Juniper Networks, Inc.

Walmart Apollo, LLC

Extreme Networks, Inc.

Spillbox Inc.

Ashish Ranjan

Harshad Bhaskar Nakil

Ankur Singla

Pedro R Marques

Kannan Achan

Sushant Kumar

Hyun Duk Cho

Rajashekar Reddy

Soumya Wadhwa

Hampapur Nagaraj Ajay

Selene Xu

Choon Kee Lee

Gaurish R Dalvi

Aysenur Inan

Divya Chaganti

Sooraj Mangalath Subrahmannian

Suresh Kumar Vinapamula Venkata

Sachin Bansal

Desikan Saravanan

Alok Kumar Sinha

Vivek Vaidyanathan

Yogananth Mahalingam

A system including one or more processors and one or more non-transitory computer-readable media storing computing instruction that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a training procedure, labels based at least in part on price band activity data from a time period; training, using the training procedure, an affinity prediction model of a machine learning architecture; analyzing, using the affinity prediction model of the machine learning architecture, as trained, the price band activity data indicating interactions of a user with items; and generating, using the labels and the affinity prediction model of the machine learning architecture, as trained, one or more price affinity predictions for one or more items for the user. Other embodiments are disclosed herein.

Systems and methods for determining price bands and user price affinity predictions using machine learning architectures and techniques

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: evaluating, using a price band determination model, degrees of expensiveness of items relative to each other in item type categories; generating, using the price band determination model, price bands associated with item type categories; assigning each of the items to a respective one of the price bands associated with a respective one of the item type categories; and presenting, to one or more end-user applications, at least one other item corresponding to at least one of the price bands associated with at least one of the item type categories. Other embodiments are disclosed.

In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Methods and apparatus for predicting a user churn event

This application relates to apparatus and methods for automatically determining and providing digital recommendations including discounted items with associated savings. In some examples, a computing device obtains savings data including savings items. In response to obtaining the savings data, the computing device obtains a plurality of clusters. Each cluster of the plurality of clusters includes a set of product types. The computing device obtains user data associated with a user, and based on the savings data and the plurality of clusters, determines a set of initial items for recommendation. The set of initial items includes a subset of the savings items. The computing device also determines final recommendation items for the user based on the set of initial items and the user data.

Systems and methods for providing personalized discounted items

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: determining price bands for an item type category; associating items included in the item type category with the price bands; analyzing, using an affinity prediction model of the machine learning architecture, price band activity data indicating interactions of a user with respective items included in each of the price bands for the item type category; and generating one or more price affinity predictions for the user based, at least in part, on the price band activity data, wherein the one or more price affinity predictions predict a preference of the user for respective items associated with one or more of the price bands. Other embodiments are disclosed herein.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing a machine learning architecture that is configured to evaluate expensiveness of items relative to each other, wherein the items are included in an item type category; receiving prices associated with the items included in the item type category; generating, using a price band determination model associated with the machine learning architecture, price bands based, at least in part, on the prices associated with the items, each of the price bands being associated with separate price range boundaries for the item type category; and assigning each of the items to one of the price bands. Other embodiments are disclosed herein.

The invention provides systems, methods and computer program products for executing remote application iteration(s) of client application software on a remote server platform, and for enabling each remote application iteration of a client application software to have access to a corresponding instance of an application software environment that contains or otherwise has access to data object(s) necessary for execution of the remote application iteration. The invention additionally enables execution of a remote application iteration of a client software application at a remote server platform, through need-based or dynamic retrieval of data objects from an on-premise device for provisioning (on the remote platform server) an instance of the client software application environment required for execution of the remote application iteration of the client software application.

Systems, methods and computer program products for application environment synchronization between remote devices and on-premise devices

Techniques are disclosed describing an In-Service Software Upgrade for a Software Defined Networking (SDN) controller of a cloud data center. A cloud data center includes a first SDN controller configured to perform session management of a plurality of session instances, an orchestration engine communicatively coupled to the first SDN controller via a northbound application program interface (API); and a plurality of compute nodes configured to perform compute functions for the plurality of session instances. In response to receiving a request to perform an in-service software upgrade (ISSU) of the first SDN controller, the orchestration engine spawns a second, upgraded SDN controller on the cloud data center. An ISSU process initializes the second SDN controller and transfers session management for the plurality of session instances from the first SDN controller to the second SDN controller. Finally, the ISSU process removes the first SDN controller from the cloud data center.

In-service software upgrade of software-defined networking controller

In general, techniques are described in which a plurality of network switches automatically configure themselves to operate as a single virtual network switch. A virtual switch is a collection of individual switch devices that operate like as single network switch. As described herein, network switches in a network that are capable of participating in a virtual switch may automatically discover one another. The participating network switches may then elect one of the participating switches as a master switch. The master switch may generate forwarding information and store the forwarding information in the participating switches, including the master switch. The forwarding information causes the participating switches to act like a single network switch.

Automatic configuration of virtual network switches

Techniques are described to provide multicast service within a virtual network using a virtual network controller and endpoint replication without requiring multicast support in the underlying network. The virtual network controller is configured to create a multicast tree for endpoint devices of a multicast group in the virtual network at a centralized location instead of in a distributed fashion. The virtual network controller communicates the multicast tree to one or more of the endpoint devices of the multicast group to instruct the endpoint devices to replicate and forward multicast packets to other endpoint devices according to the multicast tree. The replication and forwarding of multicast packets is performed by virtual switches executed on the endpoint devices in the virtual network. No replication is performed within the underlying network. The techniques enable multicast service within a virtual network without requiring multicast support in the underlying network.

Multicast service in virtual networks

A method may include receiving a reconfiguration to a first Virtual Local Area Network (VLAN)/spanning tree table, where the first VLAN/spanning tree table has a first identifier and is associated with a region of a network; updating the first VLAN/spanning tree table to generate a second VLAN/spanning tree table based on the reconfiguration; determining a second identifier of the second VLAN/spanning tree table; and generating a list of identifiers associated with the region of the network, the list including the first identifier and the second identifier.

Configuring networks including spanning trees

A method may include contacting, by a network device, another device to obtain at least one of a network address, authentication, or authorization, receiving, from the other device, software update information that identifies an up-to-date software that the network device should have installed and location information that identifies a location from which to retrieve the up-to-date software, comparing, by the network device, the software update information to software information that identifies software currently installed on the network device, retrieving, by the network device, the up-to-date software based on the software update information and the location information when the software update information and the software information do not match, and auto-installing, by the network device, the up-to-date software.

Automatic software update on network devices

In general, techniques are described for facilitating multi-tenancy of a server accessed by virtual networks of a data center. A device included within a data center comprising one or more processors may perform the techniques. The processors may be configured to execute a virtual switch that supports a number of virtual networks executing within the data center. The virtual switch may be configured to receive a request regarding data associated with an identifier that is unique within one of the virtual networks that originated the request. The virtual switch may then translate the identifier included within the request to generate a globally unique identifier that is unique within the plurality of virtual networks, update the request to replace the identifier included within the request with the globally unique identifier, and transmit the updated request to a server of the data center.

Growing community of inventors

San Jose, CA, United States of America

Ashish Ranjan