Salesforce Com, Inc.

Karl Ryszard Skucha

Jean-Marc Soumet

Kit Pang Szeto

Matvey Tovbin

Eli Levine

Qingqing Liu

Shashank Harinath

Roojuta Lalani

Ming-Yang Chen

Thomas Archie Cook, Jr

Simon Chan

Ka Hou Chan

Manju Vijayakumar

Ka Chun Au

Michael Machado

Mike Rosenbaum

Emmanual Felipe Oliveira

John Ball

Zineb Laraki

Yan Yang

Leah McGuire

Johnson Liu

Christopher James Wu

Vlad Patryshev

Eric Shahkarami

Yuk Hei Chan

Bilong Chen

Marco Vivero

Joshua Sauter

Shu Liu

Qiong Liu

Approaches to using unstructured input to update heterogeneous data stores include receiving unstructured text input, receiving a template for interpreting the unstructured text input, identifying, using an entity classifier, entities in the unstructured text input, identifying one or more potential parent entities from the identified entities based on the template, receiving a selection of a parent entity from the one or more potential parent entities, identifying one or more potential child entities from the identified entities based on the template and the selected parent entity, receiving a selection of a child entity from the one or more potential child entities, identifying an action item in the unstructured text input based on the identified entities and the template, determining, using an intent classifier, an intent of the action item, and updating a data store based on the determined intent, the identified entities, and the selected child entity.

Using unstructured input to update heterogeneous data stores

Methods, systems, and devices for data retention handling are described. In some data storage systems, data objects are stored in a non-relational database schema. The system may support configurable data retention policies for different tenants, users, or applications. For example, a data store may receive retention requests, where the retention requests may specify deletion or exportation actions to perform on records contained within data objects. The data store may determine retention rules based on these retention requests, and may periodically or aperiodically evaluate the rules to determine active actions to perform. To improve the efficiency of the system, the data store may aggregate the active actions (e.g., according to the dataset to perform the actions on), and may generate work items corresponding to the aggregate actions. A work processor may retrieve these work items and may efficiently perform the data retention actions on datasets stored in the data object store.

Data retention handling for data object stores

Techniques and architectures for data modeling and management. Data modeling services are provided to agents within multiple different operating environments of a computing environment having at least one database stored on one or more physical memory devices communicatively coupled with one or more hardware processors the one or physical memory devices. Building and versioning of data modeling projects is coordinated and data utilized for the data modeling projects with the one or more hardware processors.

Techniques and architectures for managing disparate heterogeneous cloud-based resources

Methods, systems, and devices for data access and processing are described. To set up secure environments for data processing (e.g., including machine learning), an access control system may first receive approval from an authorized user (e.g., an approver) granting access to data objects in a multi-tenant data store. The system may determine tenant-specific paths for retrieving the data objects from the data store, and may initialize a number of virtual computing engines for accessing the data. Each computing engine may be tenant-specific based on the path(s) used by that computing engine, and each may include an access role defining the data objects or data object types accessible by that computing engine. By accessing the requested data objects according to the tenant-specific path prefixes and access roles, the virtual computing engines may securely maintain separate environments for different tenants and may only allow user access to approved tenant data.

Securely accessing and processing data in a multi-tenant data store

A system may generate a score for a predictive model based on receiving a streaming data flow of events associated with a predictive model for a tenant. The system may receive the streaming data flow and calculate one or more feature values in real time based on the reception. The system may store each of the calculated features to a multi-tenant database server. The system may calculate a score for the predictive model based on the storage and may transmit an indication of the score (e.g., a prediction) based on the calculation. The system may transmit the score to, for example, a computing device.

Feature generation and storage in a multi-tenant environment

Growing community of inventors

Sunnyvale, CA, United States of America

Karl Ryszard Skucha