Google Inc.

Gabriel Leblanc

Tuan Anh Nguyen

Yuan Yuan

Qiong Huang

Ying Di Zhang

Ying Zhang

Bhaskar Mehta

Robert Fraser

Tzu-Chan Chuang

William A Truong

Alexey Galata

Yixing Cai

Xiao Meng Zhang

Tanmay Sanjay Khirwadkar

Jesse M Rosenstock

Min-yian Su

Xiao Zhang

Hot word free adaptation, of function(s) of an automated assistant, responsive to determining, based on gaze measure(s) and/or active speech measure(s), that a user is engaging with the automated assistant. Implementations relate to techniques for mitigating false positive occurrences of and/or false negative occurrences, of hot word free adaptation, through utilization of a permissive parameter set in some situation(s) and a restrictive parameter set in other situation(s). For example, utilizing the restrictive parameter set when it is determined that a user is engaged in conversation with additional user(s). The permissive parameter set includes permissive parameter(s) that are more permissive than counterpart(s) in the restrictive parameter set. A parameter set is utilized in determining whether condition(s) are satisfied, where those condition(s), if satisfied, indicate that the user is engaging in hot word free interaction with the automated assistant and result in adaptation of function(s) of the automated assistant.

Dynamic adaptation of parameter set used in hot word free adaptation of automated assistant

The technology relates to generating a wireless network access point model for determining locations within an area. In one example, a plurality of scan vectors, each scan vector representing a set of signal strengths for a set of wireless network access point identifiers and being associated with a location within the area at which a mobile device moving through the area measured the set of signal strengths are received. A set of clusters is generated by iterating through a split phase until the number of clusters in the set of clusters equals a maximum number of clusters or there are no bad scan vectors identified. A bad scan vector may include a vector having a vector difference from a center of a cluster to which that vector is assigned is less than a minimum similarity value. The set of cluster is then used to generate the model of the area.

Generating wireless network access point models using clustering techniques

Systems and methods for determining semantic place names from one or more location reports received from a user device are provided. High quality visits for a candidate semantic place location from a plurality of previously obtained location reports can be aggregated and used to generate a point cloud for the semantic place location. A high quality visit can correspond to a visit by a device that is determined to be associated with a candidate semantic place location with greater likelihood relative to a plurality of other candidate semantic place locations. Data associated with one or more point clouds can be accessed and used to support determinations of semantic place name for one or more location reports. In example embodiments, the semantic place name can be stored as part of a location history and/or provided for display in a user interface presented on a display device.

Determining semantic place names from location reports

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Sunnyvale, CA, United States of America

Gabriel Leblanc