Asapp, Inc.

Joseph Ellsworth Hackman

Gustavo Sapoznik

Shawn Henry

Tao Lei

Christopher David Fox

Hui Dai

Satchuthananthavale Rasiah Kuhan Branavan

Punyashloka Biswal

Frederick William Poe Heckel

Vicky Sehrawat

Jason Shaev

Brian Dillmann

Aaron Isaksen

Jonathan David Weese

Kelsey Taylor Ball

Priya Sawant

Adrien Brochard

Alan Nicolas Hampton

Mitchell Steven Morgan

A third-party service may be used to assist entities in responding to requests of users by determining a suggested resource corresponding to a received communication. The third party service may receive a request from a first entity, such as via an application programming interface request, that includes a message in a conversation. A conversation feature vector may be computed by processing the message with a first neural network. A suggested resource may be determined using the conversation feature vector. The third-party service may then return the suggested resource for use in the conversation. The third-party service may similarly be used to assist other entities in responding to requests of users.

Third-party service for suggesting resources for a received message

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 of 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 be presented to the user.

Customized message suggestion with user embedding vectors

A third-party service may be used to assist entities in responding to requests of users by determining a suggested response to a received communication. The third party service may receive a request from a first entity, such as via an application programming interface request, that includes a message in a conversation. A conversation feature vector may be computed by processing the message with a first neural network. A suggested respond to the message may be determined by processing the conversation feature vector with a second neural network. The third-party service may then return the suggested response for use in the conversation. The third-party service may similarly be used to assist other entities in responding to requests of users.

Third-party service for suggesting a response to a received message

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. In some instances, a first communications session may be started between a first user and a second user. During the first communications session, one or more communications may be processed to select an intent of the first user and a value of an information item communicated by the first user. An automated workflow may be selected to continue assisting the first user, and the first user may be transferred to a second communications session with the automated workflow. The automated workflow may be initialized with the value of the information item that was provided during the first communications session so that the first user does not need to repeat information.

Natural language processing for information extraction

The third-party company may provide a user interface to a customer to submit a customer support request regarding a first company. The third-party company may receive a customer identifier from the first device and may request prediction data from the first company using the customer identifier. The third-party company may process the prediction data to predict a customer support request of the customer and generate user interface data corresponding to the predicted customer support request. The user interface data may be transmitted to the user interface before a customer support request from the customer is received.

Predicting customer support requests

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.

Automated communications over multiple channels

Initialization of automated workflows

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.

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.

Maintaining machine language model state across communications channels

A third-party service may be used to assist entities in responding to requests of users. A third-party service may receive, directly or indirectly, a request of a first user for assistance from a first entity. The third-party service may request information about the first user by sending a request to a computer of the first entity. The third-party service may use the request of the first user and the information about the first user to automatically generate a response to the request of the first user. The third-party service may then transmit, directly or indirectly, the response to the first user.

Assisting entities in responding to a request of a user

A third-party company may assist other companies in providing customer support to their customers. The third-party company may provide software to a computer of a customer service representative to present a user interface to assist the customer service representative in responding to customer requests. Third-party company may also send update data to the computer of the customer service representative to cause a portion of the user interface to be updated, where the update data is determined using an intent of a message received from a customer. A message received from the customer may be processed to determine the intent of the message, a template may be obtained using the intent, and the update data may be generated by rendering the selected template. The update data may then be transmitted to the computer of the customer service representative to cause a portion of the user interface to be updated.

Using semantic processing for customer support

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.

Updating natural language interfaces by processing usage data

A company may desire to maintain a quality level for messages sent by customer service representatives to customers. The company may receive a message input by a customer service representative, modify the message with one or more neural networks, and transmit the modified message to a customer. To modify a message, an input vector may be created for each word of the message where the input vector is created using a word embedding of the word and a feature vector that represents the characters of the word. The input vectors for the words of the message may be sequentially processed with an encoding neural network to compute a message encoding vector that represents the message. The message encoding vector may then be processed by a decoding neural network to sequentially generate the words of a modified message. The modified message may then be transmitted to the customer.

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New York, NY, United States of America

Joseph Ellsworth Hackman