UPDATING NATURAL LANGUAGE INTERFACES BY PROCESSING USAGE DATA

    公开(公告)号:US20200081907A1

    公开(公告)日:2020-03-12

    申请号:US16680906

    申请日:2019-11-12

    申请人: ASAPP, INC.

    摘要: A third-party company may assist companies in providing natural language interfaces for their customers. To implement a natural language interface for a company, a configuration may be received that includes information, such as a list intents, seed messages for the intents, and hierarchical information of the intents. An intent classifier may be trained using the configuration, and the natural language interface may be deployed for use with customers. Usage data of the natural language classifier may be collected and used to improve the natural language interface. Messages corresponding to an intent may be clustered into clusters of similar messages, and a prototype message may be obtained for each cluster to provide a human understandable description of the cluster. The information about the clusters may be used to improve the natural language interface, such as by creating a new intent with a cluster or moving a cluster to a different intent.

    CUSTOMIZED MESSAGE SUGGESTION WITH USER EMBEDDING VECTORS

    公开(公告)号:US20210126881A1

    公开(公告)日:2021-04-29

    申请号:US16663872

    申请日:2019-10-25

    申请人: ASAPP, INC.

    IPC分类号: H04L12/58 G06N3/04 G06F17/28

    摘要: A message may be suggested to a user participating in a conversation using one or more neural networks where the suggested message is adapted to the preferences or communication style of the user. The suggested message may be adapted to the user with a user embedding vector that represents the preferences or communication style of the user in a vector space. To suggest a message to the user, a conversation feature vector may be computed by processing the text the conversation with a neural network. A context score may be computed for one or more designated messages, where the context score is computed by processing the user embedding vector, the conversation feature vector, and a designated message feature vector with a neural network. A designated message may be selected as a suggested message for the user using the context scores. The suggestion may then presented to the user.

    MAINTAINING MACHINE LANGUAGE MODEL STATE ACROSS COMMUNICATIONS CHANNELS

    公开(公告)号:US20200327892A1

    公开(公告)日:2020-10-15

    申请号:US16503528

    申请日:2019-07-04

    申请人: ASAPP, INC.

    摘要: Machine learning models may be used during a communications session to process natural language communications and perform actions relating to the communications session. For example, a machine learning model may be used to provide an automated response to a user, to suggest a completion of text being entered by a user, or to provide information about a relevant resource. Machine learning models may rely on machine learning model data that is updated during a communications session as communications are processed by the machine learning model. To improve the performance of a machine learning model when a user leaves a first communications session and enters a second communications session, the machine learning model data may be stored during a first communications session and then retrieved during the second communications session to initialize a machine learning model for the second communications session.

    Customized message suggestion with user embedding vectors

    公开(公告)号:US11425064B2

    公开(公告)日:2022-08-23

    申请号:US16663872

    申请日:2019-10-25

    申请人: ASAPP, INC.

    摘要: A message may be suggested to a user participating in a conversation using one or more neural networks where the suggested message is adapted to the preferences or communication style of the user. The suggested message may be adapted to the user with a user embedding vector that represents the preferences or communication style of the user in a vector space. To suggest a message to the user, a conversation feature vector may be computed by processing the text the conversation with a neural network. A context score may be computed for one or more designated messages, where the context score is computed by processing the user embedding vector, the conversation feature vector, and a designated message feature vector with a neural network. A designated message may be selected as a suggested message for the user using the context scores. The suggestion may then presented to the user.

    AUTOMATED COMMUNICATIONS OVER MULTIPLE CHANNELS

    公开(公告)号:US20200329144A1

    公开(公告)日:2020-10-15

    申请号:US16503529

    申请日:2019-07-04

    申请人: ASAPP, INC.

    IPC分类号: H04M3/51

    摘要: A company may implement automated workflows for convenience of users or to reduce support costs. For example, allowing a user to change an address using an automated workflow may be faster or less expensive than with a human agent. Companies may provide support over different types of communications channels with different capabilities, such as voice channels and text channels. Instead of implementing different workflows for different channels, a company may separate aspects of the workflow that are common to different channels from aspects of the workflow that are different for different channels. For example, a workflow may be implemented to determine an action in response to a received communication where the action may be used with multiple channels. The action may then be used to select an action implementation that is specific to a channel.

    Updating natural language interfaces by processing usage data

    公开(公告)号:US10210244B1

    公开(公告)日:2019-02-19

    申请号:US15894504

    申请日:2018-02-12

    申请人: ASAPP, INC.

    摘要: A third-party company may assist companies in providing natural language interfaces for their customers. To implement a natural language interface for a company, a configuration may be received that includes information, such as a list intents, seed messages for the intents, and hierarchical information of the intents. An intent classifier may be trained using the configuration, and the natural language interface may be deployed for use with customers. Usage data of the natural language classifier may be collected and used to improve the natural language interface. Messages corresponding to an intent may be clustered into clusters of similar messages, and a prototype message may be obtained for each cluster to provide a human understandable description of the cluster. The information about the clusters may be used to improve the natural language interface, such as by creating a new intent with a cluster or moving a cluster to a different intent.