Emc Corporation

Open Text Corporation

Salesforce Com, Inc.

Emc IP Holding Company LLC

Salesforce, Inc.

Marc Brette

Pierre-Yves Chevalier

Edward C Bueche

Francisco Borges

Frederic Ciminera

Bruno Marquié

Victor Spivak

Alex Rankov

Sunita Casula

Naren M Chittar

Anuprit Kale

Venkatesan Chandrasekaran

Guillaume Kempf

Guillaume Jean Mathieu Kempf

Shanshan Quan

Petr Pleshachkov

Frédéric Ciminera

Samir Batla

David Le Strat

Jayesh Govindarajan

Mario Sergio Rodriguez

Edgar Gerardo Velasco

Matthieu Michel Robin Landos

Ghislain Brun

Georgios Balikas

Bruno Roustant

James David Vogt

Ilan Ginzburg

Francois Lopitaux

Glenn Sorrentino

Linda Wei

Ahmet Bugdayci

Arvind Srikantan

Son Thanh Chang

Pierre Salagnac

Sahibdeep Singh

Darya Brazouskaya

Qianqian Shi

Yasaman Mohsenin

Frederic Lombardo

Pranshu Sharma

Ivan Koval

Arieta Shabani

Jean-Baptiste Charlety

Naveen Veeravalli

Bruno Marquie

A system for providing a search service application is disclosed and includes an application builder component that provides a search model for a first object of a plurality of objects. The search model is based at least on an end-user input field corresponding to a first attribute of the first object and a search result output field corresponding to a second attribute of the first object. The search model is also associated with a backend data store that supports a storage structure that stores information relating to the first object. The system also includes a deployment engine that automatically configures a search engine system associated with the backend data store to place a portion of indexed data into a first partition and to place another portion of indexed data into at least another partition based on the search model.

Methods and systems for providing a search service application

The system includes a search service application builder that provides a search model. The search model represents a search service application for an object. The search model uses an end-user input field for a first attribute for the object and a user-defined search result output field for a second attribute of the object. The search model has code to generate a user interface. The interface includes an input section for receiving values for the first attribute and a search results section for outputting values of the second attribute. The interface also includes code to generate a search query using a search query model for a selected query type. The interface also includes code to receive search result data returned in response to the search query and code to format the search result data for the search results section.

Methods and systems for building search service application

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

A method and apparatus for generating personalized suggestions for natural language search queries, where the method includes receiving a natural language query input from a user, obtaining set of suggestions for the natural language query, identifying a set of concepts in the set of suggestions, applying co-occurrence model to the set of concepts to obtain user specific concept probabilities and vocabulary probabilities, computing suggestion scores for at least one suggestion in the set of suggestions based on the user specific concept probabilities and vocabulary probabilities, and ranking the set of suggestions based on suggestion scores.

Personalized NLS query suggestions using language models

System and methods are described for parsing log data items to find events in the log data items, wherein an event comprises at least a portion of a log data item matching an event definition; aggregating a plurality of performance metric definitions into a single expression; and generating a plurality of performance metrics from the events by applying the single expression to the events.

Generating performance metrics from events derived from user interface logs

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

Searching stored content is disclosed. A first mapping is created from an object to one or more stored relational database tables. A second mapping is created from the object to an indexer schema. One or both of the following is done: 1) using the first mapping to translate a search request expressed in an abstract query language to a first query language associated with the relational database; and 2) using the second mapping to translate the search request to a second query language associated with the indexer schema.

Query translation for searching complex structures of objects

A system for providing a search service is disclosed and includes a processor-based search service application builder component that provides a search model representing a search service application for a first object of a plurality of objects. The search model is based at least on a user-defined end-user input field corresponding to a first attribute of a plurality of attributes associated with the first object and a user-defined search result output field corresponding to a second attribute of the plurality of attributes. The search model is also associated with a backend data store that supports a storage structure configured to store information relating to the first object. The system also includes a processor-based deployment engine that automatically configures a search engine system associated with the backend data store system to generate and/or update search index(es) based on at least one of the first attribute and the second attribute.

Methods and systems for building a search service application

As part of providing the services to users, an online system stores multiple records that are accessible by users of the online system. When a user provides a search query, the online system extracts morphological and dictionary features from the query. The online system provides the extracted features to a machine learning model as an input. The machine learning model outputs a score for each potential entity type that indicates a likelihood that the search query is for a record associated with the entity type. The output from the machine learning model is used by the online system to select one or more entity types that the user is likely searching for. The online system searches the stored records based on the search query but limits the searching to records associated with at least one of the selected entity types.

Predicting a type of a record searched for by a user

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.

Predicting entities for search query results

System, method, and computer product embodiments for proving a graphical user interface (GUI) for a GUI object are described. A computing device provides, within the GUI, a polymorphic lookup interface for retrieving a plurality of entity instances to relate to the GUI object. The computing device provides a plurality of types of entities within the polymorphic lookup interface. A type of entity selected from the plurality of types limits the possible entity instances that may be returned. Through the polymorphic lookup interface, the computing device enables a user to select two or more entity instances to relate to the GUI object. Each of the selected entity instances may be selected from a plurality of possible entity instances that are returned, where each possible entity instance is a same type of entity.

User interface for polymorphic lookup

An online system stores objects that may be accessed by users. The online system also stores indexes of terms related to different entity types of objects. When a user provides a search query, the online system compares the search terms with terms stored in the indexes. Based on the comparisons, the online system determines term features for entity types associated with an index. The online system provides the term features as inputs to a machine learning model. The machine learning model outputs a score for each entity type indicating a likelihood that the search query is for an object associated with the entity type. The machine learning model output is used by the online system to select one or more entity types that the user is likely searching for. The online system offers objects of the likely entity types to the user as results of the search query.

Predicting user intent based on entity-type search indexes

A database system may generate a new case associated with a customer support query. The database system may initiate a search for other cases related to the new case and identify articles linked to the related cases. The database system may identify more relevant articles by finding the related cases that use a similar vocabulary for describing similar customer problems. The database system then identifies the articles used for resolving the prior problems. Thus, the database system may bridge the gap between vocabularies used by customers to describe problems and vocabularies used in articles to describe solutions to those problems.

Identifying relevant material for cases

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Domene, France

Marc Brette