Abstract:
Computer network method and apparatus provides targeting of appropriate audience based on psychographic or behavioral profiles of end users. The psychographic profile is formed by recording computer activity and viewing habits of the end user. Content of categories of interest and display format in each category are revealed by the psychographic profile, based on user viewing of agate information. Using the profile (with or without additional user demographics), advertisements are displayed to appropriately selected users. Based on regression analysis of recorded responses of a first set of users viewing the advertisements, the target user profile is refined. Viewing by and regression analysis of recorded responses of subsequent sets of users continually auto-targets and customizes ads for the optimal end user audience.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
   This application claims the benefit of International Application No. PCT/US99/28628, filed Dec. 2, 1999, entitled Consumer Profiling and Advertisement Selection System, which claims the benefit of co-pending U.S. patent application Ser. No. 09/204,888, filed Dec. 3, 1998, entitled Subscriber Characterization System; U.S. patent application Ser. No. 09/268,526, filed Mar. 12, 1999, entitled Advertisement Selection System Supporting Discretionary Target Market Characteristics, now U.S. Pat. No. 6,216,129; and U.S. patent application Ser. No. 09/268,519, filed Mar. 12, 1999, entitled Consumer Profiling System, now U.S. Pat. No. 6,298,348. 
   BACKGROUND OF THE INVENTION 
   The advent of the Internet has resulted in the ability to communicate data across the globe instantaneously, and will allow for numerous new applications which enhance consumer&#39;s lives. One of the enhancements which can occur is the ability for the consumer to receive advertising which is relevant to their lifestyle, rather than a stream of ads determined by the program they are watching. Such “targeted ads” can potentially reduce the amount of unwanted information which consumers receive in the mail, during television programs, and when using the Internet. Examples of editorial targeting can be found on the World Wide Web, where banners are delivered based on the page content. The product literature from DoubleClick, “Dynamic Advertising Reporting and Targeting (DART), printed from the World Wide Web site http://www.doubleclick.net/dart on Jun. 19, 1998 discloses DoubleClick&#39;s advertising solution for matching advertiser&#39;s selected targeted profiles with individual user profiles and deliver an appropriate banner. The user and advertisements are matched based on geographic location or keywords on the page content. The product literature from Imgis, “Ad Force,” printed from the World Wide Web site http://www.starpt.com/core on Jun. 30, 1998 discloses an ad management system for targeting users and delivering advertisements to them. Users are targeted based on the type of content they are viewing or by keywords. 
   From an advertiser&#39;s perspective the ability to target ads can be beneficial since they have some confidence that their ad will at least be determined relevant by the consumer, and therefore will not be found annoying because it is not applicable to their lifestyle. Different systems for matching a consumer profile to an advertisement have been proposed such as the U.S. Pat. No. 5,774,170, which discloses a system for delivering targeted advertisement to consumers. In this system, a set of advertisements is tagged with commercial identifier (CID) and, from the existing marketing database, a list of prospective viewers is also identified with CID. The commercials are displayed to the consumers when the CIDs match. 
   Other systems propose methods for delivering programming tailored to subscribers&#39; profile. U.S. Pat. No. 5,446,919 discloses a communication system capable of targeting a demographically or psychographically defined audience. Demographic and psychographic information about audience member are downloaded and stored in the audience member receiver. Media messages are transmitted to audience member along with a selection profile command, which details the demographic/psychographic profile of audience members that are to receive each media message. Audience members which fall within a group identified by the selection profile command are presented with the media message. 
   U.S. Pat. No. 5,223,924 discloses a system and method for automatically correlating user preferences with a TV program information database. The system includes a processor that performs “free text” search techniques to correlate the downloaded TV program information with the viewer&#39;s preferences. U.S. Pat. No. 5,410,344 discloses a method for selecting audiovideo programs based on viewers&#39; preferences, wherein each of the audiovideo programs has a plurality of programs attributes and a corresponding content code representing the program attributes. The method comprises the steps of storing a viewer preference file, which includes attributes ratings, which represents the degree of impact of the programs attributes on the viewer and, in response to the comparison of viewer preference file with the program content codes, a program is selected for presentation to the viewer. 
   In order to determine the applicability of an advertisement to a consumer, it is necessary to know something about their lifestyle, and in particular to understand their demographics (age, household size and income). In some instances, it is useful to know their particular purchasing habits. Purchasing habits are being used by E-commerce to profile their visitors. As an example, the product literature from Aptex software Inc., “SelectCast for Commerce Servers,” printed from the World Wide Web site http://www.aptex.com/products-selectcast-commerce.htm on Jun. 30, 1998 discloses the product SelectCast for Commerce Servers. The product personalizes online shopping based on observed user behavior. User interests are learned based on the content they browse, the promotions they click and the products they purchase. 
   Knowledge of the purchasing habits of a consumer can be beneficial to a product vendor in the sense that a vendor of soups would like to know which consumers are buying their competitor&#39;s soup, so that they can target ads at those consumers in an effort to convince them to switch brands. That vendor will probably not want to target loyal customers, although for a new product introduction the strategy may be to convince loyal customers to try the new product. In both cases it is extremely useful for the vendor to be able to determine what brand of product the consumer presently purchases. 
   There are several difficulties associated with the collection, processing, and storage of consumer data. First, collecting consumer data and determining the demographic parameters of the consumer can be difficult. Surveys can be performed, and in some instances the consumer will willingly give access to normally private data including family size, age of family members, and household income. In such circumstances there generally needs to be an agreement with the consumer regarding how the data will be used. If the consumer does not provide this data directly, the information must be “mined” from various pieces of information which are gathered about the consumer, typically from specific purchases. 
   A relatively intrusive method for collecting consumer information is described in U.S. Pat. No. 4,546,382, which discloses a television and market research data collection system and method. A data collection unit containing a memory, stores data as to which of the plurality of TV modes are in use, which TV channel is being viewed as well as input from a suitable optical scanning device for collecting consumer product purchases. 
   Once data is collected, usually from one source, some type of processing can be performed to determine a particular aspect of the consumer&#39;s life. As an example, processing can be performed on credit data to determine which consumers are a good credit risk and have recently applied for credit. The resulting list of consumers can be solicited, typically by direct mail. Although information such as credit history is stored on multiple databases, storage of other information such as the specifics of grocery purchases is not typically performed. Even if each individual&#39;s detailed list of grocery purchases was recorded, the information would be of little use since it would amount to nothing more than unprocessed shopping lists. 
   Privacy concerns are also an important factor in using consumer purchase information. Consumers will generally find it desirable that advertisements and other information is matched with their interests, but will not allow indiscriminate access to their demographic profile and purchase records. 
   The Internet has spawned the concept of “negatively priced information” in which consumers can be paid to receive advertising. Paying consumers to watch advertisements can be accomplished interactively over the Internet, with the consumer acknowledging that they will watch an advertisement for a particular price. Previously proposed schemes such as that described in U.S. Pat. No. 5,794,210, entitled “Attention Brokerage,” of which A. Nathaniel Goldhaber and Gary Fitts are the inventors, describe such a system, in which the consumer is presented with a list of advertisements and their corresponding payments. The consumer chooses from the list and is compensated for viewing the advertisement. The system uses also software agents representing consumers to match the consumer interest profiles with advertisements. The matching is done using “relevance indexing” which is based on hierarchical tree structures. The system requires real-time interactivity in that the viewer must select the advertisement from the list of choices presented. 
   The ability to place ads to consumers and compensate them for viewing the advertisements opens many possibilities for new models of advertising. However, it is important to understand the demographics and product preferences of the consumer in order to be able to determine if an advertisement is appropriate. 
   Although it is possible to collect statistical information regarding consumers of particular products and compare those profiles against individual demographic data points of consumers, such a methodology only allows for selection of potential consumers based on the demographics of existing customers of the same or similar products. 
   U.S. Pat. No. 5,515,098, entitled “System and method for selectively distributing commercial messages over a communications network,” of which John B. Carles is the inventor, describes a method in which target household data of actual customers of a product are compared against subscriber household data to determine the applicability of a commercial to a household. Target households for a product or service are characterized by comparing or correlating the profile of the customer household to the profile of all households. A rating is established for each household for each category of goods/services. The households within a predefined percentile of subscribers, as defined by the rating, are targeted by the advertiser of the product or service. 
   It will also frequently be desirable to target an advertisement to a market having discretionary characteristics and to obtain a measure of the correlation of these discretionary features with probabilistic or deterministic data of the consumer/subscriber, rather than being forced to rely on the characteristics of existing consumers of a product. Such correlation should be possible based both on demographic characteristics and product preferences. 
   Another previously proposed system, described in U.S. Pat. No. 5,724,521, entitled “Method and apparatus for providing electronic advertisements to end users in a consumer best-fit pricing manner,” of which R. Dedrick is the inventor, utilizes a consumer scale as the mechanism to determine to which group an advertisement is intended. A consumer scale matching process compares the set of characteristics stored in a user profile database to a consumer scale associated with the electronic advertisement. The fee charged to the advertiser is determined by where the set of characteristics fall on the consumer scale. Such a system requires specification of numerous parameters and weighting factors, and requires access to specific and non-statistical personal profile information. 
   For the foregoing reasons, there is a need for a consumer profiling system which can profile the consumer, provide access to the consumer profile in a secure manner, and return a measurement of the potential applicability of an advertisement. There is also a need for an advertisement selection system which can match an advertisement with discretionary target market characteristics, and which can do so in a manner which protects the privacy of the consumer data and characterizations. 
   SUMMARY OF THE INVENTION 
   The present invention supports the receipt of consumer purchase information with which consumer characterization vectors are updated based on product characterization information. The consumer characterization vectors include a consumer demographic vector which provides a probabilistic measure of the demographics of the consumer, and a product preference vector which describes which products the consumer has typically purchased in the past, and therefore is likely to purchase in the future. The product characterization information includes vector information which represents probabilistic determinations of the demographics of purchasers of an item, heuristic rules which can be applied to probabilistically describe the demographics of the consumer based on that purchase, and a vector representation of the purchase itself. 
   In a preferred embodiment a computer-readable detailed purchase record is received, along with a unique consumer identifier. A demographic characterization vector corresponding to the consumer can be retrieved. In the event that there is no existing demographic characterization vector for that consumer, a new demographic characterization vector can be created. In a preferred embodiment the new demographic characterization vector contains no information. A set of heuristic rules is retrieved and contains a probabilistic measure of the demographic characteristics of a typical purchaser of an item. A new demographic characterization vector is calculated based on the purchase, the existing demographic characterization vector, and the heuristic rules. 
   In a preferred embodiment the calculation of the demographic characterization vector is performed by calculating a weighted average of a product demographics vector and the existing demographic characterization vector. A weighting factor is used in which the weighting factor is determined based on the ratio of the current product purchase amount to a cumulative product purchase amount. The cumulative product purchase amount can be measured as the amount spent on a particular category of items (e.g. groceries, clothes, accessories) over a given period of time such as one month or one year. 
   In a preferred embodiment the heuristic rules are in the form of a product demographics vector which states the demographics of known purchasers of an item. Each product can have an associated product demographics vector. 
   The present invention can be used to develop product preference descriptions of consumers which describe the brand and size product that they purchase, and which provide a probabilistic interpretation of the products they are likely to buy in the future. The product preference description can be generated by creating a weighted average of an existing product preference vector describing the consumer&#39;s historical product preferences (type of product, brand, and size) and the characteristics of recent purchases. 
   The present invention can be realized as a data processing system or computer program which processes consumer purchase records and updates their demographic and product preference profiles based on the use of product characterization information. The data processing system can also be used to receive information regarding an advertisement and to perform a correlation between the advertisement and the consumer&#39;s demographic and product preferences. 
   The present invention can be realized as software resident on one or more computers. The system can be realized on an individual computer which receives information regarding consumer purchases, or can be realized on a network of computers in which portions of the system are resident on different computers. 
   One advantage of the present invention is that it allows consumer profiles to be updated automatically based on their purchases, and forms a description of the consumer including demographic characteristics and product preferences. This description can be used by advertisers to determine the suitability of advertisements to the consumer. Consumers benefit from the system since they will receive advertisements which are more likely to be applicable to them. 
   The present invention can be used to profile consumers to support the correlation of an advertisement characterization vector associated with an advertisement with the consumer characterization vector to determine the applicability of the advertisement to the consumer. 
   Another feature of the present invention is the ability to price access to the consumer based on the degree of correlation of an advertisement with their profile. If an advertisement is found to be very highly correlated with a consumer&#39;s demographics and product preferences, a relatively high price can be charged for transmitting the advertisement to the consumer. From the consumer&#39;s perspective, if the correlation between the advertisement and the consumer&#39;s demographics or product preferences is high the consumer can charge less to view the ad, since it is likely that is will be of interest. 
   The present invention also describes a system for determining the applicability of an advertisement to a consumer, based on the reception of an ad characterization vector and use of a unique consumer ID. The consumer ID is used to retrieve a consumer characterization vector, and the correlation between the consumer characterization vector and the ad characterization vector is used to determine the applicability of the advertisement to the consumer. The price to be paid for presentation of the advertisement can be determined based on the degree of correlation. 
   The price to present an advertisement can increase with correlation, as may be typical when the content/opportunity provider is also the profiling entity. The price can decrease with correlation when the consumer is the profiler, and is interested in, and willing to charge less for seeing advertisements which are highly correlated with their demographics, lifestyle, and product preferences. 
   The present invention can be used to specify purchasers of a specific product. In a preferred embodiment the advertisement characterization vector contains a description of a target market including an indicator of a target product, i.e., purchasers of a particular product type, brand, or product size. The advertisement characterization vector is correlated with a consumer characterization vector which is retrieved based on a unique consumer ID. The correlation factor is determined and indicates if the consumer is a purchaser of the product the advertisement is intended for. This feature can be used to identify purchasers of a particular brand and can be used to target ads at those consumers to lure them away from their present product provider. Similarly, this feature can be used to target ads to loyal consumers to introduce them to a new product in a product family, or different size of product. 
   One advantage of the present invention is that discretionary target market parameters can be specified and do not necessarily need to correspond to an existing market, but can reflect the various market segments for which the advertisement is targeted. The market segments can be designated by demographic characteristics or by product preferences. 
   Another advantage of the present invention is that demographic samples of present purchasers of a product are not required to define the target market. 
   The present invention can be used to determine the applicability of an advertisement to a consumer based on demographics, product preferences, or a combination of both. 
   In a preferred embodiment of the present invention the correlation is calculated as the scalar product of the ad characterization vector and the consumer characterization vector. The ad characterization vector and consumer characterization vector can be composed of demographic characteristics, product purchase characteristics, or a combination of both. 
   In a preferred embodiment pricing for the displaying of said advertisement is developed based on the result of the correlation between the ad characterization vector and the consumer characterization vector. In a first embodiment the pricing increases as a function of the correlation. This embodiment can represent the situation in which the party which determines the correlation also controls the ability to display the advertisement. 
   In an alternate embodiment the price for displaying the advertisement decreases as a function of the degree of correlation. This embodiment can represent the situation in which the consumer controls access to the consumer characterization vector, and charges less to view advertisements which are highly correlated with their interests and demographics. A feature of this embodiment is the ability of the consumer to decrease the number of unwanted advertisements by charging a higher price to view advertisements which are likely to be of less interest. 
   One advantage of the present invention is that it allows advertisements to be directed to new markets by setting specific parameters in the ad characterization vector, and does not require specific statistical knowledge regarding existing customers of similar products. Another advantage is that the system allows ads to be directed at consumers of a competing brand, or specific targeting at loyal customers. This feature can be useful for the introduction of a new product to an existing customer base. 
   Another advantage of the present invention is that the correlation can be performed by calculating a simple scalar (dot) product of the ad characterization and consumer characterization vectors. A weighted sum or other statistical analysis is not required to determine the applicability of the advertisement. 
   The present invention can be realized as a data processing system and as a computer program. The invention can be realized on an individual computer or can be realized using distributed computers with portions of the system operating on various computers. 
   An advantage of the present invention is the ability to direct advertisements to consumers which will find the advertisements of interest. This eliminates unwanted advertisements. Another advantage is the ability of advertisers to target specific groups of potential customers. 
   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 embodiments of the present invention and, together with the description serve to explain the principles of the invention. 
     In the drawings: 
       FIGS. 1A and 1B  show user relationship diagrams for the present invention; 
       FIGS. 2A ,  2 B,  2 C and  2 D illustrate a probabilistic consumer demographic characterization vector, a deterministic consumer demographic characterization vector, a consumer product preference characterization vector, and a storage structure for consumer characterization vectors respectively; 
       FIGS. 3A and 3B  illustrate an advertisement demographic characterization vector and an advertisement product preference characterization vector respectively; 
       FIG. 4  illustrates a computer system on which the present invention can be realized; 
       FIG. 5  illustrates a context diagram for the present invention; 
       FIGS. 6A and 6B  illustrate pseudocode updating the characteristics vectors and for a correlation operation respectively; 
       FIG. 7  illustrates heuristic rules; 
       FIGS. 8A and 8B  illustrate flowcharts for updating consumer characterization vectors and a correlation operation respectively; and 
       FIG. 9  represents pricing as a function of correlation. 
       FIG. 10  illustrates a representation of a consumer characterization as a set of basis vectors and an ad characterization vector. 
   

   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 apparatus of the present invention is disclosed. 
     FIG. 1A  shows a user relationship diagram which illustrates the relationships between a consumer profiling system and various entities. As can be seen in  FIG. 1 , a consumer  100  can receive information and advertisements from a consumer personal computer (PC)  104 , displayed on a television  108  which is connected to a settop  106 , or can receive a mailed ad  182 . 
   Advertisements and information displayed on consumer PC  104  or television  108  can be received over an Internet  150 , or can be received over the combination of the Internet  150  with another telecommunications access system. The telecommunications access system can include but is not limited to cable TV delivery systems, switched digital video access systems operating over telephone wires, microwave telecommunications systems, or any other medium which provides connectivity between the consumer  100  and a content server  162  and ad server  146 . 
   A content/opportunity provider  160  maintains the content server  162  which can transmit content including broadcast programming across a network such as the Internet  150 . Other methods of data transport can be used including private data networks and can connect the content sever  160  through an access system to a device owned by consumer  100 . 
   Content/opportunity provider  160  is termed such since if consumer  100  is receiving a transmission from content server  162 , the content/opportunity provider can insert an advertisement. For video programming, content/opportunity provider is typically the cable network operator or the source of entertainment material, and the opportunity is the ability to transmit an advertisement during a commercial break. 
   The majority of content that is being transmitted today is done so in broadcast form, such as broadcast television programming (broadcast over the air and via cable TV networks), broadcast radio, and newspapers. Although the interconnectivity provided by the Internet will allow consumer specific programming to be transmitted, there will still be a large amount of broadcast material which can be sponsored in part by advertising. The ability to insert an advertisement in a broadcast stream (video, audio, or mailed) is an opportunity for advertiser  144 . Content can also be broadcast over the Internet and combined with existing video services, in which case opportunities for the insertion of advertisements will be present. 
   Although  FIG. 1A  represents content/opportunity provider  160  and content server  162  as being independently connected to Internet  150 , with the consumer&#39;s devices being also being directly connected to the Internet  150 , the content/opportunity provider  160  can also control access to the subscriber. This can occur when the content/opportunity provider is also the cable operator or telephone company. In such instances, the cable operator or telephone company can be providing content to consumer  100  over the cable operator/telephone company access network. As an example, if the cable operator has control over the content being transmitted to the consumer  100 , and has programmed times for the insertion of advertisements, the cable operator is considered to be a content/opportunity provider  160  since the cable operator can provide advertisers the opportunity to access consumer  100  by inserting an advertisement at the commercial break. 
   In a preferred embodiment of the present invention, a pricing policy can be defined. The content/opportunity provider  160  can charge advertiser  144  for access to consumer  100  during an opportunity. In a preferred embodiment the price charged for access to consumer  100  by content/opportunity provider varies as a function of the applicability of the advertisement to consumer  100 . In an alternate embodiment consumer  100  retains control of access to the profile and charges for viewing an advertisement. 
   The content provider can also be a mailing company or printer which is preparing printed information for consumer  100 . As an example, content server  162  can be connected to a printer  164  which creates a mailed ad  182  for consumer  100 . Alternatively, printer  164  can produce advertisements for insertion into newspapers which are delivered to consumer  100 . Other printed material can be generated by printer  162  and delivered to consumer  100  in a variety of ways. 
   Advertiser  144  maintains an ad server  146  which contains a variety of advertisements in the form of still video which can be printed, video advertisements, audio advertisements, or combinations thereof. 
   Profiler  140  maintains a consumer profile server  130  which contains the characterization of consumer  100 . The consumer profiling system is operated by profiler  140 , who can use consumer profile server  130  or another computing device connected to consumer profile server  130  to profile consumer  100 . 
   Data to perform the consumer profiling is received from a point of purchase  110 . Point of purchase  110  can be a grocery store, department store, other retail outlet, or can be a web site or other location where a purchase request is received and processed. In a preferred embodiment, data from the point of purchase is transferred over a public or private network  120 , such as a local area network within a store or a wide area network which connects a number of department or grocery stores. In an alternate embodiment the data from point of purchase  110  is transmitted over the Internet  150  to profiler  140 . 
   Profiler  140  may be a retailer who collects data from its stores, but can also be a third party who contracts with consumer  100  and the retailer to receive point of purchase data and profile consumer  100 . Consumer  100  may agree to such an arrangement based on the increased convenience offered by targeted ads, or through a compensation arrangement in which they are paid on a periodic basis for revealing their specific purchase records. 
   Consumer profile server  130  can contain a consumer profile which is determined from observation of the consumer&#39;s viewing habits on television  108  or consumer PC  104 . Determination of demographic and product preference information based on the consumer&#39;s use of services such as cable television and Internet access can be performed by monitoring the channel selections that a subscriber makes, and determining household demographics based on the subscriber selections and information associated with the programming being watched. 
   In one embodiment the channel selections are recorded, and based on the time of day during which the programming is watched and duration of viewing, heuristic rules are applied to make probabilistic determinations regarding the household demographics including age, gender, household size and income, as illustrated in  FIG. 2A . This can be accomplished by applying heuristic rules which associate the programming with known and assumed characteristics for viewers of the programming. As an example, it is known that the probability that the viewer of a cartoon in the morning is in the 3–8 year old age group is high, thus if the household viewing habits consistently record viewing of cartoons the probability that the household will contain one or more viewers in the 3–8 year old age group is high. 
   In one embodiment information regarding the program is extracted from the Electronic Program Guide (EPG) which contains information regarding the scheduled programming. In another embodiment information regarding the programming is retrieved from the closed caption channel transmitted in the broadcast signal. 
   The volume at which the program is watched can also be stored and forms an additional basis for subscriber characterization, wherein the muting of a channel indicates limited interest in a particular program or advertisement. In the case of an advertisement, muting of the advertisement can be used as a measure of the effectiveness (or ineffectiveness) of the advertisement and can serve as part of the basis for the subscriber characterization. The muting of a program, as well as the duration for which the program is watched, can also be used in the determination of the subscriber characterization vector. 
   By processing the recorded viewing habits in conjunction with programming related information and heuristic rules similar to those illustrated in  FIG. 7  but related to programming rather than purchases, it is possible to construct a subscriber characterization vector which contains a probabilistic demographic profile of the household. 
   When used herein, the term consumer characterization vector also represents the subscriber characterization vector previously described. Both the consumer characterization vector and the subscriber characterization vector contain demographic and product preference information which is related to consumer  100 . 
     FIG. 1B  illustrates an alternate embodiment of the present invention in which the consumer  100  is also profiler  140 . Consumer  100  maintains consumer profile server  130  which is connected to a network, either directly or through consumer PC  104  or settop  106 . Consumer profile server  130  can contain the consumer profiling system, or the profiling can be performed in conjunction with consumer PC  104  or settop  106 . A subscriber characterization system which monitors the viewing habits of consumer  100  can be used in conjunction with the consumer profiling system to create a more accurate consumer profile. 
   When the consumer  100  is also the profiler  140 , as shown in  FIG. 1B , access to the consumer demographic and product preference characterization is controlled exclusively by consumer  100 , who will grant access to the profile in return for receiving an increased accuracy of ads, for cash compensation, or in return for discounts or coupons on goods and services. 
     FIG. 2A  illustrates an example of a probabilistic demographic characterization vector. The demographic characterization vector is a representation of the probability that a consumer falls within a certain demographic category such as an age group, gender, household size, or income range. 
   In a preferred embodiment the demographic characterization vector includes interest categories. The interest categories may be organized according to broad areas such as music, travel, and restaurants. Examples of music interest categories include country music, rock, classical, and folk. Examples of travel categories include “travels to another state more than twice a year,” and travels by plane more than twice a year.” 
     FIG. 2B  illustrates a deterministic demographic characterization vector. The deterministic demographic characterization vector is a representation of the consumer profile as determined from deterministic rather than probabilistic data. As an example, if consumer  100  agrees to answer specific questions regarding age, gender, household size, income, and interests the data contained in the consumer characterization vector will be deterministic. 
   As with probabilistic demographic characterization vectors, the deterministic demographic characterization vector can include interest categories. In a preferred embodiment, consumer  100  answers specific questions in a survey generated by profiler  140  and administered over the phone, in written form, or via the Internet  150  and consumer PC  104 . The survey questions correspond either directly to the elements in the probabilistic demographic characterization vector, or can be processed to obtain the deterministic results for storage in the demographic characterization vector. 
     FIG. 2C  illustrates a product preference vector. The product preference represents the average of the consumer preferences over past purchases. As an example, a consumer who buys the breakfast cereal manufactured by Post under the trademark ALPHABITS about twice as often as purchasing the breakfast cereal manufactured by Kellogg under the trademark CORN FLAKES, but who never purchases breakfast cereal manufactured by General Mills under the trademark WHEATIES, would have a product preference characterization such as that illustrated in  FIG. 2C . As shown in  FIG. 2C , the preferred size of the consumer purchase of a particular product type can also be represented in the product preference vector. 
     FIG. 2D  represents a data structure for storing the consumer profile, which can be comprised of a consumer ID field  237 , a deterministic demographic data field  239 , a probabilistic demographic data field  241 , and one or more product preference data fields  243 . As shown in  FIG. 2D , the product preference data field  243  can be comprised of multiple fields arranged by product categories  253 . 
   Depending on the data structure used to store the information contained in the vector, any of the previously mentioned vectors may be in the form of a table, record, linked tables in a relational database, series of records, or a software object. 
   The consumer ID  512  can be any identification value uniquely associated with consumer  100 . In a preferred embodiment consumer ID  512  is a telephone number, while in an alternate embodiment consumer ID  512  is a credit card number. Other unique identifiers include consumer name with middle initial or a unique alphanumeric sequence, the consumer address, social security number. 
   The vectors described and represented in  FIGS. 2A–C  form consumer characterization vectors that can be of varying length and dimension, and portions of the characterization vector can be used individually. Vectors can also be concatenated or summed to produce longer vectors which provide a more detailed profile of consumer  100 . A matrix representation of the vectors can be used, in which specific elements, such a product categories  253 , are indexed. Hierarchical structures can be employed to organize the vectors and to allow hierarchical search algorithms to be used to locate specific portions of vectors. 
     FIGS. 3A and 3B  represent an ad demographics vector and an ad product preference vector respectively. The ad demographics vector, similar in structure to the demographic characterization vector, is used to target the ad by setting the demographic parameters in the ad demographics vector to correspond to the targeted demographic group. As an example, if an advertisement is developed for a market which is the 18–24 and 24–32 age brackets, no gender bias, with a typical household size of 2–5, and income typically in the range of $20,000-$50,000, the ad demographics vector would resemble the one shown in  FIG. 3A . The ad demographics vector represents a statistical estimate of who the ad is intended for, based on the advertisers belief that the ad will be beneficial to the manufacturer when viewed by individuals in those groups. The benefit will typically be in the form of increased sales of a product or increased brand recognition. As an example, an “image ad” which simply shows an artistic composition but which does not directly sell a product may be very effective for young people, but may be annoying to older individuals. The ad demographics vector can be used to establish the criteria which will direct the ad to the demographic group of 18–24 year olds. 
     FIG. 3B  illustrates an ad product preference vector. The ad product preference vector is used to select consumers which have a particular product preference. In the example illustrated in  FIG. 3B , the ad product preference vector is set so that the ad can be directed a purchasers of ALPHABITS and WHEATIES, but not at purchasers of CORN FLAKES. This particular setting would be useful when the advertiser represents Kellogg and is charged with increasing sales of CORN FLAKES. By targeting present purchasers of ALPHABITS and WHEATIES, the advertiser can attempt to sway those purchasers over to the Kellogg brand and in particular convince them to purchase CORN FLAKES. Given that there will be a payment required to present the advertisement, in the form of a payment to the content/opportunity provider  160  or to the consumer  100 , the advertiser  144  desires to target the ad and thereby increase its cost effectiveness. 
   In the event that advertiser  144  wants to reach only the purchasers of Kellogg&#39;s CORN FLAKES, that category would be set at a high value, and in the example shown would be set to 1. As shown in  FIG. 3B , product size can also be specified. If there is no preference to size category the values can all be set to be equal. In a preferred embodiment the values of each characteristic including brand and size are individually normalized. 
   Because advertisements can be targeted based on a set of demographic and product preference considerations which may not be representative of any particular group of present consumers of the product, the ad characterization vector can be set to identify a number of demographic groups which would normally be considered to be uncorrelated. Because the ad characterization vector can have target profiles which are not representative of actual consumers of the product, the ad characterization vector can be considered to have discretionary elements. When used herein the term discretionary refers to a selection of target market characteristics which need not be representative of an actual existing market or single purchasing segment. 
   In a preferred embodiment the consumer characterization vectors shown in  FIGS. 2A–C  and the ad characterization vectors represented in  FIGS. 3A and 3B  have a standardized format, in which each demographic characteristic and product preference is identified by an indexed position. In a preferred embodiment the vectors are singly indexed and thus represent coordinates in n-dimensional space, with each dimension representing a demographic or product preference characteristic. In this embodiment a single value represents one probabilistic or deterministic value (e.g. the probability that the consumer is in the 18–24 year old age group, or the weighting of an advertisement to the age group). 
   In an alternate embodiment a group of demographic or product characteristics forms an individual vector. As an example, age categories can be considered a vector, with each component of the vector representing the probability that the consumer is in that age group. In this embodiment each vector can be considered to be a basis vector for the description of the consumer or the target ad. The consumer or ad characterization is comprised of a finite set of vectors in a the vector space that describes the consumer or advertisement. 
     FIG. 4  shows the block diagram of a computer system for a realization of the consumer profiling system. A system bus  422  transports data amongst the CPU  203 , the RAM  204 , Read Only Memory-Basic Input Output System (ROM-BIOS)  406  and other components. The CPU  203  accesses a hard drive  400  through a disk controller  402 . The standard input/output devices are connected to the system bus  422  through the I/O controller  201 . A keyboard is attached to the I/O controller  201  through a keyboard port  416  and the monitor is connected through a monitor port  418 . The serial port device uses a serial port  420  to communicate with the I/O controller  201 . Industry Standard Architecture (ISA) expansion slots  408  and Peripheral Component Interconnect (PCI) expansion slots  410  allow additional cards to be placed into the computer. In a preferred embodiment, a network card is available to interface a local area, wide area, or other network. The computer system shown in  FIG. 4  can be part of consumer profile server  130 , or can be a processor present in another element of the network. 
     FIG. 5  shows a context diagram for the present invention. Context diagrams are useful in illustrating the relationship between a system and external entities. Context diagrams can be especially useful in developing object oriented implementations of a system, although use of a context diagram does not limit implementation of the present invention to any particular programming language. The present invention can be realized in a variety of programming languages including but not limited to C, C++, Smalltalk, Java, Perl, and can be developed as part of a relational database. Other languages and data structures can be utilized to realize the present invention and are known to those skilled in the art. 
   Referring to  FIG. 5 , in a preferred embodiment consumer profiling system  500  is resident on consumer profile server  130 . Point of purchase records  510  are transmitted from point of purchase  110  and stored on consumer profile server  130 . Heuristic rules records  530 , pricing policy  570 , and consumer profile  560  are similarly stored on consumer profile server  130 . In a preferred embodiment advertisement records  540  are stored on ad server  146  and connectivity between advertisement records  540  and consumer profiling system  500  is via the Internet or other network. 
   In an alternate embodiment the entities represented in  FIG. 5  are located on servers which are interconnect via the Internet or other network. 
   Consumer profiling system  500  receives purchase information from a point of purchase, as represented by point of purchase records  510 . The information contained within the point of purchase records  510  includes a consumer ID  512 , a product ID  514  of the purchased product, the quantity  516  purchased and the price  518  of the product. In a preferred embodiment, the date and time of purchase  520  are transmitted by point of purchase records  510  to consumer profiling system  500 . 
   The consumer profiling system  500  can access the consumer profile  560  to update the profiles contained in it. Consumer profiling system  500  retrieves a consumer characterization vector  562  and a product preference vector  564 . Subsequent to retrieval one or more data processing algorithms are applied to update the vectors. An algorithm for updating is illustrated in the flowchart in  FIG. 8A . The updated vectors termed herein as new demographic characterization vector  566  and new product preference  568  are returned to consumer profile  560  for storage. 
   Consumer profiling system  500  can determine probabilistic consumer demographic characteristics based on product purchases by applying heuristic rules  519 . Consumer profiling system  500  provides a product ID  514  to heuristic rules records  530  and receives heuristic rules associated with that product. Examples of heuristic rules are illustrated in  FIG. 7 . 
   In a preferred embodiment of the present invention, consumer profiling system  500  can determine the applicability of an advertisement to the consumer  100 . For determination of the applicability of an advertisement, a correlation request  546  is received by consumer profiling system  500  from advertisements records  540 , along with consumer ID  512 . Advertisements records  540  also provides advertisement characteristics including an ad demographic vector  548 , an ad product category  552  and an ad product preference vector  554 . 
   Application of a correlation process, as will be described in accordance with  FIG. 8B , results in a demographic correlation  556  and a product correlation  558  which can be returned to advertisement records  540 . In a preferred embodiment, advertiser  144  uses product correlation  558  and demographic correlation  556  to determine the applicability of the advertisement and to determine if it is worth purchasing the opportunity. In a preferred embodiment, pricing policy  570  is utilized to determine an ad price  572  which can be transmitted from consumer profiling system  500  to advertisement records  540  for use by advertiser  144 . 
   Pricing policy  570  is accessed by consumer profiling system  500  to obtain ad price  572 . Pricing policy  570  takes into consideration results of the correlation provided by the consumer profiling system  500 . An example of pricing schemes are illustrated in  FIG. 9   
     FIGS. 6A and 6B  illustrate pseudocode for the updating process and for a correlation operation respectively. The updating process involves utilizing purchase information in conjunction with heuristic rules to obtain a more accurate representation of consumer  100 , stored in the form of a new demographic characterization vector  562  and a new product preference vector  568 . 
   As illustrated in the pseudocode in  FIG. 6A  the point of purchase data are read and the products purchase are integrated into the updating process. Consumer profiling system  500  retrieves a product demographics vector obtained from the set of heuristic rules  519  and applies the product demographics vector to the demographics characterization vector  562  and the product preference vector  564  from the consumer profile  560 . 
   The updating process as illustrated by the pseudocode in  FIG. 6A  utilizes a weighting factor which determines the importance of that product purchase with respect to all of the products purchased in a particular product category. In a preferred embodiment the weight is computed as the ratio of the total of products with a particular product ID  514  purchased at that time, to the product total purchase, which is the total quantity of the product identified by its product ID  514  purchased by consumer  100  identified by its consumer ID  512 , purchased over an extended period of time. In a preferred embodiment the extended period of time is one year. 
   In the preferred embodiment the product category total purchase is determined from a record containing the number of times that consumer  100  has purchased a product identified by a particular product ID. 
   In an alternate embodiment other types of weighting factors, running averages and statistical filtering techniques can be used to use the purchase data to update the demographic characterization vector. The system can also be reset to clear previous demographic characterization vectors and product preference vectors. 
   The new demographic characterization vector  566  is obtained as the weighted sum of the product demographics vector the demographic characterization vector  562 . The same procedure is performed to obtain the new product preference vector  568 . Before storing those new vectors, a normalization is performed on the said new vectors. When used herein the term product characterization information refers product demographics vectors, product purchase vectors or heuristic rules, all of which can be used in the updating process. The product purchase vector refers to the vector which represents the purchase of a item represented by a product ID. As an example, a product purchase vector for the purchase of Kellogg&#39;s CORN FLAKES in a 32 oz. size has a product purchase vector with a unity value for Kellogg&#39;s CORN FLAKES and in the 32 oz. size. In the updating process the weighted sum of the purchase as represented by the product purchase vector is added to the product preference vector to update the product preference vector, increasing the estimated probability that the consumer will purchase Kellogg&#39;s CORN FLAKES in the 32 oz. size. 
   In  FIG. 6B  the pseudocode for a correlation process is illustrated. Consumer profiling system  500 , after receiving the product characteristics and the consumer ID  512  from the advertisement records retrieves the consumer demographic characterization vector  562  and its product preference vector  564 . The demographic correlation is the correlation between the demographic characterization vector  562  and the ad demographics vector. The product correlation is the correlation between the ad product preference vector  554  and the product preference vector  564 . 
   In a preferred embodiment the correlation process involves computing the dot product between vectors. The resulting scalar is the correlation between the two vectors. 
   In an alternate embodiment, as illustrated in  FIG. 10 , the basis vectors which describe aspects of the consumer can be used to calculate the projections of the ad vector on those basis vectors. In this embodiment, the result of the ad correlation can itself be in vector form whose components represent the degree of correlation of the advertisement with each consumer demographic or product preference feature. As shown in  FIG. 10  the basis vectors are the age of the consumer  1021 , the income of the consumer  1001 , and the family size of the consumer  1031 . The ad characterization vector  1500  represents the desired characteristics of the target audience, and can include product preference as well as demographic characteristics. 
   In this embodiment the degree of orthogonality of the basis vectors will determine the uniqueness of the answer. The projections on the basis vectors form a set of data which represent the corresponding values for the parameter measured in the basis vector. As an example, if household income is one basis vector, the projection of the ad characterization vector on the household income basis vector will return a result indicative of the target household income for that advertisement. 
   Because basis vectors cannot be readily created from some product preference categories (e.g. cereal preferences) an alternate representation to that illustrated in  FIG. 2C  can be utilized in which the product preference vector represents the statistical average of purchases of cereal in increasing size containers. This vector can be interpreted as an average measure of the cereal purchased by the consumer in a given time period. 
   The individual measurements of correlation as represented by the correlation vector can be utilized in determining the applicability of the advertisement to the subscriber, or a sum of correlations can be generated to represent the overall applicability of the advertisement. 
   In a preferred embodiment individual measurements of the correlations, or projections of the ad characteristics vector on the consumer basis vectors, are not made available to protect consumer privacy, and only the absolute sum is reported. In geometric terms this can be interpreted as disclosure of the sum of the lengths of the projections rather than the actual projections themselves. 
   In an alternate embodiment the demographic and product preference parameters are grouped to form sets of paired scores in which elements in the consumer characterization vector are paired with corresponding elements of the ad characteristics vector. A correlation coefficient such as the Pearson product-moment correlation can be calculated. Other methods for correlation can be employed and are well known to those skilled in the art. 
   When the consumer characterization vector and the ad characterization vector are not in a standardized format, a transformation can be performed to standardize the order of the demographic and product preferences, or the data can be decomposed into sets of basis vectors which indicate particular attributes such as age, income or family size. 
     FIG. 7  illustrates an example of heuristic rules including rules for defining a product demographics vector. From the product characteristics, a probabilistic determination of household demographics can be generated. Similarly, the monthly quantity purchased can be used to estimate household size. The heuristic rules illustrated in  FIG. 7  serve as an example of the types of heuristic rules which can be employed to better characterize consumer  100  as a result of their purchases. The heuristic rules can include any set of logic tests, statistical estimates, or market studies which provide the basis for better estimating the demographics of consumer  100  based on their purchases. 
   In  FIG. 8A  the flowchart for updating the consumer characterization vectors is depicted. The system receives data from the point of purchase at receive point of purchase information step  800 . The system performs a test to determine if a deterministic demographic characterization vector is available at deterministic demographic information available step  810  and, if not, proceeds to update the demographic characteristics. 
   Referring to  FIG. 8A , at read purchase ID info step  820 , the product ID  514  is read, and at update consumer demographic characterization vector step  830 , an algorithm such as that represented in  FIG. 6A  is applied to obtain a new demographic characterization vector  566 , which is stored in the consumer profile  560  at store updated demographic characterization vector step  840 . 
   The end test step  850  can loop back to the read purchase ID info  820  if all the purchased products are not yet processed for updating, or continue to the branch for updating the product preference vector  564 . In this branch, the purchased product is identified at read purchase ID info step  820 . An algorithm, such as that illustrated in  FIG. 6A  for updating the product preference vector  564 , is applied in update product preference vector step  870 . The updated vector is stored in consumer profile  560  at store product preference vector step  880 . This process is carried out until all the purchased items are integrated in the updating process. 
     FIG. 8B  shows a flowchart for the correlation process. At step  900  the advertisement characteristics described earlier in accordance with  FIG. 5  along with the consumer ID are received by consumer profiling system  500 . At step  910  the demographic correlation  556  is computed and at step  920  the product preference correlation  558  is computed. An illustrative example of an algorithm for correlation is presented in  FIG. 6   b . The system returns demographic correlation  556  and product preference correlation  558  to the advertisement records  540  before exiting the procedure at end step  950 . 
     FIG. 9  illustrates two pricing schemes, one for content/opportunity provider  160  based pricing  970 , which shows increasing cost as a function of correlation. In this pricing scheme, the higher the correlation, the more the content/opportunity provider  160  charges to air the advertisement. 
     FIG. 9  also illustrates consumer based pricing  960 , which allows a consumer to charge less to receive advertisements which are more highly correlated with their demographics and interests. 
   As an example of the industrial applicability of the invention, a consumer  100  can purchase items in a grocery store which also acts as a profiler  140  using a consumer profiling system  500 . The purchase record is used by the profiler to update the probabilistic representation of customer  100 , both in terms of their demographics as well as their product preferences. For each item purchased by consumer  100 , product characterization information in the form of a product demographics vector and a product purchase vector is used to update the demographic characterization vector and the product preference vector for consumer  100 . 
   A content/opportunity provider  160  may subsequently determine that there is an opportunity to present an advertisement to consumer  100 . Content/opportunity provider  160  can announce this opportunity to advertiser  144  by transmitting the details regarding the opportunity and the consumer ID  512 . Advertiser  144  can then query profiler  140  by transmitting consumer ID  512  along with advertisement specific information including the correlation request  546  and ad demographics vector  548 . The consumer profiling system  500  performs a correlation and determines the extent to which the ad target market is correlated with the estimated demographics and product preferences of consumer  100 . Based on this determination advertiser  144  can decide whether to purchase the opportunity or not. 
   Although this invention has been illustrated by reference to specific embodiments, it will be apparent to those skilled in the art that various changes and modifications may be made which clearly fall within the scope of the invention. The invention is intended to be protected broadly within the spirit and scope of the appended claims.