Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Network marketing compensation systems and methods that have been modified to include compensation for those that are not initially in the network are described. In particular, this application describes compensation methods that include creating a network compensation system for sales of products, the system containing a unilevel distribution network with at least five levels of distributors, allowing an affiliate to recommend a product to a customer, allocating a position in the network to the affiliate, and distributing compensation under the system to the affiliate and the distributor in near real time upon purchase of the product by the customer. These compensation systems and associated methods have a great potential to increase involvement by customers (and others outside the network like an affiliate) that otherwise might not be attracted to it. Other embodiments are described.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 <cc_number>. The redacted text may then be presented to a person or stored for later processing.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A neural network may be used to suggest a response to a received message. One or more messages of a conversation may be processed to generate a conversation feature vector that describes the conversation. The conversation feature vector may be used to select a template from a data store of templates. For example, each template may be associated with a template feature vector, and the template whose template feature vector is closest to the conversation feature vector may be selected. The selected template may have a slot corresponding to a class of words, such as a person's name. A text value may be obtained corresponding to the slot (e.g., a person's name), and the template and the text value may be used to suggest a response to the received message. A person may select the suggested response to cause the suggested response to be sent as a message.

Abstract: 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.