Google Inc.

Yoav Tzur

Yaniv Leviathan

Eyal Segalis

Gal Chechik

Yossi Matias

Ran El Manor

Michael Andrew Goodman

Yuval Baror

Ofer Ron

Asaf Aharoni

Sasha Goldshtein

Efrat Farkash

Jan Piotr Jedrzejowicz

Joseph Joseph Cherukara

Andrew George Shebanow

Dekel Auster

Felix Wang

Amit Sivan

Adva Wolf

Tamuz Gindes

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 engage in corresponding conversations with additional users. In various implementations, the unstructured free-form natural language input implicitly defines a corresponding dialog state map (e.g., defines corresponding dialog states and/or corresponding dialog state transitions) without defining any explicit dialog states and/or explicit dialog state transitions. In other implementations, the unstructured free-form natural language input is assigned to explicit dialog states and/or explicit dialog state transitions. Nonetheless, the unstructured free-form natural language input may be utilized to fine-tune and/or primed 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.

Structured description-based chatbot development techniques

Implementations disclosed herein optimize performance of phone calls where users interact with interactive voice response (IVR) systems. By utilizing dynamic wait time statistics and intelligent call management techniques, these implementations minimize wait times and reduce telephone network traffic. Call optimization systems can analyze real-time data to provide users with real-time wait time information to inform users decision-making prior to placing a call or at the time of placing a call. Implementations can present dynamic wait time statistics that consider personalized user experiences to provide more accurate information about any particular IVR navigation path that a user can select from. Selection and execution of a suggested IVR path can result in additional wait time data that can be utilized for refining wait time statistics for users.

Dynamic interactive voice response (IVR) route estimation to reduce telephone network traffic

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 utilizing an assistant to automatically navigate an interactive voice response (IVR) tree to arrive at a target state during an assisted telephone call. The assistant can receive input to initiate the assisted telephone call, identify an entity to engage with, on behalf of the user, and during the assisted telephone call, based on the input, and identify an IVR tree stored in association with the entity. In various implementations, navigation of the IVR tree can be modified based on interaction(s) detected at a client device subsequent to initiating the assisted telephone call. In various implementations, the assisted telephone call can be initiated from a search interface, and the target state can be associated with a given search result. In various implementations, the IVR tree can be dynamic in that only a subset of candidate state(s) of the IVR tree may be available as the target state.

Automatic navigation of an interactive voice response (IVR) tree on behalf of human user(s)

Implementations are directed to simulating automated telephone call(s) to be performed by an automated assistant. Processor(s) can receive user input and determine, based on the user input, that the user input includes: a request to cause the automated assistant to initiate an automated telephone call with an entity, and a task to be performed during the automated telephone call. In some implementations, the processor(s) can cause a simulation of the automated telephone call to be performed to simulate the task, and, based on a result of the simulation, determine whether to initiate the automated telephone call or to refrain from initiating the automated telephone call. In additional or alternative implementations, the processor(s) can determine whether to cause the simulation of the automated telephone call to be performed based on a type of the entity, a type of the task, and/or whether a prior simulation of the task has been performed.

Simulation of automated telephone call(s)

Processor(s) of a client device of a user can receive a telephone call that is initiated by an additional user, and, in response to receiving the telephone call, identify an entity that is associated with the additional user, and determine, based on the entity that is associated with the additional user, whether to (1) fully automate the telephone call, or (2) partially automate the telephone call. In fully automating the telephone call, the processor(s) can cause a chatbot to engage in a corresponding conversation with the additional user and without prompting the user for any input. In partially automating the telephone call, the processor(s) can cause the chatbot to engage in a corresponding conversation with the additional user but with prompting the user for input(s) via suggestion chip(s). In some implementations, the processor(s) can further determine whether to (3) refrain from automating the telephone call entirely.

System(s) and method(s) for implementing a personalized chatbot

Implementations relate to determination and visual display of spoken menus for calls. In some implementations, a computer-implemented method includes receiving audio data output in a call between a call device and a device associated with a target entity. The audio data includes speech indicating one or more selection options for a user of the call device to navigate through a call menu provided by the target entity in the call. Text is determined by programmatically analyzing the audio data, the text representing the speech. The selection options are determined based on programmatically analyzing at least one of the text or the audio data. At least a portion of the text is displayed by the call device during the call, as one or more visual options that correspond to the selection options. The visual options are each selectable via user input to cause corresponding navigation through the call menu.

Determination and visual display of spoken menus for calls

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 computer storage media, for selecting textual representations for entity attribute values. One of the methods includes receiving, for presentation to a user, data identifying a relevant entity and a respective presentation attribute value for each of a plurality of presentation attributes associated with the relevant entity; obtaining user profile data for the user; selecting a respective textual representation for each of the presentation attribute values, wherein selecting the textual representations comprises selecting a first alternative textual representation for a first presentation attribute value based on the user profile data; and providing data identifying the entity and the textual representations for presentation to the user.

Selecting textual representations for entity attribute values

In one example embodiment, a computer system includes at least one processor and a memory storing a data graph and instructions. The instructions, when executed by the at least one processor, cause the system to generate a template sentence based on a fact including a first node, a second node and a string, wherein the first node and the second node exist in the data graph and the string represents a fact that is absent from the data graph, search the internet for a document including the template sentence, and upon determining the internet includes the document with the template sentence, infer the fact by generating a series of connections between nodes and edges of the data graph that together with the first node and the second node are configured to represent the fact, the series of connections defining a path, in the data graph, from the first node to the second node.

Semi structured question answering system

Systems and methods are disclosed for providing an explanation of an estimate for information missing from a data graph. An example method may include receiving a query that requests information for a first entity and receiving an estimate for the information, the estimate being based on a plurality of features of a joint distribution model. The method may include determining respective contribution scores for the plurality of features, selecting a quantity of the features with highest contribution scores, generating, using the selected quantity of features, an explanation for the estimate; and providing the explanation and the estimate as part of a search result for the query.

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Tel Aviv, Israel

Yoav Tzur