Source: http://www.google.com/patents/US20030037333?dq=inventor:%22Arthur+R.+Hair%22&ei=VAy0Tsa4NYTl0QGQiqWiBA
Timestamp: 2017-08-23 21:48:10
Document Index: 442700749

Matched Legal Cases: ['art 1101', 'art) 1201', 'art 1201', 'art 1201', 'art 1201', 'art 1301', 'art 1301', 'art 1501', 'art 1601', 'art 1603']

Patent US20030037333 - Audience measurement system - Google Patents
An audience measurement system plays and records television program material on a client system's persistent storage device. Program guide objects are stored on the client system and indicate when television programs are broadcast. A client system stores a viewer's television viewing habits and behavior....http://www.google.com/patents/US20030037333?utm_source=gb-gplus-sharePatent US20030037333 - Audience measurement system
Publication number US20030037333 A1
Application number US 10/189,989
Also published as US8943527
Publication number 10189989, 189989, US 2003/0037333 A1, US 2003/037333 A1, US 20030037333 A1, US 20030037333A1, US 2003037333 A1, US 2003037333A1, US-A1-20030037333, US-A1-2003037333, US2003/0037333A1, US2003/037333A1, US20030037333 A1, US20030037333A1, US2003037333 A1, US2003037333A1
Inventors John Ghashghai, James Barton
Original Assignee John Ghashghai, Barton James M.
Patent Citations (15), Referenced by (241), Classifications (14), Legal Events (6)
US 20030037333 A1
An audience measurement system plays and records television program material on a client system's persistent storage device. Program guide objects are stored on the client system and indicate when television programs are broadcast. A client system stores a viewer's television viewing habits and behavior. Information such as what television programs a viewer watches and/or prefers, and when a viewer uses trickplay functions while watching a television program (such as: variable rate fast forward and rewind, frame step, index, pause, variable rate reverse play, variable rate play, and play) are stored on the client system which the client sends to a central server. The central server stores viewer information from all client systems such that the information obtained is anonymous. The aggregated information is used by the central server to compile statistical reports and make predictions. Data are analyzed to create reports covering information that includes, but is not limited to: aggregate viewer behavior in relation to a specific television program; aggregate viewer response to specific commercial pods; and aggregate viewer behavior in relation to tuning out of a specific television program and viewer tune-in destinations. The central server also analyzes aggregate information to predict viewing activity and program ratings.
1. A process for measuring viewer television viewing behavior and habits in a client system, comprising the steps of:
providing a persistent storage device on said client system;
providing a program guide database on said persistent storage device;
wherein said program guide indicates when television programs are broadcast during a certain time period;
storing a viewer's television program preferences on said persistent storage device;
correlating a viewer's input via a remote control with segments of a television program and storing said correlated inputs on said persistent storage device;
wherein information about a television program is obtained from said program guide and stored along with said viewer's program preferences and said correlated inputs; and
sending said television program information, said viewer's program preferences, and said correlated inputs stored on said persistent storage device to a server.
recording television programs selected by the viewer onto said persistent storage device;
displaying a list of recorded television programs to a viewer;
allowing the viewer to select a specific recorded television program from said list;
playing back said specific recorded television program to the viewer; and
correlating the viewer's input via said remote control with segments of said specific recorded television program and storing said correlated inputs on said persistent storage device along with information pertaining to said specific recorded television program.
3. The process of claim 1, wherein said server stores said television program information, said viewer's program preferences, and said correlated inputs in a central database, and wherein said television program information, said viewer's program preferences, and said correlated inputs are stored as anonymous information.
4. The process of claim 3, wherein said anonymous information is aggregated into said central database with other anonymous information received from other client systems.
providing reporting means on said server for filtering said aggregated information into statistical reports as specified by a user.
6. The process of claim 5, wherein said server analyzes said aggregate information to predict viewing activity and program ratings.
7. The process of claim 5, wherein said server analyzes said aggregate information to chart aggregate viewer behavior in relation to a specific television program.
8. The process of claim 5, wherein said server analyzes said aggregate information to chart aggregate viewer response to specific commercial pods.
9. The process of claim 5, wherein said server analyzes said aggregate information to chart aggregate viewer behavior in relation to tuning out of a specific television program and viewer tune-in destinations.
inserting tags into a television program;
providing means on said client for recognizing said tags while displaying said tagged television program to a viewer; and
wherein said client system uses said tags to more accurately track viewer behavior during certain events in said tagged television program.
11. An apparatus for measuring viewer television viewing behavior and habits in a client system, comprising:
a persistent storage device on said client system;
a program guide database on said persistent storage device;
a module for storing a viewer's television program preferences on said persistent storage device;
a module for correlating a viewer's input via a remote control with segments of a television program and storing said correlated inputs on said persistent storage device;
wherein information about a television program is obtained from said program guide objects and stored along with said viewer's program preferences and said correlated inputs; and
a module for sending said television program information, said viewer's program preferences, and said correlated inputs stored on said persistent storage device to a server.
a module for recording television programs selected by the viewer onto said persistent storage device;
a module for displaying a list of recorded television programs to a viewer;
a module for allowing the viewer to select a specific recorded television program from said list;
a module for playing back said specific recorded television program to the viewer; and
a module for correlating the viewer's input via said remote control with segments of said specific recorded television program and storing said correlated inputs on said persistent storage device along with information pertaining to said specific recorded television program.
13. The apparatus of claim 11, wherein said server stores said television program information, said viewer's program preferences, and said correlated inputs in a central database, and wherein said television program information, said viewer's program preferences, and said correlated inputs are stored as anonymous information.
14. The apparatus of claim 13, wherein said anonymous information is aggregated into said central database with other anonymous information received from other client systems.
reporting means on said server for filtering said aggregated information into statistical reports as specified by a user.
16. The apparatus of claim 15, wherein said server analyzes said aggregate information to predict viewing activity and program ratings.
17. The apparatus of claim 15, wherein said server analyzes said aggregate information to chart aggregate viewer behavior in relation to a specific television program.
18. The apparatus of claim 15, wherein said server analyzes said aggregate information to chart aggregate viewer response to specific commercial pods.
19. The apparatus of claim 15, wherein said server analyzes said aggregate information to chart aggregate viewer behavior in relation to tuning out of a specific television program and viewer tune-in destinations.
a module for inserting tags into a television program;
means on said client for recognizing said tags while displaying said tagged television program to a viewer; and
This application is a Continuation-in-part of U.S. patent application Ser. No. 09/422,121, filed on Oct. 20, 1999; and claims benefit of U.S. Provisional Patent Application Ser. No. 60/303,179, filed on Jul. 5, 2001.
The invention relates to the storing and viewing of television program material in a computer environment. More particularly, the invention relates to the storing and viewing of television program material and the measuring of audience reactions to the material in a computer environment.
Further, along with the demands of television viewers, the television production studios, stations, broadcasters, and advertisers have an increasing interest in the ability to track television viewer habits and actions. This demand for viewer information places the client systems in a very unique position to not only manage program material for television viewers, but to also serve the informational curiosity of the television community.
It would be advantageous to provide an audience measurement system that monitors viewer habits and preferences for both live and recorded television program material. It would further be advantageous to provide an audience measurement system that protects viewer identities while aggregating such information.
The invention provides an audience measurement system. The system monitors viewer habits and preferences for both live and recorded television program material. In addition, the invention provides a system that protects viewer identities while aggregating such information.
A preferred embodiment of the invention plays and records television program material on a client system's persistent storage device. Program guide objects are stored on the client system and indicate when television programs are broadcast.
A client system stores a viewer's television viewing habits and behavior. Information such as what television programs a viewer watches and/or prefers, and when a viewer uses trickplay functions while watching a television program (such as: variable rate fast forward and rewind, frame step, index, pause, variable rate reverse play, variable rate play, and play) are stored on the client system. The client sends this information to a central server.
The central server stores viewer information from all client systems such that the information obtained is anonymous. The aggregated information is used by the central server to compile statistical reports and make predictions. Data are analyzed to create reports covering information that includes, but is not limited to: aggregate viewer behavior in relation to a specific television program; aggregate viewer response to specific commercial pods; and aggregate viewer behavior in relation to tuning out of a specific television program and viewer tune-in destinations.
The central server also analyzes aggregate information to predict viewing activity and program ratings.
Tags are inserted into television programs that the client recognizes during playback. The client uses these tags to accurately track viewer behavior during certain events in the tagged television program.
[0022]FIG. 1 is a block schematic diagram of a preferred embodiment of a distributed television viewing management system according to the invention;
[0023]FIG. 2 is a block schematic diagram of the structure of a viewing object in computer storage for programmatic access according to the invention;
[0024]FIG. 3 is a block schematic diagram showing how the schema for a viewing object is structured in computer storage for programmatic access according to the invention;
[0025]FIG. 4 is a block schematic diagram showing an example graph of relationships between viewing objects which describe information about programs according to the invention;
[0026]FIG. 5 is a block schematic diagram showing an example graph of relationships generated when processing viewer preferences to determine programs of interest according to the invention;
[0027]FIG. 6 is a block schematic diagram showing the scheduling of inputs and storage space for making recordings according to the invention;
[0028]FIG. 7 is a flowchart showing the steps taken to schedule a recording using the mechanism depicted in FIG. 6 according to the invention;
[0029]FIG. 8 is a block schematic diagram of a preferred embodiment of the invention showing the bootstrap system configuration according to the invention;
[0030]FIG. 9a is a block schematic diagram of the decision flowchart for the bootstrap component according to the invention;
[0031]FIG. 9b is a block schematic diagram of the decision flowchart for the bootstrap component according to the invention;
[0032]FIG. 10 is a block schematic diagram of the decision flowchart for the software installation procedure according to the invention;
[0033]FIG. 11 is a schematic diagram of a Response Chart charting all use of trickplay features against position within an episode according to the invention;
[0034]FIG. 12 is a schematic diagram of a Tune-Out chart showing the number of households that tune away from a specific program according to the invention;
[0035]FIG. 13 is a schematic diagram of a chart that describes how an audience interacts with iPreview tags according to the invention;
[0036]FIG. 14 is a schematic diagram of a chart that shows predictions of what programs will be recorded during a certain time period according to the invention;
[0037]FIG. 15 is a schematic diagram of a chart that shows the total number of recordings categorized by the daypart during which a recording was made according to the invention;
[0038]FIG. 16 is a schematic diagram of a chart that shows what kind of programs are likely to be timeshifted according to the invention; and
[0039]FIG. 17 is a schematic diagram of a chart that shows the amount of live broadcasts that are being viewed vs. timeshifted recordings according to the invention.
The invention is embodied in an audience measurement system. A system according to the invention monitors viewer habits and preferences for both live and recorded television program material. In addition, the invention provides a system that protects viewer identities while aggregating such information.
[0043]FIG. 1 gives a schematic overview of the invention. Central to the invention is a method and apparatus for maintaining a distributed database of television viewing information among computer systems at a central site 100 and an extremely large number of client computing systems 101. The process of extracting suitable subsets of the central copy of the database is called “slicing” 102, delivering the resulting “slices” to clients is called “transmission” 103, delivering information collected about or on behalf of the viewer to the central site is called “collection” 104, and processing the collected information to generate new television viewing objects or reports is called “analysis” 107; in all cases, the act of recreating an object from one database within another is called “replication” 105. Data items to be transmitted or collected are termed “objects” 106, and the central database and each replicated subset of the central database contained within a client device is an “object-based” database. The objects within this database are often termed “television viewing objects”, “viewing objects”, or simply “objects”, emphasizing their intended use. However, one skilled in the art will readily appreciate that objects can be any type of data.
A preferred method for adding an object to the database namespace is called “indexing”. In a preferred embodiment of the invention, an “indexer” object is defined for each object type which indicates what attributes are to be used when indexing it into the database namespace. One skilled in the art will readily appreciate that this indexer object may contain executable code in some form, perhaps as a sequence of executable commands. These commands would examine and compare attributes and attribute values of object being indexed, resulting in an indication of where the object should be located in the namespace. Based on the object type, the indexer examines a specific set of attributes attached to the object. When such attributes are discovered the indexer automatically adds a name for the object, based on the value of the attribute, within the hierarchical namespace represented by the graph of directories in the database. Referring again to FIG. 4, a program object may have both an “actor” attribute with value “John Wayne” and a “director” attribute with value “John Ford” 401. The root directory might indicate two sub-directories, “byactor” 402 and “bydirector” 403. The indexer would then add the paths “/byactor/John Wayne” and “/bydirector/John Ford” to the database, both of which refer to the same object 401.
6. Using techniques similar to those described in case 2, application software may also capture promotional material that may be of interest to the viewer. This information may be presented on viewer demand, or it may be automatically inserted into the output television signal at some convenient point. For example, an advertisement in the broadcast program might be replaced by a different advertisement which has a higher preference priority. Using the time-warping apparatus, such as that described in U.S. Pat. No. 6,233,389, entitled “Multimedia Time Warping System”, and owned by the Applicant, it is possible to insert any stored program into the output television signal at any point. The time-warping apparatus allows the overlaid program to be delayed while the stored program is inserted to make this work.
Measuring Audience Activities and Behaviors Using Operations Status Objects
The client system records information relating to the viewer's viewing habit and behaviors and places this information into operations status objects. The client system uploads this information to the server. Operations status objects are collected by the server and processed. Viewer related information is sorted and placed in a relational database the central database. The following describes an example of the types of viewer information is derived from the viewer information:
What programs (or portions of programs) are recorded or viewed.
What programs plan to be recorded (predictive).
What programs are time-shifted and by how much.
How Trickplay features (e.g., variable rate fast forward and rewind, frame step, index, pause, variable rate reverse play, variable rate play, and play) are used.
Thumbs (user preference) ratings of programs.
Navigation through the interface.
Reactions to interactive content (e.g., ipreview —as described in U.S. patent application Ser. No.______ entitled “Closed Caption Tagging System” owned by the Applicant)
Reactions to any other ‘tagged’ content (as described in U.S. patent application Ser. No.______).
The server parses the accumulated viewer information object files to provide additional meaning and clarity to the data. For example, when the server observes Trickplay buttons being used on the client system's remote control, the server can infer the actual Trickplay state of the client system and record that in the relational database instead. If the server observes four presses of the fastforward button, it recognizes that the viewer started to fast forward, then increased the speed of travel twice, and then resumed playback.
The server can also track use of the Play button on the remote control differently, depending upon context, e.g., “play following fast forward” is recorded differently than a “play following a pause”.
Further, the server can infer the occurrence of events that are not themselves recorded in the viewer information. For instance, when a viewer changes channels to a new program, the server automatically determines and records the exact point where the first program was abandoned.
Each client system logs exactly which programs it has scheduled to record over a two-week period. By analyzing these records, the server can predict viewing activity and program ratings.
Using technology related to iPreview, invisible “stealth tags” can be inserted into a television broadcast signal's VBI, alongside any close caption information. The client system recognizes these tags and logs events when they are observed. These tags can be used to highlight or label events in program material where it is not known where the exact place and time of an segment of interest, e.g., the repeated airings of a specific commercial. Using two tags—one at the start and one at the end of a segment of interest—makes certain analyses much easier to perform.
The server can index the level of interest and attention a viewer base feels for a specific period of program content with a “Transit chart” described below. All observed viewer sessions that intersect the program content of interest are aggregated and the size and average speed of transit through the content are reported. For example, transit is reported as an index factor, where “1.0” represents viewing the spot at normal speed “2.0” is double speed and “0.5” is one-half speed. The program content to study can be identified by time and channel (“KTVU last Tuesday between 1 AM and 1:01 AM”) or by program and position (“Episode 107 of M*A*S*H, from 17:00 min to 17:30 min”) or through the use of stealth tags (“All airings of Coke ad #AB892, padded by 10 sec at either end”).
The server can also chart subscriber behavior in relation to a specific broadcast. For a given episode, it can chart where viewers started or stopped viewing the program. It can chart where the FF, Pause or any Trickplay feature is used. For a sufficiently large number of observations, the response chart aggregates individual events into an overall picture of average behavior. As the number of events that can be charted against the broadcast is large, it makes sense to create several response charts which display limited, related events. These can include:
Fast forward—each of the three speeds available, and the Play event that follows any fast forward.
Rewind—each of the three speeds available, and the Play event that follows any rewind.
Thumbs—The exact moments when thumbs-up or thumbs-down buttons were pressed
Bail—The exact moments in the program where viewing begins (or resumes), and the exact moments in the program when the viewer chooses to leave.
Pause—The points in a program where the Pause button is pressed, or Slow Motion or Frame Advance are used.
The response chart can be made for an entire episode or focus on a specific segment of the broadcast, e.g., the first commercial pod. Stealth tags can be utilized to automatically highlight specific content on a response chart. Each response chart has a resolution which is the minimum period of time that is reported, e.g., one to ten second values work well.
With respect to FIG. 11, by charting all use of trickplay features against position within an episode using a Response Chart 1101, the server is able to chart the audience's response to specific content within the episode. The resolution of such a chart can be anything down to one second (five seconds is shown here).
Related Trickplay events can be charted together—in this case three different speeds of fast forward, and the occurrence of a “play” that follows some fast forward. The server distinguishes events by their context and therefore recognizes a “play” following a fast-forward is different from a “play” that follows a pause. This example 1101 clearly indicates the presence of commercial pods within the program.
Other interesting Response Charts include: use of “Thumbs Up” and “Thumbs Down” buttons within the program; use of slow-motion, instant replay, pause, and frame advance within a program; and the points where the audience tunes in or tunes away from the program.
Referring to FIG. 12, while the Response Chart can indicate exactly where within a specific program viewers will enter or leave a program, a Flow Report (Tune-Out Chart) 1201 can provide insight into where the viewers come from or go to. This example illustrates how a “Tune-Out” chart 1201 can show the number of households 1202 that tune away from a specific program 1203. The chart 1201 also provides insight into what point of the program they are at when they decide to tune and what their destination is. This would be combined with a “Tune-In” chart to provide a complete picture of viewer traffic around a specific program or block of airtime.
This chart 1201 can also have a resolution down to one second. While the tuning destinations shown in the example are networks, any level of actual detail is possible, including specific broadcast or recorded programs.
With respect to FIG. 13, the server can fashion a number of reports that describe how an audience interacts with ipreview tags. The client can observe tags being displayed and selected, and report on any recordings or season passes that are created as a result of an ipreview tag. The server creates an iPreview effectiveness chart 1301. It indicates the number of times 1302 tags were displayed on each type of channel 1303 and how often those tags were selected 1304. The ratio is a measure of tag effectiveness. This is an “uncorrected” chart 1301, because it does not yet consider cases where the promoted program already has a recording scheduled in the home—in such cases there is no reason to react to the ipreview tag.
Referring to FIG. 14, each client system maintains a schedule of all recordings it plans to make in the next two weeks as a “To Do list”. Utilizing this information, the server is able to predict what programs will be recorded at any time within the next two weeks. This example shows the prediction for an entire Sunday 1401, 1402. That prediction can easily be focused into a specific daypart or segment of airtime. The server can also utilize the To Do lists to predict program viewings, and even to predict Nielsen ratings results.
With respect to FIG. 15, program recordings or viewings can be easily charted against geography of the household, day of the week, daypart, program genre, etc. 1501. The number of recordings made in each daypart is shown 1502 this is a count of shows recorded by client systems. This chart 1501 shows the total number of recordings categorized by the daypart during which the recording was made. The server's reporting tools would allow any segments of the chart to be “drilled down” into additional detail—such as program genre. It can also differentiate between recordings that are made as a result a Season Pass, Wish List, iPreview tag interaction, or a user-scheduled event.
Referring to FIG. 16, client systems make it easier than ever for households to timeshift their television viewing, deferring viewing until it is convenient or viewing a complete program over a number of disconnected sessions. Timeshifting Reports 1601 provide insight into what kind of programs are likely to be timeshifted, and by how much.
This chart 1601 shows the average amount of timeshifting 1602 for all programs by the daypart 1603 in which they originally aired. Again, it is possible to view this data by day of the week, program genre, household geography, or any combination. The server's reporting tools allow drilling down to specific segments to provide additional detail and understanding.
With respect to FIG. 17, the server can report the amount of live broadcasts that are being viewed vs. timeshifted recordings and can additionally chart this against attributes such as day of the week, geography, daypart or genre 1701, 1702, 1703, 1704, 1705, 1706. Other related measures that are interesting include the number of distinct viewing sessions, and the average length of viewing sessions.
US7646960 * Jun 9, 2005 Jan 12, 2010 International Business Machines Corporation Determining chapters based on presentation of a program
US7770193 * Dec 26, 2001 Aug 3, 2010 Lg Electronics Inc. Apparatus and method for calculating audience rating using interactive television
US7774294 * Mar 6, 2007 Aug 10, 2010 Veveo, Inc. Methods and systems for selecting and presenting content based on learned periodicity of user content selection
US8006258 Dec 11, 2009 Aug 23, 2011 The Nielsen Company (Us), Llc. Methods and apparatus for media source identification and time shifted media consumption measurements
US8180216 Dec 20, 2007 May 15, 2012 Verizon Patent And Licensing Inc. Latency measurement in optical networks
US9088823 * Apr 29, 2011 Jul 21, 2015 Amazon Technologies, Inc. Customized insertions into digital items
US9515754 Dec 4, 2009 Dec 6, 2016 Iheartmedia Management Services, Inc. Measuring audience reaction
US9654840 Jun 12, 2015 May 16, 2017 Amazon Technologies, Inc. Customized insertions into digital items
US9679302 Nov 15, 2016 Jun 13, 2017 Iheartmedia Management Services, Inc. Audience migration information
US20060282859 * Jun 9, 2005 Dec 14, 2006 International Business Machines Corporation Determining chapters based on presentation of a program
US20090162052 * Dec 20, 2007 Jun 25, 2009 Verizon Services Organization Inc. Latency measurement in optical networks
US20110280545 * Mar 15, 2010 Nov 17, 2011 Lawrence Kates Multi-channel parallel digital video recorder
CN103518215A * Mar 27, 2012 Jan 15, 2014 英特尔公司 System and method for viewership validation based on cross-device contextual inputs
EP1905239A2 * Jun 3, 2005 Apr 2, 2008 Nielsen Media Research, Inc. Methods and apparatus to detect a time-shift event associated with the presentation of media content
EP1905239A4 * Jun 3, 2005 Jul 25, 2012 Nielsen Media Res Inc Methods and apparatus to detect a time-shift event associated with the presentation of media content
WO2009079142A1 * Nov 18, 2008 Jun 25, 2009 Verizon Data Services Inc. Indicating program popularity
WO2012135239A3 * Mar 27, 2012 Dec 27, 2012 Intel Corporation System and method for viewership validation based on cross-device contextual inputs
U.S. Classification 725/46, 725/55, 725/9, 725/13
International Classification G06F13/00, H04N5/445, H04N7/16, G06F3/00, H04H60/31, H04H60/39
Cooperative Classification H04H60/31, H04H60/39
European Classification H04H60/31, H04H60/39
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:GHASHGHAI, JOHN;BARTON, JAMES M.;REEL/FRAME:013431/0533