Methods and systems for determining usage are described. Initially, site-centric data and panel-centric data are accessed and pre-processed. Initial usage measurement data is determined based on the pre-processed site-centric data. One or more adjustment factors are determined based on the pre-processed panel-centric data. The one or more adjustment factors are applied to the initial usage measurement data to generate an adjusted usage measurement data. Reports based on the adjusted usage measurement data are generated.
Methods and systems for determining usage are described. Initially, site-centric data and panel-centric data are accessed and pre-processed. Initial usage measurement data is determined based on the pre-processed site-centric data. One or more adjustment factors are determined based on the pre-processed panel-centric data. The one or more adjustment factors are applied to the initial usage measurement data to generate an adjusted usage measurement data. Reports based on the adjusted usage measurement data are generated.
Methods and systems for determining usage are described. Initially, site-centric data and panel-centric data are accessed and pre-processed. Initial usage measurement data is determined based on the pre-processed site-centric data. One or more adjustment factors are determined based on the pre-processed panel-centric data. The one or more adjustment factors are applied to the initial usage measurement data to generate an adjusted usage measurement data. Reports based on the adjusted usage measurement data are generated.
Data indicative of a plurality of content output sessions associated with a first time period may be received. Based on the received content output session data, a first subset of the plurality of content output sessions that are outliers may be determined. Data associated with a device associated with each of the first subset of content output sessions may be received. Based on the received device data, each device may be classified as an outlier device or an inlier device. Based on the classification, a second subset of the first subset of content output sessions that are associated with an outlier device may be determined. A duration of each content output session of the second subset may be compared to a duration threshold associated with a second time period. Based on the comparison, a total duration of the content output sessions of the second subset may be adjusted.
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/658 - Transmission by the client directed to the server
Systems and methods herein log traffic to and from a device on a network. Logging can occur using a metering device, router, proxy, or other elements. For example, a metering device operatively coupled to a routing device can log the traffic directed to and originating from a user device. Logged traffic can be analyzed to identify users, devices, and/or sessions. For example, an identifier unique to the user device in the session, a device type of the user device, and a specific user of the device during the session can be identified.
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
H04L 43/0811 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity
H04L 41/046 - Network management architectures or arrangements comprising network management agents or mobile agents therefor
H04L 41/142 - Network analysis or design using statistical or mathematical methods
H04L 41/0853 - Retrieval of network configuration; Tracking network configuration history by actively collecting configuration information or by backing up configuration information
G06F 16/9535 - Search customisation based on user profiles and personalisation
H04L 43/04 - Processing captured monitoring data, e.g. for logfile generation
H04L 43/062 - Generation of reports related to network traffic
H04L 61/5014 - Internet protocol [IP] addresses using dynamic host configuration protocol [DHCP] or bootstrap protocol [BOOTP]
Techniques for projecting household-level viewing events are described herein. Population data may be accessed including classes of a plurality of demographic attributes for households in a market. Representative household units (RHUs) may be generated, and the RHUs may be assigned a class for each of the demographic attributes and a quota based on the demographic attributes of a plurality of panelist households. Each of the panelist households may be assigned to one of the RHUs based on at least one panelist classes matching the classes for respective demographic attributes of the RHU, and the number of matching panelist households assigned to each of the RHU may be based on the quota. Panelist viewing data representing viewing events associated with the panelist household may be accessed. A report may be generated with the classes of the RHUs and the panelist viewing data of the assigned panelist households.
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
G06Q 30/0242 - Determining effectiveness of advertisements
H04H 60/45 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying users
G06Q 30/0201 - Market modelling; Market analysis; Collecting market data
H04H 60/52 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying locations of users
Data indicative of content associated with at least one content category may be received from a content provider. Data indicative of a plurality of consumer categories may also be received. A correlation between at least one consumer category of the plurality of consumer categories and the at least one content category may be determined. It may be determined if the correlation between the at least one consumer category and the at least one content category satisfies a threshold. If the correlation between the at least one consumer category and the at least one content category satisfies the threshold, the at least one consumer category may be associated to a profile associated with the at least one content category.
Data indicative of times at which at least one item of supplemental video content was output via a plurality of client devices may be received. A first time at which the at least one item of supplemental video content was output via a greatest quantity of client devices of the plurality of client devices may be determined. A second time at which the at least one item of supplemental video content was output via a second greatest quantity of client devices of the plurality of client devices may also be determined. A schedule associated with output of the at least one item of supplemental video content may be generated based at least on the first time and the second time.
H04N 21/262 - Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission or generating play-lists
Geographic identifiers and television network affiliate data may be received. A primary local affiliate station for each of the plurality of national television networks may be determined for each geographic identifier. The geographic identifiers that have the same primary local affiliate for each of the plurality of national television networks may be grouped together to form a first plurality of markets. The markets from the first plurality of markets that have the same primary local affiliate for a first subset of the plurality of national television networks may be merged together, resulting in a second, smaller plurality of markets. Each of the geographic identifiers may be assigned to a market from the second plurality of markets, and a report indicating the second plurality of markets may be output.
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies
Systems and methods herein log traffic to and from a device on a network. Logging can occur using a metering device, router, proxy, or other elements. For example, a metering device operatively coupled to a routing device can log the traffic directed to and originating from a user device. Logged traffic can be analyzed to identify users, devices, and/or sessions. For example, an identifier unique to the user device in the session, a device type of the user device, and a specific user of the device during the session can be identified.
H04L 43/0811 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity
H04L 41/046 - Network management architectures or arrangements comprising network management agents or mobile agents therefor
H04L 41/142 - Network analysis or design using statistical or mathematical methods
H04L 41/0853 - Retrieval of network configuration; Tracking network configuration history by actively collecting configuration information or by backing up configuration information
H04L 43/04 - Processing captured monitoring data, e.g. for logfile generation
H04L 43/062 - Generation of reports related to network traffic
H04L 61/5014 - Internet protocol [IP] addresses using dynamic host configuration protocol [DHCP] or bootstrap protocol [BOOTP]
H04L 67/00 - Network arrangements or protocols for supporting network services or applications
H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
Predict whether active viewability is taking place and/or the likelihood that active viewability will be taking place in the future is described. Historical viewing data may be obtained. One or more probability distribution functions may be generated based on the historical viewing data. One or more survival curves may be determined based on the one or more probability distribution functions. Current viewing data may be obtained. Whether viewability is active in at least one of a current time or a specified future time may be predicted based on the one or more probability distribution functions and the current viewing data. Alternatively, this prediction may be performed via a machine learning model trained on the historical viewing data. Whether to perform a function may be determined based on the prediction.
Determining attributes for a stream of live content (e.g., live-streaming videos) is described. Initially, a stream of live content is received. One or more attributes are then extracted from a first segment and a second segment of the stream. The first segment and the second segment of the stream may have the same predetermined duration and the second segment may at least partially overlap with the first segment. The one or more attributes extracted from the first segment may be transmitted at a first time, such as at a first break in the stream of content. The one or more attributes extracted from the second segment may be transmitted at a second time, such as at a second break in the stream of content. The attributes from the first and second segments may be transmitted to a content provider and/or a creative provider, such as an advertiser.
A system identifies redundancies among a plethora of viewership data of each of several markets. Some embodiments may obtain, from each of a plurality of different devices, a different set of viewership data, each set comprising different subsets that respectively relate to different entities; each of the subsets may indicate several time-based views of content over a period of time. These or other embodiments may determine a set of values by performing a set of deterministic functions using the time-based views of each of the subsets; compare the values, which relate to same entities and to same time intervals, of the determined sets of each distinct pair of the devices, and identify, from among the device pairs, candidate pairs. One exemplary output of this approach may be identifiers of the devices of a first pair of devices that is identified from among the candidate pairs.
Online consumption data may be secured by receiving data associated with first online interactions actually performed during a predetermined time period, generating, via a machine learning model for each of a plurality of different personas, data associated with second online interactions that simulate Internet traffic, selecting a plurality of the received data associated with the first online interactions that matches the generated data associated with the second online interactions of one or more of the personas, replacing the generated data associated with the second online interactions of the one or more personas with the selected data, and outputting the one or more personas with the replaced data.
Prediction models for managing viewership data are disclosed. An amount of time users are displayed content is initially obtained. The obtained amounts may be for each content distributor that distributes channels, for each of the channels with respect to which sets of content are displayed, for each of the sets that comprises content displayed during past periods, and for each of the displayed content. A set of features associated with each of the displays is obtained and a target period is selected from among the past periods. A model is used to predict an amount of time users were displayed content during the target period, for each of the sets, channels, and distributors, based on the obtained sets of features associated with the displays during the target period and on the obtained amounts for the displays during the past periods that precede the target period. A comparison, respectively for the same displays during the target period, of each of the obtained amounts to each of the predicted amounts is performed, and an anomaly is detected based on the comparisons. Finally, the anomaly is alerted.
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
16.
System and method for multi-modal image classification
Systems and methods for classifying images (e.g., ads) are described. An image is accessed. Optical character recognition is performed on at least a first portion of the image. Image recognition is performed via a convolutional neural network on at least a second portion of the image. At least one class for the image is automatically identified, via a fully connected neural network, based on one or more predictions, each of the one or more predictions being based on both the optical character recognition and the image recognition. Finally, the at least one class identified for the image is output.
Systems and methods herein log traffic to and from a device on a network. Logging can occur using a metering device, router, proxy, or other elements. For example, a metering device operatively coupled to a routing device can log the traffic directed to and originating from a user device. Logged traffic can be analyzed to identify users, devices, and/or sessions. For example, an identifier unique to the user device in the session, a device type of the user device, and a specific user of the device during the session can be identified.
Measuring a networked audience is described. Initially, a first set of network usage data based on access of a resource by a first set of client systems is received. Next, a second set of network usage data based on access of the resource by a second set of client systems using a monitoring application installed on the second set of client systems is determined. Usage of the resource based on the first set of network usage data during a time period and one or more adjustment factors based on the second set of network usage data are determined. The determined usage is adjusted using the one or more adjustment factors. Finally, one or more audience reports for the resource using the adjusted usage are generated.
A first image of a mobile device screen is recorded into memory of the mobile device. The first image includes at least one icon that represents an application installed on the mobile device. A second image of the mobile device screen is recorded into the memory of the mobile device. A graphical change in an area of the mobile device screen corresponding to a position of the icon is detected by comparing at least a portion of the second image to the first image. The graphical change results from a user selection of the icon to activate the application represented by the icon. In response to detecting the graphical change, determine an identifier of the application represented by the icon. Send a record of the user selection of the icon to a collection server. The record includes at least the identifier of the application.
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
G06F 9/451 - Execution arrangements for user interfaces
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06K 9/18 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints using printed characters having additional code marks or containing code marks, e.g. the character being composed of individual strokes of different shape, each representing a different code value
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
20.
System and method for measuring the relative and absolute effects of advertising on behavior based events over time
The systems and techniques described herein measure advertisement effectiveness of behavior-based outcomes (e.g., site visit, number of pages consumed, searches, online and offline transactions). The system implemented an automated model to measure the impact of exposures and impressions on outcomes using uses panel data, cookie-based data, and combinations thereof. The techniques use test and control approach to calculate effectiveness, where the test group are those exposed to a campaign and a control group who is not exposed. For those exposed, a running analysis of impressions (and other variables) in a pre period is used to determine behavior based outcomes over a set time period after that exposure. As a result, the automated model is able to generate metrics that show absolute and relative impacts on future behavior.
Estimating the effectiveness of an online ad campaign is disclosed. Impression data indicative of online ad activity associated with the ad campaign, such as a plurality of online ad impressions, is received. Awareness data indicative of an awareness of select users of the subject of the ad campaign is received. One or more timing characteristics associated with the online ad activity are determined. Using a survival analysis and based on the impression data, the awareness data, and the one or more timing characteristics, one or more factors are determined. The factors may indicate the degree to which one or more associated attributes of online ad activity influence the effectiveness of the ad campaign. The factors may be applied to second impression data indicative of second online ad activity associated with the ad campaign to determine the estimate of the effectiveness of the ad campaign.
Methods and systems are provided herein for collecting web browser click events across a plurality of web sites from a data collection agent (DCA), as a click-stream, at a data collection server (DCS) to record and provide user on-line activity, filtering the user online activity to include activity from a time period prior to a sale from the sales transaction data and identifying one or more shopping touch-points based on the filtered user online activity and the sales transaction data. Further, an engagement index, an influence index, and an opportunity index is calculated. A digital touch-points facility may perform the identifying and calculating.
The systems and techniques described herein measure advertisement effectiveness of behavior-based outcomes (e.g., site visit, number of pages consumed, searches, online and offline transactions). The system implemented an automated model to measure the impact of exposures and impressions on outcomes using uses panel data, cookie-based data, and combinations thereof. The techniques use test and control approach to calculate effectiveness, where the test group are those exposed to a campaign and a control group who is not exposed. For those exposed, a running analysis of impressions (and other variables) in a pre period is used to determine behavior based outcomes over a set time period after that exposure. As a result, the automated model is able to generate metrics that show absolute and relative impacts on future behavior.
Mobile usage data representing the access of one or more resources on a network by mobile devices is accessed. The mobile usage data includes information received from mobile devices as a result of beacon instructions included with the one or more resources. A first set of data representing information about accesses to the one or more resources by mobile devices with persistent beacon cookies is determined. A second set of data representing information about accesses to the one or more resources by mobile devices with non-persistent beacon cookies is determined. One or more adjustment factors are determined based on the first set of data. A count of unique visitors accessing the one or more resources from the mobile devices is determined based on a count of accesses by the mobile devices with persistent beacon cookies and a count of accesses by the mobile devices with non-persistent beacon cookies adjusted by the one or more adjustment factors.
Audience engagement is analyzed by receiving at least one analysis parameter comprising a content selection parameter, identifying one or more content based on the content selection parameter, determining viewership for the one or more content, determining one or more keywords for the one or more content based on the content, filtering social media messages based on the determined one or more keywords, and calculating an audience engagement measurement corresponding to the one or more content based on the viewership and the social media messages.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
G06Q 50/00 - Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
H04N 21/4788 - Supplemental services, e.g. displaying phone caller identification or shopping application communicating with other users, e.g. chatting
H04H 60/33 - Arrangements for monitoring the users' behaviour or opinions
H04H 60/66 - Arrangements for services using the result of monitoring, identification or recognition covered by groups or for using the result on distributors' side
H04N 21/658 - Transmission by the client directed to the server
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies
H04N 21/462 - Content or additional data management e.g. creating a master electronic program guide from data received from the Internet and a Head-end or controlling the complexity of a video stream by scaling the resolution or bit-rate based on the client capabi
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
26.
DETECTION AND ESTIMATION OF FRAUDULENT CONTENT ATTRIBUTION
Methods and systems for detection and estimation of fraudulent online advertisement attribution are disclosed. A plurality of network domains and a corresponding plurality of network locations for each network domain are determined based on network traffic data associated with webpage ad placement. The number of the plurality of network locations corresponding to each plurality of network domains is greater than a first threshold value. From the plurality of network locations, a second plurality of network locations is determined in which each network location is associated with two or more network domains of a predefined set of network domains. The number of the two or more network domains exceeds a second threshold value. A report is generated indicating the second plurality of network locations.
Techniques for projecting person-level viewership from household-level tuning events are described. Initially, panelist viewing data are accessed and a plurality of state values based on the panelist viewing data are determined. Then, tuning data representing tuning events associated with particular households are accessed. For at least one tuning event represented by the tuning data, household member data is accessed, a portion of the panelist viewing data whose panelist information matches at least a portion of the member data is determined, a total number of watched minutes of the program by an individual member and a number of continuous series of watched states of the program by the individual member is determined, and an output representative of a probability that the particular portion of the program was watched by one or more of the individual members is generated.
H04N 7/025 - Systems for transmission of digital non-picture data, e.g. of text during the active part of a television frame
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/2668 - Creating a channel for a dedicated end-user group, e.g. by inserting targeted commercials into a video stream based on end-user profiles
28.
Demographic attribution of household viewing events
Aggregating viewership data is disclosed. Initially, household viewership data assigned to a tuning event in multiple households is accessed. Then, the household viewership data for the tuning event in the multiple households representing the same episode is aggregated to generate episode viewership data. Next, episode viewership data representing multiple episodes of the same program is aggregated to generate program viewership data. Next, the program viewership data for multiple programs of the same network is aggregated to generate network viewership data. Finally, the network viewership data for multiple commonly-owned networks is aggregated to generate entity viewership data.
H04H 60/33 - Arrangements for monitoring the users' behaviour or opinions
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04H 60/37 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying segments of broadcast information, e.g. scenes or extracting programme ID
H04H 60/39 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying broadcast time or space for identifying broadcast space-time
H04H 60/45 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying users
H04H 60/46 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for recognising users' preferences
H04H 60/66 - Arrangements for services using the result of monitoring, identification or recognition covered by groups or for using the result on distributors' side
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
Systems and methods for estimating media audience are disclosed. In one aspect, a method includes receiving a media signal to be transmitted via a media distribution system, the media signal including multiple pieces of media content that each contain video and audio signals. One of the multiple pieces of media content within the received media signal are identified. It is determined that the identified piece of media content exceeds a threshold length, and, in response to determining that the identified piece of media content exceeds a threshold length, the audio signal of the identified piece of media content is encoded with a sequence of discrete codes. Each discrete code has a period that includes a set of sequential frequency components imperceptible to humans. The threshold length is greater than the period of a discrete code multiplied by the number of discrete codes in the sequence.
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs
H04N 21/439 - Processing of audio elementary streams
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/8358 - Generation of protective data, e.g. certificates involving watermark
Methods and systems for determining usage are described. Initially, site-centric data and panel-centric data are accessed and pre-processed. Initial usage measurement data is determined based on the pre-processed site-centric data. One or more adjustment factors are determined based on the pre-processed panel-centric data. The one or more adjustment factors are applied to the initial usage measurement data to generate an adjusted usage measurement data. Reports based on the adjusted usage measurement data are generated.
H04L 67/146 - Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
Measuring a networked audience is described. Initially, a first set of network usage data is determined based on a first set of signaling information obtained from a first subset of the client systems as a result of the first subset accessing common resources. In addition, a second set of network usage data is determined based on a second set of information obtained from a second subset of the client systems as a result of an application installed thereon monitoring accessing of the common resources. Usage is measured based on the first set of network usage data by determining an initial count of unique visitors that accessed the common resources by determining a count related to signaling of the first set of signaling information. Adjustment factors are determined based on the second set of network usage data. Finally, audience reports are generated by adjusting the measured usage using the one or more adjustment factors.
A computer system may include at least one processor and at least one memory storing instructions that, when executed, cause the at least one processor to perform a process. The process may include receiving audio data from a user device, and accessing content data including at least one audio signature associated with video content. The process may also include correlating the audio data with the at least one audio signature and identifying recognized video content based on the correlation of the audio data with the at least one audio. The process may also include receiving tuning data including content being presented on a display component. The process may further include correlating the recognized video content with the tuning data, determining viewed video content based on the correlation of the recognized video content with the tuning data, and storing the viewed video content in a user array.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/439 - Processing of audio elementary streams
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs
H04N 21/233 - Processing of audio elementary streams
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/414 - Specialised client platforms, e.g. receiver in car or embedded in a mobile appliance
H04N 21/422 - Input-only peripherals, e.g. global positioning system [GPS]
Techniques for projecting household-level viewing events are described herein. Population data may be accessed including classes of a plurality of demographic attributes for households in a market. Representative household units (RHUs) may be generated, and the RHUs may be assigned a class for each of the demographic attributes and a quota based on the demographic attributes of a plurality of panelist households. Each of the panelist households may be assigned to one of the RHUs based on at least one panelist classes matching the classes for respective demographic attributes of the RHU, and the number of matching panelist households assigned to each of the RHU may be based on the quota. Panelist viewing data representing viewing events associated with the panelist household may be accessed. A report may be generated with the classes of the RHUs and the panelist viewing data of the assigned panelist households.
H04N 7/025 - Systems for transmission of digital non-picture data, e.g. of text during the active part of a television frame
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
H04H 60/45 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying users
H04H 60/52 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying locations of users
34.
SYSTEMS AND METHODS FOR GENERATING AND TRANSMITTING CONTENT BASED ON ASSOCIATION OF A COMMON DEVICE
A computerized method of generating a report is disclosed, along with a corresponding system and non-transitory computer-readable medium. The method may include receiving training data including labeled feature sets and an indicator of a common device. The method may include receiving a first identifier with a first feature set, and a second identifier with a second feature set. The method may include correlating the first and second feature sets, and generating a common device score based on the correlated first and second feature sets and the training data. The method may also include comparing the common device score to a threshold, and associating, in response to the comparison, the first identifier and the second identifier with a device. The method may further include generating the report that indicates that the first and second identifiers are associated with the device.
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
G06F 7/14 - Merging, i.e. combining at least two sets of record carriers each arranged in the same ordered sequence to produce a single set having the same ordered sequence
Rating a network interaction is disclosed. A rating system includes an interface for receiving, a rating determiner and an interface for providing. The interface for receiving receives one or more data regarding a new incoming network interaction originated from a third party device over a network. The rating determiner determines a rating of the network interaction based at least in part on the one or more data regarding the network interaction. The interface for providing provides the rating of the network interaction.
Providing analysis of exposure of users to content without the use of personally identifiable information (PII) is described. Initially, first user activity data is obtained from a server associated with a collection service. The first user activity data comprises a first unique identifier (ID) deterministically created based on an Internet protocol (IP) address of a first entity using a first one-way function. Next, second user activity data is obtained from a service provider. The second user activity data comprises a second unique ID deterministically created based on PII of a second entity using a second one-way function. It is determined whether the first unique ID is the same as the second unique ID, and responsive to that determination, the first and second user activity data are continuously aggregated such that a trend is determined in real-time and analysis of the aggregated user activity data is performed and reported in compliance with mandated legal or policy privacy provisions.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/658 - Transmission by the client directed to the server
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/61 - Network physical structure; Signal processing
Techniques for measuring the visibility of video content presented within video players are presented. Initialization code is incorporated within a video player. The initialization code examines metadata associated with video content to determine whether to measure visibility information associated with the video content. If a measurement flag is encountered, the initialization code initializes measurement code to measure visibility information associated with the video content. The measurement code executes to measure visibility information associated with the video content and transmits the visibility information to a measurement server.
Systems, methods, and computer-readable medium are provided for identifying content displayed by a media device and associating the content with demographics of one or more users. An indication to capture an image of the content displayed by the media device is initially received. The image of the content is then captured and stored. One or more image fingerprints in the image of the content are identified. The monitoring device transmits the one or more image fingerprints, a timestamp of the time at which the image was captured, and an identifier of the monitoring device to a server. The server compares the one or more image fingerprints to fingerprints in a database to identify the content displayed by the media device. The identity of the content is then associated with demographic information of the one or more users. A report of the content viewership of the content displayed by the media device is then generated.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for audience measurement using a mobile device accessory are disclosed. In one aspect, a mobile device accessory includes a power source configured to provide power to the mobile device. The mobile device accessory further includes an audio capturing device configured to capture an ambient sound, and a microprocessor coupled to the audio capturing device and configured to identify distinctive features of the ambient sound and generate a digital representation of the ambient sound. The mobile device accessory further includes a memory coupled to the microprocessor and configured to store at least the digital representation, and a transmitter coupled to the microprocessor and configured to transmit at least the digital representation.
G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
G10L 25/72 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for transmitting results of analysis
G06F 17/30 - Information retrieval; Database structures therefor
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/41 - Structure of client; Structure of client peripherals
40.
Protecting user privacy during collection of demographics census data
In general, the systems, components, methods, and techniques are provided for gathering, recording, and developing accurate user demographics attributed to users viewing content across different media platforms while protecting user privacy and providing compliance with legal or policy mandated privacy provisions.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/61 - Network physical structure; Signal processing
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/658 - Transmission by the client directed to the server
A method of classifying a product placement in a video using rule sets is disclosed. Each rule of the rule set includes a value and one or more defining rule elements. An attribute rule set is created with attribute values and attribute elements that define levels of audio visual prominence of a product in the video. An integration rule set is created with integration values and integration elements where the integration elements define levels of integration of the product with video continuity. The video is partitioned at product scene changes to create product blocks. For each product block, an attribute value is selected based on the attribute elements and an integration value is selected based on the integration elements. An impact parameter for the video is derived as a function of the selected attribute values and integration value.
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
H04N 21/4725 - End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content for requesting additional data associated with the content using interactive regions of the image, e.g. hot spots
The present disclosure relates generally to monitoring internet usage on home networks of panelist users. One example method includes, after receiving informed consent from a user, identifying the user's home network including one or more devices and a gateway configured to receive network traffic from the devices and send the received network traffic to an external network; configuring the home network to send network traffic from the devices to a measurement device connected to the gateway; and after configuring the home network, monitoring, at the measurement device, network traffic received from the devices on the home network.
Systems and methods herein log traffic to and from a device on a network. Logging can occur using a metering device, router, proxy, or other elements. For example, a metering device operatively coupled to a routing device can log the traffic directed to and originating from a user device. Logged traffic can be analyzed to identify users, devices, and/or sessions. For example, an identifier unique to the user device in the session, a device type of the user device, and a specific user of the device during the session can be identified.
Usage data representing the access of a network entity by a plurality of client devices each associated with one or more Internet Protocol (IP) addresses of a plurality of IP addresses is initially accessed. Then a set of static IP addresses of the plurality of IP addresses is determined based on the usage data. A first count of static IP addresses of the set of static IP addresses that are all associated with mobile traffic or that are all associated with non-mobile traffic is determined. A second count of static IP addresses of the set of static IP addresses that are associated with the mobile traffic and the non-mobile traffic is also determined. An overlap factor is determined based on a ratio between the first count of static IP addresses and the second count of static IP addresses. Based on the overlap factor, a total count of unique static IP addresses via which the network entity was accessed is finally determined.
Techniques for measuring the visibility of video content presented within video players are presented. Initialization code is incorporated within a video player. The initialization code examines metadata associated with video content to determine whether to measure visibility information associated with the video content. If a measurement flag is encountered, the initialization code initializes measurement code to measure visibility information associated with the video content. The measurement code executes to measure visibility information associated with the video content and transmits the visibility information to a measurement server.
A computerized method of transmitting content to a first device and a second device may include receiving a first identifier and first location data of the first device, and a second identifier and second location data of the second device. The method may include comparing the first location data with the second location data, and generating a co-location score in response to the comparison. The method may include determining that the co-location score is greater than a threshold, and responsively generating household data indicative of a relationship between the first device and the second device. The method may further include generating and transmitting a report indicating the relationship to a content provider that transmits content to the first device and the second device.
A computerized process of detecting content blocking software may include forwarding, to a client device, instructions to enable scanning of a web browser and a file with features resembling advertisement content, and receiving a report from the forwarded instructions indicative of a response of a webpage generated by a web browser of the client device in response to the loaded file. The computerized process may also include analyzing the report based on an expected response of the web browser, and indicating the presence of the content blocking software based on the analysis.
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
48.
Systems and processes for detecting content blocking software
A process of detecting an implemented blacklist may include downloading data from at least one blacklist. The process may also include compiling a list of image elements from the at least one blacklist based on a feature of the image elements, and selecting an image element from the list of image elements. The process may further include receiving a report indicative of the selected image successfully loading while a webpage is loaded on a web browser of a client device, and analyzing the report based on an expected response of the web browser to detect the implemented blacklist of the at least on blacklist.
G06F 21/55 - Detecting local intrusion or implementing counter-measures
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
H04L 29/06 - Communication control; Communication processing characterised by a protocol
49.
Demographic attribution of household viewing events
Tuning data representing a television viewing event associated with a particular household is accessed. Household member data representing information on individual members of the particular household is accessed. Viewing profile data representing information on individual members of other households regarding viewership by the individual members of the other households is accessed. Fractional viewership values for the individual members of the particular household are determined based on the tuning data, the household member data, and the viewing profile data. Household viewership data is determined based on the fractional viewership values.
H04H 60/33 - Arrangements for monitoring the users' behaviour or opinions
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04H 60/37 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying segments of broadcast information, e.g. scenes or extracting programme ID
H04H 60/39 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying broadcast time or space for identifying broadcast space-time
H04H 60/45 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying users
H04H 60/46 - Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for recognising users' preferences
H04H 60/66 - Arrangements for services using the result of monitoring, identification or recognition covered by groups or for using the result on distributors' side
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
A combined reach can be determined for multiple resources. Usage data, including audience duplication data, is used to generate audience duplication measurements for pairs of the resources. A child-resource pair is identified, and the audience duplication measurement for that resource pair is modified. The modified measurement is used to determine a combined reach for the multiple resources. A report is also generated based on the combined reach.
A copy of a request for content from a content provider is initially received from a client device. The copy of the request indicates a unique identifier of the client device, an originating network address of the client device, and a destination network address of the content provider. The client device is associated with a network provider based on the originating network address. It is determined that the network provider belongs to a predetermined class. Activation of a private network switch is enabled on the client device to provide an encrypted connection between the client device and a private network server before the request is forwarded to the content provider based on the determination that the network provider belongs to the predetermined class.
Evaluating web activity is disclosed. Initially, activity data for one or more resources on a network is received for a predetermined time period. The resources have been accessed by a plurality of client systems, and the activity data includes a unique identifier and a category of an accessing client system for each access to the one or more resources. Next, at least one persistent identifier of a client system within the activity data is identified. A subset of the activity data associated with the at least one persistent identifier is also identified. Based on the subset of the activity data, a total number of accesses to the one or more resources from the client system having the persistent identifier is determined. Finally, an estimated number of accesses to the one or more resources from the client system is determined if the persistent identifier persisted on the client system during the entire predetermined time period.
A system includes a configuration server, a client device, and a VPN system. The configuration server is configured to send a configuration profile to a client device. The configuration profile is configured to cause the client device to connect to a VPN system without user input and send network traffic through the VPN system. The client device is configured to receive the configuration profile and apply the received configuration profile such that the client device is configured to connect to the VPN system without user input and send network traffic through the VPN system. The VPN system is configured to receive the network traffic sent by the client device through the VPN system and record information about the network traffic sent by the client device through the VPN system.
Techniques for measuring the visibility of video content are presented. The video content, such as a video advertisement, may be played or presented by a video player, for example as part of a web page. Initialization code may be incorporated within a video player. The initialization code may examine metadata associated with video content to determine whether to measure visibility information associated with the video content. If a measurement flag is encountered in the metadata, the initialization code may initialize measurement code designed to measure visibility information associated with the video content. The measurement code may execute to measure visibility information associated with the video content and transmit the visibility information to a measurement server.
Tuning data representing a television viewing event associated with a particular household is accessed. Household member data representing information on individual members of the particular household is accessed. Viewing profile data representing information on individual members of other households regarding viewership by the individual members of the other households is accessed. Fractional viewership values for the individual members of the particular household are determined based on the tuning data, the household member data, and the viewing profile data. Household viewership data is determined based on the fractional viewership values.
H04H 60/33 - Arrangements for monitoring the users' behaviour or opinions
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
Webpage or other resource accesses by client systems may be recorded, and those accesses may be analyzed to develop audience measurement reports. At times, it may be desirable to segment those reports according to classes of client systems (e.g., work vs. home client systems). A given client system can be classed into one of the reporting classes based on one or more classes of network service providers that provide the client with access to a network. The recorded resource accesses and classes of the client systems can then be used to generate audience measurement reports that are segmented according to one or more of the client system classes.
Systems and methods for measuring consumption of entertainment commodities distributed through at least two distribution channels by at least one distribution network operator to one or more media receivers. The methods include collecting consumption data from at least one distribution network operator. The at least one distribution network operator distributes entertainment commodities through at least a first distribution channel and a second distribution channel. The collected consumption data relates to entertainment commodities distributed through at least the first distribution channel and the second distribution channel. The collected consumption data includes at least one inconsistency. The collected consumption data is loaded into a computerized database adapted to store, process, and report on consumption. Cleaned consumption data is produced by reconciling consumption data inconsistencies. A consumption data report is based at least in part on the cleaned consumption data. The consumption data report communicates consumption of at least one entertainment commodity.
Panel and census data representing accesses by sets of users with multiple types of media platforms to media content associated with multiple media entities is accessed. An overlap in the accessed panel data that represents users who have accessed media content associated with the media entity with more than one of the multiple types of media platforms is determined. Based on the accessed panel data, the determined overlap in the accessed panel data, and the accessed census data, an overlap function that estimates an overlap in the accessed census data is derived. The derived overlap function is applied to census data associated with a media entity to estimate an overlap in the census data associated with the media entity. The overlap in the census data represents users who have accessed media content associated with the media entity with more than one of the multiple types of media platforms.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
A method for analyzing network interaction data is disclosed. Initially, network interaction data is received from a network over time. A predetermined model comprising predetermined values associated with network interaction parameters is also received. The received network interaction data is processed to determine the network interaction parameters and information regarding the network interaction data. A score for the network interaction data is calculated based on the predetermined model and the determined network interaction parameters. The score is compared to a threshold. The information regarding the network interaction data is then forwarded based on the comparison of the score to the threshold.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/06 - Communication control; Communication processing characterised by a protocol
G06F 17/30 - Information retrieval; Database structures therefor
A system includes a configuration server, a client device, and a VPN system. The configuration server is configured to send a configuration profile to a client device. The configuration profile is configured to cause the client device to connect to a VPN system without user input and send network traffic through the VPN system. The client device is configured to receive the configuration profile and apply the received configuration profile such that the client device is configured to connect to the VPN system without user input and send network traffic through the VPN system. The VPN system is configured to receive the network traffic sent by the client device through the VPN system and record information about the network traffic sent by the client device through the VPN system.
A first set of data including a first tracking identifier for a first webpage component and a destination address for the first webpage component is accessed. A second set of data that includes an address for retrieving a second webpage component, is accessed. The address includes a second tracking identifier. Whether the second tracking identifier is the same as the first tracking identifier is determined. The second webpage component is associated with the destination address when the second tracking identifier is the same as the first tracking identifier.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06F 17/30 - Information retrieval; Database structures therefor
62.
Systems, methods, and devices for monitoring content viewership using short-range wireless communication
Systems, methods, and computer-readable medium are provided for monitoring content viewership using short-range wireless communication by transmitting a short-range wireless signal, detecting a user device that responds to the transmitting, detecting content being presented by a media device, storing monitoring data that includes an indication of the user device and an indication of the content being presented, and transmitting the monitoring data to a server. Systems, methods, and computer-readable medium are also provided for receiving monitoring data from multiple monitoring devices, determining users associated with user devices that were detected, identifying content based on indications of content detected by the monitoring devices, matching the users to the content using the monitoring data, and generating a report of content viewership based on the matching.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies
A system may analyze beacon data over a particular period of time in order to estimate a total number of unique devices that visited web entities during that period.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
Usage data representing resource accesses on a network by client devices in a plurality of households is accessed. Based on the accessed usage data, a set of the households that the usage data indicates are associated with a specified number and type of client devices is determined. A subset of the usage data is extracted, where the subset corresponds to resource accesses by client devices associated with the subset of households. Usage information is determined for the specified number and type of client devices based on the extracted subset of the usage data.
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
65.
System and methods for analyzing content engagement in conjunction with social media
Various embodiments disclose a system and methods for media content analysis based at least in part upon social media data. In some embodiments, a computer system may determine a viewership associated with a selected piece of televised content such as a television show or advertisement. The computer system may also identify social media data such as social media messages associated with the selected piece of televised content. The computer system may calculate an audience engagement measurement corresponding to the selected piece of televised content based upon the social media data and the viewership.
G06Q 50/00 - Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
H04H 60/33 - Arrangements for monitoring the users' behaviour or opinions
H04H 60/66 - Arrangements for services using the result of monitoring, identification or recognition covered by groups or for using the result on distributors' side
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies
H04N 21/422 - Input-only peripherals, e.g. global positioning system [GPS]
H04N 21/462 - Content or additional data management e.g. creating a master electronic program guide from data received from the Internet and a Head-end or controlling the complexity of a video stream by scaling the resolution or bit-rate based on the client capabi
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/658 - Transmission by the client directed to the server
H04N 21/4788 - Supplemental services, e.g. displaying phone caller identification or shopping application communicating with other users, e.g. chatting
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
66.
Systems and method for analyzing advertisement pods
Systems and methods for analyzing viewing behavior of users viewing video content and advertisements are provided. A system may periodically or continuously receive tune data reflecting the viewing behavior of users, analyze the viewing behavior to determine a location and time period of advertisement blocks in a viewed segment, and determine parameters reflecting a comparison between a number of viewers that viewed the advertisement blocks and a number of viewers that viewed non-advertised content. The system may also deliver a report of the analysis to advertisers, agencies, media sellers, or other parties that are interested in measuring the effectiveness of advertisements on users. The analysis by the system can be done over multiple different types of content-distribution platforms.
H04N 21/422 - Input-only peripherals, e.g. global positioning system [GPS]
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/472 - End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
H04N 21/658 - Transmission by the client directed to the server
67.
Systems and methods for calibrating user and consumer data
Methods and systems are provided herein for calibrating subject data based on reference data, so that the calibrated subject data more closely represents a target population. The methods and systems include partitioning a reference data set into a plurality of reference data partitions using a data partitioning scheme, each reference data partition associated with a characteristic; and partitioning a subject data set into a plurality of subject data partitions using the data partitioning scheme, each subject data partition associated with a characteristic that corresponds to the characteristic associated with a reference data partition of the plurality of reference data partitions; identifying a variable present in the reference data set that is not present in the subject data set; and calculating a value of the variable for each reference data partition based on a rate of occurrence of the variable in each reference data partition.
Methods and systems for delivering a creative are disclosed. Initially, a first tag including a first link to the creative to be displayed on a webpage to be displayed on a first device is received. Next, a second tag corresponding to the first tag is generated, where the second tag includes a second link to a first set of instructions including instructions to forward a first rating request. The second tag is forwarded to a creative provider. Then, it is determined, based on first metadata of the first rating request, that a first rating satisfies a first set of parameters. Finally, the first tag including the first link to the creative is forwarded in response to the determination that the first rating satisfies the first set of parameters.
Usage data representing the access of a set of resources on a network is accessed. The usage data is based at least in part on information received from client systems sent as a result of beacon instructions included with the set of resources. First and second sets of usage data representing access by client systems classified as a first type and a second type, respectively, are determined based on the accessed usage data. Counts of unique visitors accessing the network resources from each of the first and second types of client systems, based on the first and sets of usage data, respectively, are each determined. A total count of unique visitors accessing the network resources from the first and second types of client systems is determined based on data representing the usage overlap of devices of the first type with devices of the second type.
Methods and systems for fraudulent traffic detection and estimation are disclosed. Initially, an empirical distribution of a plurality of features based on a first plurality of datapoints for the plurality of features is received. Next, a model distribution of the plurality of features based on a second plurality of datapoints for the plurality of features is received. Then, it is determined, a minimum number of datapoints to remove from the first plurality of datapoints to create a modified empirical distribution corresponding to the model distribution within a first significance level. Finally, an alert that the first plurality of web traffic includes at least one fraudulent instance of web traffic is generated in response to the determination that the minimum number of datapoints is greater than a first threshold.
A computer system may include at least one processor and at least one memory storing instructions that, when executed, cause the at least one processor to perform a process. The process may include receiving audio data from a user device, and accessing content data including at least one audio signature associated with video content. The process may also include correlating the audio data with the at least one audio signature and identifying recognized video content based on the correlation of the audio data with the at least one audio. The process may also include receiving tuning data including content being presented on a display component. The process may further include correlating the recognized video content with the tuning data, determining viewed video content based on the correlation of the recognized video content with the tuning data, and storing the viewed video content in a user array.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/439 - Processing of audio elementary streams
H04N 21/44 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs
H04N 21/233 - Processing of audio elementary streams
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/414 - Specialised client platforms, e.g. receiver in car or embedded in a mobile appliance
H04N 21/422 - Input-only peripherals, e.g. global positioning system [GPS]
72.
Attribution of household viewership information to individuals
A system and method for the assignment of person-level viewership. The system receives viewership information describing the viewing of video content at a household. The system additionally receives demographic information for that household, including the numbers of persons associated with the household. For each combination of viewers, the system calculates the probability that the viewers viewed the content based on the demographic attributes of those viewers and the probabilities that individuals sharing those attributes would view the content. The system then attributes the viewing information to one or more persons from the household based on the calculated probabilities. The system additionally updates the probabilities that individuals having different demographic attributes would view the content based on the selection of persons.
H04H 60/32 - Arrangements for monitoring conditions of receiving stations, e.g. malfunction or breakdown of receiving stations
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/658 - Transmission by the client directed to the server
73.
Projecting person-level viewership from household-level tuning events
The present disclosure describes techniques for projecting person-level viewership from household-level tuning events. One example method includes accessing tuning data representing television tuning events associated with particular households; accessing panelist viewing data representing television viewing events associated with panelists; and for at least one tuning event represented by the tuning data: accessing household member data associated with the tuning event; determining a member minutes value associated with each individual member of the particular household for the tuning event; and determining a fractional viewership value for each individual member of the particular household associated with the tuning event.
H04H 60/33 - Arrangements for monitoring the users' behaviour or opinions
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
Panel and census data representing accesses by sets of users with multiple types of media platforms to media content associated with multiple media entities is accessed. An overlap in the accessed panel data that represents users who have accessed media content associated with the media entity with more than one of the multiple types of media platforms is determined. Based on the accessed panel data, the determined overlap in the accessed panel data, and the accessed census data, an overlap function that estimates an overlap in the accessed census data is derived. The derived overlap function is applied to census data associated with a media entity to estimate an overlap in the census data associated with the media entity. The overlap in the census data represents users who have accessed media content associated with the media entity with more than one of the multiple types of media platforms.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
Tuning data representing a television viewing event associated with a particular household is accessed. Household member data representing information on individual members of the particular household is accessed. Viewing profile data representing information on individual members of other households regarding viewership by the individual members of the other households is accessed. Fractional viewership values for the individual members of the particular household are determined based on the tuning data, the household member data, and the viewing profile data. Household viewership data is determined based on the fractional viewership values.
The present disclosure relates generally to monitoring internet usage on home networks of panelist users. One examples method includes after determining that a user has accepted an offer to modify a home network of the user to monitor network traffic generated by devices connected to the home network, identifying a gateway device on the home network configured to receive network traffic from devices connected to the home network and communicate with an external network on behalf of the devices; determining that the gateway device is operable to be modified over the home network to include a monitoring application; and in response to determining that the gateway device is operable to be modified, modifying the gateway device over the home network to include the monitoring application.
Transferring metadata is disclosed. Information about a network interaction is processed to generate metadata describing the network interaction. Based on the metadata it is determined whether the metadata is to be transferred to an aggregator. In the event that the metadata is to be transferred, one or more aggregators are determined to which the metadata is to be transferred. The metadata is transferred to the one or more aggregators.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/06 - Communication control; Communication processing characterised by a protocol
78.
Audience duplication for parent-child resource pairs
A combined reach can be determined for multiple resources. Usage data, including audience duplication data, is used to generate audience duplication measurements for pairs of the resources. A child-resource pair is identified, and the audience duplication measurement for that resource pair is modified. The modified measurement is used to determine a combined reach for the multiple resources. A report is also generated based on the combined reach.
An aspect of the present invention relates to tracking and analyzing a computer user's behavior after viewing a particular search result or a particular advertisement to assess the impact of having viewed the search result or advertisement.
An identifier associated with a client machine is accessed, and parameters that define a control group of client machines for a particular advertising campaign are accessed. The advertising campaign is associated with one or more standard advertisements. Whether the client machine is a member of the control group based on the identifier associated with the client machine and the parameters that define the control group is determined. If the determination of whether the client machine is a member of the control group indicates that the client machine is a member of the control group, a control advertisement is caused to be presented on the client machine. The control advertisement is an advertisement not associated with the advertising campaign.
Requests for data received from multiple subscribers are accessed. At least some of the requests for data originate from one or more addresses associated with a particular subscriber of the multiple subscribers. The accessed requests for data are organized into sets of requests based on the one or more addresses such that a set of requests corresponds to the particular subscriber, and a characteristic of the particular subscriber is determined based on aspects of the set of requests corresponding to the particular subscriber and a behavior model.
Requests for data received from multiple subscribers are accessed. At least some of the requests for data originate from one or more addresses associated with a particular subscriber of the multiple subscribers. The accessed requests for data are organized into sets of requests based on the one or more addresses such that a set of requests corresponds to the particular subscriber, and a characteristic of the particular subscriber is determined based on aspects of the set of requests corresponding to the particular subscriber and a behavior model.
The effectiveness of advertisement(s) that are included in an advertising campaign may be determined by displaying a control advertisement to a control group of webpage visitors and inviting the users in the control group and/or the users exposed to the advertisement(s) that are in the campaign to complete a survey regarding the advertisement(s) or overall objectives of the campaign.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
The disclosure herein concerns a method of estimating Internet traffic including taking a size of a target population based on at least one of census data and third party data, identifying a percentage of the target population that displays an online behavior, receiving clickstream data at a host processing facility from a clickstream panel that connotes Internet activity of individual members of the clickstream panel, identifying a fraction of participants within the clickstream panel that exhibit the online behavior, and producing, at the host processing facility, an estimate of the target population's Internet activity by first scaling the clickstream data for the participants in the clickstream panel exhibiting the online behavior so that it matches the percentage in the target population and then scaling the data for all members of the clickstream panel by the relative size of the target population.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
An appliance for analyzing a network interaction is disclosed. The appliance includes an input to monitor network traffic that includes a network interaction without interfering with the network traffic. The appliance further includes a processor to analyze network traffic to derive information about the network interaction wherein the information about the network interaction is used to distinguish whether the network interaction is a qualified network interaction with a user.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
A first set of data including a first tracking identifier for a first webpage component and a destination address for the first webpage component is accessed. A second set of data that includes an address for retrieving a second webpage component, is accessed. The address includes a second tracking identifier. Whether the second tracking identifier is the same as the first tracking identifier is determined. The second webpage component is associated with the destination address when the second tracking identifier is the same as the first tracking identifier.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06F 9/44 - Arrangements for executing specific programs
G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
A first set of usage data for a first set of resources on a network and a second set of usage data for a second set of resources on a network are accessed. The first set of resources were accessed by a first group of client systems and the first set of usage data is determined based on information received from the first group of client systems sent as a result of beacon instructions included with the first set of resources. The second set of usage data is determined based on information received from monitoring applications installed on a second group of client systems that accessed the second set of resources. Users of the second group of client systems are a sample of a larger group of users that use resources on the network. Initial usage measurement data for a third set of resources on the network is determined based on the first set of usage data. The third set includes one or more common resources that are included in the first set of resources and the second set of resources. One or more adjustment factors are determined based on the second set of usage data and applied to the initial usage measurement data to generate adjusted usage measurement data. One or more reports are generated based on the adjusted usage measurement data.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06F 17/30 - Information retrieval; Database structures therefor
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
A first set of data including a first tracking identifier for a first webpage component and a destination address for the first webpage component is accessed. A second set of data that includes an address for retrieving a second webpage component, is accessed. The address includes a second tracking identifier. Whether the second tracking identifier is the same as the first tracking identifier is determined. The second webpage component is associated with the destination address when the second tracking identifier is the same as the first tracking identifier.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
A first data set associated with a first group of users is accessed. The first data set includes demographic data, online behavior data, and additional user data associated with the users in the first group. A second data set associated with a second group of users is accessed. The second data set includes demographic data and online behavior data but not additional user data associated with the users in the second group. One or more sets of matched users are determined based on the demographic data and online behavior data included in the first data set and the demographic data and online behavior data included in the second data set. Each set includes a user from the first group matched with a user from the second group. Based on the one or more sets of matched users, an augmented second data set that includes additional user data associated with the users in the second group is generated. One or more reports are generated based on the augmented second data set.
Methods and systems are provided herein for collecting web browser click events across a plurality of web sites from a data collection agent (DCA), as a click-stream, at a data collection server (DCS) to record and provide user on-line activity, filtering the user online activity to include activity from a time period prior to a sale from the sales transaction data and identifying one or more shopping touch-points based on the filtered user online activity and the sales transaction data. Further, an engagement index, an influence index, and an opportunity index is calculated. A digital touch-points facility may perform the identifying and calculating.
Transferring metadata is disclosed. Information about a network interaction is processed to generate metadata describing the network interaction. Based on the metadata it is determined whether the metadata is to be transferred to an aggregator. In the event that the metadata is to be transferred, one or more aggregators are determined to which the metadata is to be transferred. The metadata is transferred to the one or more aggregators.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/06 - Communication control; Communication processing characterised by a protocol
Information associated with responses and requests that result from one or more selections of a webpage component is collected. The webpage component corresponds to at least one identifier. At least one set including the at least one identifier and the collected information associated with the responses and requests is determined. A destination webpage for the webpage component corresponding to the webpage component based at least on the information associated with the responses and requests is determined.
Panel and census data representing accesses by sets of users with multiple types of media platforms to media content associated with multiple media entities is accessed. An overlap in the accessed panel data that represents users who have accessed media content associated with the media entity with more than one of the multiple types of media platforms is determined. Based on the accessed panel data, the determined overlap in the accessed panel data, and the accessed census data, an overlap function that estimates an overlap in the accessed census data is derived. The derived overlap function is applied to census data associated with a media entity to estimate an overlap in the census data associated with the media entity. The overlap in the census data represents users who have accessed media content associated with the media entity with more than one of the multiple types of media platforms.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
A first set of usage data for a first set of resources on a network and a second set of usage data for a second set of resources on a network are accessed. The first set of resources were accessed by a first group of client systems and the first set of usage data is determined based on information received from the first group of client systems sent as a result of beacon instructions included with the first set of resources. The second set of usage data is determined based on information received from monitoring applications installed on a second group of client systems that accessed the second set of resources. Users of the second group of client systems are a sample of a larger group of users that use resources on the network. Initial usage measurement data for a third set of resources on the network is determined based on the first set of usage data. The third set includes one or more common resources that are included in the first set of resources and the second set of resources. One or more adjustment factors are determined based on the second set of usage data and applied to the initial usage measurement data to generate adjusted usage measurement data. One or more reports are generated based on the adjusted usage measurement data.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
Various embodiments disclose a system and methods for media content analysis based at least in part upon social media data. In some embodiments, a computer system may identify social media messages corresponding to televised content. For example, following the uploading of a television show (or advertisement) to an Internet website, or transmission across a television network, viewers and non-viewers of the show may begin commenting upon the show. The system may correlate the number of viewers of the show with the social media messages to generate one or more metrics describing relations between the show's viewership and the show's social media effects.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/4788 - Supplemental services, e.g. displaying phone caller identification or shopping application communicating with other users, e.g. chatting
H04N 21/658 - Transmission by the client directed to the server
G06Q 50/00 - Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
H04H 60/33 - Arrangements for monitoring the users' behaviour or opinions
H04H 60/66 - Arrangements for services using the result of monitoring, identification or recognition covered by groups or for using the result on distributors' side
96.
Direct page view measurement tag placement verification
Disclosed herein are strategies for verifying placement of a direct measurement tag useful for measuring Internet traffic of a plurality of users at a website. For example, a method may include receiving web page identification data that is derived from user clickstream data, determining a URL associated with a domain based on the webpage identification information, and providing a measurement code verification web crawler with the URL and the depth to which to explore the domain for verifying measurement code placement with the web crawler.
A system and method for cleaning television viewing behavior data collected from set top boxes by detecting and correcting various problems that can occur in the viewing data. Three problems that may be detected and corrected by the system include: overflows of memory buffers; inclusion of non-human generated tuning events; and presence of clock offsets. After cleaning the television viewing behavior data, the cleaned data may be used to analyze audience viewing behavior in a manner that achieves a higher degree of accuracy than can be achieved by using uncleaned television viewing behavior data.
H04N 9/00 - PICTORIAL COMMUNICATION, e.g. TELEVISION - Details of colour television systems
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 7/173 - Analogue secrecy systems; Analogue subscription systems with two-way working, e.g. subscriber sending a programme selection signal
H04N 7/16 - Analogue secrecy systems; Analogue subscription systems
G06F 15/173 - Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
98.
Demographic attribution of household viewing events
Tuning data representing a television viewing event associated with a particular household is accessed. Household member data representing information on individual members of the particular household is accessed. Viewing profile data representing information on individual members of other households regarding viewership by the individual members of the other households is accessed. Fractional viewership values for the individual members of the particular household are determined based on the tuning data, the household member data, and the viewing profile data. Household viewership data is determined based on the fractional viewership values.
Usage data representing resource accesses on a network by client devices in a plurality of households is accessed. Based on the accessed usage data, a set of the households that the usage data indicates are associated with a specified number and type of client devices is determined. A subset of the usage data is extracted, where the subset corresponds to resource accesses by client devices associated with the subset of households. Usage information is determined for the specified number and type of client devices based on the extracted subset of the usage data.
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
G06F 15/173 - Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
100.
System and methods for calibrating user and consumer data
A system and method that calibrates subject data for which a relationship to a target population is not known, so that the calibrated subject data can more accurately represent the target population. In many cases the calibration will involve the use of a differential weighting scheme applied to the data at the constituent level. The system and method allows the values of the observed variables in the subject data set to be weighted so that their incidence is equivalent to that of a reference population represented by a reference data set, even if the variables used in the reference data set to make estimates for the reference population were not collected or measured for the subject data set.
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