Salesforce, Inc.

Salesforce Com, Inc.

Guillaume Jean Mathieu Kempf

Marc Brette

Francisco Borges

Matthieu Michel Robin Landos

Qianqian Shi

Darya Brazouskaya

Mario Sergio Rodriguez

Arvind Srikantan

Georgios Balikas

Francisco Dellatorre Borges

A method for managing features for a search system using declarative metadata. The method includes receiving search metadata including declarative statements identifying at least one search feature to be enabled across a plurality of components of the search system, performing functional verification of the at least one search feature, testing the at least one search feature, and enabling the at least one search feature in at least one of the plurality of components of the search system in response to positive functional verification and positive testing.

Management of search features via declarative metadata

A database system may receive a natural language query that is associated with a tenant of a multi-tenant system. The natural language query may be parsed into a set of tokens, and the set of tokens may be tagged, using a tenant specific tagging model associated with the tenant, the set of tokens with at least one pre-configured data type identifier that is configured for the plurality of tenants. A global tagging model that supports the plurality of tenants of the multi-tenant system may tag the set of tokens with at least one category identifier. The global tagging model may use the natural language query and the pre-configured data type identifier to identify the at least one category identifier. The system may execute a database query on a database associated with the tenant using the at least one pre-configured data type identifier and the at least one category identifier.

Tenant specific and global pretagging for natural language queries

Methods, systems, and devices supporting detecting and processing conceptual queries are described. A device (e.g., an application server) may receive a search query from a user device. The search query may include one or more parameters. The device may tag the search query using one or more tags associated with the one or more parameters. In some examples, the one or more tags may be determined based on a neural network. The device may determine that the search query is supported as a conceptual query based on a tag of the one or more tags corresponding to a data object stored in a database. The device may then generate a database query in a query language based on the search query, retrieve a set of results for the search query using the database query in the query language, and transmit the set of results to the user device.

Detecting and processing conceptual queries

For a multi-tenant database accessible by a plurality of separate organizations, a system is provided for capturing organization specificities in a model for the multi-tenant database. The system includes a neural network. The system is configured to: receive an organization encoding for one or more separate organizations making previous search queries into the multi-tenant database; generate a vector matrix from the organization encoding to embed organization specificities for training a model of the neural network; and using the vector matrix, train the model of the neural network for processing a present search query into the multi-tenant database. In some embodiments, the model of the neural network is global across the separate organizations accessing the database.

Capturing organization specificities with embeddings in a model for a multi-tenant database system

For a database accessible by a plurality of separate organizations, a system is provided for predicting entities for database query results. The system includes a multi-layer neural network. The system is configured to receive a query encoding for one or more previous queries made into the database, a user entity view frequency encoding for a frequency of views by one or more users, and an organization encoding for one or more separate organizations accessing the database; and based on the query encoding, the user entity view frequency encoding, and the organization encoding, generate a neural model for predicting entities for results to a present query into the database. In some embodiments, the neural model is global across the separate organizations accessing the database.

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San Francisco, CA, United States of America

Guillaume Jean Mathieu Kempf