Google Inc.

Alexander Fabrikant

Andrew Stephen Tomkins

Robin Dua

Kevin Snow McCurley

Renaud Bourassa-Denis

Atish Das Sarma

Prathab Murugesan

Tanmay Sanjay Khirwadkar

Nitish John Korula

Gagan Aggarwal

Jesse M Rosenstock

Daniel Wyatt

Lujun Fang

Galen Pickard

Rob Schonberger

Anna Teytelman

Yaron Singer

Kristen Lefevre

Shanmugasundaram Ravikumar

Brett Rolston Lider

Sreenivas Gollapudi

Andrew Ames Bunner

Joseph Robert Smarr

Erik Nathan Vee

Zachary Yeskel

Balaji Srinivasan

Abhijit Bose

David Andrew Gibson

Zach Yeskel

David Andrew Huffaker

Rachel Ida Rosenthal Schutt

Makoto Uchida

Bin Chang

Amirali Abdullah

Gueorgi Kossinets

James Alexander Cook

Temporal proximity of newly formed social relationships is used to determine the likelihood of common features being shared by users in the newly formed social relationships. The respective times in which each of a plurality of new relationships are formed are determined to be related to each other, and user information associated each of a plurality of respective contact accounts who are the subject of the new relationships are compared, and a common feature likely shared by the respective contact accounts is identified based on the comparing.

Using temporal proximity of newly formed relationships to predict common features

A method is disclosed for estimating a duration that an instance of subject matter-related content will remain relevant. An archive of data sources spanning a period of time is analyzed to identify past instances in time in which a subject matter was newsworthy, and the duration of each instance. On receiving an indication that a user is interested in a current instance of the subject matter, an estimated period of time that the current instance will be of interest to the user is may be determined based on the duration of the past instances. In some aspects, the archive may be a corpus of social media data compiled from a social network, with each data source including or representative of an interaction between users in the social network, and the current instance of the subject technology may include content provided to the user through a social stream.

Measuring media attention over time based on long term heterogeneous archive data

Implementations generally relate to hashtags. In some implementations, a method includes providing one or more location-based contextual hashtags to a user by receiving, from a first user device associated with a first user, information indicative of a physical location of the first user device. The method further includes identifying, with one or more processors, a place of interest based on the information indicative of the physical location of the first user device. The method further includes determining a category associated with the place of interest. The method further includes retrieving one or more hashtags from one or more databases based on the place of interest or the category associated with the place of interest. The method further includes providing the one or more hashtags and information about the place of interest to the first user device.

Determining location-based contextual hashtags

In one aspect, a method includes receiving an indication that a first user has interacted with a second user in a social network, calculating a social affinity score between the first user and the second user based on first user information, second user information, or relationship information between the first user and the second user and scheduling delivery of a notification to the recipient based on the social affinity score. Systems and machine-readable media are also provided.

Ranking to determine relevance of social connections

Computer-implemented methods and systems of determining semantic place data include receiving a plurality of location data reports from a plurality of mobile devices, partitioning them into localized segments, and estimating a geographic region bucket for each segment. For clustering canopies of localized segments identified as satisfying a potential geographic overlap characterization, an overlap score is calculated that correlates the overlap among actual geographic regions covered by movement of the mobile devices generating the localized segments in that given clustering canopy. A data structure that provides a hierarchical clustering configuration of the localized segments in each geographic region bucket is generated from the determined overlap scores. Additional semantic data for nodes in the data structure can also be provided.

Analyzing semantic places and related data from a plurality of location data reports

Systems and methods for generating a user location history are provided. One example method includes obtaining a plurality of location reports from one or more devices associated with the user. The method includes clustering the plurality of location reports to form a plurality of segments. The method includes identifying a plurality of location entities for each of the plurality of segments. The method includes determining, for each of the plurality of segments, one or more feature values associated with each of the location entities identified for such segment. The method includes determining, for each of the plurality of segments, a score for each of the plurality of location entities based at least in part on a scoring formula. The method includes selecting one of plurality of locations entities for each of the plurality of segments.

Systems and methods for generating a user location history

Systems and methods for identifying an entity associated with a wireless network access point are provided. An estimated location of a wireless network access point and a network name associated with a wireless network access point can be accessed. The network name can be analyzed to identify at least one text signal. An entity associated with the wireless network access point can be identified based at least in part on the text signal. For instance, a confidence score for a plurality of candidate entities identified using the estimated location of the wireless network access point can be determined based on the text signal. The confidence score can be used to identify the entity associated with the wireless network access point. Information associated with the entity can be presented in a graphical user interface.

Identifying an entity associated with wireless network access point

Aspects of the subject technology relate to a social-networking system, including one or more computers communicatively coupled via a network. In certain aspects, the computers are configured to perform operations including, receiving one or more indications that a common user has initiated a connection with each of a plurality of contacts in a social network and identifying two or more contacts, from among the plurality of contacts, that share a temporal relationship with respect to the connections formed between the common user and the respective two or more contacts in the social network. In certain implementations, the operations can further include comparing information associated with the two or more contacts to determine a likelihood that a common feature is shared by the two or more contacts. Computer-implemented methods and computer-readable media are also provided.

Methods for using temporal proximity of social connection creations to predict properties of a social connection

A method of setting privacy level values for a user. The method includes receiving a designation of a relative privacy level for the user and receiving a selection of a group of other users for the user. Plural sets of privacy level values are associated with the group of other users. The method also includes setting privacy level values for the user based on the designation of the relative privacy level and the plural sets of privacy level values associated with the selected group of other users.

Privacy selection based on social groups

Provided is a process and system for ascertaining the operating hours of a business. The process includes obtaining wireless-environment data indicative of the location of business and a timestamp; determining that a user device is located at the business responsive to both the wireless-environment data and wireless-environment data from checked-in user devices that have checked-in to the business via a social network; storing the timestamp in a timestamp data store; estimating operating hours of the business responsive to stored timestamps; and storing the estimated operating hours in a business-hours data store.

Apparatus and method for ascertaining the operating hours of a business

Aspects and implementations of the present disclosure are directed to methods and systems of pre-processing a social network structure for fast discovery of cohesive groups. In general, in some implementations, a data processing system identifies a cohesive user group in a social network for delivery of a tailored content item. Generally, the data processing system identifies an affinity criteria; generates a set of user identifiers having characteristics that satisfy the affinity criteria; and generates graphs of users with at least one direct or indirect social network user connection with other user identifiers in the graph. The data processing system returns or stores the graph on computer readable media for later use. A graph may be generated with edges representing connections between user identifiers; edges may be weighted for the number and lengths of connection paths for indirect connections, and for similarities between users.

Pre-processing of social network structures for fast discovery of cohesive groups

A system and method for facilitating automatic objective-based feature improvement, the method including receiving a request to identify an optimal alternative for each of one or more features of a computer-implemented entity, determining an alternative feature range for each of the one or more features, the alternative feature range defining a range of possible alternative features available with respect to the feature, selecting one or more alternative features for each feature from the alternative feature range of the feature, generating a plurality of alternative sets, each including an alternative feature for at least one of the one or more features, selecting a plurality of user groups from a pool of users and assigning each user group of the plurality of users groups to one of the plurality of alternative sets based on user characteristics of the users and historical information regarding the interaction of the user with the computer-implemented entity.

Automated objective-based feature improvement

Growing community of inventors

East Palo Alto, CA, United States of America

Alexander Fabrikant