(12) United States Patent
`Eldering et al.
`
`(10) Patent N0.:
`(45) Date of Patent:
`
`US 6,820,277 Bl
`Nov. 16, 2004
`
`USOO6820277B1
`
`(54)
`
`(75)
`
`ADVERTISING MANAGEMENT SYSTEM
`FOR DIGITAL VIDEO STREAMS
`
`Inventors: Charles A. Eldering, Doylestown, PA
`(US); Gregory C. Flickinger,
`Horsham, PA (US); Jeffrey S.
`Hamilton, Doylestown, PA (US)
`
`W0
`wo
`wo
`wo
`W0
`wo
`W0
`
`9901984
`9904561
`9952235
`003002
`0014951
`0033224
`0054504
`
`171999
`171999
`1071999
`272000
`372000
`672000
`972000
`
`OTHER PUBLICATIONS
`
`(73)
`
`Assignee: Expense Networks, Inc., Pipersville,
`PA (US)
`
`(*)
`
`Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 0 days.
`
`(21)
`
`(22)
`
`(60)
`
`(51)
`(53)
`
`(58)
`
`(56)
`
`W0
`W0
`W0
`W0
`W0
`
`Appl. N0.: 09,553,637
`
`Filed:
`
`Apr. 20, 2000
`
`Related U.S. Application Data
`Provisional application No. 60718.3,411, filed on Feb. 18,
`2000, and provisional application No. 607130102, filed on
`Apr. 20, 1999.
`
`Int. Cl.7 ......................... H04W 77025; H04W 7710
`
`U.S. Cl.
`725735; 725732; 725734;
`725736
`Field of Search ........................ 725714, 32, 34—36,
`725742, 46, 52; 705726
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`4,602,279 A
`5,099,319 A
`5,231,494 A
`5,446,919 A
`5,532,735 A
`
`771986 Freeman ...................... 358786
`371992 Esch et al.
`...... 358786
`
`771993 Wachob .......
`3587146
`
`
`871995 Wilkins ................ 45576.2
`771996 Blahut et al.
`................. 348713
`
`(List continued on next page.)
`FOREIGN PATENT DOCUMENTS
`
`9712486
`9717774
`9741673
`9821877
`9828906
`
`471997
`571997
`1171997
`571998
`771998
`
`SCTE Digital Video Subcommittee, Digital Program Inser-
`tion Ad Hoc Group, Business and Operationai Dependen-
`cies, Nov. 5, 1997, 2 pages.
`SCTE Digital Video Subcommittee, Digital Progrmn Inser-
`tion Comments from Cableiiabs, Sep. 18, 1997, 2 pages.
`Davis, Joe Digital Video Systems, New Media Division,
`Recononendations for New Ad Hoc Group, Sep. 16, 1997, 4
`pages.
`SCTE Digital Video Subcommittee, Digital Program Inser-
`tion Requirements, Mar. 17, 1997, 1 page.
`Digital Video Systems, Technical Issues Nov. 5, 1997, 5
`pages.
`Shen, Paul Imedia Corporation, Imedia ChemrPicket-TM,
`Router 1Re—MaitipiexerTM for Digital Programming Jun,
`1998, 12 pages.
`
`Primary Examiner—Vivek Srivastava
`Assistant Examiner—Matthew Demicco
`
`(74) Attorney, Agent, or Firm—Douglas J Ryder; Ryder IP
`Law, PC
`
`(57)
`
`ABSTRACT
`
`An Ad Management System (AMS) for managing sales and
`insertion of targeted advertisements into advertising oppor-
`tunities (“avails”). The AMS provides advertisers an ability
`to describe their advertisements (ad characteristics) in terms
`of target market demographics, required ad bandwidth, ad
`duration, and other ad specific parameters. The AMS
`receives the ad characteristics and matches the ads to the
`avails. The AMS tracks different avails including duration
`and bandwidth of the avail, and uses a number of schemes
`to determine if the ad can be placed in the avail. The AMS
`targets advertisements by correlating subscriber character-
`istics to the ad characteristics. The subscriber characteristics
`
`may be associated with groups of subscribers (e.g. nodes in
`cable television environments) or to individual subscribers.
`
`1 Claim, 12 Drawing Sheets
`
`0%
`
`190
`
`”W
`' OPERATOR
`C
`I'
`'|
`SUBSCRIBER
`
`||
`‘
`ADVERTISER
`
`FD"
`|
`|
`I
`|
`CONTENT
`FROVIIZER
`
`00011011100
`MODULE
`
`
`SUBSCRIBER
`
`
`OPPORTUNITIES
`CHARACTERIZATION
`
`
`
`MODULE
`MODULE
`
`
`IQI
`II
`‘I
`PFIOFILER
`
`AD NSERTION
`MODULE
`
`114
`
`NTFX—1004 I Page 1 of 21
`
`

`

`US 6,820,277 B1
`Page 2
`
`6,009,409 A
`'9
`.
`2,3203; :
`'
`-
`g’gggrgg: :
`6,108,637 A
`6’134’532 A
`691607389 A
`6017-12331 Bl
`6324219 B] a:
`6,463,585 B] 3:
`’
`"
`6.470079 Bl
`*
`6487,72] B]
`’
`’
`
`1211999
`12.11999
`212000
`312000
`712000
`812000
`1012000
`1212000
`112001
`1112001
`1012002
`1012002
`1112002
`
`.
`
`
`705114
`Adler el al.
`..... 705114
`LeMole et al.
`3451327
`Herz el al.
`. ............... 7091206
`Wolfe et a].
`Herz et a]. ......
`7091217
`
`Blumenau ...................... 70517
`Lazarus et al.
`705114
`.
`Hendricks el al.
`45514.2
`.
`Alexander el 0].
`3451327
`
`Eldering ...................... 705114
`Hendricks et al.
`725135
`Benson . 379,014.13
`
`......................... 725130
`Safadi
`
`* cited by examiner
`
`U.S. PATENT DOCUMENTS
`
`911996
`2.11997
`211997
`811997
`311998
`511998
`611998
`611998
`611999
`611999
`811999
`911999
`1011999
`12.11999
`
`34316
`Hendricks et al.
`34811
`Hendricks et al.
`..
`
`.. 3431552
`Dedrick ..........
`
`Carles
`34818
`
`.. 3951226
`Dedrick
`
`..... 45512
`Han et al.
`Nemirofsky et al.
`45513.1
`Hite et al.
`..................... 34819
`
`Buehl
`..... 34815.5
`Robinson .....
`395120049
`
`.
`705114
`Angles et a].
`.......... 7091219
`Merriman et al.
`
`705110
`Anderson et al.
`Slezak ........................ ”1091219
`
`A >
`
`>>>>>>>>>>>>
`
`5,559,549
`5,0(KI,304
`5,604,542
`5,661,516
`5,724,521
`5,758,257
`5,761,601
`5,774,170
`5,912,696
`5,918,014
`5,933,811
`5,948,061
`5,974,306
`6,006,257
`
`NTFX—1004 1 Page 2 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 1 of 12
`
`US 6,820,277 Bl
`
`._
`
`o9
`
`zo:§mm_5§s._oLL_mohémao2__i
`
`zowfimmfioo
`
`JUL3:223
`
`mmmaowmgmQ3802L__92.20.52
`
`£282
`
`'mem_Em_>o<
`
`mmzmoEmLL_«ESEmM58923:825528
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`
`55:82
`zopmmwzad.
`
`“GE
`
`NTFX—1004 I Page 3 of 21
`
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 2 0f 12
`
`US 6,820,277 Bl
`
`
`
`ZIP CODE
`
`MEDIAN HOME PRICE
`
`
`
`$175,000
`
`
`
`$64,000
`
`$80,000
`
`$1 10,000
`
` $225,000
`
`FIG. 2A
`
`NTFX—1004 I Page 4 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 3 0f 12
`
`US 6,820,277 Bl
`
`STARTER HOME PRICES
`
`ZIP CODE
`
`TOWN
`
`AVERAGE SALE
`
`BOSTON
`
`NEWTON, MA
`
`NEW YORK
`
`NEW YORK
`
`HORSHAM, PA
`
`PHILADELPHIA
`
`WASHINGTON, DC.
`
`ALEXANDRIA, VA
`
`RALEIGH, NC
`
`DECATUR, GA
`
`ATLANTA
`
`MIAMI
`
`TAMPA, FL
`
`BELLEVUE, TN
`
`FARMINGTON, MI
`
`CHICAGO
`
`CHICAGO
`
`AURORA, CO
`
`PHOENIX
`
`325,378
`
`422,500
`
`387,800
`
`151,411
`
`184,562
`
`337,402
`
`263,323
`
`190,863
`
`169,271
`
`318,602
`
`121,568
`
`186,794
`
`155,399
`
`208,558
`
`234,124
`
`327,601
`
`176,517
`
`205,099
`
` $204,889
`
`REDONDO BEACH, CA
`
`329,251
`
`WINNETKA, CA
`
`SAN DIEGO
`
`SAN BRUNO, CA
`SAN FRANCISCO
`
`KIRKLAND, WA
`
`164,000
`
`201,620
`
`255,110
`
`418,731
`
`260,334
`
`FIG. 2B
`
`NTFX—1004 I Page 5 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 4 of 12
`
`US 6,820,277 Bl
`
`mum—mommzm
`m5._<>omwmww<fin._m_om_<n_XE.wwmmonfi
`
`
`80de9WmémIHNFommrmoomzfl,
`
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`80,9.9NFTmém9.53m.N_.mmzow522w219.
`
`08.3%09-98_._._.omNS?I._.=>_w<2m_m_Imw29.
`
`
`
`
`m.GE
`
`NTFX—1004 I Page 6 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 5 0f 12
`
`US 6,820,277 Bl
`
`<$30K
`
`$31K-$50K
`
`$51 K-$75K
`
`$76K-$100K
`
`>$100K
`
`HOUSEHOLD INCOME
`
`FIG. 4A
`
`1
`
`2
`
`3-4
`
`4-6
`
`>6
`
`HOUSEHOLD SIZE
`
`FIG. 4B
`
`NTFX—1004 I Page 7 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 6 0f 12
`
`US 6,820,277 Bl
`
`<25
`
`25-35
`
`36-45
`
`46-55
`
`>56
`
`MEDIAN HOUSEHOLD AGE
`
`FIG. 4C
`
`1
`
`CAUCASIAN
`
`AFRICAN
`
`AMERICAN
`
`HISPANIC
`
`ASIAN-
`
`PACIFIC
`
`ETHNIC GROUP
`
`FIG. 40
`
`NTFX—1004 I Page 8 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 7 of 12
`
`US 6,820,277 Bl
`
`o_In_<mOOEm_n_
`
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`0
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`
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`
`$2002DAOImmDOIOm:
`
`m.OE
`
`NTFX—1004 I Page 9 of 21
`
`ZO_._.(N_mm_.5<m_<_._omoozEmmEomew
`
`zO_._.¢N_mm_._b<m_¢Ioo<
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 8 0f 12
`
`US 6,820,277 Bl
`
`ADVERTISEMENT: VOLKSWAGEN. DRIVERS WANTED
`
`DURATION:
`
`303
`
`MINIMUM BW:
`
`4Mbls
`
`AVAIL
`
`DATEHIME
`
`PROGRAM
`
`CORRELATION
`
`#23
`
`3MARCHOD : 15:28
`
`DAYS OF OUR LIVES
`
`#72
`
`3MARCH00 : 20:15
`
`BUFFY THE VAMPIRE SLAYER
`
`#51
`
`3MARCH00 : 21 :00
`
`60 MINUTES
`
`0.2
`
`0.7
`
`0.6
`
`FIG. 6
`
`NTFX—1004 I Page 10 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 9 0f 12
`
`US 6,820,277 Bl
`
`ADVERTISEMENT: VOLKSWAGEN, DRIVERS WANTED
`
`DURATION:
`
`305
`
`MINIMUM BW:
`
`4Mb/s
`
`CORRELATION WI MODE/SUBSCRIBER
`
`HOUSEHOLD INCOME W 0.6
`
`HOUSEHOLD SIZE WW 0,7
`
`MEDIAN HOUSEHOLD AGE W 0‘4
`
`ETHNIC GROUP WW 0]
`
`S‘éii’é‘iifigfioempmc W/////////////_ 0.6
`
`CORRELATION W! AVAIL
`
`VIEWER‘S INCOME WM 0.8
`
`VIEWER‘S HOUSEHOLD SIZE W 0.6
`
`VIEWER'S AGE W 0.9
`
`VIEWER'S ETHNIC GROUP W 0.5
`
`
`AVERAGE AVAIL CORRELATIONW 0.7
`
`EXPECTED VIEWERSHIP:
`
`ACTUAL VIEWERSHIP:
`
`2E6
`
`1.8E6
`
`FIG. 7
`
`NTFX—1004 I Page 11 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 10 0f 12
`
`US 6,820,277 Bl
`
`08.85
`
`08.9%mumwe
`
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`
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`
`a
`
`0E
`
`NTFX—1004 I Page 12 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 11 0f 12
`
`US 6,820,277 Bl
`
`ADVERTISER!
`
`AD SOURCE
`
`AMS
`
`100
`
`MODULE 1 14
`
`"'“i-
`
`T0 AD
`
`INSERTION
`
`FIG. 9
`
`NTFX—1004 I Page 13 of 21
`
`

`

`US. Patent
`
`Nov. 16, 2004
`
`Sheet 12 0f 12
`
`US 6,820,277 Bl
`
`m=>__._.
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`NTFX—1004 I Page 14 of 21
`
`

`

`US 6,820,277 B1
`
`1
`ADVERTISING MANAGEMENT SYSTEM
`FOR DIGITAL VIDEO STREAMS
`
`This application claims the priority, under 35 U.S.C.
`§119(e), of Provisional Applications 60f130,102 filed on
`Apr. 20, 1999 and 60f183,411 filed on Feb. 18, 2000. Both
`of these applications are herein incorporated by reference
`but are not admitted to be prior art.
`BACKGROUND OF TI IE INVENTION
`
`Advertising forms an important part of broadcast pro-
`gramming including broadcast video (television), radio and
`printed media. The revenues generated from advertisers
`subsidize and in some cases pay-entirely for programming
`received by subscribers. For example, over the air broadcast
`programming (non-cable television) is provided entirely free
`to viewers and is essentially paid for by the advertisements
`placed in the shows that are watched. Even in cable televi-
`sion systems and satellite-based systems, the revenues from
`advertisements subsidize the cost of the programming, and
`were it not for advertisements,
`the monthly subscription
`rates for cable television would be many times higher than
`at present. Radio similarly ofl‘ers free programming based on
`payments for advertising. The low cost of newspapers and
`magazines is based on the subsidization of the cost of
`reporting, printing and distribution from the advertising
`revenues.
`
`Techniques for inserting pre-recorded spot messages into
`broadcast transmission have been known. Generally, broad-
`cast video sources (i.e., TV networks, special
`interest
`channels, etc.) schedule their air time with two types of
`information: “programming” for the purpose of informing or
`entertaining, and “avails” for the purpose of advertising. The
`avails may occupy roughly 20—25% of the total transmitting
`time, and are usually divided into smaller intervals of 15, 30,
`or 60 seconds.
`
`I.» I).
`
`In many prior art systems, the insertion of advertisements
`in avails is handled by a combination of cue-tone detectors,
`switching equipment and tape players which hold the adver-
`tising material. Upon receipt of the cue tones, an insertion
`controller automatically turns on a tape player containing the
`advertisement. Switching equipment then switches the sys-
`tem output from the video and audio signals received from
`the programming source to the output of the tape player. The
`tape player remains on for the duration of the advertising,
`after which the insertion controller causes the switching
`equipment to switch back to the video and audio channels of
`the programming source. When switched, these successive
`program and advertising segments usually feed to a radio-
`frequency (RF) modulator for delivery to the subscribers.
`Many subscriber television systems, such as cable tele-
`vision are currently being converted to digital equipment.
`These new digital systems compress the advertising data
`according to a decompression standards, such as a Motion
`Picture Experts Group (MPEG) compression standard
`(currently MPEG-2 standard). The compressed data is then
`stored as a digital file on a large disk drive (or several
`drives). Upon receipt of the cue tone,
`the digital file is
`spooled (“played”) off of the drive to a decompressor. The
`video and accompanying audio data are decompressed back
`to standard video and audio, and switched into the video;f
`audio feed of the RF modulator for delivery to the sub-
`scriber.
`
`A prior art (present model) of providing advertisements
`along with actual programming is based on linked sponsor-
`ship. In the linked sponsorship model, the advertisements
`
`50
`
`55
`
`60
`
`2
`are inserted into the actual programming based on the
`demographic information related to the viewersfsubscribers.
`However, the ability to transmit information digitally allows
`programming and advertisements to be transported from
`various geographic locations and arranged in a fashion
`which permits an optimized program to be presented to a
`subscriber.
`
`10
`
`15
`
`20
`
`The transition to the digital age permits a migration to
`new methods of advertising based on what
`is termed
`orthogonal sponsorship.
`In orthogonal sponsorship,
`the
`advertisements are targeted at subscribers based on a deter-
`mination that the advertisement will be of interest to the
`
`the subscriber is likely to ultimately
`subscriber and that
`purchase the product or service being advertised.
`The digital systems are capable of handling both linked
`sponsorship, orthogonal sponsorship and a combination of
`both. However, what is required is a method and apparatus
`for identifying advertising opportunities, presenting those
`opportunities to advertisers, receiving information about the
`advertisements, determining the ability to insert
`the
`advertisements, managing the insertion process, and retum-
`ing to the program in the digital video arena.
`SUMMARY OF THE INVENTION
`
`The present invention is a method and apparatus for the
`managing advertisements in a digital environment, including
`methods for selecting suitable advertising based on sub-
`scriber profiles, and substituting advertisements in a pro-
`gram stream with targeted advertisements.
`The Ad Management System (AMS) of the present inven-
`tion manages the sales and insertion of digital video adver-
`tisements (hereinafter "ads")
`in telecommunications
`systems, such as cable television (CATV), switched digital
`video (SDV), and streaming video (Internet) based environ-
`ments. The AMS provides advertisers an ability to describe
`their advertisements in terms of target market demographics,
`required ad bandwidth, ad duration, and other ad specific
`parameters.
`The AMS receives the ad descriptions that include some
`or all of the aforementioned parameters, and matches the ads
`to the advertising opportunities (“avails”) available in the
`programming stream. The AMS tracks difiemnt avails
`including duration and bandwidth of the avail, and uses a
`number of schemes to determine if the ad can be placed in
`the avail. In one embodiment, the ads are received in a high
`resolution state with minimum compression, and are com-
`pressed to a predetermined available bit rate (ABR) band-
`width.
`
`One of the key functions of the AMS is its ability to allow
`ads to be matched to groups of subscribers (e.g. nodes in
`CATV environments) or to individual subscribers in the
`SDV or streaming video environments. The service is pro-
`vided at no cost to the subscribert’consumer, however, the
`economic efliciencies are created and may be used to pro-
`vide a revenue stream to the cable operator, profiler and ad
`service operator.
`Another key aspect of the present invention is one or more
`privacy features wherein the raw consumerfsubscriber data
`is maintained private on a Secured Correlation Server
`(SCS). The raw consumerfsubscriber data is not available for
`sale or is not accessible by third parties. Thus, the AMS
`forms part of a matching service, in which advertisers work
`in conjunction with subscribers, profilers (such as video
`surfstream profilers, Internet profilers, and retail outlets),
`and network operators to allow subscribers to receive more
`targeted ads while protecting the privacy of the subscribers.
`
`NTFX—1004 I Page 15 of 21
`
`

`

`US 6,820,277 B1
`
`3
`
`The network operator may be a cable, Digital Subscriber
`Line (DSL), or satellite network operator. Subscribers
`receive the benefits of being able to have advertisements
`which are more targeted to their lifestyle in addition to
`receiving discounts from retailers and service providers.
`Furthermore, a method of dynamic ad linking is presented
`in which a present ad in an actual programming (e.g., a
`primary program stream) can be replaced by another ad
`targeted at the subscriber. A plurality of diiferent schemes
`may be used for dynamic linking, e.g., the ads may be
`statistically multiplexed within a program stream in real-
`time. Alternatively, a local storage may be used to store the
`ad for subsequent insertion into the program stream.
`These and other features and objects of the invention will
`be more fully understood from the following detailed
`description of the preferred embodiments, which should be
`read in light of the accompanying drawings.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`The accompanying drawings, which are incorporated in
`and form a part of the specification, illustrate the embodi-
`ments of the present
`invention and,
`together with the
`description serve to explain the principles of the invention.
`In the drawings:
`FIG. 1 illustrates an advertisement management system
`(AMS) in accordance with the one embodiment of the
`present invention;
`FIGS. 2A and 2B illustrate exemplary use of public
`information based on median home prices or starter home
`prices;
`FIG. 3 illustrates an exemplary tax assessment data that
`can be used for determining the applicability of an adver-
`tisement;
`FIGS. 4A—4D illustrate exemplary graphical representa-
`tion of ad characterization vectors;
`FIG. 5 illustrates an exemplary case of demographic
`correlation;
`FIG. 6 illustrates an exemplary case of utilizing avail
`opportunities in conjunction with correlation data to match
`the advertisements;
`
`FIG. 7 illustrates a bar graph indicator utilized for corre-
`lating advertisements and subscribers;
`FIG. 8 illustrates an exemplary pricing scheme;
`FIG. 9 is a functional diagram showing difl‘erent functions
`of avail salesfauctioning module; and
`FIG. 10 illustrates an exemplary method of dynamic ad
`linking.
`
`DETAILED DESCRIPTION OF THE
`PREFERRED EMBODIMENT
`
`In describing a preferred embodiment of the invention
`illustrated in the drawings, specific terminology will be used
`for the sake of clarity. However, the invention is 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.
`With reference to the drawings, in general, and FIGS. 1
`through 10 in particular,
`the method and system of the
`present invention is disclosed.
`Generally, an advertisement management system (AMS)
`in accordance with the principles of the present invention
`consists of one or more subsystems which allow for the
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`characterization of the advertisement, determination of
`advertising opportunities (avails), characterization of the
`subscriber, correlation of the advertisement with a sub-
`scriber or group of subscribers, and sale of the
`advertisement, either through a traditional placement (sale),
`an Internet based sale, or an Internet based auction.
`As illustrated in FIG. 1, an AMS 100 comprises an ad
`characterization module 102, an avail opportunities module
`104, a subscriber characterization module 108, a correlation
`module 110, and an avail salesfauctioning module 112. The
`AMS 100 is also configured to communicate to an ad
`insertion module 114. The ad insertion module 114 may be
`located within the AMS 100 or may be located externally.
`The ad characterization module 102 allows one or more
`
`advertisers to enter key characterization data regarding the
`advertisement and the target market. The avail opportunities
`module 104 allows the content providersfproducers of pro-
`gram streams to indicate various avails that are available in
`the programming stream, their basic characteristics, and the
`extent
`to which they can be substituted. The subscriber
`characterization module 108 allows for the collection of
`subscriber data. The subscriber data can be collected from a
`variety of sources including private databases external to the
`system or public databases that contain information relevant
`to the subscriber.
`
`With respect to private data, the subscriber has generally
`paid for the access to this data, e.g.,
`the subscriber may
`receive product promotions or other offers. The subscriber is
`also provided access to his private data. The subscriber may
`access the private data to assure the integrity of the data, e.g.,
`the data accurately reflects his interests and lifestyle.
`The subscriber data may be based on an individual
`subscriber, a group of subscribers, a household or a group of
`households. Techniques evolving the coarse discrimination
`of subscribers and grouping of subscribers into large groups
`can be used to associate a serving area with a particular
`advertisement. For example, in a cable television (CATV)
`system, it may be determined that a group of subscribers
`associated with a particular optical distribution node speak
`a particular language. This knowledge may then be used to
`direct a particular set of advertisements to that node. As an
`example, a node associated with Spanish-speaking individu-
`als can have advertisements in Spanish inserted in the
`programming streams.
`The specific targeting can also be based on public infor-
`mation such has median home prices or starter home prices.
`These prices can be further associated with zip codes, as
`shown in FIGS. 2A and 2B. The publicly available data may
`be subscriber specific. FIG. 3 illustrates an example of tax
`assessment data that can be used as a factor in determining
`the applicability of an advertisement. In the case of tax
`assessment data, the subscriber’s name, address and tax
`parcel number are known along with an assessed value of the
`property. The assessed value of the property can be used to
`determine an approximate income range for the family and
`thus specifically target advertisements.
`The publicly available data is not restricted to real estate
`data, as illustrated in FIGS. 2 and 3, but can include a variety
`of demographic data including median household age,
`household income, race and other characteristics which can
`be determined on a group or individual level.
`Private data can also be amassed and can include specific
`viewing habits or purchase records of the subscriber.
`Alternatively, the subscriber may complete questionnaires
`and forms that
`indicate lifestyle, product preference and
`previous purchases. All
`the available private and public
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`information is used by the subscriber characterization mod-
`ule 108 for characterizing one or more subscribers. The
`subscriber characteristics may be based upon some known
`features. For example,
`it is known that the Nielsen data
`tracks the number of households watching particular TV
`programming.
`In accordance with the principles of the
`present invention, such information may be used to charac-
`terize one or more characteristics of the subscribers.
`The ad characterization module 102 has an advertiser
`
`interface, e.g., a Web (browser) interface, that allows adver-
`tisers to enter parameters which characterize their advertise-
`ment and are used to form ad characterization vectors. The
`
`advertisers may manually create ad characterization vectors
`by entering useful information via the browser interface. In
`this case, the ad characterization vector contains a simple
`deterministic value (Oor 1) for each category. Alternatively,
`the vectors may contain probabilistic distributions and may
`allow advertisers to develop more complex models for the
`target market. The principles of the present invention are
`flexible and may operate with either simple deterministic
`values or with complex models. Furthermore, heuristic rules
`may be defined for generating ad characterization vectors.
`These heuristic rules are logical rules or conditional prob—
`abilities that aid in the formation of adcharacterization
`vectors. The heuristic rules in logic form allow the system to
`apply generalizations that have been learned from external
`studies. In the case of conditional probabilities, determina-
`tions are based on statistical probabilities that define ad
`characterization vectors.
`
`Furthermore, the ad characterization module 102 supports
`entry of the one or more parameters that are used by
`advertisers to target the advertisement and create advertise-
`ment vectors. The choices for these parameters may be
`presented as pull down selections in a browser utilizing a
`graphical user interface (GUI). In an exemplary case, the
`following categories may be used:
`Advertisement duration: 10 s; 15 s; 30 s; 60 5
`Minimum advertisement bandwidth: 2 bes, 4 bes, 6
`bes, 8 bes, 10 bes
`Household Income: ($30 K, $31 K—$50 K, $51 K—75 K,
`$76 K—$10'0 K, >$100 K, no preference
`Household size: 1, 2, 3—4, 4—6, >6, no preference
`Median household age: (25, 25—35, 36—45, 46—55, >56, no
`preference
`Ethnic group: Caucasian, African American, Hispanic,
`Asian-Pacific, no preference
`In one implementation, when "no preference” is chosen,
`equal weighting is given to each category within the par-
`ticular demographic parameter. For example, if no prefer-
`ence is selected for household income, all categories within
`the household income demographic are assigned a value of
`0.2 (1 divided by the number of choices, which in this case
`is 5). After weights have been assigned to all the categories,
`one or more ad characterization vectors may be generated
`based on weighted categories. These ad characterization
`vectors assist in characterization of various advertisements.
`
`An exemplary graphical representation of these vectors is
`presented in FIGS. 4A—4D. Other categories based on
`demographic factors, socio-economic factors, and consump-
`tion factors (purchase information) may also be used.
`The avail opportunities module 104 permits an operator or
`a video programming manager an ability to list and organize
`the particular avails in a programming stream. The avail
`opportunities module 104 comprises an interface that may
`be used for manual entry of data, or may be used for
`collection of avail data from network or other content related
`
`databases. The avail data may include specifics about the
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`avail opportunities, such as duration, broadcast time, etc.
`and may include demographic data pertaining to the pro-
`gram the avail is associated with, such as household income,
`ethnic group, etc. The avail data may be used for formation
`of one or more avail characterization vectors. These avail
`characterization vectors are correlated with the ad charac-
`terization vectors to determine how avails should be corre-
`lated with the ads. The result of this correlation (avail
`correlation) is an enhanced measurement of how well the
`correlation exists between an ad and an avail. One or more
`heuristic rules may be defined for the generation avail
`characterization vectors. These heuristic rules may be
`expressed in terms of logical rules as well as conditional
`probabilities.
`In an exemplary case, the avail opportunities module 104
`may have a GUI and the operator may be presented with the
`following menus to assist in generation of the avail infor-
`mation:
`fill-in line regarding the pro-
`Programming opportunity:
`gramming in which the avail is located (e.g. Buffy the
`Vampire Slayer, or Monday Night Football)
`Avail duration: the exact time duration of the avail (e.g. 30
`5)
`Initial bandwidth: the minimum bandwidth which is given to
`the avail, and to which the initial advertisement is maxi-
`mally compressed. As an example, if an advertisement is
`initially placed in a program stream which is compressed
`to 6 Mb7s, the initial bandwidth of the avail is 6 bes.
`Initial scheduled broadcast
`time:
`the initial dateftime
`(Universal Standard Time, UST) at which the avail will
`appear.
`Local preemption authorized: this checkbox indicates if an
`avail can be substituted at
`the local
`level or if such
`
`substitution is prohibited.
`Household Income: ($30 K, $31 K—$50 K, $51 K—75 K,
`$76 K—$100 K, >$100 K, not designated
`Household size: 1, 2, 3—4, 4—6, >6, not designated
`Median household age: (25, 25—35, 36—45, 46—55, >56, not
`designated
`Ethnic group: Caucasian, African American, Hispanic,
`Asian-Pacific, not designated
`The subscriber characterization module 108 provides the
`operator the ability to characterize the subscriber
`in
`Switched Digital Video (SDV) mode or in non-SDV mode.
`The characterizations of the subscriber are used to form a
`subscriber characterization vector.
`
`In a non-SDV mode, the operator is presented with a node
`demographics interface that allows the operator to manually
`program the node characteristics using pull-down menus, or
`to import the data from a file. The node characteristics are
`determined from information manually collected by the
`operator, or assembled using agents that collect the infor-
`mation from publicly available sources. The node demo-
`graphics interface presents both an input screen and a node
`characteristics screen, wherein the node characteristics
`screen further includes a graphical representation of the
`node demographics. Generally, a browser-based interface
`allows the operator to analyze the input characteristics, and
`to characterize the node. The characteristics that are input
`and displayed include the following:
`Household Income: ($30 K, $31 K—$50 K, $51 K—75 K,
`$76 K—$100 K, >$100 K
`Household size: 1, 2, 3—4, 4—6, >6
`Median household age: (25, 25—35, 36—45, 46—55, >56
`Ethnic group: Caucasian, African American, Hispanic,
`Asian-Pacific
`
`The browser-based interface also permits the subscriber
`characterization module 108 to fill in probabilistic values for
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`each of the parameters. A pull down menu may be utilized
`with increments of 0.1, and a normalization error message
`may be generated if the operator generates a series of values
`which when summed exceed one. If the operator enters
`values that do not sum to 1.0, another normalization error
`message may be generated-and displayed. As an example, if
`the operator characterizes the node as having equal prob-
`ability of the household income being in any one of the
`ranges shown above, the value that must be entered in each
`category is 0.2.
`In an SDV mode, the operator is presented with a sub-
`scriber information interface. By utilizing this interface, the
`system is capable of retrieving (based on a unique subscriber
`ID) demographic and product preference characteristics for
`each subscriberr'household. Generally, to protect privacy, the
`subscriber private information is not used in the subscriber
`ID, therefore the subscriber is not identifiable by the ID. The
`demographic and product preference characteristics may be
`stored locally or may be stored in one or more network
`databases configured to directly communicate with the AMS
`100.
`
`In an exemplary case, information for a limited number of
`subscribers may be stored and may be retrievable and
`displayable on the interface. The principal characteristics of
`the displayed subscriber information include:
`Household Income: <$30 K, $31 K—$50 K, $51 K—75 K,
`$76 K—$100 K, >$100 K
`Household size: 1, 2, 3—4, 4-6, >6
`Median household age: <25, 25—35, 36—45, 46—55, >56
`Ethnic group: Caucasian, African American, Hispanic,
`Asian-Pacific
`
`The subscriber characteristics may be determined in a
`plurality of ways including by utilizing previously described
`public and private data. These characteristics may also be
`determined based on probabilistic measures in an external
`surfstream characterization module (not shown). The surf-
`stream characterization module monitors the subscriber
`viewing habits and determines subscriber preferences by
`utilizing one or more pre-determined heuristic rules.
`The correlation module 110 correlates the ad character-
`ization vectors with the subscriberfnode characterization
`vectors to produce a demographic correlation, and also
`correlates the ad characterization vectors with the avail
`
`characterization vectors to produce an avail correlation. The
`correlation values may be calculated for each ad character-
`ization vector and the corresponding subscriben’node char-
`acterization vector, as well as for each ad characterization
`vector and the avail characterization vectors.
`In one
`implementation, the correlations are generated by multiply-
`ing corresponding elements of the vector and summing the
`result (dot product). Different correlation values are normal-
`ized such that the resulting correlation value is normalized
`to 1, with a value of 1
`indicating that
`the maximum
`correlation has been obtained.
`
`An exemplary case of a demographic correlation is illus-
`trated in FIG. 5. The calculation for the avail correlation may
`be performed similarly. The average value (sum of correla-
`tions divided by 2) of the demog