Adobe, Inc.

Samsung Electronics Co., Ltd.

Gaurav Sinha

Ayush Chauhan

Shiv Kumar Saini

Madhan Raj Kanagarathinam

Karthikeyan Arunachalam

Sujith Rengan Jayaseelan

Gunjan Kumar Choudhary

Aurghya Maiti

Siddharth Jain

Aayush Makharia

Sourav Suman

Vineet Malik

Fan Du

Atanu Ranjan Sinha

Ritwik Sinha

Sunghee Lee

Chounjong Nam

Niranjan Shivanand Kumbi

Dronamraju Siva Sabareesh

Omar Rahman

Vibhor Porwal

Bhagwan Dass Swami

Jaheon Gu

Vaidyanathan Venkatraman

HariKrishnan Natarajan

Akangsha Sunil Bedmutha

Kai Yeung Lau

Xiao Xie

Naman Poddar

Pulkit Goel

Ruchi Sandeep Pandya

Amit Doda

Disclosed herein is a method and network handover system for handling a data session in a user equipment (UE). The method comprises initiating a data session of at least one application from a plurality of applications with a first communication interface using a first socket of the UE having a first socket file descriptor (SOCKFD) for the data session, detecting a deterioration in a network connection of the first communication interface, identifying a second communication interface, establishing a second socket having a second SOCKFD associated with the second communication interface and migrating the data session from the first communication interface to the second communication interface by mapping the first SOCKFD corresponding to the first socket to the second SOCKFD corresponding to the second socket.

Method and system for network handover on transport layer

In implementations of systems for estimating terminal event likelihood, a computing device implements a termination system to receive observed data describing values of a treatment metric and indications of a terminal event. Values of the treatment metric are grouped into groups using a mixture model that represents the treatment metric as a mixture of distributions. Parameters of a distribution are estimated for each of the groups and mixing proportions are also estimated for each of the groups. In response to receiving a user input requesting an estimate of a likelihood of the terminal event for a particular value of the treatment metric, the termination system generates an indication of the estimate of the likelihood of the terminal event for the particular value based on a distribution density at the particular value for each of the groups and a probability of including the particular value in each of the groups.

Systems for estimating terminal event likelihood

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.

Discovering causal relationships in mixed datasets

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

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.

Determining feature contributions to data metrics utilizing a causal dependency model

Automated discovery of causal relationships in mixed datasets

A method for Multipath Quick User Datagram Protocol (UDP) Internet Connections (MPQUIC) over Quick SOCKS (QSOCKS) in a wireless network is provided. The method includes receiving, by a QSOCKS server, a Client Hello (CHLO) message from a QSOCKS client device using a QSOCKS method tag, wherein the CHLO message comprises a plurality of client-supported SOCKS Authentication (AUTH) procedures, selecting, by the QSOCKS server, a candidate client-supported SOCKS AUTH procedure from the plurality of client-supported SOCKS AUTH procedures, and transmitting, by the QSOCKS server, a reject packet using the QSKM tag to the QSOCKS client device, wherein the reject packet includes information indicating the selected candidate client-supported SOCKS AUTH procedure.

Method and system for MPQUIC over QSOCKS in wireless network

The present disclosure describes systems, methods, and non-transitory computer readable media for generating and providing a causal-graph interface that visually depicts causal relationships among dimensions and represents uncertainty metrics for such relationships as part of a streamlined visualization of a causal graph. The disclosed systems can determine causality among dimensions of multidimensional data and determine uncertainty metrics associated with individual causal relationships. Additionally, the disclosed system can generate a visual representation of a causal graph with nodes arranged in stratified layers and can connect the layered nodes with uncertainty-aware-causal edges to represent both the causality between the dimensions and the uncertainty metrics. Further, the disclosed systems can provide interactive tools for generating and visualizing predictions or causal relationships in intuitive user interfaces, such as visualizations for dimension-specific (or dimension-value-specific) interventions and/or attribution determinations.

Generating visualizations of analytical causal graphs

Growing community of inventors

Bengaluru, India

Gaurav Sinha