Adobe, Inc.

Fortinet, Inc.

Walmart Apollo, LLC

Computer Associates Think, Inc.

Wepay, Inc.

Jun He

Zhenyu Yan

Wuyang Dai

Lei Zhang

Partha P Bhattacharya

Abhishek Pani

Shankara Bhargava Subramanya

Xinyue Liu

Jae Yoo

Chang Liu

Scott Ball

Jonathan Caron

Joseph Greenwald

Christopher A Buia

David K Taylor

Yi-Hong Kuo

Luyang Chen

Shiyuan Gu

Kushal Bhatt

Shengyun Peng

Suofei Wu

Hsiang-Yu Yang

Lijun Yu

Tingting Xu

Jalaj Bhandari

Systems and methods for adaptively provisioning a distributed event data store of a multi-tenant architecture are provided. According to one embodiment, a managed security service provider (MSSP) maintains a distributed event data store on behalf of each tenant of the MSSP. For each tenant, the MSSP periodically determines a provisioning status for a current active partition of the distributed event data store of the tenant. Further, when the determining indicates an under-provisioning condition exits, the MSSP dynamically increases number of resource provision units (RPUs) to be used for a new partition to be added to the partitions for the tenant by a first adjustment ratio. While, when the determining indicates an over-provisioning condition exists, the MSSP dynamically decreases the number of RPUs to be used for subsequent partitions added to the partitions for the tenant by a second adjustment ratio.

Adaptive resource provisioning for a multi-tenant distributed event data store

Methods and systems are provided for improved electronic communication campaign technologies, which can automatically balance objectives or goals of an electronic communication campaign against an overall opt-out rate for the electronic communication campaign. An electronic communications frequency optimizer can generate individual contact frequencies for individual email recipients. Embodiments can avoid unnecessary or counterproductive communications while achieving overall campaign goals, and can use processes to improve the efficiency of systems. In some cases, embodiments cluster communication recipients into different groups based on their past actions, then optimizes the communication contact frequency on different groups, to avoid performing optimization directly on millions of recipients. Some embodiments automatically self-update, for example with recipients' recent responses, to generate and/or implement campaign communication schedules on an individual level.

Multi-objective electronic communication frequency optimization

Methods and systems are provided for facilitating time zone prediction using electronic communication data. Electronic message data associated with a message recipient of electronic communications is obtained. The electronic message data includes message delivery data associated with an electronic message and message response data associated with a response, by the message recipient, to a received electronic message. Using a machine learning model and based on the message delivery data and the message response data, a time-zone score is determined for a time zone. Such a time-zone score can indicate a probability the time zone corresponds with the message recipient. Based on the time-zone score, the time zone is identified as corresponding with the message recipient.

Facilitating time zone prediction based on electronic communication data

Systems and methods for customer journey optimization in email marketing are described. The systems and methods may identify a plurality of messages for a first time period, wherein the plurality of messages are categorized according to a plurality of messages types, identify user information for a customer, wherein the user information includes user interaction data, determine a message type from the plurality of message types for the first time period based on the user information, wherein the message type is determined using a decision making model comprising a deep Q-learning neural network, select a message from the plurality of messages based on the determined message type, and transmit the message to the customer during the first time period based on the selection.

Multi-objective customer journey optimization

The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.

Utilizing a bayesian approach and multi-armed bandit algorithms to improve distribution timing of electronic communications

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a target distribution schedule for providing electronic communications based on predicted behavior rates by utilizing a genetic algorithm and one or more objective functions. For example, the disclosed systems can generate predicted behavior rates by training and utilizing one or more behavior prediction models. Based on the predicted behavior rates, the disclosed systems can further utilize a genetic algorithm to apply objective functions to generate one or more candidate distribution schedules. In accordance with the genetic algorithm, the disclosed systems can select a target distribution schedule for a particular user/client device. The disclosed systems can thus provide one or more electronic communications to individual users based on respective target distribution schedules.

Utilizing a genetic algorithm in applying objective functions to determine distribution times for electronic communications

Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply 'marketing platform') that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.

Optimization of send time of messages

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving an online query from an electronic device of a user, the online query comprising natural language of the user, determining an intent of the online query as at least one of a product-related intent or a non-product-related intent, predicting an entity from the online query if the intent of the online query is determined to be a product-related intent, mapping the entity predicted from the online query to product metadata associated with one or more products, and coordinating a display of product information of the one more products on the electronic device. The entity can include a product name, a product attribute, a product price range, an average customer review, or a product brand.

Systems and methods for mapping a predicted entity to a product based on an online query

The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.

Utilizing a recommendation system approach to determine electronic communication send times

Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of accessing purchase information history from a website of an online retailer, training a first machine learning model configured to obtain first population demographic data for first products, coordinating a display of an advertisement associated with the first products on social media webpages of new users corresponding to at least a portion of the first population demographic data, receiving an indication that some of the new users selected the first advertisement, accessing additional information of the new users, training a second machine learning model configured to obtain additional data for the one or more first products, and coordinating a display of the first advertisement associated with the first products on additional social media webpages of additional new users.

Systems and methods for optimizing targeted advertising through social media interactions

Dynamic reporting rates for a log management system are adaptively allocated. Each individual controller node device of plurality of controller nodes is initially allocated an EPS rate limit for submitting event records to a log management system (e.g., an SIEM log management system) out of a licensed EPS rate. When surges are detected, the log management system dynamically reallocates proportions of EPS rates, within the licensed EPS rate. The individual EPS rate limit for at least one collector node is adjusted in real-time for a specific controller node based on under usage by other collector nodes. Another technique is to prioritize or weight events causing the surge to determine adjustments to EPS rate.

Adaptive allocation for dynamic reporting rates of log events to a central log management server from distributed nodes in a high volume log management system

A new approach is proposed to support two variants of the approach to effectively generate reason codes for an ensemble model. Both variants involve treating the ensemble model as a black box, identifying trivial values for input variables to the ensemble model, replacing each of the input variables to the ensemble model with its corresponding trivial values and then evaluating the impact on a score of the model after the trivial value replacement. The evaluation result of the impact is then used to generate the reason codes for the ensemble model. Specifically, the first variant is configured to perform one round of replacement, wherein variables with top drops in the score will be the reason codes. The second variant is configured to perform multiple rounds of replacement, which in each round, keeps the identified reason codes variables replaced with trivial values and analyzes incremental drops of replacing remaining variables.

System and methods for generating reason codes for ensemble computer models

A network management system includes a fault diagnosis engine, a topology mapper, an impact analyzer and a help desk system. The topology mapper includes a discovery module, a memory, and a presentation module. The discovery module is constructed and arranged to discover network elements in a communications network. The memory constructed and arranged to store topology data received from the discovery module. The presentation module constructed and arranged to present data related to topology of the communication network.

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San Jose, CA, United States of America

Jun He