Abstract: A method and system are provided for assigning metadata candidates to a commercial by performing image analysis on a plurality of the video frames to identify video frames that include one or more of identifiable product-related logos, brand-related logos, product-related text, or brand-related text which appear in the video frames, capturing frame data for such video frames, categorizing a temporal position of the identified video frames within the commercial as being in either a beginning or ending portion of the commercial, or a middle portion of the commercial, and assigning to the commercial the identified products or brands as primary metadata candidates when the temporal position of the identified video frames within the commercial is in either the beginning or ending portion of the commercial.

Abstract: A system and method are provided for detecting the presence of potential commercials in a video data stream. Each of the commercials has an expected time length that is an integer multiple of a first predetermined time length, and has an overall time length that is equal to or less than a second predetermined time length. Transitions in the audio or video of the video data stream are detected and the time of the transitions are recorded. Time differences between one or more successive transitions are detected. Any time differences that are an integer multiple of the first predetermined time length, and that have an overall time length that is equal to or less than the second predetermined time length are identified. The contents of the video data stream associated with the identified time differences are flagged as potential commercials and sent to a content processing platform for further analysis.

Abstract: A content recommendation system that recommends content based on previous user content information and information regarding the user and user device. The system is capable of accurately recommending content using collaborative filtering by comparing the user's profile to other user profiles. User profiles rely on implicit ratings to determine user preferences. Implicit ratings may be determined by collecting the content that a user consumed, in addition to user and user device information. The system recommends content that similar users have consumed. The system is also able to identify and recommend trending content by analyzing changes in metrics over time. The system may also rely on automatic content recognition to identify content that the user is consuming without requiring active user input.

Abstract: Unknown potential commercials are detected in a video data stream that contains segments of program type content, and blocks of commercial content. Each block includes a plurality of successive individual commercials. A library of known commercials is maintained in a first database. A video data stream is received in a video processing engine which includes a search engine that is in communication with the first database. The search engine identifies all known commercials in the video data stream and their respective start and end times. The video processing engine identifies all time segments that are sandwiched between the known commercials. The video processing engine filters out as a potential commercial any identified time segments that are significantly longer than the time length of a commercial. The video processing engine designates content of each of the time segments that were not filtered out as being one or more unknown potential commercials.

Abstract: Systems and methods are provided to generate a personalized video channel for a viewer. Selected parameters of the viewer's viewing behavior with respect to one or more media devices that are associated with the viewer are automatically monitored and detected during one or more sampling periods. Media content sources that are available to the viewer on the one or more media devices are automatically detected. A personalized video channel is then generated for the viewer using at least the monitoring parameters and the detected media content sources. During a viewing session on one of the media devices, the generated personalized video channel is displayed.

Abstract: A method and system are provided for assigning metadata candidates to a commercial by performing image analysis on a plurality of the video frames to identify video frames that include one or more of identifiable product-related logos, brand-related logos, product-related text, or brand-related text which appear in the video frames, performing audio analysis on the commercial to identify audio associated with one or more identifiable products or brands in the commercial, comparing the products and brands identified by the image analysis with the products and brands identified by the audio analysis, and identifying similar products and brands; and assigning to the commercial a product or brand as a primary metadata candidate when a similar product or brand is identified by the image analysis and the audio analysis.

Abstract: Methods and systems are provided to create a media consumer-personalized database table of selectable actions related to media content of interest to a media consumer that is being displayed on a media device. The media content of interest is identified by the media consumer by recording an indication of the media content of interest via a signal received from a user input device when the media content of interest is being displayed or presented. The database table of selectable actions is then presented to the media consumer who can initiate one of the actions.

Abstract: A content recommendation system that recommends content based on previous user content information and information regarding the user and user device. The system is capable of accurately recommending content using collaborative filtering by comparing the user's profile to other user profiles. User profiles rely on implicit ratings to determine user preferences. Implicit ratings may be determined by collecting the content that a user consumed, in addition to user and user device information. The system recommends content that similar users have consumed. The system is also able to identify and recommend trending content by analyzing changes in metrics over time. The system may also rely on automatic content recognition to identify content that the user is consuming without requiring active user input.

Abstract: A system and method for adjusting bid forming in a real-time bidding advertisement auction system. The method may be implemented for a bidding agent or in connection with a campaign database specifying campaign objectives by segment and campaign duration.

Abstract: Methods and apparatus are provided for detecting potential repeating content, such as commercials, in a video data stream by receiving one or more video data streams, parsing each video data stream into a plurality of segments, creating a representation of each segment such as a segment fingerprint, storing the plurality of segment representations in a database, and identifying any segment representations in the received and parsed one or more video data streams that match segment representations in the database that were previously stored from video data streams that were previously received and parsed.

Abstract: An system for determining time offset of an audio signature from a reference signature time stamp may have a server connected to one or more remote devices. The server may have a receiver connected to one of more communication channels configured to receive an audio signature generated by the remote device and transmitted over the communication channel. The system may have a database at or connected to the server that contains one or more reference audio signatures. Each of the reference audio signatures may have a time stamp also stored in the database. A query engine may compare the remote audio signature to one or more reference audio signatures stored in the database. A processor may be provided to compare a reference timestamp to a timestamp associated with said remote audio signature. The system may be used to evaluate viewing habits and particularly delayed viewing and program playback manipulation such as Fast-forward, slow-motion, skip, rewind and program abandonment.

Abstract: A system and method are provided for detecting the presence of potential commercials in a video data stream. Each of the commercials has an expected time length that is an integer multiple of a first predetermined time length, and has an overall time length that is equal to or less than a second predetermined time length. Transitions in the audio or video of the video data stream are detected and the time of the transitions are recorded. Time differences between one or more successive transitions are detected. Any time differences that are an integer multiple of the first predetermined time length, and that have an overall time length that is equal to or less than the second predetermined time length are identified. The contents of the video data stream associated with the identified time differences are flagged as potential commercials and sent to a content processing platform for further analysis.

Abstract: Methods and apparatus are provided for detecting potential repeating content, such as commercials, in a video data stream by receiving one or more video data streams, parsing each video data stream into a plurality of segments, creating audio fingerprints of each segment, storing the plurality of audio fingerprints in a database, and identifying any audio fingerprints in the received and parsed one or more video data streams that match audio fingerprints in the database that were previously stored from video data streams that were previously received and parsed. Video fingerprints are then created for these same pairs of segments and a similarity analysis is performed. The results of the video fingerprint analysis is used to make a determination of subsequent actions to be taken by a content processing platform that performs recognition processing of the content associated with the segments identified as being potentially repeating content.

Abstract: Unknown potential commercials are detected in a video data stream that contains segments of program type content, and blocks of commercial content. Each block includes a plurality of successive individual commercials. A library of known commercials is maintained in a first database. A video data stream is received in a video processing engine which includes a search engine that is in communication with the first database. The search engine identifies all known commercials in the video data stream and their respective start and end times. The video processing engine identifies all time segments that are sandwiched between the known commercials. The video processing engine filters out as a potential commercial any identified time segments that are significantly longer than the time length of a commercial. The video processing engine designates content of each of the time segments that were not filtered out as being one or more unknown potential commercials.

Abstract: Methods and apparatus are provided for detecting potential repeating content, such as commercials, in a video data stream by receiving one or more video data streams, parsing each video data stream into a plurality of segments, creating a representation of each segment such as a segment fingerprint, storing the plurality of segment representations in a database, and identifying any segment representations in the received and parsed one or more video data streams that match segment representations in the database that were previously stored from video data streams that were previously received and parsed.

Abstract: A method and system are provided for assigning metadata candidates to a commercial by performing image analysis on a plurality of the video frames to identify video frames that include one or more of identifiable product-related logos, brand-related logos, product-related text, or brand-related text which appear in the video frames, capturing frame data for such video frames, calculating a dominance or prominence metric from the frame data for each of the identifiable product-related logos, brand-related logos, product-related text, or brand-related text, comparing the dominance or prominence metric for each of the identifiable product-related logos, brand-related logos, product-related text, or brand-related text to a predetermined threshold percentage or value, and assigning to the commercial, the identified products or brands as metadata candidates when the dominance or prominence metric exceeds the predetermined threshold percentage or value.

Abstract: Audio information is monitored by a user device that performs audio content recognition of any received audio content. The user device includes a scheduling logic unit, a probe, and an audio signature generator. The scheduling logic unit maintains a set of scheduling rules that define conditions that were present when previous audio content recognition of audio content received by the user device was successful. The scheduling logic unit receives currently present conditions of the user device, and compares the currently present conditions to the set of scheduling rules to determine if the currently present conditions match any scheduling rules. The user devices captures ambient audio content via the probe and generates audio signatures of the captured audio content using the audio signature generator if a match occurs, and inhibits capturing audio content by the user device and/or inhibits generating audio signatures if a match does not occur.

Abstract: A method and system are provided for assigning metadata candidates to a commercial by performing image analysis on a plurality of the video frames to identify video frames that include one or more of identifiable product-related logos, brand-related logos, product-related text, or brand-related text which appear in the video frames, capturing frame data for such video frames, calculating a persistence metric from the frame data for each of the identifiable product-related logos, brand-related logos, product-related text, or brand-related text, comparing the persistence metric for each of the identifiable product-related logos, brand-related logos, product-related text, or brand-related text to a predetermined threshold value, and assigning to the commercial, the identified products or brands as metadata candidates when the persistence metric exceeds the predetermined threshold value.

Abstract: A method and system are provided for assigning metadata candidates to a commercial by performing image analysis on a plurality of the video frames to identify video frames that include one or more of identifiable product-related logos, brand-related logos, product-related text, or brand-related text which appear in the video frames, performing audio analysis on the commercial to identify audio associated with one or more identifiable products or brands in the commercial, comparing the products and brands identified by the image analysis with the products and brands identified by the audio analysis, and identifying similar products and brands; and assigning to the commercial a product or brand as a primary metadata candidate when a similar product or brand is identified by the image analysis and the audio analysis.

Abstract: Unknown potential commercials are detected in a video data stream that contains segments of program type content, and blocks of commercial content. Each block includes a plurality of successive individual commercials. A library of known commercials is maintained in a first database. A video data stream is received in a video processing engine which includes a search engine that is in communication with the first database. The search engine identifies all known commercials in the video data stream and their respective start and end times. The video processing engine identifies all time segments that are sandwiched between the known commercials. The video processing engine filters out as a potential commercial any identified time segments that are significantly longer than the time length of a commercial. The video processing engine designates content of each of the time segments that were not filtered out as being one or more unknown potential commercials.