Patent Grant
11212565
I have not found any relevant prior art at the present time.
With the ever increasing number of consumer choices for television viewing, it is important for advertisers, content producers, and service providers such as cable television and satellite television and internet protocol television companies to be able to accurately measure audience viewership. I have discussed this problem extensively in my prior applications. This application teaches how to analyze video viewing activity using the Hadoop MapReduce distributed computing framework.
Existing Tools for Data Analysis
In my prior applications I taught how to analyze video viewing activity (channel tuning data) using various methods that rely on loading data into arrays in the memory of a computer. In certain cases, an analyst may wish to use the Hadoop MapReduce distributed computing framework to analyze video viewing activity. I have not identified any patents that teach how to use MapReduce to solve this problem.
In accordance with one embodiment, I disclose a computer-implemented method of aggregating video viewing activity data using the Hadoop MapReduce distributed computing framework. This will allow an analyst to aggregate second-by-second video viewing activity for various kinds of video content. Once this data has been aggregated, it can be used in any number of downstream analytic processes to provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers.
By using the Hadoop MapReduce distributed computing framework to aggregate the video viewing activity, an analyst can harness the power of hundreds or even thousands of processors working in parallel to solve the problem of aggregating video viewing activity data. This will allow an analyst to work with data sets of all sizes including extremely large data sets. The resulting files can be loaded to a relational database for various analytics similar to what I have taught in my other Patent Applications referenced previously. Additionally, the resulting files can be used in other Hadoop processes to correlate video viewing activity with other social media activity, with weather, with other programming content, and similar uses.
The following are definitions that will aid in understanding one or more of the embodiments presented herein:
Computer readable format means any data format that can be read by a computer program or a human being as necessary. Nonlimiting examples include:
(i) formatted text files,
(ii) pipe delimited text files,
(iii) data base tables,
(iv) Extensible Markup Language (XML) messages,
(v) a printed report,
(vi) JavaScript Object Notation messages.
Data analysis computer system means a combination of one or more computers on which a Data Analysis Program or Programs or Hadoop or MapReduce processes can be executed.
Nonlimiting examples include:
(i) one or more computers where video viewing activity data can be used to create video viewing detail records,
(ii) a single computer running the MapReduce distributed computing framework for parallel processing,
(iii) a cluster of many computers running the MapReduce distributed computing framework for parallel processing where many means a few to hundreds or even thousands,
(iv) a Hadoop cluster of computers.
Data analysis computer of known type means any commonly available computer system running a commonly known operating system. Nonlimiting examples include:
(i) a standard personal computer running WINDOWS 7 Professional operating system from MICROSOFT® Corporation,
(ii) a computer running the UNIX operating system,
(iii) a computer running the Linux operating system,
(iv) a computer in a cloud computing environment,
(v) a mainframe computer with its operating system.
Data analysis program means a computer program or programs that are able to execute on a Data analysis computer of known type. Nonlimiting examples include:
(i) a Pig Latin script running MapReduce,
(ii) a JAVA program running MapReduce,
(iii) a Python script running MapReduce,
(iv) a COBOL program.
Demographic information means any data item that can describe a characteristic of a viewer or a subscriber or a household associated with a viewer who is operating the video asset viewing device. Nonlimiting examples include income, ethnicity, gender, age, marital status, location, geographic area, postal code, census data, occupation, social grouping, family status, any proprietary demographic grouping, segmentation, credit score, dwelling type, homeownership status, property ownership status, rental status, vehicle ownership, tax rolls, credit card usage, religious affiliation, sports interest, political party affiliation, cable television subscriber type, cable television subscriber package level, and cell phone service level.
Device Characteristic means any feature or capability or aspect or descriptive qualifier or identifier of a video viewing device. Nonlimiting examples include that this may identify the type of device such as a set-top box, a tablet, a smart phone; a capability of the device such as the ability to record video or to support multiple viewing windows, or a manufacturer identifier.
Device Type is a subset of Device Characteristic where device type may, as a nonlimiting example, identify the type of device such as a set-top box, a tablet, a smart phone.
Geographic information means any service area or any network hierarchy designation or marketing area or other designated area used by a cable television company or a satellite television company or IP Television delivery company or video asset delivery system. The boundary or description of a geographic area is defined based on the needs of the service provider. Nonlimiting examples include a Market in a cable company network, a Headend in a cable company network, a Hub in a cable company network, a census tract, a cell tower identifier, a service area for satellite TV, advertising zone, a zip code, or some other geographic identifier. The geographic information may then be used to identify the location of a video asset viewing device or geographic information about the house associated with the device or the location of the device at the time of the viewer interaction in the event that the viewer interaction occurs in a location different than the location of the house associated with the device.
Network means any computer network. Nonlimiting examples include:
(i) a cable television network,
(ii) a cellular telephony network,
(iii) hybrid fiber coax system,
(iv) a satellite television network,
(v) a wi-fi network,
(vi) any means that supports communication among video asset viewing devices or electronic devices or computers or computer systems.
Pipe delimited text files means data files where the fields are separated by the “|” character.
Set-top box means a video asset viewing device that receives external signals and decodes those signals into content that can be viewed on a television screen or similar display device. The signals may come from a cable television system, a satellite television system, a network, or any other suitable means. A set-top box may have one or more tuners. The set-top box allows the user to interact with it to control what is displayed on the television screen. The set-top box is able to capture the commands given by the user and then transmit those commands to another computer system. For purposes of this application, stating that a set-top box tunes to a channel is equivalent to stating that a tuner in a set-top box has tuned to a channel. A set-topbox may also play back previously recorded video content.
STB means Set-top box.
Tuner means a tuner in a Set-top box.
Tuner index means an identifier of a tuner in a Set-top box.
Video asset means any programming content that may be viewed and/or heard. A Video Program may contain multiple Video Assets. Nonlimiting examples of Video Asset include:
(i) advertisements or commercials,
(ii) movies,
(iii) sports programs,
(iv) news casts,
(v) music,
(vi) television programs,
(vii) video recordings.
Video asset viewing device means any electronic device that may be used either directly or indirectly by a human being to interact with video content where the video content is provided by a cable television system or a satellite television system or a computer system accessed through a network. Nonlimiting examples include: Gaming station, web browser, MP3 Player, Internet Protocol phone, Internet Protocol television, mobile device, mobile smart phone, set-top box, satellite television receiver, set-top box in a cable television network, set-top box in a satellite television system, cell phone, personal communication device, personal video recorder, personal video player, two-way interactive service platforms, personal computer, tablet device.
Video server delivering video content through a network means any computer system, any individual piece of computer equipment or electronic gear, or any combination of computer equipment or electronic gear which enables or facilitates the viewer interaction with the video asset viewing device. Nonlimiting examples include:
(i) cable television system,
(ii) cable television switched digital video system,
(iii) cellular phone network,
(iv) satellite television system,
(v) web server,
(vi) any individual piece of computer equipment or electronic gear,
(vii) any combination of computer equipment or electronic gear.
Video viewing activity means any identifiable activity that a Video asset viewing device operator may do in regard to a Video asset viewing device and where such activity can be captured by the video asset viewing device or by the video server delivering video content through a network that supports the device. Nonlimiting examples include:
(i) power on/power off, open web page, close web page,
(ii) channel up/channel down/channel selection, play video content on web browser,
(iii) volume up/volume down/mute/unmute,
(iv) any trick play such as fast forward, rewind, pause
(v) recording video content,
(vi) playing back recorded video content,
(vii) invoking a menu, choosing a menu option,
(viii) any response to a screen prompt
(ix) playing live video content.
Video viewing activity means any measurements or aggregations produced by the MapReduce distributed computing framework as it aggregates video viewing detail records or any value calculated by a Data Analysis Program as part of this process.
Viewer means the human being causing a Viewer interaction; the user of a Set-top box or a Video asset viewing device.
When reading the information below, it can be appreciated that these are merely samples of table layouts, format and content, and many aspects of these tables may be varied or expanded within the scope of the embodiment. The table layouts, field formats and content, algorithms, and other aspects are what I presently contemplate for this embodiment, but other table layouts, field formats and content, algorithms, etc. can be used. The algorithms are samples and various aspects of the algorithms may be varied or expanded within the scope of the embodiment.
In one embodiment the MapReduce Aggregation Engine 200 can be implemented on computer clusters running a standard Hadoop distribution from Apache under the Linux operating system. The MapReduce Aggregation Engine 200 can be implemented in JAVA or Pig. The reader may find more information about various Apache open source projects from The ApacheSoftware Foundation at http://apache.org. Pig is a dataflow scripting language used to run dataflows on Hadoop. Pig uses the Hadoop Distributed File System and the Hadoop processing system which is MapReduce. Pig is an Apache open source project. The reader may find more information about Pig at http://apache.org. Those skilled in the art will readily recognize these tools.
Note on Media Measurement Data Model
Cable Television Laboratories, Inc. has published an “Audience Data Measurement Specification” as “OpenCable™ Specifications, Audience Measurement, Audience Measurement Data Specification” having Document Control Number “OC-SP-AMD-101-130502” Copyright© Cable Television Laboratories, Inc. 2013 which contains a Media Measurement Data Model database design which can be used as a source of data for the MapReduce Aggregation Engine 200 which I teach how to build in this Application. The teaching in my present application can be implemented in conjunction with that Media Measurement Data Model or with any number of data models as long as the required input data is provided as described herein.
Additionally, my MapReduce Aggregation Engine 200 creates files which may be used to load additional tables in a Media Measurement Data Model such as the one published by Cable Television Laboratories, Inc. These files are described in
Note: Numbering in the Drawings—The numbers in the drawings are usually, but not always, in sequential order.
In this nonlimiting example, the purpose is not to describe in detail the operations of video content delivery network or a data collection process, but simply to show how the data that is collected from that system can be made available to my MapReduce Aggregation Engine 200.
It begins with Viewer Viewing Linear Content 9200 who is interacting with a set-top box 9210 and television 9220 as he views linear content. The set-top box 9210 interacts with a Video Content Delivery System 9250 which delivers the content across a Network 9230.
It continues with Viewer Viewing DVR Content 9202 who is interacting with a set-top box 9210 and television 9220 as he interacts with DVR content recording content and playing back recorded content using various modes including trick plays. The set-top box 9210 interacts with a Video Content Delivery System 9250 which delivers the content across a Network 9230.
It continues with Viewer Viewing VOD Content 9203 who is interacting with a set-top box 9210 and television 9220 as he interacts with VOD content playing the content using various modes including trick plays. The set-top box 9210 interacts with a Video Content Delivery System 9250 which delivers the content across a Network 9230.
It continues with Viewer viewing video content using tablet, smart phone, IP TV, or other video viewing device 9204 who is interacting with a variety of Video Viewing Devices 9212, including but not limited to tablet, smart phone, IP TV, PC, etc. The video viewing device interacts with a Video Content Delivery System 9250 which delivers the content across a Network 9230.
Video Content Delivery System 9250 then interacts with a Viewer Interaction Data, Data Collection System 9260 which collects all manner of viewer interaction data including Linear viewing including time-shifted linear viewing, Digital Video Recorder recording and playback/viewing, and Video on Demand viewing. The Viewer Interaction Data, Data Collection System 9260 then processes the data as needed to load it to a Media Measurement Data Base 100. The data in the Media Measurement Data Base 100 can then be used as input to my Aggregation Engine 200 as described in
As noted previously, the video viewing activity may be sourced from a Media Measurement Data Base such as the one described in the Cable Television Laboratories, Inc. specification. The populating of the Media Measurement Database 100 is beyond the scope of this application and so only brief remarks will be made in reference to that. There are video viewing data collection systems that are commonly used in the industry for collecting channel tuning or video viewing activity data including switched digital video systems, set top box applications, internet protocol video viewing applications, and other video viewing applications. These systems enable the collection of the video viewing events which can be loaded to a Media Measurement Database 100. From such a database, Video Viewing Activity Data can be extracted in a format similar to that shown in
Proceeding with the review of
Other data preparation activities can be done according to business needs. Those with reasonable skill in the art will readily recognize how to perform these activities.
Proceeding with the review of
The computer algorithm that the Data Explosion Process 140 runs to create the Video ViewingDetail File 150 is as follows:
Note:
The following fields were optionally included in Video Viewing Activity Data File 130 for data validation purposes. During Data Explosion Process 140 they are dropped so that they do not pass forward to Video Viewing Detail File 150.
The explosion process can be run in several ways to achieve the same result. I have included two alternative embodiments.
If the tune duration is provided, the looping construct can be done as follows:
Note: In this case, the SECOND_OF_DAY_WHEN_TUNED 1250 will represent a UNIX EPOCH time stamp.
Note: In each case the Video Viewing Detail File 150 records can be written directly to the Hadoop Distributed File System (HDFS) so that the video viewing detail records are ready for use by the MapReduce distributed computing framework.
Note:
The Video Viewing Activity Data File 130 can be provided by the Extract 120 process in any computer readable format including, but not limited to, database tables, flat files, JSON messages, and XML messages. Alternatively, such video viewing events can be collected directly from the source without the need for a Media Measurement Database 100. In such a case, those events can still be provided as video viewing activity in a format similar to that shown in
For all of the above embodiments, at the completion of Data Explosion Process 140, one record has been written to the Video Viewing Detail File 150 for each second of the tune duration represented in the video viewing activity record. The Sample Data in
Those skilled in the art will readily recognize that the Data Explosion Process 140 is suitable for running in parallel on multiple computers simultaneously with each process creating Video Viewing Detail File records that can be fed into the MapReduce Aggregation Engine 200.
Proceeding with the review of
The MapReduce process can be coded in JAVA or in Pig. I have coded this in Pig. The code below can be used to create the four output files reviewed in the Drawings (
Using these four outputs, the reader will have a comprehensive set of aggregated video viewing metrics. The reader should recognize that the aggregation logic shown below provides several illustrations of what can be done. Additional aggregation combinations will be obvious to those skilled in the art.
The reader will note that I have used very descriptive names in the Pig Latin code below so as to convey the meaning of what is happening. Much shorter names could be used to produce the same result.
Creating the Aggregated Video Viewing Geo+Server+Content+Demo File 220
The Pig Latin coding to create the Aggregated Video Viewing Geo+Server+Content+Demo File 220 is shown next.
This summarization aggregates viewing activity for each combination of geographic identifier and server identifier and content identifier and demographic identifier for each second of the aggregation period. The result provides viewing metrics for each combination of geographic area and video server and content and demographic identifier as represented in the input data. As a nonlimiting example, a Video Content Identifier may be a channel call sign; this summary then provides a count of how many devices were tuned to that channel within each geographic area (a city or a region) and within each video server and for each demographic group. As an example, how many devices in the DENV Geo area served by SERVER-01 were tuned to ABC from Demo code 40-60 k during each second of the time period. A second example, how many devices in the DENV Geo area served by SERVER-01 were tuned to Program Monday Night Football from Demo code 40-60 k during each second of the time period.
Note: A sample of the file created by the aggregation is shown in
Creating the Aggregated Video Viewing Geo+Server+Content File 230
The Pig Latin coding to create the Aggregated Video Viewing Geo+Server+Content File 230 is shown next.
This summarization aggregates viewing activity for each combination of geographic identifier and server identifier and content identifier for each second of the aggregation period. The result provides viewing metrics for each combination of geographic area and video server and content id as represented in the input data. As a nonlimiting example, a Video Content Identifier may be a channel call sign; this summary then provides a count of how many devices were tuned to that channel within each geographic area (a city or a region) and within each video server. As an example, how many devices in the DENV Geo area served by SERVER-01 were tuned to ABC during each second of the time period.
Note: A sample of the file created by the aggregation is shown in
Creating the Aggregated Video Viewing Content File 240
The Pig Latin coding to create the Aggregated Video Viewing Content File 240 is shown next. This summarization aggregates viewing across all geographic identifiers, all servers, and all demographic groups for each second of the aggregation period. The result provides viewing metrics for the content (channel) across all geographic areas, video servers, and demographic groups as represented in the input data. As a nonlimiting example, a Video Content Identifier may be a channel call sign; this summary then provides a count of how many devices were tuned to that channel during each second of the viewing period.
Note: A sample of the file created by the aggregation is shown in
Creating the Aggregated Video Viewing File 250
The Pig Latin coding to create the Aggregated Video Viewing File 250 is shown next. This summarization aggregates viewing activity across all geographic identifiers, all servers, all content, and all demographic groups for each second of the aggregation period. The result provides viewing metrics across all geographic areas, video servers, content ids, and demographic groups as represented in the input data. As a nonlimiting example, this aggregation will provide insight into total viewing activity during each second of the measurement period. This is creating the denominator which can be used in calculations which measure the percentage of the total viewing audience that a particular piece of content earned.
Note: A sample of the file created by the aggregation is shown in
There is Summary Information followed by the Data Structure including field definitions. After the Data Structure there is a set of Sample Data.
There is Summary Information followed by the Data Structure including field definitions. After the Data Structure there is a set of Sample Data.
Overview of
There is Summary Information followed by the Data Structure including field definitions. After the Data Structure there is a set of Sample Data.
There is Summary Information followed by the Data Structure including field definitions. After the Data Structure there is a set of Sample Data.
There is Summary Information followed by the Data Structure including field definitions. After the Data Structure there is a set of Sample Data.
There is Summary Information followed by the Data Structure including field definitions. After the Data Structure there is a set of Sample Data.
Although the description above contains much specificity, this should not be construed as limiting the scope of the embodiments but as merely providing illustrations of some of several embodiments. As a nonlimiting example, additional qualifiers may be added along with geographic identifiers, video server identifiers, video content identifiers, and demographic identifiers. Additional aggregations can be done using other combinations of these identifiers.
Scope of Viewer Interaction Data Loaded
I presently contemplate that the MapReduce Aggregation Engine 200 will process viewer interaction data for whatever set of viewing activity is provided to it. This may be one Video Program at a time, one hour of the day, a primetime television viewing period, an entire 24 hour day of viewing, a week of viewing, or another time period decided by the analyst. Another embodiment may simply process viewing activity within the context of a single program, or a single advertisement, or some other combination.
Identifiers for Data
I presently contemplate using a combination of numeric and mnemonics for the various identifiers such as geographic identifiers, video server identifiers, video content identifiers, and demographic identifiers, and other similar fields, but another embodiment could use only numeric values as identifiers with links to reference tables for the descriptions of the numeric identifiers or only mnemonic identifiers.
Data Explosion Process
I presently contemplate that the Data Explosion Process 140 will generate one record for each second of the tuning activity. Another embodiment may generate one record for each video frame of viewing activity. In this case, the second of the day for tune-in and tune-out would be replaced by a frame number of the tune-in and a frame number of the tune-out.
Yet another embodiment may generate records at a one minute level with the count being the number of seconds tuned to the content during that minute (in this case there would be 1,440 possible one minute intervals during a 24 hour day).
Yet another embodiment may generate records at a 10-second level with the count being the number of seconds tuned to the content during that 10-second interval (in this case there would be 8,640 possible 10-second intervals during a 24 hour day).
Programming Algorithm Scope
I presently contemplate executing the algorithms described herein separately in some sequence, but another embodiment could combine multiple simple algorithms into fewer complex algorithms.
Receiving Date and Time Information
I presently contemplate that the various file formats which provide date and time information will provide an actual date and time whether represented in a format such as YYYY-MM-DD HH:MM:SS AM/PM, or Epoch time (seconds since Jan. 1, 1970). Another embodiment may provide the tune-in and tune-out times as seconds relative to the true beginning of the program content. Any of these embodiments can be used as input to create the metrics.
I presently contemplate receiving all of the date and time values in local time, but another embodiment may provide these in Coordinated Universal Time (UTC time).
General Information
I presently contemplate using variables having the data types and field sizes shown, but another embodiment may use variables with different data types and field sizes to accomplish a similar result.
I presently contemplate tracking viewing activity at the granularity of one second, but another embodiment may track viewing activity at a finer granularity, perhaps half-second, or tenth-second, or millisecond. Yet another embodiment may receive data at a granularity finer than one second and round to the nearest second for use by the MapReduce Aggregation Engine 200.
I presently contemplate using record layouts similar to those defined herein, but another embodiment may use a different record layout or record layouts to accomplish a similar result. As a non limiting example, another embodiment may use database tables or other objects instead of record layouts similar to those I have defined herein to accomplish a similar result while still working within the spirit and scope of this disclosure.
Implementation Information
I presently contemplate using the generic Apache Hadoop distribution, but another embodiment may use a different Hadoop distribution.
I presently contemplate using Linux operating system, but another embodiment may use a different operating system.
I presently contemplate using the Pig along with the Pig Latin dataflow language, but another embodiment may use Java or Python or some other language alone or in combination with PigLatin.
General Remarks
It will be apparent to those of ordinary skill in the art that various changes and modifications may be made which clearly fall within the scope of the embodiments revealed herein. In describing an embodiment illustrated in the drawings, specific terminology has been used for the sake of clarity. However, the embodiments are not intended to be limited to the specific terms so selected, and it is to be understood that each specific term includes all technical equivalents which operate in a similar manner to accomplish a similar purpose.
In general, it will be apparent to one of ordinary skill in the art that various embodiments described herein, or components or parts thereof, may be implemented in many different embodiments of software, firmware, and/or hardware, or modules thereof. The software code or specialized control hardware used to implement some of the present embodiments is not limiting of the present embodiment. For example, the embodiments described hereinabove may be implemented in computer software using any suitable computer software language type such as, for example, Python of JAVA using, for example, conventional or object-oriented techniques. Such software may be stored on any type of suitable computer-readable medium or media such as, for example, a magnetic or optical storage medium. Thus, the operation and behavior of the embodiments are described in Pig Latin dataflow language purely as a matter of convenience. It is clearly understood that artisans of ordinary skill would be able to design software and control hardware to implement the embodiments presented in the language of their choice based on the description herein with only a reasonable effort and without undue experimentation.
The processes associated with the present embodiments may be executed by programmable equipment, such as computers. Software or other sets of instructions that may be employed to cause programmable equipment to execute the processes may be stored in any storage device, such as, for example, a computer system (non-volatile) memory, a compact disk, an optical disk, magnetic tape, or magnetic disk. Furthermore, some of the processes may be programmed when the computer system is manufactured or via a computer-readable medium.
It can also be appreciated that certain process aspects disclosed herein may be performed using instructions stored on a computer-readable memory mediumor media that direct a computer or computer system to perform process steps. A computer-readable medium may include, for example, memory devices such as diskettes, compact discs of both read-only and read/write varieties, optical disk drives, memory sticks, and hard disk drives. A computer-readable medium may also include memory storage that may be physical, virtual, permanent, temporary, semi-permanent and/or semi-temporary.
In various embodiments disclosed herein, a single component or algorithm may be replaced by multiple components or algorithms, and multiple components or algorithms may be replaced by a single component or algorithm, to perform a given function or functions. Except where such substitution would not be operative to implement the embodiments disclosed herein, such substitution is within the scope presented herein. Thus any element expressed herein as a means or a method for performing a specified function is intended to encompass any way of performing that function including, for example, a combination of elements that performs that function. Therefore, any means or method that can provide such functionalities may be considered equivalents to the means or methods shown herein.
It can be appreciated that the “data analysis computer system” may be, for example, any computer system capable of running MapReduce, whether it be a one node system or a system with thousands of nodes.
While various embodiments have been described herein, it should be apparent, however, that various modifications, alterations and adaptations to those embodiments may occur to persons skilled in the art with the attainment of some or all of the advantages described herein. The disclosed embodiments are therefore intended to include all such modifications, alterations and adaptations without departing from the scope and spirit of the embodiments presented herein as set forth in the appended claims.
Accordingly, the scope should be determined not by the embodiments illustrated, but by the appended claims and their legal equivalents.
In my previous Applications, I have identified numerous Conclusions, Ramifications, and Scope items. Many of those are similar for this application. The Conclusions, Ramifications, and Scope items from my U.S. Pat. No. 8,365,212 B1 issued on Jan. 29, 2013, and my U.S. Pat. No. 8,365,213 B1 issued on Jan. 29, 2013, and my U.S. application Ser. No. 13/360,704 filed on Jan. 28, 2012, and my U.S. application Ser. No. 13/567,073 filed on Aug. 5, 2012 and my U.S. application Ser. No. 13/740,199 filed on Jan. 13, 2013 are included herein by reference but not admitted to be prior art.
In this discussion below, I will focus on new ramifications introduced by this Application.
From the description above, a number of advantages of some embodiments of my MapReduceAggregation Engine 200 and its supporting processes become evident:
In this specification I have taught how to measure or analyze video viewing activity at a second-by-second level using the Hadoop MapReduce framework. Within this context, I have taught how to measure such viewing activity within multiple levels: (a) a geographic area, (b) a video server, (c) a video content identifier, and (d) a demographic grouping. Additionally, I have taught how to measure viewing across all of these to provide denominators for calculating percentage of viewing audience. All of these metrics can be calculated at a second-by-second level for each second of the video content.
Once the metrics are calculated, the resulting files can be loaded to a database for longitudinal analysis. As a nonlimiting example, the program level metrics can be tracked to identify week-to-week activity. Then the more detailed metrics can provide additional insight into the causes behind the overall trends.
The ability to produce these metrics using the Hadoop MapReduce framework provides a new tool for data analysts to use in understanding viewing behavior.
This method of using the Hadoop MapReduce framework to calculate second-by-second viewing activity by aggregating individual viewing records that were created by exploding the viewing period into individual records where each record represents one second of viewing activity is contrary to the teaching of those who work with start time and duration (seconds viewed). Thus I am able to solve problems previously found insolvable when limited to using the existing techniques. I am able to provide metrics that could not be produced using existing techniques.
Subsequent Usage of the Metrics
The metrics produced by the MapReduce Aggregation Engine 200 readily lend themselves to dimensional analysis using contemporary data warehouse methods. I have reviewed this extensively in my prior applications.
The metrics produced by the MapReduce Aggregation Engine 200 can be loaded to a data warehouse to support additional longitudinal analysis beyond what is done by the Engine 200. Thus we can readily envision a myriad of uses for the metrics produced by the MapReduce Aggregation Engine 200.
Numerous additional metrics can readily be identified by those skilled in the art. Additionally, numerous additional uses for the metrics identified herein will be readily evident to those skilled in the art.
In accordance with one embodiment, I have disclosed a computer-implemented method of using video viewing activity data as input to an aggregation engine built on the Hadoop MapReduce distributed computing framework for parallel processing which calculates second-by-second video viewing activity aggregated to the analyst's choice of (a) geographic area, (b) video server, (c) video content (channel call sign, video program, etc.), or (d) viewer demographic, or any combination of these fields, for each second of the day represented in the video viewing activity data. The engine also calculates overall viewing for use as a denominator in calculations. The source data may be extracted from a database defined according to the Cable Television Laboratories, Inc. Media Measurement Data Model defined in “Audience Data Measurement Specification” as “OpenCable™ Specifications, Audience Measurement, Audience Measurement Data Specification” document OC-SP-AMD-101-130502 or any similar format. These metrics provide detailed data needed to calculate information on customer viewing behavior that can drive business decisions for service providers, advertisers, and content producers. The ability to use the Hadoop MapReduce framework to aggregate this data will meet pressing needs for detailed audience viewership information that is not presently available and thus the metrics will be of great value to the industry.
This application is a continuation of and claims priority to U.S. patent application Ser. No. 16/730,423, filed on Dec. 30, 2019, which is a continuation of and claims priority to U.S. patent application Ser. No. 14/013,031, filed on Aug. 29, 2013, now U.S. Pat. No. 10,645,433, which are hereby incorporated by reference in their entirety. This application is related to U.S. Pat. No. 8,365,212 B1 issued on Jan. 29, 2013 entitled “SYSTEM AND METHOD FOR ANALYZING HUMAN INTERACTION WITH ELECTRONIC DEVICES THAT ACCESS A COMPUTER SYSTEM THROUGH A NETWORK” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art. This application is also related to U.S. Pat. No. 8,365,213 B1 issued on Jan. 29, 2013 entitled “SYSTEM AND METHOD FOR MEASURING TELEVISION ADVERTISING AND PROGRAM VIEWING AT A SECOND-BY-SECOND LEVEL AND FOR MEASURING EFFECTIVENESS OF TARGETED ADVERTISING” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art. This application is also related to the co-pending application U.S. application Ser. No. 13/360,704 filed on Jan. 28, 2012 entitled “SYSTEM AND METHOD FOR MEASURING LONGITUDINAL VIDEO ASSET VIEWING AT A SECOND-BY-SECOND LEVEL TO UNDERSTAND BEHAVIOR OF VIEWERS AS THEY INTERACT WITH VIDEO ASSET VIEWING DEVICES THAT ACCESS A COMPUTER SYSTEM THROUGH A NETWORK” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art. This application is also related to the co-pending application U.S. application Ser. No. 13/567,073 filed on Aug. 5, 2012 entitled “SYSTEM AND METHOD FOR MEASURING LINEAR, DVR, AND VOD VIDEO PROGRAM VIEWING AT A SECOND-BY-SECOND LEVEL TO UNDERSTAND BEHAVIOR OF VIEWERS AS THEY INTERACT WITH VIDEO ASSET VIEWING DEVICES DELIVERING CONTENT THROUGH A NETWORK” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art. This application is also related to the co-pending application U.S. application Ser. No. 13/740,199 filed on Jan. 13, 2013 entitled “SYSTEM AND METHOD FOR MEASURING DEMOGRAPHIC-BASED HOUSEHOLD ADVERTISING REACH; IMPRESSIONS, SHARE, HUT, RATING, AND CUMULATIVE AUDIENCE; AND VIDEO PROGRAM VIEWING, BASED ON SECOND-BY-SECOND HOUSE LEVEL VIEWING ACTIVITY, TO UNDERSTAND BEHAVIOR OF VIEWERS AS THEY INTERACT WITH VIDEO ASSET VIEWING DEVICES DELIVERING CONTENT THROUGH A NETWORK” by the present inventor which is incorporated by reference in its entirety but is not admitted to be prior art.
| Number | Name | Date | Kind |
|---|---|---|---|
| 5374951 | Welsh | Dec 1994 | A |
| 5790935 | Payton | Aug 1998 | A |
| 5872588 | Aras et al. | Feb 1999 | A |
| 6286005 | Cannon | Sep 2001 | B1 |
| 6449350 | Cox | Sep 2002 | B1 |
| 6820277 | Eldering et al. | Nov 2004 | B1 |
| 7383243 | Conkwright et al. | Jun 2008 | B2 |
| 7490045 | Flores et al. | Feb 2009 | B1 |
| 7509663 | Maynard et al. | Mar 2009 | B2 |
| 7590993 | Hendricks et al. | Sep 2009 | B1 |
| 7614064 | Zigmond | Nov 2009 | B2 |
| 7729940 | Harvey et al. | Jun 2010 | B2 |
| 7930391 | Holt | Apr 2011 | B1 |
| 8001561 | Gibbs et al. | Aug 2011 | B2 |
| 8214867 | Hudspeth | Jul 2012 | B2 |
| 8280996 | Lu et al. | Oct 2012 | B2 |
| 8351645 | Srinivasan | Jan 2013 | B2 |
| 8352984 | Gogoi et al. | Jan 2013 | B2 |
| 8365212 | Orlowski | Jan 2013 | B1 |
| 8365213 | Orlowski | Jan 2013 | B1 |
| 8453173 | Anderson et al. | May 2013 | B1 |
| 8548991 | Zamir et al. | Oct 2013 | B1 |
| 8694396 | Craner et al. | Apr 2014 | B1 |
| 8739197 | Pecjak et al. | May 2014 | B1 |
| 8745647 | Shin et al. | Jun 2014 | B1 |
| 8924993 | Niebles Duque et al. | Dec 2014 | B1 |
| 8949873 | Bayer et al. | Feb 2015 | B1 |
| 8959540 | Gargi et al. | Feb 2015 | B1 |
| 9277275 | Arini | Mar 2016 | B1 |
| 20020055854 | Kurauchi et al. | May 2002 | A1 |
| 20020059576 | Feininger et al. | May 2002 | A1 |
| 20020062393 | Borger et al. | May 2002 | A1 |
| 20020083451 | Gill et al. | Jun 2002 | A1 |
| 20020194196 | Weinberg et al. | Dec 2002 | A1 |
| 20030046696 | Mizuno et al. | Mar 2003 | A1 |
| 20030088715 | Chaudhuri et al. | May 2003 | A1 |
| 20030115585 | Barsness et al. | Jun 2003 | A1 |
| 20030145323 | Hendricks et al. | Jul 2003 | A1 |
| 20030172374 | Vinson et al. | Sep 2003 | A1 |
| 20030174160 | Deutscher et al. | Sep 2003 | A1 |
| 20030237095 | Srinivas | Dec 2003 | A1 |
| 20040019899 | Pelletier | Jan 2004 | A1 |
| 20040215698 | Bertin | Oct 2004 | A1 |
| 20040268226 | McMullin | Dec 2004 | A1 |
| 20050086110 | Haley et al. | Apr 2005 | A1 |
| 20050229199 | Yabe | Oct 2005 | A1 |
| 20050235307 | Relan et al. | Oct 2005 | A1 |
| 20050286860 | Conklin | Dec 2005 | A1 |
| 20060015891 | Lazzaro et al. | Jan 2006 | A1 |
| 20060075420 | Ludvig et al. | Apr 2006 | A1 |
| 20060075421 | Roberts et al. | Apr 2006 | A1 |
| 20060168609 | Chen | Jul 2006 | A1 |
| 20060184961 | Lee et al. | Aug 2006 | A1 |
| 20060223495 | Cassett et al. | Oct 2006 | A1 |
| 20070067794 | Russell et al. | Mar 2007 | A1 |
| 20070074258 | Wood et al. | Mar 2007 | A1 |
| 20070092204 | Wagner et al. | Apr 2007 | A1 |
| 20070157249 | Cordray et al. | Jul 2007 | A1 |
| 20070186228 | Ramaswamy et al. | Aug 2007 | A1 |
| 20070214483 | Bou-Abboud | Sep 2007 | A1 |
| 20070283409 | Golden | Dec 2007 | A1 |
| 20070288950 | Downey et al. | Dec 2007 | A1 |
| 20080077951 | Maggio et al. | Mar 2008 | A1 |
| 20080127252 | Eldering et al. | May 2008 | A1 |
| 20080300965 | Doe | Dec 2008 | A1 |
| 20090007171 | Casey et al. | Jan 2009 | A1 |
| 20090052864 | Ohde | Feb 2009 | A1 |
| 20090070798 | Lee et al. | Mar 2009 | A1 |
| 20090077577 | Allegrezza et al. | Mar 2009 | A1 |
| 20090077579 | Li et al. | Mar 2009 | A1 |
| 20090094630 | Brown | Apr 2009 | A1 |
| 20090100456 | Hughes | Apr 2009 | A1 |
| 20090133047 | Lee et al. | May 2009 | A1 |
| 20090150814 | Eyer et al. | Jun 2009 | A1 |
| 20090172725 | Heilbron et al. | Jul 2009 | A1 |
| 20090183210 | Andrade | Jul 2009 | A1 |
| 20090193460 | Barnett | Jul 2009 | A1 |
| 20090268905 | Matsushima et al. | Oct 2009 | A1 |
| 20090313232 | Tinsley et al. | Dec 2009 | A1 |
| 20090327208 | Bittner et al. | Dec 2009 | A1 |
| 20100043021 | Torsiello et al. | Feb 2010 | A1 |
| 20100088716 | Ellanti et al. | Apr 2010 | A1 |
| 20100145791 | Canning et al. | Jun 2010 | A1 |
| 20100161492 | Harvey et al. | Jun 2010 | A1 |
| 20100211439 | Marci et al. | Aug 2010 | A1 |
| 20100235852 | Mears | Sep 2010 | A1 |
| 20100262986 | Adimatyam et al. | Oct 2010 | A1 |
| 20100330954 | Manning Cassett et al. | Dec 2010 | A1 |
| 20110072448 | Stiers et al. | Mar 2011 | A1 |
| 20110110515 | Tidwell et al. | May 2011 | A1 |
| 20110126241 | Beattie, Jr. et al. | May 2011 | A1 |
| 20110145847 | Barve et al. | Jun 2011 | A1 |
| 20110289524 | Toner et al. | Nov 2011 | A1 |
| 20110307913 | Wang et al. | Dec 2011 | A1 |
| 20110321077 | Wang et al. | Dec 2011 | A1 |
| 20120005527 | Engel et al. | Jan 2012 | A1 |
| 20120079518 | Wan et al. | Mar 2012 | A1 |
| 20120151511 | Bernard et al. | Jun 2012 | A1 |
| 20120191815 | Tabbal et al. | Jul 2012 | A1 |
| 20120222058 | el Kaliouby et al. | Aug 2012 | A1 |
| 20120240143 | Mathews | Sep 2012 | A1 |
| 20120260278 | Lambert et al. | Oct 2012 | A1 |
| 20120278161 | Lazzaro | Nov 2012 | A1 |
| 20120278828 | Yazdani et al. | Nov 2012 | A1 |
| 20120296909 | Cao et al. | Nov 2012 | A1 |
| 20120304210 | Zaslavsky et al. | Nov 2012 | A1 |
| 20120304211 | Berezowski et al. | Nov 2012 | A1 |
| 20130007789 | Wang et al. | Jan 2013 | A1 |
| 20130024901 | Sharif-Ahmadi et al. | Jan 2013 | A1 |
| 20130124309 | Traasdahl et al. | May 2013 | A1 |
| 20130145385 | Aghajanyan et al. | Jun 2013 | A1 |
| 20130198125 | Oliver et al. | Aug 2013 | A1 |
| 20130283304 | Wan et al. | Oct 2013 | A1 |
| 20140075465 | Petrovic et al. | Mar 2014 | A1 |
| 20140109124 | Morales et al. | Apr 2014 | A1 |
| 20140150005 | Kalmes et al. | May 2014 | A1 |
| 20140181019 | Bajaria et al. | Jun 2014 | A1 |
| 20140359649 | Cronk et al. | Dec 2014 | A1 |
| 20150113153 | Lin | Apr 2015 | A1 |
| 20150128162 | Ionescu | May 2015 | A1 |
| Number | Date | Country |
|---|---|---|
| 102236867 | Nov 2011 | CN |
| 1995878 | Nov 2008 | EP |
| 2012162693 | Nov 2012 | WO |
| 2013033123 | Mar 2013 | WO |
| Entry |
|---|
| Jun. 23, 2021—Canadian Office Action—CA 2,864,621. |
| Jun. 22, 2021—European Office Action—EP 14186382.9. |
| Cisco Systems, Inc., “Channel Viewership Analyzer”, 2009, Web page: http://www.cisco.com/en/US/prod/collateral/video/ps9119/ps9883/7016867.pdf, pp. 1-2. |
| Ineoquest Technologies, Inc., “Switched Digital Video Solutions”, http://www.ineoquest.com/switched-digital-video-solutions, Dec. 28, 2010, pp. 1-2. |
| Motorola, Inc., Solutions Paper, “Implementing Switched Digital Video Solutions”, http://www.motorola.com/staticfiles/Business/Products/_Documents/_Static%20files/SDV%20Implementation%20Solutions%20paper%20-555998-001-a.pdf?localeId=33, Copyright 2008, p. 6. |
| Strickland, Jonathan, “How Switched Digital Video Works”, Nov. 20, 2007. HowStuffWorks.com. <http://electronics.howstuffworks.com/switched-digital-video.htm>, pp. 1-4. |
| Rentrak Corporation, Television, TV Essentials, Web source: http://www.rentrak.com/section/media/tv/linear.html, Feb. 1, 2011, p. 1. |
| Wayne Friedman, Rentrak's ‘Stickiness’ Mines TV Value on Granular Level, MediaPost, Jan. 27, 2010, Web source: http://www.tvb.org/media/file/TVB_Measurement_Rentraks_Stickiness_Mines_TV_Value_on_Granular_Level_1-27-10.pdf. |
| Rentrak Corporation, Reaching Your Target Audience Using Viewership Segments, Rentrak Case Studies, http://rentrak.com/downloads/Viewership_Segment_Case_Study.pdf, Oct. 18, 2013, p. 1-2. |
| Rentrak Corporation, Reaching Your Target Audience Using Commercial Ratings and Pod Analysis, Rentrak Case Studies, http://www.rentrak.com/downloads/Commercial_and_Pod_Analysis_Case_Study.pdf, Oct. 18, 2013, p. 1-2. |
| Rentrak Corporation, Rentrak Overview: Exact Commercial Ratings®, http://www.rentrak.com/downloads/Exact_Commercial_Ratings_Presentation.pdf, Jan. 22, 2013, p. 1-30. |
| Tim Brooks, Stu Gray, Jim Dennison, “The State of Set-Top Box Viewing Data as of Dec. 2009”, STB Committee of the Council for Research Excellence. Research Report, Feb. 24, 2010, http://researchexcellence.com/stbstudy.php, pp. 1-9. |
| FourthWall Media, Product information from web page, MassiveDataTM, http://www.fourthwallmedia.tv, Oct. 18, 2013, p. 1. |
| Cisco Systems, Inc., “Network Efficiency with Switched Digital”, Web page: http://www.cisco.com/en/US/products/ps9258/index.html, accessed Oct. 13, 2014, 2 pages. |
| Cisco Systems, Inc., “Access Viewership Data, Monitor Performance”, Web page: http://www.cisco.com/en/US/products/ps9122/index.html, accessed May 20, 2013, 1 page. |
| Extended European Search Report—EP 14183827.6—dated Oct. 23, 2014. |
| Extended European Search Report—EP 14182927.5—dated Dec. 16, 2014. |
| Terry A. Welch, Sperry Research Center, “A Technique for High-Performance Data Compression,” 1984. |
| Extended European Search Report, EP Application 14186382.9, dated Feb. 4, 2015. |
| Response to European Search Report—EP Appl. 14182927.5—dated Sep. 4, 2015. |
| Response to European Search Report—EP 14183827.6—dated Sep. 10, 2015. |
| Response to EP Search Report—EP 14186382.9—dated Sep. 29, 2015. |
| EP Office Action—EP App 14182927.5—dated Mar. 31, 2016. |
| Konstantin Shvachko et al.: “ The Hadoop Distributed File System”, Mass Storage Systems and Technologies (MSST), 2010 IEEE 26th Symposium On, IEEE, Piscataway, NJ, USA, May 3, 2010 (May 3, 2010), pp. 1-10, XP031698650, ISBN: 978-1-4244-7152-2. |
| Anonymous: “Apache Hadoop”, Sep. 5, 2013 (Sep. 5, 2013), XP055394634, Retrieved from the Internet: URL: https://en.wikipedia.org/w/index.php?title=Apache_Hadoop&oldid=571641303 [retrieved on Jul. 28, 2017]. |
| Aug. 4, 2017—(EP) Office Action—App No. 14183827.6. |
| May 22, 2018—European Office Action—EP 14183827.6. |
| Mark Landler, Digital TV Converter Boxes to Expand Cable Offerings, 1996, The New York Times. |
| Mar. 22, 2019—EP Office Action—EP 14186382.9. |
| Nov. 10, 2020—CA Office Action—CA 2,864,621. |
| Nov. 6, 2020—EP Office Action—EP 14186382.9. |
| Jan. 12, 2021—Canadian Office Action—CA 2,860,802. |
| Jan. 13, 2021—Canadian Office Action—CA 2,861,861. |
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|---|---|---|---|
| 20210289244 A1 | Sep 2021 | US |
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| Parent | 16730423 | Dec 2019 | US |
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