Distributed Representations of Words and Phrases and their Compositionality

The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible

ASSISTANT WITH HISTORICAL LOCATION BASED TRIGGERS

A virtual, intelligent, or computational assistant (e.g., also referred to simply as an “assistant”) is described that performs actions based on an inferred user location, user direction of movement, and/or historical actions performed for previous locations or directions of movement. In some implementations, a user may explicitly command the assistant to perform a particular action when the user is moving relative to, or at, a particular location. In other implementations, the assistant may learn what actions the user performs or causes the assistant to perform when the user is moving relative to, or at, a particular location. In either case, the assistant may monitor location or movement information of the user (e.g., a location history, a current location, etc.) and perform the requested or learned action when the current location or movement information matches the commanded or learned behavior. This way, the assistant is enabled to trigger performance of previously defined actions or tasks based on changes in user’s future location or future movement

CREATION OF THEME-BASED AND/OR GENRE-BASED MUSIC PLAYLISTS USING AN INTERACTIVE ASSISTANT

An interactive assistant, referred to herein as “an interactive assistant,” “a virtual assistant,” or simply “an assistant,” may be configured to create comprehensive playlists based on singer/artist, composer, genre, theme, or other criteria, based on queries from a user. For example, an interactive assistant may accept queries from a user, perform searches for songs and related content based on certain theme-based and/or genre-based criteria specified in the queries (e.g., criteria for one or more love songs, happy songs, scary songs, sad songs, hard-rock songs, and the like), and generate or modify music playlists based on the search results. The interactive assistant may also execute one or more applications, such as a music application, to play the songs included in the generated playlists

OBSERVATION-BASED FORM ASSISTANT

A virtual, intelligent, or computational assistant (e.g., also referred to simply as an “assistant”) is described that is configured to selectively aid users when completing forms (e.g., documents, surveys, orders, fields, questionnaires, set of questions, etc.) or other online activities for which aid can be offered. When a user is filling out a form, the assistant may monitor the user’s rate of progress (e.g., filling out) of the form. If the rate decreases, the assistant may offer aid in completing the form. If the user accepts the aid, the assistant may fill in some or all of the form

DEVICE SETTINGS ASSISTANT

An interactive assistant, referred to herein as “an interactive assistant,” “a virtual assistant,” or simply “an assistant” may execute on counter-top devices, mobile phones, automobiles, and many other type of computing devices. The interactive assistant may be configured to guide the user to access features and settings of the computer device, and may also introduce features and settings of the computer device to the user

PROCESSING MULTIPLE TASKS BY VIRTUAL ASSISTANTS

An interactive assistant, referred to herein as “an interactive assistant,” “a virtual assistant,” or simply an “assistant,” is described that processes multiple tasks presented in a single query by a user. The user may, in other words, issue a query requesting that the virtual assistant perform multiple tasks. The virtual assistant may parse and buffer (or, stated differently, cache) each of the tasks of the multiple tasks, and process them appropriately, such as serially or concurrently (which may be referred to as being processed “in parallel”)

PROVISION OF PERSONALIZED DATE-BASED CONTENT

An interactive assistant computing system, referred to herein as “an interactive assistant,” “a virtual assistant,” “a computational assistant,” or simply an “assistant,” is described that stores and/or retrieves information related to items associated with particular days for a user such that the assistant can automatically retrieve one or more items associated with the current day (e.g., a local weather forecast, a current weather report, upcoming calendar items for the current day, current news stories, stock prices, indications of the day having personal importance, etc.) and provide a personalized report that includes the items specific to the current day. The assistant may also provide an audible indication of one or more of the items, such as an audible verbalization of a birthday wish to the user. This way, the assistant can present an improved, personalized indication of important aspects of the current day, eliminating the need for the user to access multiple different applications in order to access information regarding matters of the immediate future

PROACTIVE ASSISTANCE FOR A PREDICTED DESTINATION

A virtual, intelligent, or computational assistant (e.g., also referred to simply as an “assistant”) is described that relies on supplemental data (e.g., contextual information, user information, etc.) to predict a user’s destination and offer to assist the user with actions the user will likely want to take at the predicted destination. With explicit permission from a user, the assistant may access a user’s location history, calendar, e-mail, messages, past assistant interactions, contacts, photos, search history, sensor data, and other contextual or user information to predict a destination of a user as well as actions the user will likely want to take at the destination. The supplemental data can be stored locally on a device that is executing the assistant or in a cloud computing environment that is accessible to the assistant from the device. This way, the assistant is enabled to proactively offer assistance to a user when he or she will most likely need it, without requiring the user to consider requesting such assistance

ASSISTANT ENABLED ENTERTAINMENT SYSTEM

A virtual, intelligent, or computational assistant (e.g., also referred to simply as an “assistant”) is described that relies on supplemental data (e.g., contextual information, user information, etc.) to determine media, including movies, television shows, music, etc., that a user may want to consume and provide recommendations or automatically perform actions based on to assist the user in consuming the media. With explicit permission from a user, the assistant may access a user’s location history, calendar, e-mail, messages, past assistant interactions, contacts, photos, search history, sensor data, social network accounts, and other contextual or user information develop recommendations. The supplemental data can be stored locally on a device that is executing the assistant or in a cloud computing environment that is accessible to the assistant from the device. This way, the assistant can better understand what kinds of media a user may be interested in consuming and automatically make recommendations for, or take specific actions on, specific media that will satisfy the user’s interest, without requiring the user to search for, pre-record, or otherwise be aware that such media exists

ASSISTANT TEXT NORMALIZATION

A virtual, intelligent, or computational assistant (e.g., also referred to simply as an “assistant”) is described that is configured to perform text normalization when converting text to speech (e.g., when synthesizing audio data for output to a user). The assistant may perform text normalization by determining how pronounce a particular set of characters (e.g., word, homonyms, number, date, acronym, abbreviation, etc.) based on the context in-which the particular set of characters is used. For instance, when performing text to speech on the text “1233 St. Andrew St.” (e.g., when reading an address aloud), the assistant may determine that the first use of the set of characters “St.” should be pronounced as “saint” as it is a prefix of a street address and that the second use of the set of characters “St.” should be pronounced as “street” as it is a suffix of a street address