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

Vol. IX, Tab 47 - Ex. 12 - Email from AdWords Support - Your Google AdWords Approval Status

Exhibits from the un-sealed joint appendix for Rosetta Stone Ltd., v. Google Inc., No. 10-2007, on appeal to the 4th Circuit. Issue presented: Under the Lanham Act, does the use of trademarked terms in keyword advertising result in infringement when there is evidence of actual confusion

Vol. IX, Tab 41 - Ex 6 - Google Three Ad Policy Changes

Exhibits from the un-sealed joint appendix for Rosetta Stone Ltd., v. Google Inc., No. 10-2007, on appeal to the 4th Circuit. Issue presented: Under the Lanham Act, does the use of trademarked terms in keyword advertising result in infringement when there is evidence of actual confusion

Vol. XXII, Tab 59 - Google\u27s Opposition to Rosetta Stone\u27s Motion for Sanctions

Exhibits from the un-sealed joint appendix for Rosetta Stone Ltd., v. Google Inc., No. 10-2007, on appeal to the 4th Circuit. Issue presented: Under the Lanham Act, does the use of trademarked terms in keyword advertising result in infringement when there is evidence of actual confusion

Vol. VIII, Tab 39 - Ex. 3 - Google\u27s Trademark Complaint Policy

Exhibits from the un-sealed joint appendix for Rosetta Stone Ltd., v. Google Inc., No. 10-2007, on appeal to the 4th Circuit. Issue presented: Under the Lanham Act, does the use of trademarked terms in keyword advertising result in infringement when there is evidence of actual confusion

Vol. IX, Tab 46 - Ex. 40 - Document TMprocess.txt Trademark meeting 3/4

Exhibits from the un-sealed joint appendix for Rosetta Stone Ltd., v. Google Inc., No. 10-2007, on appeal to the 4th Circuit. Issue presented: Under the Lanham Act, does the use of trademarked terms in keyword advertising result in infringement when there is evidence of actual confusion

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

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