachit Bansal

Guillaume L Escarguel

Tanay Anand

Gautam Choudhary

John Anderson

Vaidyanathan Venkatraman

Manoj Ghuhan Arivazhagan

Prithvi Bhutani

Ayush Chauhan

Arpit Ajay Narechania

Akangsha Sunil Bedmutha

Kai Yeung Lau

Abhisek Trivedi

Navjot Singh

Sarthak Chakraborty

Saarthak Sandip Marathe

Brahmbhatt Paresh Anand

Manupriya Gupta

Liana Vagharshakian

Camille Girabawe

Scott Trafton

Vivek Sinha

Goutham Srivatsav Arra

Jean Bernard Hishamunda

Amit Doda

Harshita Chopra

Sarthak Kapoor

Sai Shashank Kalakonda

Shivi Gupta

Atharv Tyagi

Yuvraj

Atharva Anand Joshi

Saili Myana

Atishay Ganesh

Purnima Grover

Liza Dahiya

Andrei Zugravu

Ishita Sunity Kumar Chakraborty

Brandon Galen Mooso

Kartavya Jain

Avani Samdariya

Deepanshu Bansal

Yash Shrivastava

Madhav Goel

Aniket Raj

Tirupati Saketh Chandra

Titas Chakraborty

Techniques for data question answering with auxiliary recommendations are described to enable efficient querying of data sets for answers to data questions based on a natural language input. In an example, a processing device is operable to receive a natural language input including a query, determine an additional query based on a context of the query, and query a machine-learning model using the query and the additional query. The processing device is further operable to receive, from the machine-learning model, a result including a quantitative answer to the query, an additional answer based on the additional query, and an explanation by the machine-learning model of how the machine-learning model generated the quantitative answer or the additional answer in response to the querying. The processing device is operable to present the result for display in a user interface.

Data question answering with auxiliary recommendations

Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: 'quality'—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, an interactive tree view may visually represent a nested attribute schema and attribute quality or consumption metrics to facilitate discovery of bad data before ingesting into a data lake.

Interactive tree representing attribute quality or consumption metrics for data ingestion and other applications

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.

Artificial intelligence approaches for predicting conversion activity probability scores and key personas for target entities

Systems and methods for identifying key moments, such as key moments within a livestream, are described. Embodiments of the present disclosure obtain video data and text data. In some cases, the text data is aligned with a timeline of the video data. The system then computes a moment importance score for a time of the video data using a machine learning model based on the video data and the text data, and presents content to a user at the time of the video data based on the moment importance score.

Livestream key moment identification

Systems and methods for content customization are provided. One aspect of the systems and methods includes receiving dynamic characteristics for a plurality of users, wherein the dynamic characteristics include interactions between the plurality of users and a digital content channel; clustering the plurality of users in a plurality of segments based on the dynamic characteristics using a machine learning model; assigning a user to a segment of the plurality of segments based on static characteristics of the user; and providing customized digital content for the user based on the segment.

Systems and methods for content customization

A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.

Generating concise and common user representations for edge systems from event sequence data stored on hub systems

In some embodiments, a computing system computes, with a state prediction model, probabilities of transitioning from a click state represented by interaction data to various predicted next states. The computing system computes an interface experience metric for the click with an experience valuation model. To do so, the computing system identifies base values for the click state and the predicted next states. The computing system computes value differentials for between the click state's base value and each predicted next state's base value. Value differentials indicate qualities of interface experience. The computing system determines the interface experience metric from a summation that includes the current click state's base value and the value differentials weighted with the predicted next states' probabilities. The computing system transmits the interface experience metric to an online platform, which can cause interface elements of the online platform to be modified based on the interface experience metric.

Machine-learning models applied to interaction data for facilitating experience-based modifications to interface elements in online environments

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.

Cloud-based resource allocation using meters

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.

Systems for predicting a terminal event

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.

Provisioning interactive content based on predicted user-engagement levels

In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

Creating a knowledge graph based on text-based knowledge corpora

Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

Leading indicators and monitor business KPIs and metrics for preemptive action

A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.

Machine-learning models applied to interaction data for determining interaction goals and facilitating experience-based modifications to interface elements in online environments

A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.

Artificial intelligence tool to predict user behavior in an interactive environment

A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.

Facilitating changes to online computing environment by assessing impacts of actions using a knowledge base representation

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Bengaluru, India

Atanu Ranjan Sinha