AU2008202539A1 - Method and system for managing purchase of media assets - Google Patents

Description

S&P Ref: 862738 AUSTRALIA PATENTS ACT 1990 COMPLETE SPECIFICATION FOR A STANDARD PATENT Name and Address Brandscreen Pty Ltd, of Applicant: an Australian company, ACN 123 253 958, of The Old Dairy, 31 Gore Street, Greenwich, New South Wales, 2065, Australia Actual Inventor(s): Julian Peter Nicholas, Alexander Vincent Danilo Address for Service: Spruson & Ferguson St Martins Tower Level 35 31 Market Street Sydney NSW 2000 (CCN 3710000177) Invention Title: Method and system for managing purchase of media assets The following statement is a full description of this invention, including the best method of performing it known to me/us: 5845c(1 267487_1) - 1 METHOD AND SYSTEM FOR MANAGING PURCHASE OF MEDIA ASSETS Field of the Invention The present invention relates generally to purchase and sale of media assets and, in particular, to a method and system to assist clients to make a selection between available media assets. 5 Background The field of advertising incorporates a large number of media assets in which advertising may be placed. Such media assets incorporate areas such as sub-sections of newspaper publications, sub-sections of magazine publications, television advertisements, radio advertisements, Internet web-page display areas, cell-phone targeted advertisements 10 and the like. All media assets may be categorized in terms of the expected audience that will be exposed to each given media asset. For example, a front page advertisement in a well known publication such as 'The Wall Street Journal' may be categorized as likely to have exposure to people with high disposable income, whilst an advertisement in a lesser known 15 publication such as 'Auto Mechanics Monthly' may be categorized as likely to have exposure to people with a moderate disposable income. Such categorising of media assets may be used to 'rate' the quality of a given target media. However, there may be a number of different categories into which the given media asset may be classified. In addition to subjective classifications of media assets, 20 there may exist some well specified statistical data that may be associated with the given media asset. Such statistical data may for example include the number of distinct viewings of the media asset by unique viewers within a given month.

-2 In a conventional advertising purchasing model, as would be well understood by any practitioner well versed in the purchasing of advertising target media, the process of selecting which media assts to purchase based on a customers perception of a target audience is commonly based on expert evaluation of the classification of many media assts 5 against the target audience and number of target audience individuals exposed to the target media given the customers direction of desired demographic or target categories. However, such expert evaluation is largely subjective and based on the experience of the expert. Hence, the process of quantifying customer's target categories and target audience size to an inventory of advertising media assets is performed based on experience as 10 opposed to an optimized mathematical process. Furthermore, the price of a given media asset is either fixed, subject to manual manipulation or set through an auction process by competing customers. In setting the price manually, an estimate of the anticipated level of demand of the given media asst is typically made. However, in the absence of known high-demand periods for advertising, 15 such as during the Christmas shopping season, such estimates are often incorrect, resulting in the seller (publisher) to either obtain a sub-optimal price, or for the media asset not to be sold by the time of publication. An alternate and well-known pricing model for target media may take the form of a so-called 'Dutch auction' whereby a plurality of customers engage in a time-constrained 20 bidding round whereby the highest bid offered by a customer for the target media secures the media asset being actioned. Such auction based target media purchasing systems require a large amount of customer involvement in the process of securing the media asset, and thus require an excessive time burden for the customer in securing optimal media assets.

-3 It is a deficiency of existing target media purchasing systems that a given customer must either agree to pay a given fixed price for a given media asset or choose to participate in a time-consuming auction process whereby media assets are acquired. Summary 5 It is an object of the present invention to substantially overcome, or at least ameliorate, one or more disadvantages of existing arrangements. According to a first aspect of the present disclosure, there is provided a method method of adjusting the purchase price of media asset, said method comprising the steps of: 10 generating a valuation ranking of each media asset based on specified ranking criteria; grouping said media assets based upon respective valuation rankings; determining a minimum price for each media asset; determining a time factor for each group of media assets as a function of the 15 period remaining until publication of media assets within respective groups; and adjusting the price of each media asset based upon at least said minimum price of that media asset and said time factor. According to a second aspect of the present disclosure, there is provided a method of adjusting the purchase price of media asset, said method comprising the steps of: 20 generating a valuation ranking of each media asset based on specified ranking criteria; grouping said media assets based upon respective valuation rankings; determining a minimum price for each media asset; -4 determining the percentage of media assets sold within the group to which said media asset belongs; and adjusting the price of each media asset based upon at least said minimum price and said percentage. 5 According to a third aspect of the present disclosure, there is provided a method of generating a valuation ranking of a media asset, said method comprising the steps of: selecting a plurality of individual ranking characteristics; associating a weighting to each ranking characteristic, said weighting representing an importance of said ranking characteristic; and 10 combining said plurality of individual ranking characteristics into a one dimensional measure, wherein the contribution of each individual ranking characteristic to said one-dimensional measure is weighted by associated weightings. According to another aspect of the present disclosure, there is provided an apparatus for implementing any one of the aforementioned methods. 15 According to another aspect of the present disclosure there is provided a computer program product including a computer readable medium having recorded thereon a computer program for implementing any one of the methods described above. Other aspects of the invention are also disclosed. Brief Description of the Drawings 20 One or more embodiments of the present invention will now be described with reference to the drawings, in which: Fig. 1 illustrates a typical online media exchange system within which embodiments described herein may be practiced; - 5 Fig. 2 shows a schematic flow diagram of a method of valuating a media asset in order to compile a set of statistics for that media asset; Fig. 3 shows a schematic flow diagram of a method of generating a combined ranking for a particular media asset from the set of statistics of the media asset; 5 Fig. 4 shows a schematic flow diagram of a method of adjusting the pricing of media assets; and Fig. 5 is a schematic block diagram of a general purpose computer upon which the methods of Figs. 3 and 4 may be practiced. Detailed Description 10 Fig. 1 illustrates a typical online media exchange system 170 enabling publishers 205 to sell media assets 200 to clients 105 of the online media exchange system 170. A media asset may be defined as an advertising space available for a defined time period. The online media exchange system 170 is embodied in a web server to which both clients 105 and publishers 205 connect to through computers (not illustrated) connected to the 15 online media exchange system 170 through the Internet. The media exchange system 170 trades in media asserts 200 made available by the publishers 205. Clients 105 may purchase such media assets 200, or bid for the media assets 200 through an auction process controlled by the media exchange system 170. The media exchange system 170 maintains a database containing details about the 20 clients 105, the publishers 205, and all the media assets 200 available to the media exchange system 170. The database also contains details of offers and bids 180 received for available media assets 200, and contracts 190 issued for media assets 200 that have been sold. Typically the details kept for each media asset include the publisher 205 the -6 media asset 200 belongs to, demographics of the target audience of the media asset 200, publication timing, pricing, etc. A particular client 105 of the media exchange system 170 accesses the media exchange system 170 by entering an appropriate Universal Resource Locator (URL) into 5 an Internet browser application executing in the client's computer. That action downloads various web-pages to the client's computer, with the web-pages being displayed on the display of the client's computer. The client may move from one web-page to another through selection of a hyperlink, or is taken to another web-page following a client entry, either using a pointing device or a keyboard of the client's computer. 10 The client 105 starts interaction with the media exchange system 170 by logging on through a 'log on' web-page 110, which allows the media exchange system 170 to verify the identity of the client 105, typically through a password provided by the client 105. Upon logging on, the client 105 has access to a number of further web-pages. 15 Those further web pages include a 'create offer' web-page 120, a 'review offer' web-page 130, a 'review performance' web-page 140, a 'payment' web-page 150 and a 'review account' web-page 160. The 'create offer' web-page 120 typically provides a form that allows the client 105 of the media exchange system 170 to place a bid for a particular media asset. The 20 fields in that form typically include a starting date and duration of use of the particular media asset to display an advertisement, offered price or bid, and optionally the selection of a plurality of media assets to be purchased as a group. The 'review order' web-page 130 allows the client 105 to view existing offers that have been made by the client 105 and optionally modify such existing offers prior to final -7 placement of the offers with the media exchange system 170. Similarly, offers which have failed to successfully purchase a media asset of interest may be reviewed using the 'review order' web-page 130. Failed offers may also optionally be modified prior to resubmission. The 'review performance' web-page 140 allows the client 105 to view graphs of 5 successfully placed advertisements and review statistics relating to the number of unique visitors that have accessed the media asset into which the advertisements were placed, the demographics of the visitors, the value per visit for the client (cost per unique visit) plus any other derived statistics or details relating to the placed advertisement. The 'payment' web-page 150 is presented for any successful offer for media 10 assets 200, and allows the client 105 to submit payment for media assets 200 acquired. This web-page 150 also allows the client 105 to set a budget for automatic payment, such as perhaps $1,000/month for a given set of media asset offers. The 'payment' web-page 150 is the main screen the client uses to control costs associated with an advertising budget. 15 The 'review account' web-page 160 provides a summary page containing overall statistics, history of purchases and so forth with the media exchange system 170. The publisher 205 is the owner of one or more of the media assets 200 made available to the media exchange system 170 for sale. The publisher 205 connects to the media exchange system 170 via a 'log on' web 20 page 210, after which the publisher 205 is validated by the media exchange system 170. The publisher 205 also accesses the media exchange system 170 using an Internet browser application executing in a computer and downloading various web-pages. Following verification of the publisher 205 by the media exchange system 170, the publisher 205 is provided access to a number of further web-pages for managing the -8 media assets belonging to that publisher 205. The web-pages available include a 'create bids/set price' web-page 230, a 'review/search offers' web-page 240, a 'define assets' web page 220, a 'define accept rules' web-page 250, a 'review contract performance' web-page 260, a 'review account' web-page 270 and a 'payment' web-page 280. 5 The 'define assets' web-page 220 allows the publisher 205 to specify parameters about a particular media asset 200. Such parameters may include the size of the media asset 200, date of publication, duration of publication, expected number of views, demographics of the target audience, etc. The 'create bids/set price' web-page 230 allows the publisher 205 to define 10 parameters for the sale of the media assets. Such parameters may include minimum price, duration of bidding cycle in the case where the media asset 200 is to auctioned, and so forth. The 'review/search offers' web-page 240 allows the publisher 205 to manually review any client offers for media assets 200, and select whether to accept or decline offers 15 explicitly. The 'review/search offers' web-page 240 also allows the publisher 205 to perform targeted searches amongst many offers to find a subset of offers relating to media assets 200 the publisher 205 wants extra information on. The 'define/accept rules' web-page 250 allows the publisher 205 to define automated offer acceptance rules. In order to remove the need for manual review of offers 20 to determine whether to accept or decline the respective offers, the system 170 accepts offers automatically when such offers satisfy the defined automated offer acceptance rules. The 'review contract performance' web-page 260 allows the publisher 205 to review issued contracts 190 to gather statistics and generate reports about media assets 200 -9 that have been sold on behalf of the publisher 205. In general the publisher 205 may view the reports online or have a summary exported to their computer for archiving purposes. The 'review account' web-page 270 is used by the publisher 205 to view financial information relating to the publisher's account, check subscription information, and so 5 forth. The 'payment' web-page 280 allows the publisher 205 to view payments made by clients 105 for media assets 200 that have been sold. It is also possible to track the performance of the media exchange system 170 in acting as a broker for media assets 200 owned by the publisher 205. 10 Fig. 2 shows a schematic flow diagram of a method 300 of valuating a media asset in order to compile a set of statistics for that media asset. The set of statistics allows clients 105 to 'rate' the value of a particular media asset. The method 300 of valuating the media asset starts at step 310 where it is determined whether the media asset relates to media assets on the Internet. If it is 15 determined that the media asset under consideration is an Internet based media asset, then the method 300 proceeds to step 320 where viewing statistics of the given media asset is collected. A filtering operation is then performed in step 340 in order to remove from the viewing statistics multiple viewing instances by the same individual. In the case of on-line 20 advertising, it's possible that an individual will view the same web-page containing the on line advertisement a number of times and this will be reflected in the viewing statistics. Step 340 filters such multiple viewing instances by using web-page access logging to calculate which views are replicated for the same individual. Step 340 thus 'cleans-up' the raw viewing statistics to generated statistics of unique views.

- 10 If it is determined in step 310 that the media asset under consideration is not Internet based, then viewing statistics for that media asset is collected through surveying a sample of the audience of the media asset. For example, a number of people in a given demographic area may be contacted and interviewed to ascertain their viewing habits, 5 whether they have encountered the advertising space being evaluated etc. This allows classification of media assets, such as television/radio advertising, to be performed where direct feedback can't be obtained. Method 300 continues from either step 330 or 340 to steps 350 to 410 where the collected view statistics are classified using a number of different ranking criteria. 10 Step 350 categorizes the unique view statistics into age related categories, usually in a band of ages with similar interests such as children, teenagers, young adults, etc. Step 360 categorizes the unique view statistics by industry, thereby generating a statistical distribution of the industries in which the viewers of the media asset are employed in. 15 Step 370 categorizes the unique view statistics by the geographic area from which the viewers of the media asset reside. This is useful for local advertisement targeting. Step 380 categorizes the unique view statistics by the sex of the viewers. Step 390 categorizes the unique view statistics by the income of the viewers of the media asset. 20 Step 400 categorizes the unique view statistics by the education level achieved by the viewers of the media asset. Step 410 calculates the number of views per month of the media asset.

- 11 The method 300 ends in step 420 where the set of statistics for the media asset under consideration, including the distributions resulting from the categorizations performed in steps 350 to 400 and the number of views per month, is output. Method 300 described above use a number of ranking criteria to compile the set of 5 statistics for a particular media asset, with those ranking criteria including: age, industry, geographic area, sex, income and education level associated with viewers, together with the number of views per month. However, a person in the art would recognise that a different set of ranking criteria may be used. The set of statistics generated by method 300 for each media asset may be 10 presented to the client 105 of the media exchange system 170, allowing the client 105 to manually evaluate the presented sets of statistics to choose which media assets 200 to make offers on, or purchase. However, in the case where there are many media assets 200 to choose from, it may be difficult for the client 105 to discriminate between the many media assets with similar rankings. It may also be time-consuming to review the sets of statistics 15 for a large number of media assets 200 to decide which represent the best purchasing value for the client 105. Fig. 3 shows a schematic flow diagram of a method 500 of generating a combined ranking for a particular media asset from the set of statistics of the media asset, for example compiled through method 300 described with reference to Fig. 2. The method 20 500 quantifies the plurality of ranking criteria used to classify the respective media assets in order to produce the combined ranking, which is a single one-dimensional measure. The combined ranking may then be used to rank the media assets 200 for the purposes of selecting target media assets which best match ranking criteria specified by the client 105 - 12 by application of a weighted evaluation function for the purposes of purchasing such target media assets. The method 500 may be implemented using a computer system 700, such as that shown in Fig. 5 wherein the steps of method 500 are implemented as software executable 5 within the computer system 700. The software may be stored in a computer readable medium. The software is loaded into the computer system 700 from the computer readable medium, and then executed by the computer system 700. A computer readable medium having such software or computer program recorded on it is a computer program product. The use of the computer program product in the computer system 700 preferably effects an 10 advantageous apparatus for generating a combined ranking for a particular media asset from the set of statistics of the media asset. As seen in Fig. 5, the computer system 700 is formed by a computer module 701, input devices such as a keyboard 702 and a mouse pointer device 703, and output devices including a printer 715, and a display device 714. An external Modulator-Demodulator 15 (Modem) transceiver device 716 may be used by the computer module 701 for communicating to and from a communications network 720 via a connection 721. The network 720 may be a wide-area network (WAN), such as the Internet. The computer module 701 typically includes at least one processor unit 705, and a memory unit 706. The module 701 also includes an number of input/output (I/O) 20 interfaces including a video interface 707 that couples to the video display 714, an 1/0 interface 713 for the keyboard 702 and mouse 703, and an interface 708 for the external modem 716 and printer 715. Storage devices 709 are provided and typically include a hard disk drive (HDD).

- 13 The components 705, to 713 of the computer module 701 typically communicate via an interconnected bus 704 and in a manner which results in a conventional mode of operation of the computer system 700 known to those in the relevant art. Referring again to Fig. 3, the method 500 starts in step 510 where the set of 5 statistics of the media asset is received. Step 520 then normalizes each of the ranking criteria into a fixed range. Normalization means that the ranges of possible values are scaled to fall within a fixed range, such as 0 to 100. Processing in method 500 then continues in step 540 where a weighting function is applied to each of the normalized ranking criteria to determine a ranking R(n) for each 10 ranking criterion. In other words, the distributions resulting from the classification when the ranking criterion was applied is weighted in order to calculate a single weighted ranking criterion R(n) for each of the ranking criteria. For example, the weighting function could weigh one or more classifications within the age demographic ranking criterion of the media asset with a factor of 0, thus 15 removing any contribution of those classifications to the associated weighted ranking criterion R(n), and ultimately the combined ranking. Similarly, with a specific advertising campaign that is heavily industry oriented, certain industry classifications within the industry ranking criterion could be increased, thereby increasing the contribution of the statistics associated with those industries to the associated weighted ranking criterion R(n). 20 It may for example be desired that viewing statistics resulting from kids be removed when the media asset is a publication directed to investment property. In such a case the weighting function includes a weighting factor of 0 for the age classification(s) under 18 years, and perhaps a low weighting factor for the age classification between 18 and 25 years.

- 14 The individually generated weighted ranking criteria R(n) are then combined in step 560 to generate the combined ranking FinalRank which is used as an overall discriminator for classification of the media asset. Such a combined ranking FinalRank for N ranking criteria may be generated by: 5 FinalRank = X(N)R(N) + X(N-)R(N - 1) +...+ X(1)R(1) (1) wherein R(n) are the weighted ranking criteria and X(n) are the weighing factors determining the contribution of the various weighted ranking criteria R(n) to the combined ranking FinalRank. The combined rankings FinalRank of different media assets may then be 10 compared to determine which media assets are most desirable for a specific purpose represented by the weighting functions and weighting factors X(n). Thus, the method 500 provides the ability to automate the ordering of a set of media assets based on the classification data, while using client supplied preferences, encapsulated in the weighting functions and weighting factors X(n), for the desired targeting of their advertising 15 expenditure. An example of comparing a media asset according to its set of ranking criteria against a set of client ranking criteria may be performed by assigning a scaled value to each ranking criteria and calculating the difference between the media rated and client desired rank for each classification ranking and combining the set of resultant rankings into 20 a single value representing the best match value. The operation of method 500 is further illustrated by way of example. In the example 6 six ranking criteria are used, namely 'sex', 'age', 'family status', 'profession', 'wealth' and 'education'.

- 15 In step 510 the set of statistics of the media asset is received, and in step 520 the distributions of the ranking criteria are normalized to a range 0 through 100. The 'sex' ranking criterion has classifications 'don't care', 'male' and 'female', having distributions dist(don'tcare), dist(male) and dist(female) respectively. The 5 weighting function applied to the statistics of the 'sex' ranking criterion having classifications 'don't care', 'male' and 'female', are 0, 50 and 100 respectively. Applying that weighing function to determine the ranking R(sex) for the 'sex' ranking criterion provides: R(sex) = Oxdist(don't care) + 50xdist(male) + I 00xdist(female) (2) 10 Similarly, the 'age' ranking criterion may have classifications: 2-11 years, 12-17 years, 18-24 years, 25-34 years, 35-49 years, 50-64 years and 65 and older. The weighting function applied to the 'age' classifications is 0, 17, 34, 51, 68, 85 and 100 respectively. The ranking R(age) for the 'age' ranking criterion is then: R(age) = Oxdist(2-11) + 17xdist(12-17) + 34xdist(18-24) + 51xdist(25-34) 15 + 68xdist(35-49) + 85xdist(50-64) + 100xdist(65+) (3) Upon generating a ranking R(n) for each of the ranking criteria, a combined ranking FinalRank may be derived as the sum of all the rankings R(n) weighted with respective weighting factors X(n): FinalRank = k(sex)R(sex) + X(age)R(age) + X(family status)R(family status) 20 + X(profession)R(profession) + X(wealth)R(wealth) + X(education)R(education) (4) Thus, the method 500 provides the ability to automate the ordering of a set of media assets based on a set of classification data for each media asset while using client -16 supplied preferences in the form of the weighting functions and weighting factors X(n). The media asset having the highest combined ranking FinalRank is the best match between client preferences and media assets being evaluated. However, it is not only the closeness of match between client preferences and 5 media assets being evaluated that determines the perceived value to the client. The value of media assets may be expressed as: Value(j) = (Estimated Views(j) / Price(j)) x FinalRank(j) (5) wherein Estimated Views(j) is the number of view estimated for the media assets during publication (estimated by the publisher 205), and Price(j) is the lowest sales price 10 of media assets. The respective values Value(j) of the media assets may be sorted in descending order to provide a list wherein the top entries represent the best value purchases of available media assets. Given a set of media assets that are ordered according to their respective 15 combined rankings Value, a method 600 of adjusting the pricing of media assets is next described with reference to the schematic flow diagram shown in Fig. 4. The method 600 may also be implemented using a computer system 700 shown in Fig. 5. The method 600 starts at step 610 where a set of combined rankings FinalRank for a set of media assets is received as input, with the combined rankings FinalRank being 20 generated by the method 500 described above with reference to Fig. 3. Step 620 follows where all the media assets are subdivided into groups according to the difference between the current time and projected publication of the respective media assets. Preferably the groups are similarly sized.

- 17 For example, in the case where the media assets have publication times between one day and 100 days, each asset may be assigned to a group in which the publication time is one day from the present time, two days from the present time, and so forth up to the 100 day from the present time. The distribution of media assets within the groups is dependent 5 on the distribution of time until publication for each media asset. Given the groupings according to time until publication, in step 625 the media assets within each grouping are arranged according to descending combined rankings FinalRank. Step 630 then retrieves the minimum selling price for each media asset. The 10 minimum selling price of the respective media assets is recorded in the database of the media exchange system 170 described with reference to Fig. 1. Given the minimum price for media asset i is $x, a maximum price for each media asset is also determined in step 630 as follows: Max Price(i) = x + (MaxMarkup x x) (6) 15 wherein MaxMarkup is a predetermined variable establishing the relationship between the minimum and maximum selling prices. Step 640 follows where a time factor T is calculated for each group of the media assets, with the time factor T being dependent upon the time remaining until publication, as follows: T(remain) = (Time(end) - Time(now)) / Time(duration) (7) 20 wherein Time(end) is the latest publication time for the media assets within the group, Time(now) is the current time, and Time(duration) is the time duration between the first media asset to be published and the last media asset to be published within the group.

- 18 Step 650 then calculates the percentage of media assets within the group that have been sold to clients as follows: Percentage = (Number of assets sold! assets in group) (8) The method 600 may optionally use real-time feedback of sale performance to 5 adjust pricing for remaining media assets with the group. Accordingly, in step 660 it is determined whether feedback is to be used. If it is determined in step 660 that real-time feedback of sale performance is not to be used, then processing continues at step 680 where the final price of each media asset is determined as a function of the minimum price x, the time factor T, and the percentage of media assets within the group which have been 10 sold. For example, the final price of a media asset may be calculated as: Final Price = x + (markup x x) (9) wherein markup = MaxMarkup x Percentage x T(remain) (10) If it is determined in step 660 that real-time feedback of sale performance is to be 15 used, then processing continues from step 660 to step 670 where sales data is retrieved. The sales data includes the starting time of the current set of media asset sales, the timing history of each media asset sale and the rate of sale of media items in the group against the time duration available for the sale of the group of the media assets. Step 690 then determines the final price of each media asset as a function of the 20 minimum price x, the time factor T, and the percentage of media assets within the group which have been sold, time remaining for sale of the given media asset. As an example, as items within the group of available media assets are sold, the price is increased of each remaining media asset, whilst simultaneously the price is decreased as the time to - 19 publication is reduced. For example, the final price of a media asset may be calculated using Equation (9) wherein: markup = MaxMarkup x (Percentage x TimeElapsed) x ((1-Percentage) x T(remain)) (11) 5 wherein TimeElapsed is a value between 0 and 1 which represents the fraction of the time duration available which has elapsed for the sale of all media assets within the group and may be calculated as follows: TimeElapsed = I - T(remain) (12) 10 Following step 680 or 690 where the final price Final Price for each media asset is calculated, the method 600 terminates in step 695. The method 600 thus provides a mechanism to dynamically adjust the respective prices of the remaining media assets, with the prices being dependent upon at least the time remaining until publication of the media assets. 15 The foregoing describes only some embodiments of the present invention, and modifications and/or changes can be made thereto without departing from the scope and spirit of the invention, the embodiments being illustrative and not restrictive.

Claims (12)

1. A method of adjusting the purchase price of media asset, said method comprising the steps of: 5 generating a valuation ranking of each media asset based on specified ranking criteria; grouping said media assets based upon respective valuation rankings; determining a minimum price for each media asset; determining a time factor for each group of media assets as a function of the 10 period remaining until publication of media assets within respective groups; and adjusting the price of each media asset based upon at least said minimum price of that media asset and said time factor.

2. The method according to claim 1 wherein said price of each media asset is 15 further adjusted based upon the percentage of media assets sold within the group to which said media asset belongs.

3. A method of adjusting the purchase price of media asset, said method comprising the steps of: 20 generating a valuation ranking of each media asset based on specified ranking criteria; grouping said media assets based upon respective valuation rankings; determining a minimum price for each media asset; -21 determining the percentage of media assets sold within the group to which said media asset belongs; and adjusting the price of each media asset based upon at least said minimum price and said percentage. 5

4. A method of generating a valuation ranking of a media asset, said method comprising the steps of: selecting a plurality of individual ranking characteristics; associating a weighting to each ranking characteristic, said weighting representing 10 an importance of said ranking characteristic; and combining said plurality of individual ranking characteristics into a one dimensional measure, wherein the contribution of each individual ranking characteristic to said one-dimensional measure is weighted by associated weightings. 15

5. The method according to claim 4 wherein said weightings include a first weighting with respect to ranking characteristics from a same ranking criterion, and a second weighting with respect to ranking characteristics from different ranking criteria.

6. The method according to claim 4 or 5 wherein said method comprises the 20 further step of: calculating a one-dimensional value measure as a function of said measure, an estimate of the number of views of said media asset, and the price of said media asset. -22

7. Apparatus for adjusting the purchase price of media asset, said apparatus comprising: means for generating a valuation ranking of each media asset based on specified ranking criteria; 5 means for grouping said media assets based upon respective valuation rankings; means for determining a minimum price for each media asset; means for determining a time factor for each group of media assets as a function of the period remaining until publication of media assets within respective groups; and means for adjusting the price of each media asset based upon at least said 10 minimum price of that media asset and said time factor.

8. Apparatus for adjusting the purchase price of media asset, said apparatus comprising: means for generating a valuation ranking of each media asset based on specified 15 ranking criteria; means for grouping said media assets based upon respective valuation rankings; means for determining a minimum price for each media asset; means for determining the percentage of media assets sold within the group to which said media asset belongs; and 20 means for adjusting the price of each media asset based upon at least said minimum price and said percentage.

9. Apparatus for generating a valuation ranking of a media asset, said apparatus comprising: - 23 means for selecting a plurality of individual ranking characteristics; means for associating a weighting to each ranking characteristic, said weighting representing an importance of said ranking characteristic; and means for combining said plurality of individual ranking characteristics into a 5 one-dimensional measure, wherein the contribution of each individual ranking characteristic to said one-dimensional measure is weighted by associated weightings.

10. A computer program product including a computer readable medium having recorded thereon a computer program for implementing any one of the method 10 defined in claims I to 6.

11. A method of adjusting the purchase price of media asset, said method being substantially as described herein with reference to the accompanying drawings. 15

12. A method of generating a valuation ranking of a media asset, said method being substantially as described herein with reference to the accompanying drawings. DATED this Sixth Day of June, 2008 BRANDSCREEN PTY LTD 20 Patent Attorneys for the Applicant SPRUSON&FERGUSON