公开(公告)号：US20180101527A1

公开(公告)日：2018-04-12

申请号：US15730574

申请日：2017-10-11

Applicant: salesforce.com, inc.

Inventor： Jayesh Govindarajan ,  Ammar Haris ,  Nicholas Beng Tek Geh ,  Francisco Borges

IPC: G06F17/30

CPC classification number: G06F16/93 ,  G06F16/248 ,  G06F16/3323 ,  G06F16/986

Abstract: An online system stores documents for access by users. The online system also stores query independent information about the documents. Query independent features include data that can be used to score or rank a document independent of any terms entered as a search query. The online system periodically determines whether the values of query independent features have changed, such as by checking activity logs. The online system updates records of query independent features accordingly, and sends information about the updated records to an enterprise search platform for re-indexing. When a user sends a search query to the online system, the enterprise search platform determines whether documents are relevant to the query based on the document contents and the query independent features associated with the documents.

公开(公告)号：US20180101617A1

公开(公告)日：2018-04-12

申请号：US15730591

申请日：2017-10-11

Applicant: salesforce.com, inc.

Inventor： Jayesh Govindarajan ,  Nicholas Beng Tek Geh ,  Francisco Borges ,  Ammar Haris

IPC: G06F17/30 ,  G06N99/00 ,  H04L29/08

CPC classification number: G06F16/9535 ,  G06N20/00 ,  H04L67/02 ,  H04L67/306

Abstract: An online system identifies and ranks records using multiple machine learning models in response to a search query. Therefore, the online system can provide selected records that are of the most relevance to a user of a client device that provided the search query. More specifically, the online system applies a first machine learning model that is of low complexity, such as a regression model. Therefore, the first machine learning model can quickly narrow down the large number of records of the online system to a first set of candidate records. The online system analyzes candidate records in the first set by applying a more complex, second machine learning model that more accurately determines records of interest for the user. In various embodiments, the online system can apply subsequent machine learning models of higher complexity for selecting and ranking records for provision to the client device.

公开(公告)号：US10733241B2

公开(公告)日：2020-08-04

申请号：US15730574

申请日：2017-10-11

Applicant: salesforce.com, inc.

Inventor： Jayesh Govindarajan ,  Ammar Haris ,  Nicholas Beng Tek Geh ,  Francisco Borges

IPC: G06F17/00 ,  G06F16/93 ,  G06F16/248 ,  G06F16/958 ,  G06F16/332

Abstract: An online system stores documents for access by users. The online system also stores query independent information about the documents. Query independent features include data that can be used to score or rank a document independent of any terms entered as a search query. The online system periodically determines whether the values of query independent features have changed, such as by checking activity logs. The online system updates records of query independent features accordingly, and sends information about the updated records to an enterprise search platform for re-indexing. When a user sends a search query to the online system, the enterprise search platform determines whether documents are relevant to the query based on the document contents and the query independent features associated with the documents.

公开(公告)号：US11210304B2

公开(公告)日：2021-12-28

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: G06F7/00 ,  G06F16/2457 ,  G06N20/00 ,  G06N5/00 ,  G06F16/242 ,  G06N20/20 ,  G06N7/00 ,  G06N3/02 ,  G06N20/10

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：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：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.

公开(公告)号：US11475048B2

公开(公告)日：2022-10-18

申请号：US16736577

申请日：2020-01-07

Applicant: salesforce.com, inc.

Inventor： Rohit Kapoor ,  Christian Posse ,  Francisco Borges ,  Guillaume Kempf ,  Arvind Srikantan

IPC: G06F16/28 ,  G06F16/29 ,  G06F16/22 ,  G06F16/242

Abstract: In disclosed techniques, a computing system causes presentation of a user interface having an input field operable to receive, from a user, a search query for a database. The computing system may classify the search query by: determining whether the search query includes terms that are within a specified vocabulary indicative of a natural language query and determining whether the search query includes terms that identify an object defined in a schema of the database. In response to classifying the search query as a natural language query, the computing system returns query results determined by identifying values in the database corresponding to the object defined in the schema. In response to classifying the search query as a keyword query, the computing system returns query results determined by comparing terms of the search query to values within records in the database.

公开(公告)号：US20210081436A1

公开(公告)日：2021-03-18

申请号：US16736577

申请日：2020-01-07

Applicant: salesforce.com, inc.

Inventor： Rohit Kapoor ,  Christian Posse ,  Francisco Borges ,  Guillaume Kempf ,  Arvind Srikantan

IPC: G06F16/28 ,  G06F16/242 ,  G06F16/22 ,  G06F16/29

Abstract: In disclosed techniques, a computing system causes presentation of a user interface having an input field operable to receive, from a user, a search query for a database. The computing system may classify the search query by: determining whether the search query includes terms that are within a specified vocabulary indicative of a natural language query and determining whether the search query includes terms that identify an object defined in a schema of the database. In response to classifying the search query as a natural language query, the computing system returns query results determined by identifying values in the database corresponding to the object defined in the schema. In response to classifying the search query as a keyword query, the computing system returns query results determined by comparing terms of the search query to values within records in the database.

公开(公告)号：US10606910B2

公开(公告)日：2020-03-31

申请号：US15730591

申请日：2017-10-11

Applicant: salesforce.com, inc.

Inventor： Jayesh Govindarajan ,  Nicholas Beng Tek Geh ,  Francisco Borges ,  Ammar Haris

IPC: G06F17/30 ,  G06F16/9535 ,  H04L29/08 ,  G06N20/00

Abstract: An online system identifies and ranks records using multiple machine learning models in response to a search query. Therefore, the online system can provide selected records that are of the most relevance to a user of a client device that provided the search query. More specifically, the online system applies a first machine learning model that is of low complexity, such as a regression model. Therefore, the first machine learning model can quickly narrow down the large number of records of the online system to a first set of candidate records. The online system analyzes candidate records in the first set by applying a more complex, second machine learning model that more accurately determines records of interest for the user. In various embodiments, the online system can apply subsequent machine learning models of higher complexity for selecting and ranking records for provision to the client device.