Source: http://www.google.com/patents/US8046798?dq=10027854
Timestamp: 2017-07-21 10:52:12
Document Index: 754411119

Matched Legal Cases: ['Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'art 3060']

Patent US8046798 - Profiling and identification of television viewers - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsProfiling a television viewer by monitoring and processing, locally at a television viewing environment, the viewer's interactivity (e.g., via a remote control unit) with the television viewing environment. Generating one or more profiles for each viewer based on one or more of the multitude of interactions...http://www.google.com/patents/US8046798?utm_source=gb-gplus-sharePatent US8046798 - Profiling and identification of television viewersAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS8046798 B1Publication typeGrantApplication numberUS 11/751,154Publication dateOct 25, 2011Filing dateMay 21, 2007Priority dateJan 11, 2001Fee statusPaidAlso published asUS7260823, US8667536, US9473814, US20020129368Publication number11751154, 751154, US 8046798 B1, US 8046798B1, US-B1-8046798, US8046798 B1, US8046798B1InventorsJohn A. Schlack, Andrew Sutton, Andrew J. Rampulla, Charles A ElderingOriginal AssigneePrime Research Alliance E, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (38), Referenced by (70), Classifications (66), Legal Events (3) External Links: USPTO, USPTO Assignment, EspacenetProfiling and identification of television viewers
US 8046798 B1Abstract
1. A method of differentiating viewers interacting with a content presentation device, the method comprising:
(a) monitoring a first plurality of inputs from at least one first viewer to the content presentation device during a first session, wherein the first plurality of inputs form at least one first session profile;
(b) merging, based on a correlation threshold, the at least one first session profile into at least one signature profile, wherein the at least one signature profile is formed independently of viewer characteristics stored in the content presentation device prior to the monitoring of (a);
(c) monitoring a second plurality of inputs from a second viewer to the content presentation device during a second session, wherein the second plurality of inputs form at least one second session profile;
(d) comparing the at least one second session profile to the at least one signature profile; and
(e) identifying a matching signature profile corresponding to the at least one second session profile if the at least one signature profile is sufficiently correlated to the second session profile, wherein the matching signature profile is selected from the at least one signature profile.
2. The method of claim 1, wherein the matching signature profile is identified without knowing the actual identity of the second viewer.
(f) delivering content to the content presentation device, wherein the content is targeted to the matching signature profile identified in (e).
4. The method of claim 3, wherein the content is targeted based on determining a demographic profile of the matching signature profile.
5. The method of claim 3, wherein the content is targeted based on determining preference probabilities from the matching signature profile.
6. The method of claim 1, wherein the at least one first session profile and the at least one signature profile identify viewing habits of the at least one first viewer including at least some subset of program genre viewing habits, program category viewing habits, network viewing habits, and dwell time habits.
7. The method of claim 1, wherein the at least one first session profile and the at least one signature profile identify probable demographic traits of the at least one first viewer.
8. The method of claim 7, wherein the demographic traits distinguish the at least one second session profile from the at least one signature profile.
9. The method of claim 7, wherein the demographic traits are generated by applying heuristic rules to the at least one first session profile and the at least one signature profile.
10. The method of claim 9, wherein the heuristic rules are applied to program genre and program categories viewed.
11. The method of claim 9, wherein the heuristic rules are applied based on time that the first and second plurality of inputs are received.
12. The method of claim 1, wherein the at least one signature profile includes a time weighted average of the at least one first session profile generated based on previous interactions with the content presentation device.
determining, based on the matching signature profile that the second viewer is different than the at least one first viewer.
determining, based on the matching signature profile that the second viewer is the same as the at least one first viewer.
15. A method of differentiating viewers using a content presentation device, the method comprising:
monitoring a first plurality of inputs from at least one first viewer to the content presentation device during a first session, wherein the first plurality of inputs form at least one first session profile;
merging, based on a correlation threshold, the at least one first session profile into at least one signature profile, wherein the at least one signature profile is formed independently of viewer characteristics stored in the content presentation device prior to the monitoring of the first plurality of inputs;
monitoring a second plurality of inputs from a second viewer to the content presentation device during a second session, wherein the second plurality of inputs form at least one second session profile;
determining at least one signature channel search sequence corresponding to the at least one signature profile;
retrieving a session channel search sequence corresponding to the at least one second session profile; and
identifying a matching signature profile corresponding to the at least one second session profile based on a comparison of the session channel search sequence to the at least one signature channel search sequence for each of the least one signature profiles.
determining based on the identifying a matching signature profile that the second viewer is likely a viewer corresponding to the matching signature profile, without knowing the actual identity of the viewer, and therefore has characteristics determinable by analysis of the matching signature profile.
retrieving a signature channel dwell time for the identified matching signature profile; and
determining, based on the signature channel dwell time, a list of channels in the session channel search sequence to be included in determining the characteristics of the matching signature profile.
determining to which program each channel in the session channel search sequence corresponds, based on the determined list of channels.
This application is a divisional of U.S. patent application Ser. No. 09/998,979, filed Oct. 31, 2001, and entitled Profiling and Identification of Television Viewers, the entire disclosure of which is incorporated herein by reference.
This application claims the benefit of U.S. Provisional Application Nos. 60/260,946, filed Jan. 11, 2001, entitled Viewer Profiling within a Set-top Box, and 60/263,095, filed Jan. 19, 2001, entitled Session Based Profiling in a Television Viewing Environment, the entire disclosures of which are incorporated herein by reference.
This application is related to all of the Applicants co-pending applications, patents and publications listed below. The entire list below is herein incorporated in their entirety, but are not admitted to be prior art:
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BACKGROUND OF THE INVENTION
For the foregoing reasons, a need exists for a method and system for monitoring click-stream and other interactivity of a viewer with the viewer's television viewing environment and generating one more viewer profiles therefrom. Additionally, a need exists for the monitoring of interactivity and generation of viewer profiles to be performed within the television viewing environment (i.e., TV, STB, PVR). Furthermore, a need exists for such profiling to be done in a secure and privacy-protected manner. Moreover, a need exists for a reliable way of automatically, detecting or inferring, which specific individual or individuals, are actually watching the TV in a household comprising more than one individual at a particular time, and for generating one or more profiles per each individual.
FIGS. 3A-B illustrate an exemplary head-end channel map table format and channel map table respectfully, according to one embodiment;
FIGS. 4A-B illustrate an exemplary head-end network table format and network table respectfully, according to one embodiment;
FIGS. 5A-B illustrate an exemplary head-end BpID table format and BpID table respectfully, according to one embodiment;
FIGS. 6A-B illustrate an exemplary head-end program table format and program table respectfully, according to one embodiment;
FIGS. 7A-B illustrate an exemplary set-top box (STB) channel map header format and channel map table format respectfully, according to one embodiment;
FIGS. 9A-B illustrate exemplary STB fixed length program header format and fixed length program record format respectively, according to one embodiment;
FIGS. 10A-C illustrate exemplary STB variable length program header format, network record format, and program record format respectively, according to one embodiment;
FIGS. 18A-C illustrate exemplary tables for probabilities based on program categories, adjustments to those probabilities based on day part, and normalization of the adjusted probabilities respectively, according to one embodiment;
FIGS. 21A-B illustrate examples of records and graphs of network viewership by day part profile table and graph respectively, according to one embodiment;
FIGS. 22A-B illustrate exemplary average viewing duration profile table and graph respectively, according to one embodiment;
FIGS. 25A-D illustrate exemplary channel order, surf session and dwell time profiles, and a dwell time graph respectively, according to one embodiment;
FIGS. 27A-B illustrate examples of how the time span of a viewing session can be determined and delimited, according to one embodiment;
FIGS. 28A-C illustrate examples of how viewing sessions can be defined based on viewer activity or inactivity, according to one embodiment;
FIGS. 31A-B illustrate how session data is compared with the signature data already stored on the VCPS, according to one embodiment;
FIGS. 32A-B illustrate an exemplary process flow chart of how events are processed and associated with a particular session, and how the sliding window is managed, according to one embodiment;
FIGS. 35A-B illustrate an exemplary process flow generating the session profile and adding the session profile to a correlated signature profile based on the event queue, according to one embodiment.
The present invention can be implemented for use with various television (TV) delivery systems including, but not limited to, digital broadcast satellite (DBS) systems, switched digital video (SDV) systems, local multipoint distribution systems (LMDS), multichannel multipoint distribution systems (MMDS), hybrid fiber coax (HFC) systems, the Internet, other cable TV (CTV) systems, or other terrestrial wireless networks. The TV delivery system can deliver programming in various forms, including but not limited to digital video, analog video, or streaming media. The programming may be compressed in accordance with a variety of now known or later discovered compression standards, such as the current Motion Picture Expert Group (MPEG-2) standard for digital video.
The communications manager 222 handles the communications with the HE 210. The communications manager 222 may receive program content or database downloads (i.e., program data, channel maps) from the HE 210 and may transmit commands (i.e., channel changes), profiles (i.e., updated), or other information including anonymous system statistics (i.e., audience measurements). The user interface 224 allows the viewer to interact with the STB 220, for example, via a conventional remote control unit. The viewer interactions include, but are not limited to, channel and volume changes, EPG activity, and participation in interactive entertainment and advertisements.
In a preferred embodiment, the HE 210, or other upstream system, generates and stores the program database 214 and transmits relevant program data to the STB 220. According to one embodiment, the relevant program data are placed on a download carousel, where they are transmitted in their entirety to the STB 220. Alternatively, the relevant program data can be downloaded periodically in smaller increments. In another embodiment, the program data is transmitted with the programs. For example, the program data is transmitted within the vertical blanking interval (VBI) or the program specific information (PSI) of a transport stream, such as, a Motion Picture Expert Group (MPEG-2) transport stream. It should be understood however, that many alternate methods for getting the program data exist and the utility and scope of the present invention is not limited by how the data is obtained.
FIGS. 3A and 3B illustrate an exemplary channel map table format and channel map table that may be stored in the program database 214. As illustrated in FIG. 3A, the channel map table format includes for each field a column for name 300, type 302 (i.e., integer, string), size 304 (number of bytes), flags 306, and description 308. The fields captured in the channel map table include user perceived channel indicator (UPCI) 310 and broadcast program ID (BpID) 312, which are both required 2 byte integers. The UPCI 310 is the channel number illustrated on the front panel of the STB 220 and the BpID 312 is a service provider's reference number for a particular network or presentation stream. As illustrated in FIG. 3B, the table simply converts the UPCI 310 that the viewer recognizes into the BpID 312 that the HE 210 recognizes. For example, a UPCI of 02 is a BpID of 27.
FIGS. 4A and 4B illustrate an exemplary network table format and network table that would be stored in the program database 214. As illustrated in FIG. 4A, the network table format includes for each field a column for name 400, type 402 (i.e., integer, string), size 404 (number of bytes), flags 406, and description 408. The fields captured in the network table include network ID (NET_ID) 410, network name (NET_NAME) 412, network call sign (NET_CALL) 414, and network reference (NET_REF) 416. Each of the fields has the characteristics defined in the table. For example, the NET_ID 410 is a 4-byte long integer that is a unique and required field. FIG. 4B illustrates how for each NET_NAME 412 and NET_CALL 414 there is a unique NET_ID 410 assigned and potentially a unique NET_REF 416. For example, WHYY FOX Philadelphia is assigned a NET_ID of 0001, but does not have a NET_REF.
preferred programs—measure of the amount of time a viewer(s) watch a particular program or program category; preferred networks—measure of the amount of time viewer(s) watch a particular network; viewing duration—amount of viewing time per viewing session; channel change frequency—average number of channel changes per time period; holding factor per program or program category—how much of an entire program or program category a household or individual watches; and surf sequence—typical order networks are visited in a surf routine. As one of ordinary skill in the art would recognize, there are numerous other profile categories that could be included that would fall within the scope of the current invention. The current invention is in no way not limited to the categories described herein.
The VCPS 290 may adjust the probability data based on the day part. For example, because the probability that a daytime viewer is a man is lower than the probability that the viewer is a woman or a child, this fact results in an adjusted and reduced probability that the daytime viewer is male. FIG. 18B illustrates an exemplary day part adjustment table, according to one embodiment. The table includes a day part column 1820 and adjustment columns for men 1822, women 1824, and children 1826. The adjustment factor is multiplied by the probability defined in the table of FIG. 18A to determine an adjusted probability. An adjustment value of 1.0 indicates that no adjustment is required, while values smaller than 1.0 will adjust the probability downwards, and values larger than 1.0 will adjust the probability upwards. For example, the adjustment factor for weekdays between 09:00-16:00 is 0.3, 0.9 and 1.0, for men, women and children respectively.
FIG. 19 illustrates an exemplary table of conditional probabilities of the likely viewer demographic makeup for different program categories (genres). The table includes different demographic groups 1910 (i.e., age, income) for different program categories 1920 (i.e., news, fiction). Each demographic group 1910 has numerous categories 1930 defined thereunder (i.e., ages 0-10, 10-18). Each cell in the table contains a conditional probability that represents the likelihood that the viewer or viewing group watching a particular category (genre) of program 1920 is within a particular category 1930 for each demographic group 1910. For example, as illustrated there is a 10% chance that a viewer watching the news will be between the ages of 0-10. As will be evident to those of ordinary skill in the art, other sets of rules and probabilities could also be utilized without departing from the scope of the present invention.
In order to generate a preferred network profile, the VCPS 290 tracks the networks that are most watched by each viewer or household. FIG. 20 illustrates an exemplary preferred networks profile 2000, reflecting the top five networks 2010 chosen by this viewer, the associated percentage 2020 and relative duration 2030 those networks were watched. Horizontal bars 2040 also graphically illustrate the relative duration of network viewing. The VCPS 290 responds to all channel change and power on/off events. When a channel change occurs, the VCPS 290 records the time and network, and when the next channel change occurs, the VCPS 290 notes the elapsed time and stores that elapsed time in an array. The VCPS 290 may also track the preferred networks by day or day part. Note that this exemplary profile is an aggregated profile comprising more than a weeks worth of data. A similar session profile comprising a much smaller span of viewing time may also be generated by the VCPS 290.
FIG. 21A illustrates an exemplary network viewership table broken out by day part. The day parts may be associated with a single day, weekdays, weekends, or other intervals that may be appropriate. As illustrated, the breakout is for weekdays. The network viewership table includes a network column 2110 and various day part columns 2120. As illustrated, there is a total of eight-day parts having varying time durations associated therewith. The viewership duration for each day part for each network is recorded in the appropriate cell of the table. As illustrated, the time is in seconds, but could be in minutes, hours or other suitable time parameters. As one of ordinary skill in the art would recognize, the number and duration of the day parts could vary without departing from the scope of the current invention.
FIG. 25A illustrates an exemplary table with data related to surf sequence channel order being captured and processed. The table includes a column for networks 2500, channel surf sequences 2510, 2520, 2530 and a typical search order 2540. The networks column 2500 lists networks that are desirable to track. As illustrated five networks are tracked (CNN, ESPN, HIST, TBS, TLC). The channel surf sequence columns 2510, 2520, 2530 capture the order that each network was selected during a surf sequence. For example, in the first surf sequence 2510, ESPN was the first network that was selected. The typical search order 2540 is a weighted average of all surf sequences 2510, 2520, 2530. Notice that in third surf sequence 2530, the viewer did not visit the HIST channel In this case, the typical order for the history channel relies only on the two channel surf sequences 2510, 2520 that it has data for.
FIG. 25B illustrates an exemplary table having data related to number of times each network is selected during a viewing session being captured. The table includes a network column 2500 and a surf session column 2550. The networks column 2550 lists networks desirable to track (CNN, ESPN, LIFE, TBS, TNT, USA are illustrated). The surf session column 2550 lists how many times a particular network was selected in a viewing session. As illustrated, ESPN was selected in 3 surf sequences and USA was not selected. Many networks typically carry certain genres of programs, such as sports, drama, comedy, talk illustrates, etc. for a specific day part. Because viewers have varying preferences for certain types of programs, particular viewers will look for specific programs on certain networks based on the time of day and day of week. Different viewers also have certain differing dislikes and each viewer may avoid surfing disliked networks entirely. The VCPS 290 monitors the networks visited during channel surfing for the current day part and compares the data to signatures to see if there is similar data.
FIG. 27A illustrates viewing sessions being determined based on when the STB 220 is powered on or off A first session 2710 starts when the STB 220 is turned on 2712 and terminates when it is turned off 2714. A second session 2720 begins the next time the STB 220 is powered on 2722 and ends at the next power off event 2724.
FIGS. 28A-C illustrate how viewing sessions can be defined based on viewer activity or inactivity, and how session start and end points can be based on remote control activity or inactivity. In FIG. 28A, the initiation of remote activity 2800, such as channel surfing, marks the start of a first session 2810, and occasional remote activities 2820 keep the session alive. However, if the remote control is idle for an extended period of time (period of inactivity) 2830, the VCPS 290 considers the next activity from the remote control 2840 to be indicative of the start of a second session 2850. The period of inactivity 2830 used to determine a new session can vary widely depending on the application and may be dynamically configurable.
In a preferred embodiment, the VCPS 290 uses two sliding windows, a small window to detect radical changes in viewer behavior and a larger window to detect more subtle changes. FIG. 30 illustrates implementing sliding windows by collecting data into fixed-width data bins termed sub-sessions. Each sub-session represents a unit of time during which viewer interactivity is monitored. A window consists of a fixed number of the sub-sessions, starting with the most recent. As the amount of time represented by a single sub-session elapses, the window slides so that it always incorporates the most recent set of sub-sessions. According to one embodiment, the VCPS 290 uses six 5-minute sub-sessions 3002-3012 with two sliding windows, one of 15 minutes 3020 (3 sub-sessions) and one of 30 minutes 3030 (6 sub-sessions).
As events occur, the data is stored in the 0-4 minute sub-session 3002. After 5 minutes has elapsed, the data for all sub-sessions except the 30+ minute sub-sessions 3040, 3050 is shifted to an adjacent bin (as illustrated, shifted to the right). Data coming out of the 25-29 minute bin 3012 is added to both the 30+ minute day-part bin 3040 and the 30+ minute session bin 3050. The 0-4 minute sub-session 3002 is cleared. The 15-minute sliding window 3020 encompasses the first three sub-sessions 3002-3006 while the 30-minute sliding window 3030 encompasses the first six sub-sessions 3002-3012.
In a preferred embodiment, the signature profile comprises the same profile categories as the session profile allowing for a direct correlation between categories. The VCPS 290 writes session data to the signature history at the end of day parts and sessions. Each 5-minute sub-session tracks the day part that it resides in, and the 30-minute day-part sub-session 3040 does the same. When data is being added from the 25-29 minute sub-session 3012 into the 30+ minute day-part sub-session 3040, the VCPS 290 verifies that the sub-sessions are the same day part 3060. If the sub-sessions are for a different day part, the data from the 30+ minute day-part bin 3040 is written to the signature data that best matches the current session and the 30+ minute day-part bin 3040 is cleared.
If the 15-minute sliding window 3020 detected the behavior change, then data in the 5-minute sub-sessions outside the 15-minute sliding window 3020 (last 3 five-minute sub-sessions 3008-3012) are also written to the signature file and cleared. When a session terminates due to power off or lack of user interaction, all data in the 5-minute sub-sessions 3002-3012 and the 30+ minute day-part sub-session 3040 are written to the signature file and cleared.
According to one embodiment, once a matching signature has been established, a fully populated 15-minute sliding window 3020 (all three 5 minute bins 3002-3006 are filled) is used to determine viewer behavior changes. The VCPS 290 compares the data in the 15-minute sliding window 3020 against the signature data. The data within the 15-minute sliding window 3020 must differ significantly (e.g., exceed a difference threshold or not meet a correlation threshold) from the signature data for the VCPS 290 to terminate the session. As would be obvious to one of ordinary skill in the art, reducing the difference threshold (or increasing the correlation threshold) would likely result in earlier termination of sessions, and increasing the difference threshold (or decreasing the correlation threshold) would likely result in longer sessions.
The following example demonstrates all of these concepts. This example has six signature bins and a total viewing time of 60,000 seconds. Thus, the top three signatures that have a minimum signature time of 5000 seconds (60000/12) may be marked as off limits. However, as illustrated only two of the bins meet the viewing time and are marked off limits. Out of the remaining four signatures, signature 5 has the lowest score of 2000 [4000/(16/10)+1)].
FIGS. 35A-B illustrate an exemplary method for updating the signature profile with the session profile based on activity (events captured in the event queue). After the process starts (step 3501), the event queue is initialized (step 3503), and the VCPS 290 waits for the next event (step 3505). Once the next event is received, the VCPS 290 determines if the event is a power on event (step 3507). If the event was a power on event, the VCPS 290 determines when the sliding window will end (i.e., 15 minute window and 30 minute window) and adds these times as upcoming events in the event queue (step 3509). The power on event is then processed (step 3517). If the event was determined to not be a power on event, a determination of whether the event was a channel change event is made (step 3511). If the determination is that the event was a channel change, the VCPS 290 deletes the end of program event associated with the previous channel from the event queue (step 3513) and processes the channel change (step 3517). If the event was not a channel change, the VCPS 290 determines if the event was an end of program event (step 3515). The end of program determination is made by comparing the current time to the time marked in the event queue for the end of the current program. If the event was an end of program, then the end of program event is processed by the VCPS (step 3517).
According to one embodiment, the VCPS 290 compares most of the elements of the signature data in order to determine if signatures correlate with one another. Unlike the session to signature comparisons, category data is used in the signature correlation process because the signatures represent many sessions, and the correlation of category data is particularly useful when comparing large data sets. However, viewer type is not used to correlate signatures, it is only used to exclude matches. That is, if the VCPS 290 determines that two signatures correlate, the VCPS 290 will look at viewer type. As long as the viewer type is not significantly different, the VCPS 290 will merge the two correlated signatures. However, if the viewer type is significantly different (i.e., signature 1=0.8 male, 0.1 female, 0.1 child and signature 2=−0.1 male, 0.8 female, 0.1 child) then the VCPS 290 will not merge the two signatures.
session profile v. itself—to determine when one session is terminated and another is began; session profile v. numerous signature profiles—to determine which, if any, signature profile the session profile should be added to; a session profile v. matching signature profile—to determine when a session is terminated and another is began; and updated signature profile v. other signature profiles—to determine if updated signature profile now correlates with the other signature profiles. There are numerous ways that profiles can be correlated with each other. The correlation can range from simple to complex. The complexity of the correlation depends on such factors as what is being correlated, and how much data is contained within the profiles. For example, a correlation of a session profile against itself may be quite simple. That is, a single profile (i.e., network profile) may be monitored to determine when a session ends. On the other hand, a correlation of a session profile to a signature profile may be more complex and may correlate numerous profiles (i.e., genre, networks, category, surf) to determine when there is a match. Some possible profile correlations and the value of each are discussed below.
Channel surf dwell time profiles capture how long a particular channel or network will maintain the viewers' interest during a surf routine. Because a channel surf sequence may only take one or two minutes, a major benefit of tracking channel surf behavior is that the dwell time data is collected over a short period of time when compared with viewing interests. The sessions and signatures track dwell times by network, and each one has an array of both the number of times a network is visited during the surf sequence and the total dwell time on each network, in order to calculate an average dwell time per network per channel surf sequence. The average dwell time per network is correlated.
genre for overall history; network viewership for prime time for each weekday (Mon-Fri); network viewership for evenings for all weekdays; network viewership for late night for all weekdays; network viewership for daytime for all weekdays; network viewership for weekends; channel surf sequence for overall history; category data for overall history; and viewer type for overall history. The VCPS 290 may track prime time viewership (8 PM-11 PM) for the weekdays, evening viewing (6 PM-8 PM) for all weekdays, late night viewing (11 PM-5 AM) for all weekdays, daytime viewing (5 AM-6 PM) for all weekdays and weekend viewership. According to this embodiment, when comparing session data to signature data, the VCPS 290 compares the following data as the primary match criteria: session genre data and signature genre data; session network data by day part with signature network data by same corresponding day part; and session surf dwell time with signature surf dwell time. Each element or category may be weighted based on the importance of the data in the correlation. Genre has the largest weighting factor in this embodiment, but other relative weightings are also possible. The correlation result is a score for the signature match. The signature that has the highest correlation with the session is considered the best match, and the session data may be associated with that signature.
The signature profiles may be used to target content (advertisements, pay per view (PPV) events, video on demand (VOD) programming) to the subscribers or to customize their viewing environment (i.e., favorite programs listed first in EPG, format of EPG). The signature profiles stored in the STB 220 may be transmitted to the HE at determined intervals (i.e., every night at off-peak hours, every week). The HE may aggregate the data received from each of the STBs 220 connected thereto in order to form groups of viewers with similar characteristics. The groups having similar characteristics may receive specific content targeted to their characteristics (signature profiles). According to another embodiment, the HE may also use other external data to help group subscribers. The external data may be in the form of geodemographic data, such as the demographic data associated with distinct ZIP+4 geographies provided by Claritas Ser. Nos. 60/238,059, 09/635,542 and Ser. No. 09/928,024 which were previously referred to and incorporated by reference for additional details). The external data may also be in the form of other transactional profiles, such as purchase transactions Ser. Nos. 09/951,577, 09,268,519 and Ser. No. 09/928,024 which were previously referred to and incorporated by reference for additional details).
The groups may be formed based on the topography of the television viewing network (i.e., based on nodes within the system). The groups would then consist of clusters of subscribers, wherein each of the clusters could be associated with a node, branch or other element of the system (see Ser. Nos. 60/278,612 and 09/928,024 which were previously referred to and incorporated by reference for additional details). In an alternative embodiment, the STB may receive the additional data (i.e., geodemographic data, other transaction data) directly and incorporate this data into the generated profiles (i.e., signature profiles) to develop enhanced profiles. These enhanced profiles can then be forwarded to the HE for aggregation and potential grouping as described above.
Regardless of where the enhanced profiles are generated, there is likely a practical limit to how many different groups are formed (i.e., 5). The groups may be formed and the advertisements targeted thereto, or ad profiles defining characteristics about the target market of the ad are created and the users are grouped to these profiles. As discussed above, each of the groups may receive material targeted for them. In one embodiment, everybody will receive all content and will select the appropriate content for display based on matching the group number associated with the content and the group number of the STB, or particular user or group of users interacting with the STB. In a preferred embodiment, each STB will receive only the content associated with it based on what group it falls in, thus saving bandwidth. If the content is targeted ads, the targeted ads may be inserted in place of the default ads to create a plurality of presentation streams (programs with targeted ads). Local cable companies have the equipment necessary to insert targeted ads as they are permitted to substitute local ads in place approximately 20% of the default ads. In the preferred embodiment, it is necessary for the television system to be able to route the different presentation streams to different areas (i.e., nodes, branches) within the system so that each user only receives the appropriate presentation stream (see Ser. No. 09/553,637, PCT/US00/10633, Ser. No.s 09/553,099, 09,694,848, PCT/ US00/29034 and Ser. No. 09/712,790 which were previously referred to and incorporated by reference for additional details).
In an alternative embodiment, the STB will insert specific content (i.e., targeted ads) locally. Thus, each subscriber may, in effect, have ads targeted directly to them. The ads may be inserted in avails (advertisement opportunities) regardless of the programming being viewed. The STB may receive the targeted ads via an ad channel. The ads may be delivered ahead of time and stored on the STB, or be received at approximately the same time as the avail and inserted on the fly. If the ads are to be stored on the STB, the ad channel may deliver a plurality of ads on the ad channel with the STB selecting the appropriate ads to store thereon. The selection may be made based on group designations or may be made by comparing the ad profile with the signature profiles on the STB. In addition to the ads, an ad queue will need to be generated and stored on the STB to define what order the ads are inserted and what criteria affect the criteria (see Ser. No. 09/712,790, PCT/US01/27217, Ser. Nos. 09/731,606, 09/748,943, 09/750,800 and Ser. No. 09/824,434 which were previously referred to and incorporated by reference for additional details).
In an alternative embodiment, the ad queue may be stored on the STB while the ads are stored somewhere else within the network. When the ad queue determines the next ad to be inserted, the ad is retrieved from the network and delivered to the STB. The ad may be delivered over an ad channel or over a dedicated line (such as an Internet connection). If the ad channel was used the ads may be delivered in real time (would require coordination between program(s) and ad channel), or ahead of time and temporarily stored (i.e., start delivering one avail in advance). The ads could be delivered on the ad channel either at the actual bit rate for the ad or at a slower bit rate where portions of the ad are stored as they are received. As one skilled in the art would recognize, the Internet connection provides both benefits and drawbacks. The drawback is the need the separate connection. The benefit is that the queue points to the URL address for the ad so the ad is displayed upon selection from the queue (see Ser. No. 09/742,506 which was previously referred to and incorporated by reference for additional detail).
In a preferred embodiment, the ad queue is actually a plurality of ad queues with a different queue associated with each signature, or group of signatures. When the VCPS 290 determines that a session has ended and thus a new user or group of users (identified by a correlation with a new signature) is interacting with the TV, the ad queue changes to the one associated with that signature. Moreover, the ad queue may be adjusted based on the programs being watched as certain advertisers may not want ads displayed during certain programs or may pay a premium for insertion in other programs (See Ser. No. 09/742,852 which was previously referred to and incorporated by reference for additional detail).
According to another embodiment, the STB may also store ads and ad queues for other types of targeted advertisements such as EPGs, program bugs (overlay in the corner of the video), product placement (illustration of product placed in program, such as a Coke can placed in actors hand), trick play ads (shortened version of ad displayed when user fast forwards through recorded ad), and record ads (alternative ads placed in a recorded program if it is determined that different subscriber is viewing or if it is being viewed for more than the first time). The ad queue could be used to manage how all of these ads are displayed so as not to saturate the viewer but yet enhance the advertisement effectiveness. The various types of ads and possible coordination therebetween are discussed in Ser. Nos. 60,238,056, 09/749,255, PCT/US01/31682, Ser. Nos. 09/680,622, 60/281,037 and Ser. No. 60/329,992 which have all previously been referred to and incorporated by reference for additional detail.
Regardless of what is being correlated (ad profile to signature profile, session profile to signature profile), how many different elements are being correlated (program genre; program genre, channel changes and networks), and whether the different elements are weighted or not, there are numerous ways to perform these correlations. If the profiles are in the form of vectors you can perform a scalar dot product on the vectors to determine the correlation between the two vectors. In order for a scalar dot product to work the vectors must be normalized. That is, the magnitude of the vector must be 1.0. To calculate the magnitude, take the square root of the sum of the squares of the components as shown below.
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H04N21/25891, G06Q30/02, H04N7/17318, H04H60/66, H04N21/23424European ClassificationH04H60/66, H04N7/173B2, H04H60/45, H04H60/33, H04H60/46, H04N21/231H, H04N21/466, H04N21/441, H04N21/466C, H04N21/466L, H04N21/44S, H04N21/81C, H04N21/658S, H04N21/258U2, H04N21/45M3, H04N21/2668, H04N21/234S, H04N21/25A1, H04N21/442E2, H04N21/258U3, H04N21/236R, H04N21/434R, G06Q30/02Legal EventsDateCodeEventDescriptionAug 14, 2007ASAssignmentOwner name: PRIME RESEARCH ALLIANCE E., INC., VIRGIN ISLANDS,Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:EXPANSE NETWORKS, INC.;REEL/FRAME:019689/0320Effective date: 20040818Jun 21, 2011ASAssignmentOwner name: EXPANSE NETWORKS, INC., PENNSYLVANIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:SCHLACK, JOHN A.;SUTTON, ANDREW;RAMPULLA, ANDREW J.;AND OTHERS;SIGNING DATES FROM 20011026 TO 20011031;REEL/FRAME:026469/0681Apr 23, 2015FPAYFee paymentYear of fee payment: 4RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - 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