Asapp, Inc.

Michael Griffiths

Kilian Quirin Weinberger

Ethan Russell Elenberg

Shawn Henry

Lei Xu

Yi Yang

Robert McDowell

Tao Ma

Hugh Nicholas Perkins

Jeremy Elliot Azriel Wohlwend

Samuel Andrew Altschul

Sam Altschul

Tianyi Zhang

Geoffrey Michael Pleiss

Cosima Travis

Theodore David Burke

Connor Daniel McNabb

A communications session with a user may be automated using a language model. The language model may be instructed to select a next action to be performed where the next action may include transmitting a responsive communication to the user or performing an API call. The prompt used to query the language model may include one or more of the following: a representation of text of the communications session, a list of available API calls, instructions to select a next action, a representation of API calls performed, or a representation of API call responses received. The language model may be sequentially queried to continue the communications session by transmitting responsive communications or performing API calls. In some implementations, a prompt template may be used to generate the prompt and a prompt template may be selected using text of the communications session.

Prompting language models to select API calls

Collar press

A user using a messaging application may be in conversations with multiple people and may inadvertently send a message intended for a first person to a second person. The user may be warned before making such mistakes by processing the text of an entered message and/or the text of the conversations with a mathematical model. A match score may be computed that indicates the match between the entered message and the conversation in which it was entered. Where the match score indicates a possible mistake, a warning may be presented to the user. In some implementations, a match score may be computed using a conversation encoding vector and a message encoding vector. In some implementations, a match score may be computed by processing a sequence of tokens for the conversation and the entered message that includes special token separators.

Identifying messages entered into an incorrect conversation

Machine learning-based approaches are used to detect trends of behavior and anomalous events from customer support messages between customers and customer support agents or other appropriate resources in an electronic environment. For example, for a plurality of time periods, a prediction model can be trained. The prediction models can be trained on messages that correspond to each prediction models' period of time. The prediction models can process messages to determine a score (e.g., a representative confidence score) for the time period a prediction model is associated with. For a selected time period, a model (e.g., a trend detection model) can be applied to the scores for time periods before the selected time period to determine whether the score for the selected time period is associated with an anomalous event. Thereafter, an alert can be presented with, for example, the messages that triggered the alert, among other such information.

Trend detection via machine learning models

In some applications, it may be desired to process a message to determine an intent of the message, where the intent indicates the meaning of the message. An intent classifier may be used to determine the meaning of a message by processing the message to compute a message embedding vector that represents the message in a vector space. Each possible intent may be represented by a prototype vector, and the intent of the message may be determined by comparing the message embedding to one or more prototype vectors, such as by selecting an intent whose prototype vector is closest to the message embedding. An intent classifier may be used, for example, (i) to implement an automated communications system with states where each state is associated with a subset of the possible intents or (ii) for processing usage data of a communications system to update the intents of the communications system.

Intent discovery with a prototype classifier

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

Growing community of inventors

Brooklyn, NY, United States of America

Michael Griffiths