Apple Inc.

Strands, Inc.

Buildgroup Data Services Inc.

Jim Shur

Francisco J Martin

Marc Torrens

Rick Hangartner

Craig Rowley

Guillermo Caudevilla-Laliena

Andrew Yip

David Del Ser Bartolome

Matt McLaughlin

Javier S Alperte Pérez Rejón

Luis Javier Placer Mendoza

Xavier Canals Orriols

Francisco J Garcia Moreno

Alvaro Otero Perez

Candido Zuriaga Garcia

Systems and methods for predicting the outcome of a business entity are presented. In embodiments, a system may receive explicit data reporting or indicating activities of a business entity, and other data from which information regarding the activities or level of operations of the entity may be inferred. Using one or more data processors, the system may generate inferred data regarding the business entity from a selected portion of the other data, and use at least some of the explicit data and the inferred data to determine which one of a series of defined sequential active states of development the entity currently is in. The system may further, using the result of the determination as the current state of the business, predict a final stage of the business entity, and a probability of evolving to that final stage from the current state. Other embodiments may be disclosed or claimed.

Adaptive and reusable processing of retroactive sequences for automated predictions

Systems and methods are disclosed for identifying a new set of media items in response to an input set (or 'query set') of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or 'similarity' metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of media items.

Recommender system for identifying a new set of media items responsive to an input set of media items and knowledge base metrics

Disclosed are various embodiments of systems and methods for generating composite mediasets from mediasets, each comprising media items, associated with a plurality of users. In some embodiments, individual and/or group recommendations are provided for creating a group playlist by aggregating user taste data for a plurality of users in a group. In other embodiments, systems and methods are provided which allow for sharing and playing of a group playlist by users in a group, each of which has a media playback device. In still other embodiments, systems and methods are provided for dynamically building a composite playlist that may be updated as users enter and/or leave the group. One example of an interactive entertainment application also is described for use at a venue to entertain live guests with music selections that are dynamically influenced by the guests' individual tastes.

Dynamic interactive entertainment venue

A system and method to navigate through a media item catalog and generate recommendations using behavioral metrics such as correlation metrics (FIGS.) from a knowledge base () of mediasets (FIG.-). One implementation uses a knowledge base comprising a collection of mediasets such as groupings of selected video items. Various metrics (Metric-Metric m) among media items (m. . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (). Such metrics preferably are stored in a matrix () that allows the system to dynamically build appropriate navigation lists (FIG.) from media items that a user selects (FIG.).

System for browsing through a music catalog using correlation metrics of a knowledge base of mediasets

Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Systems and methods for prioritizing mobile media player files

Systems and methods for prioritizing media files in a presentation device

A system and method to navigate through a media item catalog and generate recommendations using behavioral metrics such as correlation metrics (FIGS.) from a knowledge base () of mediasets (FIG.-). One implementation uses a knowledge base comprising a collection of mediasets. Various metrics (Metric-Metric m) among media items (m. . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (). Such metrics preferably are stored in a matrix () that allows the system to dynamically build appropriate navigation lists (FIG.) from media items that a user selects (FIG.).

A system to navigate through a media item catalog using correlation metrics (FIGS.) from a knowledge base () of mediasets (FIG.-). The system uses a knowledge base consisting of a collection of mediasets. Various metrics (Metric-Metric m) among media items (m. . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (). Such metrics preferably are stored in a matrix () that allows the system to dynamically build appropriate navigation lists (FIG.) from media items that a user selects (FIG.).

Growing community of inventors

Corvallis, OR, United States of America

Jim Shur