Google Inc.

Eyal Segalis

Yaniv Leviathan

Yossi Matias

Daniel Walevski

Yoav Tzur

Gal Chechik

Asaf Aharoni

Sasha Goldshtein

Tomer Amiaz

Ran El Manor

Ofer Ron

Razvan Mathias

Justin Min

Deborah Cohen

Weizhao Wang

Gal Elidan

Matthew Streit Coursen

Efrat Farkash

Monica Priya Garde

Jiarui Li

Michael Fink

Michal Levin

Niv Efron

Brian Patton

Nir Bar-Lev

Yair Shimshoni

Michal Neufeld

Nimrod Tamir

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, relating to synthetic call status updates. In some implementations, a method includes determining, by a task manager module, that a triggering event has occurred to provide a current status of a user call request. The method may then determine, by the task manager module, the current status of the user call request. A representation of the current status of the user call request is generated. Then, the generated representation of the current status of the user call request is provided to the user.

Automated call requests with status updates

Implementations are directed to receiving unstructured free-form natural language input, generating a chatbot based on the unstructured free-form natural language input and in response to receiving the unstructured free-form natural language input, and causing the chatbot to perform task(s) associated with an entity and on behalf of the user. In various implementations, the unstructured free-form natural language input conveys details of the task(s) to be performed, but does not define any corresponding dialog state map (e.g., does not define any dialog states or any dialog state transitions). Nonetheless, the unstructured free-form natural language input may be utilized to fine-tune and/or prime a machine learning model that is already capable of being utilized in conducting generalized conversations. As a result, the chatbot can be generated and deployed in a quick and efficient manner for performance of the task(s) on behalf of the user.

Unstructured description-based chatbot development techniques

Implementations are directed to providing a voice bot development platform that enables a third-party developer to train a voice bot based on training instance(s). The training instance(s) can each include training input and training output. The training input can include a portion of a corresponding conversation and a prior context of the corresponding conversation. The training output can include a corresponding ground truth response to the portion of the corresponding conversation. Subsequent to training, the voice bot can be deployed for conducting conversations on behalf of a third-party. In some implementations, the voice bot is further trained based on a corresponding feature emphasis input that attentions the voice bot to a particular feature of the portion of the corresponding conversation. In some additional or alternative implementations, the voice bot is further trained to interact with third-party system(s) via remote procedure calls (RPCs).

Example-based voice bot development techniques

Implementations are directed to updating a trained voice bot that is deployed for conducting conversations on behalf of a third-party. A third-party developer can interact with a voice bot development system that enables the third-party developer to train, update, validate, and monitor performance of the trained voice bot. In various implementations, the trained voice bot can be updated by updating a corpus of training instances that was initially utilized to train the voice bot, and updating the trained voice bot based on the updated corpus. In some implementations, the corpus of training instances may be updated in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party. In additional or alternative implementations, the corpus of training instances may be updated in response to determining the trained voice bot does not include a desired behavior.

Updating trained voice bot(s) utilizing example-based voice bot development techniques

Methods, systems, and apparatus for an automated calling system are disclosed. Some implementations are directed to using a bot to initiate telephone calls and conduct telephone conversations with a user. The bot may be interrupted while providing synthesized speech during the telephone call. The interruption can be classified into one of multiple disparate interruption types, and the bot can react to the interruption based on the interruption type. Some implementations are directed to determining that a first user is placed on hold by a second user during a telephone conversation, and maintaining the telephone call in an active state in response to determining the first user hung up the telephone call. The first user can be notified when the second user rejoins the call, and a bot associated with the first user can notify the first user that the second user has rejoined the telephone call.

Automated calling system

Systems and methods are provided for searching and identifying quotes in response to a query from a user. Consistent with certain embodiments, systems and methods are provided for identifying one or more subject entities associated with the query and for identifying, from a database or from search results obtained in response to the query, a set of quotes corresponding to the one or more subject entities. Further, systems and methods are provided for determining quote scores for the identified quotes based on at least one of the relationship of each quote to the one or more subject entities, the recency of each quote, and the popularity of each quote. Additionally, systems and methods are provided for organizing the identified quotes in a rank order based on the quote scores and selecting quotes based on the rank order or based on the quote scores. In addition, systems and methods are provided for transmitting information to display the selected quotes on a display device.

Systems and methods for searching quotes of entities using a database

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving data identifying contexts; selecting an initial set of questions based at least on the contexts; providing a respective representation of the questions of the initial set, for output; receiving data indicating a selection of a particular representation associated with a particular question of the initial set of questions; selecting a set of answers associated with the particular question; providing a respective representation of the answers of the set that are associated with the particular question, for output; receiving data indicating a selection of a particular representation associated with a particular answer of the set that are associated with the particular question; determining that an additional set of other questions is associated with the particular answer; and in response to the determining, providing a respective representation of the other questions of the additional set, for output.

Question and answer interface based on contextual information

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Tel Aviv, Israel

Eyal Segalis