Rovi Technologies Corporation

Jens Nicholas Wessling

Robert Kahlbaum

Dustin James Williams

A method and a system are provided for constructing album data using discrete track data from multiple sources. In one example, the system identifies one or more set of tracks having a similar album title, wherein the one or more set of tracks are obtained from one or more client devices. The system then searches across the one or more set of tracks for tracks having a matching fingerprint and a matching album title. The system groups tracks that match according to an original album title in metadata to obtain grouped tracks. The system mines across the grouped tracks to generate a juxtaposition of track data from the one or more client devices. The system then generates album data for one or more albums based on the juxtaposition of track data.

Constructing album data using discrete track data from multiple sources

A method and a system are provided for managing metadata for occurrences of a recording. In one example, the system receives a recording. The recording includes computer readable media data. The system generates an internal identifier of the recording. The internal identifier is a signature that uniquely identifies the recording. The system then adds the internal identifier to metadata that are associated with at least one occurrence of the recording. The metadata are data that describe the recording.

Managing metadata for occurrences of a recording

Data searching performed by a server system. A query is received to search data stored in an organizing system in which data has been organized from a highest confidence quality tier to a lowest confidence quality tier. Data of the highest confidence quality tier of the organizing system is searched to satisfy the query, wherein searching the data of the highest confidence quality tier is performed before searching data of a lower confidence quality tier of the organizing system.

Structuring and searching data in a hierarchical confidence-based configuration

A method and a system are provided for generating a representative sub-signature of a particular cluster of signatures. In one example, the system reads a sub-signature of a signature of the particular cluster. The system calculates a first factor for the sub-signature. The first factor is based on how many signatures contain the sub-signature. The signatures are in the particular cluster. The system calculates a second factor for the sub-signature. The second factor is based on how many signatures in other clusters contain the sub-signature. These other signatures in other clusters are not in the particular cluster. The system calculates an effectiveness score for the sub-signature. The effectiveness score is based on at least the first factor and the second factor. The system then determines one or more representative sub-signatures for the particular cluster. The one or more sub-signatures are based on one or more effectiveness scores calculated by the system.

Generating a representative sub-signature of a cluster of signatures by using weighted sampling

A method and a system are provided for matching a fingerprint, for example, an audio fingerprint. In one example, the system receives, from a user device, a chapter and a query about the chapter. The chapter includes computer readable data generated from a waveform of an audio signal. The query is a request to receive data related to the chapter. The system generates, at a computer, a fingerprint of the chapter. The fingerprint includes at least a digital measure of certain properties of the waveform of the audio signal. The system generates, at a computer, a hash value of the fingerprint by applying a hash function to at least a portion of the fingerprint of the chapter. The hash value serves as an identifier for the fingerprint. The system looks up, in a database system, a matching hash value for the hash value of the fingerprint.

Matching a fingerprint

A method and a system are provided for structuring and searching data in a hierarchical confidence-based configuration. In one example, the system receives data and assigns a confidence quality tier to the data. The confidence quality tier is in a range that spans from a highest confidence quality tier inclusive to a lowest confidence quality tier inclusive. Data of a particular confidence quality tier is configured to be searched before data of a relatively lower confidence quality tier. The system organizes the data in a selected server of a server system according to the confidence quality tier. The selected server is in the range that spans from the highest confidence quality tier inclusive to the lowest confidence quality tier inclusive.

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Ann Arbor, MI, United States of America

Jens Nicholas Wessling