Apple Inc.

Michael Froimowitz Greenzeiger

Mehul Kirtikant Sanghavi

Ravindra Marotirao Phulari

Steven C Bredenberg

Ryan S Mehlmauer

Mark C Lay

Christopher John Foster Bullen

Ayman Khalil

Greg Labmeier

James L Bell

Weining Zhou

Daniel Bates

Transient user profiles are generated and broadcast to nearby recipient devices. In some implementations, a transient user profile is generated from a master user profile that includes user information. The master user profile can be stored in encrypted form on the user's mobile device or on a network-based computer system where it can be accessed upon authentication. The master user profile can include information provided by the user or retrieved from other network-based computer systems. The master user profile can include information that is labeled with security tags that indicate the class of users that may receive the user information. From the master user profile, a transient user profile can be generated in response to a sharing event and broadcast to other nearby recipient devices.

Creation and consumption of transient user profiles

Methods and arrangements are provided to valuate advertising slots for a client device based on events that are detected on the device. The cost of the advertising slots can vary according to the timing of the user interactions or according to different classifications for the client device that are determined based on the events. The cost of the advertising slot can be determined before or after the invitational content is provided to the client device.

Valuation of invitational content slots based on user attentiveness

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for increasing the performance of an advertisement network by monitoring requests for advertisements from applications, detecting patterns, and developing and implementing remedial actions to increase system performance.

Optimization of promotional content campaigns

Systems, methods, and computer-readable storage media for determining user engagement levels during a presentation of content. The system first collects data associated with a user session at a client device. Next, the system predicts a user engagement level during the user session by applying an engagement predicting rule to the data. The system can predicts respective user engagement levels for various segments of the presentation by applying one or more engagement predicting rules to the data. The system then presents invitational content based on the user engagement level.

Determining user engagement

Systems, methods, and computer-readable storage media for optimizing and reporting time spent by a user engaged in a session. The system first obtains data associated with a presentation of an item of content at a mobile device, the presentation being divided into multiple partitions. Based on the data, the system adjusts at least one respective length of time associated with the multiple partitions to yield at least one adjusted length of time, the at least one adjusted length of time reflecting estimated time of user engagement with content associated with at least one of the multiple partitions. The system then determines an amount of time spent by a user at the mobile device engaging in the presentation based on the at least one adjusted length of time and the multiple partitions.

Active time spent optimization and reporting

Methods and arrangements are provided for the personalized targeting and customization of media stations in an online media service. A key set of inputs, with values unique to each user, is used to arrive at a personalized group of featured media stations that a user has access to. Demographic data, media preferences, user actions detected through the user's input, and other criteria allow for the content and screen time of featured stations to be personalized for each user.

Personalized targeting of media stations

Systems, methods, and computer-readable storage media for dynamic radio content. The system generates a media item to be inserted into a media stream. The media item can include multiple segments associated with a set of discrete components. Next, the system inserts the media item into the media stream. In response to a triggering event, the system then re-generates the media item by assembling the multiple segments using a new set of the discrete components. The system then inserts the re-generated media item into the media stream.

Dynamic media content

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for presenting event data in a heat-map interface. Event data describing user interactions with a content item can be gathered and used to generate the heat map. The gathered event data can be filtered prior to processing to exclude unnecessary or irrelevant event data. The event data can be filtered based on numerous criteria such as a determined type of user interaction, demographic data, location of the event data, content item, etc. Filtering rules can be based on an analysis of the content item to determine what event data to exclude and current performance of the system. The filtered event data can be presented in a heat-map interface that represents the frequency at which event data occurs on an item of content based on location. The event data is analyzed to determine optimization rules that dictate selection of content items.

Heat-map interface

Method and arrangements are provided to generate supplemental analysis modules for items of invitational content, the modules including an event dictionary and event handlers. Such modules are configured to collect event information at an end user device associated with the operation of an item of invitational content, determine whether the event information corresponds to an entry in the event dictionary, and, upon determining that the event information corresponds to an entry, identify an event handler corresponding to the entry and routing the event information to the event handler, where the event handler is configured for generating an event message.

Supplemental analysis module for invitational content

An online media station can be automatically generated based on a user's media preference data. Media preference data can include a user's media item purchase history. The media preference data is analyzed and media preference clusters are generated from the analyzed media preference data. Generated media preference clusters are ranked based on a predetermined set of ranking rules. The top ranked media preference clusters are selected dependent upon the user's number of slots available for customized media stations. One or more media station seeds are selected from each media preference cluster selected based on a set of predetermined selection rules. An algorithmic media station is automatically generated from the one or more media station seeds and provided to an electronic device of the user.

Automatic generation of online media stations customized to individual users

Disclosed are systems, methods, and non-transitory computer-readable storage media for providing a marker with an item of invitational content. A marker provided with an initial view of invitational content communicates features associated with a subsequent view, including the specifications or content of the invitational content, user terminal, or content provider. The marker is determined from input gathered from numerous data sources and according to a set of rules specifying what features and where and how the features are communicated. The rules can dictate that certain variants be selected based on gathered input concerning the user terminal or a user profile. The marker can be determined and changed based on real time status changes of the user terminal. A marker can receive input from the user which can be used to select a variant or change a setting. Marker data is stored and used to create reports and optimize future campaigns.

Technologies for presenting indications of content features in invitational content

Systems, methods, and computer-readable storage media for determining user journeys during presentations of content. The system first determines an average time spent for sessions associated with a presentation of content. Next, the system identifies a representative group of sessions from the sessions by identifying each of the sessions having a respective time spent within a statistical range from the average time spent for the sessions. The system then determines a most common path of events from the representative group of sessions to yield a most common user journey associated with the presentation of content.

Representative user journeys for content sessions

The disclosed technology addresses the need in the art for determining whether a selection of an invitational content item was accidental or intentional based on selection signals that describe the selection. For example, a selection signal can be a selection point, response time, multiple selection count, context of the selection, user preference data, etc. A response to the selection can be based on whether the selection was determined to be accidental or intentional. If the selection was determined to be intentional, the response can be the regular response associated with selection of the invitational content item, i.e. registering the selection for billing and presenting secondary content to the user. If the selection was determined to be accidental, the selection can be ignored. Thus, the selection would not register for billing purposes and secondary content would not be presented to the user.

Growing community of inventors

Sunnyvale, CA, United States of America

Michael Froimowitz Greenzeiger