Gracenote, Inc.

Roku, Inc.

The Nielsen Comapny (us), LLC

Konstantinos Antonios Dimitriou

Wilson Harron

Markus Kurt Cremer

Steven D Scherf

Sherman Ling Fung Li

David Henry Pearce

Peter Dunker

Dewey Ho Lee

Jeffrey William Scott

Robert Coover

Matthew James Wilkinson

A client device accesses a video input stream from an intermediate device for display. The client device analyzes the video input stream to determine that the video input stream matches a template corresponding to a screen portion. Based on the video input stream matching the template, a video output stream is generated and caused to be presented on a display. In some example embodiments, the analysis is performed while the client device is replacing video content received from a content source via the intermediate device. For example, commercials transmitted from a national content provider to a smart TV via a set-top box may be replaced with targeted commercials. During the replacement, menus generated by the set-top box may be detected and the replacement video altered by the smart TV to include the menus.

Dynamic video overlays

Methods, apparatus, systems and articles of manufacture are disclosed to select reference sub-fingerprints for comparison to query sub-fingerprints based on a determination that a query sub-fingerprint is a match with a reference sub-fingerprint, generate a count vector that stores total counts of matches between the query sub-fingerprints and different subsets of the reference sub-fingerprints, each of the different subsets being aligned to the query sub-fingerprints at a different offset from a reference point, each of the different offsets being mapped by the count vector to a different total count, calculate a maximum count among the total counts, a median of the total counts, and a difference between the maximum count and the median of the total counts, and classify the reference sub-fingerprints as a match with the query sub-fingerprints based on the difference between the maximum count in the count vector and the median.

Matching audio fingerprints

Methods, apparatus, systems and articles of manufacture are disclosed for efficient media indexing. An example method disclosed herein includes means for initiating a list of hash seeds, the list of hash seeds including at least a first hash seed value and a second hash seed value among other hash seed values, means for generating to generate a first bucket distribution based on the first hash seed value and a first hash function and generate a second bucket distribution based on the second hash seed value used in combination with the first hash seed value, means for determining to determine a first entropy value of the first bucket distribution, wherein data associated with the first bucket distribution is stored in a first hash table and determine a second entropy value of the second bucket distribution.

Methods and apparatus for efficient media indexing

In one aspect, an example method includes (i) receiving, by a computing system, media content; (ii) generating, by the computing system, a fingerprint of the received media content; (iii) determining, by the computing system, that a channel-change operation was performed; (iv) responsive to determining that the channel-change operation was performed, transmitting, by the computing system, the generated fingerprint to a content identification server to identify the received media content; and (v) performing an action based on the identified media content.

Computing system with channel-change-based trigger feature

In one aspect, an example method includes (i) receiving, by a computing system, media content; (ii) generating, by the computing system, a fingerprint of a portion of the received media content; (iii) determining, by the computing system, that the received media content has a predefined characteristic; (iv) responsive to determining that the received media content has the predefined characteristic, transmitting, by the computing system, the generated fingerprint to a content identification server to identify the portion of the received media content; and (v) performing an action based on the identified portion of media content.

Computing system with content-characteristic-based trigger feature

A client device accesses a video input stream from an intermediate device for display. The client device analyzes the video input stream to determine that the video input stream matches a template indicating a change of speed. In some example embodiments, the analysis is performed while the client device is generating a video output stream that replaces video content received from a content source via the intermediate device. Based on the video input stream matching the template, the client device modifies the video output stream to show the change of speed. For example, commercials transmitted from a national content provider to a smart TV via a set-top box may be replaced with targeted commercials. During the replacement, fast-forwarding of the commercial may be detected by matching a template with the video input stream. While the video input stream is being fast-forwarded, the smart TV fast-forwards the targeted commercial.

Methods, apparatus, systems and articles of manufacture are disclosed for efficient media indexing. An example method disclosed herein includes selecting a first hash seed value based on a first entropy value calculated for a first bucket distribution resulting from use of the first hash seed value to store data in a first hash table, selecting a second hash seed value to be used in combination with the first hash seed value based on a second entropy value calculated on a second bucket distribution resulting from use of the first hash seed value in combination with the second hash seed value, and storing data in the first hash table based on the first hash seed value and a second hash table based on the second hash seed value.

A matching machine accordingly accesses a query fingerprint that includes query sub-fingerprints that have been generated from query segments of a portion of query audio. After selecting reference sub-fingerprints for comparison to the query sub-fingerprints, the matching machine identifies a best-matching subset of the reference sub-fingerprints by evaluating total matches between the query sub-fingerprints and different subsets of the reference sub-fingerprints. The match machine then generates a count vector that stores the total counts mapped to respective offsets from a reference point in the reference sub-fingerprints. The matching machine determines a maximum count among the total counts and classifies the reference sub-fingerprints as a match with the query sub-fingerprints based on the maximum count.

Growing community of inventors

Bavaria, Germany

Konstantinos Antonios Dimitriou