公开(公告)号：US20200233874A1

公开(公告)日：2020-07-23

申请号：US16815958

申请日：2020-03-11

Applicant: salesforce.com, inc.

Inventor： Naren M. Chittar ,  Jayesh Govindarajan ,  Edgar Gerardo Velasco ,  Anuprit Kale ,  Francisco Borges ,  Guillaume Kempf ,  Marc Brette

IPC: G06F16/2457 ,  G06N20/00 ,  G06N5/00 ,  G06F16/242 ,  G06N20/20

Abstract: 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.

公开(公告)号：US20200034493A1

公开(公告)日：2020-01-30

申请号：US16049559

申请日：2018-07-30

Applicant: salesforce.com, inc.

Inventor： Guillaume Jean Mathieu Kempf ,  Marc Brette

IPC: G06F17/30 ,  G06N3/02

Abstract: 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.

公开(公告)号：US20190005089A1

公开(公告)日：2019-01-03

申请号：US15636199

申请日：2017-06-28

Applicant: salesforce.com, inc.

Inventor： Guillaume Kempf ,  Marc Brette ,  Naren M. Chittar ,  Anuprit Kale ,  Yasaman Mohsenin ,  Pranshu Sharma

IPC: G06F17/30

Abstract: 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.

公开(公告)号：US20180293241A1

公开(公告)日：2018-10-11

申请号：US15481366

申请日：2017-04-06

Applicant: salesforce.com, inc.

Inventor： Naren M. Chittar ,  Jayesh Govindarajan ,  Edgar Gerardo Velasco ,  Anuprit Kale ,  Francisco Borges ,  Guillaume Kempf ,  Marc Brette

IPC: G06F17/30 ,  G06N99/00 ,  G06N7/00

Abstract: 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.