公开(公告)号：US11061955B2

公开(公告)日：2021-07-13

申请号：US16233420

申请日：2018-12-27

Applicant: salesforce.com, inc.

Inventor： Zachary Alexander ,  Naren M. Chittar ,  Alampallam R. Ramachandran ,  Anuprit Kale ,  Tiffany McKenzie ,  Sitaram Asur ,  Jacob Nathaniel Huffman

IPC: G06F16/35 ,  G06T11/20 ,  G06N20/00 ,  G06F16/332 ,  G06F16/33

Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

公开(公告)号：US11061954B2

公开(公告)日：2021-07-13

申请号：US16138514

申请日：2018-09-21

Applicant: salesforce.com, inc.

Inventor： Zachary Alexander ,  Naren M. Chittar ,  Alampallam R. Ramachandran ,  Anuprit Kale ,  Tiffany Deiandra McKenzie ,  Sitaram Asur ,  Jacob Nathaniel Huffman

IPC: G06F16/35 ,  G06F16/61 ,  G06F16/33

Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

公开(公告)号：US20200097563A1

公开(公告)日：2020-03-26

申请号：US16138514

申请日：2018-09-21

Applicant: salesforce.com, inc.

Inventor： Zachary Alexander ,  Naren M. Chittar ,  Alampallam R. Ramachandran ,  Anuprit Kale ,  Tiffany Deiandra McKenzie ,  Sitaram Asur ,  Jacob Nathaniel Huffman

IPC: G06F17/30

Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

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：US20200097496A1

公开(公告)日：2020-03-26

申请号：US16233420

申请日：2018-12-27

Applicant: salesforce.com, inc.

Inventor： Zachary Alexander ,  Naren M. Chittar ,  Alampallam R. Ramachandran ,  Anuprit Kale ,  Tiffany McKenzie ,  Sitaram Asur ,  Jacob Nathaniel Huffman

IPC: G06F16/35 ,  G06T11/20 ,  G06F16/33 ,  G06F16/332 ,  G06N20/00

Abstract: A data processing system analyzes a corpus of conversation data collected at an interactive conversation service to train an intent classification model. The intent classification model generates vectors based on the corpus of conversation data. A set of intents is selected and an intent seed input for each intent of the set of intents is input into the model to generate an intent vector corresponding to each intent. Vectors based on user inputs are generated and compared to the intent vectors to determine the intent.

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.