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 method for managing a bidding process by keeping track of the sources for ad auction opportunity on the basis of an app generating the opportunity or web activity generating the opportunity. The bidding platform may limit bids on opportunities for a campaign when the successful placement of ads or opportunities from any source exceeds the source threshold set for that source.

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 closed loop attribution system may include a user location history storage system containing information indicative of user location history for a plurality of users based on location of a mobile device associated with a user; a user database storage system containing placement tracking information indicative of advertising content presented to the user; and a closed loop attribution processor responsive to said user location history storage system and said user database storage assessing correlations between a user's exposure and a user's location. The user database may include records indicative of user behavior and characteristics. The closed loop attribution processor may be connected to the campaign database and the campaign database may contain an indication of one or more locations of interest to an advertiser. The correlation between user exposure and a user's location may be a correlation between user exposure and the location or locations of interest.

Abstract: A method for maximizing the number of users who receive an optimal number of ad impressions. The method may be implemented for a bidding agent or in connection with a system for augmenting ad placement data across bidding platforms that bid on ad opportunities in a real-time bidding ad exchange. The system is able to use tracking pixels in order to optimize bid placements. The system is intended to manage user ad placement so as to increase the number of placements received by individual users so that the campaign has an increased impact on eligible users and reduces the diluted exposures to users who receive less than optimal exposure.

Abstract: A method for maximizing the number of users who receive an optimal number of ad impressions including a system for augmenting ad placement data across bidding platforms that bid on ad opportunities in a real-time bidding ad exchange. The system is able to use tracking pixels in connection with a plurality of bidding platforms in order to optimize bid placements. The system may also limit reach by subcategory classification of viewership information.

Abstract: A bid management system may review ad auction opportunity parameters including an identification of a source of the ad auction opportunity. The system may determine a correlation between the ad auction opportunity source and source targeting parameters for a campaign and using the determination to inform bid forming logic. The system may consider a correlation between ad opportunity source and media consumption in users where media consumption is known and use the correlation between source and consumption to inform bid forming logic. The ad opportunity source may be an identification of an app whose use generated the ad auction opportunity. The ad opportunity source may be an identification of web browsing activity that generated said auction opportunity.

Abstract: A system for augmenting ad placement data across bidding platforms that bid on ad opportunities in a real-time bidding ad exchange. The system can use tracking pixels in connection with a plurality of bidding platforms to optimize bid placements. A data augmenter in connection with a plurality of bidding platforms can facilitate coordination between bidding platforms such that the platforms optimize bidding strategy. The data augmenter may facilitate coordination in several ways. The data augmenter may aggregate data across all connected bidding platforms and distribute aggregated data to the bidding platforms. The data augmenter is also able to track the status of campaign benchmarks and other metrics based on received tracking pixel data and subsequently inform the bidding platforms or request or command that the bidding platforms change bidding behavior based on the status of the benchmark.

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: A method is provided to classify whether video content is likely to be an advertisement or a non-advertisement. A curated database of video content items that includes a plurality of different video content items that were each previously identified as being an advertisement, and a plurality of different video content items that were each previously identified as not being an advertisement, are used to train a 2D CNN and a 3D CNN. The training of the 2D CNN includes learning characteristic visual and spatial features of advertisement images in the video content items compared to non-advertisement images in the video content items, the training resulting in weights being defined for the 2D CNN. The training of the 3D CNN includes learning a temporal structure and relationship over multiple image frames of the advertisements in the video content items compared to non-advertisement image frames in the video content items, the training resulting in weights being defined for the 3D CNN.

Abstract: Systems and methods are provided for selectively replacing commercials that are in a video data stream with alternative commercials. Automatic content recognition (ACR) is performed on the video data stream to detect the identity of each of the commercials played in a commercial block during a commercial break. Commercials in the video data stream which are detected as being displayed on a video display device are stored in a first database, and commercials in the video data stream which are detected as not being displayed on the video display device are stored in a second database. A rules engine defines how commercials in the video data stream should be replaced with other commercials. Commercials in the video data stream that were previously detected as being displayed on the video display device are selectively replaced with commercials that were previously detected as not being displayed on the video display device.

Abstract: An artificial neural network architecture is provided for processing raw audio waveforms to create speaker representations that are used for text-independent speaker verification and recognition. The artificial neural network architecture includes a strided convolution layer, first and second sequentially connected residual blocks, a transformer layer, and a final fully connected (FC) layer. The strided convolution layer is configured to receive raw audio waveforms from a speaker. The first and the second residual blocks both include multiple convolutional and max pooling layers. The transformer layer is configured to aggregate frame level embeddings to an utterance level embedding. The output of the FC layer creates a speaker representation for the speaker whose raw audio waveforms were inputted into the strided convolution layer.

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: 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: 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.