Asapp, Inc.

Asaap, Inc.

Shawn Henry

Gustavo Sapoznik

Joseph Ellsworth Hackman

Punyashloka Biswal

Tao Lei

Frederick William Poe Heckel

Vicky Sehrawat

Jason Shaev

Brian Dillmann

Hui Dai

Lei Xu

Michael Griffiths

Howard Chen

Lisa Lijia Jiang

Alan Nicolas Hampton

Kevin Yang

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 description of a conversation may be generated to allow a person to understand important aspects of the conversation without needing to review the conversation. The conversation description may be generated by identifying one or more events that occurred in the conversation and then generating the description using the identified events. A set of possible events may be determined in advance for a particular application. The events may be identified by using an event neural network for each event. Each event neural network may process the messages of the conversation to generate an event score that indicates a match between the conversation and the corresponding event. The event scores may then be used to select one or more events. Message scores from the event neural network of a selected event may then be used to select one or more messages of the conversation as a rationale for the selected event.

Generating conversation descriptions using neural networks

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 user may respond to a request of another user by entering text, such as a customer service representative responding to a customer. As the user enters text, a suggested completion of the text may be suggested to the user so that the user may select the suggested completion instead of continuing to enter text. Previous messages between the two users and other information may be used to determine an appropriate suggested completion to the entered text. A neural network language model and a search graph may be used to select a suggested completion from a search graph of possible suggested completions.

Automatically suggesting completions of text

When processing a request containing personal information, personal information may be needed to respond to the request, but it may be desired to limit storage of personal information for privacy reasons. To accomplish both objectives, text of a message may be processed using multiple levels of redaction. A first level of redaction may replace digits of the text with a token so that sensitive numbers, such as credit card numbers or social security numbers are no longer present. A second level of redaction may replace one or more words of the text with a label indicating the text that was present, such as by replacing an address with a label indicating that an address was present or by replacing a credit card number with a label indicating that a credit card number was present.

Removing personal information from text using multiple levels of redaction

Text of an incomplete message entered by a user may be processed using a neural network to suggest messages similar to the message the user is in the process of entering. Word embeddings may be obtained for the words of the text that represent the words in a first vector space. The word embeddings may then be processed by the neural network to compute an input message feature vector that represents the incomplete message in a second vector space. The input message feature vector may be used to select a first designated message as a suggestion from a set of designated messages, and the first designated message may be selected using a similarity score computed from the input message feature vector and a first designated message feature vector corresponding to the first designated message. The first designated message may then be presented as a suggestion to the user.

Processing an incomplete message with a neural network to generate suggested messages

A plurality of conversations may be processed to obtain one or more representative conversations to allow a better understanding of the plurality of conversations. A representative conversation may be determined by representing each conversation as a sequence of states where a state may represent messages with similar meanings. Distances may be computed between pairs of conversations, and the conversations may be clustered using the distances. To obtain a representative conversation for a cluster of conversations, a representative sequence of states may be obtained for the cluster and a representative message may be obtained for each state of the sequence of states. The representative conversation may then be presented to a user.

Identifying representative conversations using a state model

During a conversation between a customer and a customer support representative, suggestions may be presented to the customer support representative to upsell a product to the customer. Information about the customer and/or information about the conversation may be processed by a computer to determine when to suggest the upsell to the customer support representative and the one or more products to be upsold. The determination may be performed by computing features from the information about the customer and the information about the conversation, and processing the features with one or more classifiers.

Automated upsells in customer conversations

A neural network may be used to remove personal information from text (such as names, addresses, credit card numbers, or social security numbers), and replace the personal information with a label indicating the type or class of the removed information. The neural network may comprise multiple layers that compute a context vector for words of the text, compute label scores for words of the text using the context vectors, and select a label for each word using the label scores. Words corresponding to certain labels may be replaced with a label, such as replacing the digits of a credit card number with a label &#x3c;cc_number&#x3e;. The redacted text may then be presented to a person or stored for later processing.

Removing personal information from text using a neural network

Users may request assistance or information from a limited number of resources, such as submitting a user request by speaking or entering text. A user request from among the pending user requests may be selected using a selection model. A selection model may process features relating to each of the pending user requests and generate a probability distribution for the pending user requests. A user request may then be selected using the probability distribution, such as by making a random selection. The selection model may be updated over multiple time periods by computing reward scores for the selection decisions made by the selection model and using the reward scores to update the parameters of the selection model.

Using a neural network to optimize processing of user requests

A first user and a second user may exchange messages during a session and the exchanged messages may be processed to suggest resources to the first user. For example, the first user may be a customer service representative assisting a customer, and the suggested resources may include text of a message to send to the customer. The resources may be suggested by computing a semantic representation of the messages in the session, computing a context vector that describes a context of the session, computing a context hash vector from the context vector, and retrieving one or more resources from a data store of resources using the context hash vector. Information about a resource may be presented to the first user to assist the first user in responding to the second user.

Suggesting resources using context hashing

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 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.

Maintaining quality of customer support messages

A language model may be used in a variety of natural language processing tasks, such as speech recognition, machine translation, sentence completion, part-of-speech tagging, parsing, handwriting recognition, or information retrieval. A natural language processing task may use a vocabulary of words, and a word hash vector may be created for each word in the vocabulary. A sequence of input words may be received, and a hash vector may be obtained for each word in the sequence. A language model may process the hash vectors for the sequence of input words to generate an output hash vector that describes words that are likely to follow the sequence of input words. One or words may then be selected using the output word hash vector and used for a natural language processing task.

Word hash language model

A user may respond to a request of another user by entering text, such as a customer service representative responding to a customer. Suggestions of resources may be provided to the responding user to assist the responding user in providing a response. For example, a resource may provide information to the responding user or allow the responding user to perform an action. Previous messages between the two users and other information may be used to select a resource. A conversation feature vector may be determined from previous messages, and feature vectors may be determined from the resources. The conversation feature vector and the feature vectors determined from the resource may be used to select a resource to suggest to the responding user.

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Longmont, CO, United States of America

Shawn Henry