Apple Inc.

Andrew Williams

Ashish C Nagre

Jarad M Fisher

Akila Suresh

Shanni A Weilert

Andrei Makhanov

Fang Ji

Ievgeniia Gutenko

Ren Wang

Ryan Studelska

Aditya Rane

Jeffrey L Robbin

Patrice Olivier Gautier

Eswar Priyadarshan

Usama Mikael Hajj

Steve Saro Gedikian

Maria Dimitrova Ioveva

Eduardo Humburto Cue

Christopher Laurence Bell

David E Ralston

Daniel Joseph Sherman

Victor Shugaev

Evan C Krasts

The present technology improves points-of-interest (POIs) in applications by the gathering and use of data available from various sources to improve metadata of POIs in applications (e.g., map applications) or any other metadata or information that may be of interest to a user regarding any given POI. The present technology resolves transactions to POIs or Brands (in a map application, for example) and improves, updates, creates, and removes POls/Brands. The present technology can also gain a clear name, granular and correct categorization, a URL, phone/chat contact info, etc. of the transactions.

Systems and methods for resolving points of interest on maps

The present technology improves points-of-interest (POIs) in applications by the gathering and use of data available from various sources to improve metadata of POIs in applications (e.g., map applications) or any other metadata or information that may be of interest to a user regarding any given POI. The present technology resolves transactions to POIs or Brands (in a map application, for example) and improves, updates, creates, and removes POIs/Brands. The present technology can also gain a clear name, granular and correct categorization, a URL, phone/chat contact info, etc. of the transactions.

A media item recommendation can be based on application purchase data. Application purchase data of a population of users can be used to create application preference clusters that represent unique application preference profiles. Media preference data of the same population of users can be used to create media preference clusters that represent unique media preference profiles. A preference relationship between an application preference cluster and a media preference cluster can be identified based on the number of members of the media preference cluster that are also members of the application preference cluster. Upon receiving a recommendation request from a user, the user's application preference profile can be determined based on the user's application purchase data. A media item can then be recommended based on a media preference cluster that has a preference relationship with an application preference cluster that represents the application preference profile of the user.

Recommending media items based on purchase history

Separate user accounts can be linked into a group of linked user accounts so that content items assigned to each of the user accounts can be accessed by each user account in the group. Linking user accounts in this way allows the individual user accounts to share content items while also retaining their individual properties such as username, password, preference data, etc. Linking user accounts allows each user account to retain the content items assigned to the user account when the user account is unlinked from the group. Linking user accounts can be restricted according to linking rules that dictate how many user accounts can be included in a group, when a user account can be added or removed from a group, etc. A master user account can set parameters restricting content items accessible to the user accounts in the group, as well as money spent be each user account.

Linked user accounts

Growing community of inventors

Mountain View, CA, United States of America

Andrew Williams