Luminix, Inc.

Azul Systems, Inc.

H2o.ai Inc.

Tom Kraljevic

Fernando Rodriguez, Iii

Geetha Vallabhaneni

Gil Tene

Michael A Wolf

Ivan Posva

Daniel Dwight Grove

SriSatish Ambati

Pasha Stetsenko

Sanjay Joshi

Data associated with one or more data sources is transformed into a format associated with a common ontology using one or more transformers. One or more machine learning models are generated based at least in part on the transformed data. The one or more machine learning models and the one or more transformers are provided to a remote device.

Embedded predictive machine learning models

A method for synchronizing Customer Relationship Management data between a Software as a Service ('SaaS') CRM provider and a mobile device. This method enables both read and write access from the mobile device whether a network connection to the SaaS provider is available or not. The method involves creating a local mobile device database to track portions or all of the SaaS provider database. In the case where a network separation occurs and the device and SaaS databases diverge, the synchronization method may be used to make the mobile device database and the SaaS database consistent and coherent again. In one embodiment, multiple local database tables are used to represent a single SaaS CRM table to facilitate synchronization, and a status indicator is used to visually and quickly convey status to the mobile user.

Systems and methods for facilitation communications among customer relationship management users

Executing a set one or more instructions is disclosed. A set of one or more register states is saved in a software data structure. The set of instructions is speculatively executed. At least one store made to a memory location during the speculative execution is not committed until the speculative execution is successfully completed. If an abort indication is received, the state of one or more registers restored.

Concurrent atomic execution

Executing a set one or more instructions atomically is disclosed. Executing includes saving a set of one or more register states in a software data structure, speculatively executing the set of instructions, and restoring the state of one or more registers when an abort indication is received.

Executing a set of one or more instructions atomically is disclosed. Executing includes determining whether speculatively executing the instructions is advised based at least in part on dynamic information associated with synchronization data and speculatively executing the instructions when it is determined that speculatively executing the instructions is advised.

Growing community of inventors

San Jose, CA, United States of America

Tom Kraljevic