Patent Document

[0001]    This application is a divisional of U.S. application Ser. No. 14/855,312 filed Sep. 15, 2015, which is a continuation in part of U.S. application Ser. No. 14/331,116 filed on Jul. 14, 2014 which is a continuation of U.S. application Ser. No. 13/368,037 filed on Feb. 7, 2012, claiming priority from U.S. Provisional patent application No. 61/545,211, filed Oct. 10, 2011, all of which are hereby incorporated by reference. 
     
    
     BACKGROUND 
       [0002]    1. Technical Field 
         [0003]    The present invention relates to a method and system for generating personalized digital videos over websites delivering advertisement content. The present invention also relates to methods and systems for verifying that such personalized videos meet certain quality assurance performance criteria. The present invention also makes use of a unique technique for sharing user information to enable personalization of video advertisements 
         [0004]    2. Discussion of the Related Art 
         [0005]    Wide spread digital mediums, such as the Internet; have provided digital providers an almost unlimited platform for reaching and distributing content to almost any region or any country in the world. Indeed, advertisers and vendors currently user the World Wide Web as an extensive marketing tool for disseminating information relating their products and services in a wide variety of digital forms and venues. One such venue that has become quite popular are digital videos, such as those that are intermittently downloaded during regularly watched web video including a wide variety of content, some of which may include advertising ads and commercials appearing before, between or after the webpage and/or video the viewer is watching. Normally displayed on a computer screen, handheld device, a smart phone, or the like or a few minutes or so, a user usually views such ads as clips illustrating moving images, objects, actors and all elements typically involved in a movie video adapted to promote, enhance, or otherwise convey marketing and related information. 
         [0006]    While technological advancement have been made in developing and distributing marketing videos to many users, such videos will always remain generic in nature and those may not provide specific content with which certain users located in certain region or users belonging to certain segments, i.e., genders, cultures can personally associate. For example, a commercial video adapted to promote items, such as an automobiles, household products, clothes, beverages and the like, may inherently include scenery, sounds, or other type of features to which certain users may not necessarily relate or have any personal affiliation. More specifically, as part of an advertisement, vendors can mass distribute commercial a video to a variety of locales, behavioral or demographic information dispersed over area such that those sceneries, sounds and images (i.e., weather condition, certain population segments) may not be part of the everyday life or experience of the viewer because watching such a videos. Thus, specific viewers may not connect on a personal level with the provided wide spread and generic commercial content. Consequently, vendors may miss out on promoting their products and/or services to a wide range of audience as would be desired for by any vendor for achieving a robust marketing effort. In addition, effective distribution of such videos over wide network may be computationally demanding and costly to the extent it may discourage distributing vendors from disseminating their promotional material to users over the network. 
         [0007]    Still further, the production and distribution of advertising videos over a network may involve rendering processes that may be very computationally demanding and rather complex. Indeed, intricate processing and execution of certain computer routines, such as those pertaining to digital videos, may be prone to significant errors that could harm or otherwise degrade the overall quality of rendered videos. This may become even more accentuated when the number of rendered videos becomes large such that it may diminish the feasibility of generating and distributing large amounts of videos over the network. 
       BRIEF SUMMARY 
       [0008]    Exemplary embodiments of the present technique disclose a method and systems for personalizing video data generally provided in a digital medium like Internet and network connected devices. In some embodiments of the present technique, the processing, encoding and distributing of personalized videos is performed by a core rendering engine, such as one distributed over a computing cluster, or a computing cloud having multiple servers and/or dedicated processing devices adapted to efficiently and handle large amount of data. Such devices may include an orchestrating device for overseeing the retrieval of original ad information, as well as the dynamic processing, rendering (or partial rendering), and/or encoding of numerous personalized groups of which adapted to appeal and/or target various segments of network users in accordance with certain personalized, preferences and/or attributes of the users. 
         [0009]    In further aspects of the present technique, the disclosed computing cluster operates to retrieve original data feeds, as well as voluminous ad information, such as webpages, movie ads, and so forth. The cluster is adapted to alter the retrieved information, in accordance with personalized data, to ultimately encode and distribute custom fitted videos intended for various users having various personal preferences and/or users located in different regions and places or using different devices/screens. In other aspects of the present technique, videos processed and rendered by the rendering engine of the computing cloud are custom fitted to appeal to certain users based on specific behavioral, demographic, and/or gender-based attributes with certain users may be associated. Accordingly, personalization of the videos may be based upon information provided by vendors or third party entities, or other entities wishing to promote or offer various products. In other aspects according to the present technique, the computing cluster may generate videos personalized in accordance to certain objective, or user independent criteria, such as those taking into account certain times of a day, days of a week, or those shown in certain time of a year such holidays and/or special commemorative occasions to which certain individuals accessing the network can relate. While the present technique may alter an original video ad to create multiple ad versions targeting the various dispersed users, nevertheless, the present technique is also adapted to preserve the substantive content the overall uniformity conveyed by the different ads ultimately created. 
         [0010]    In so doing, the present technique makes use of certain software and/or hardware elements for defining dynamic templates of an advertisement to accommodate personalized user preferences, such certain local lifestyles, local weather conditions, gender, demographic make-up, times of day, week, year, holidays, local prices and personalized visual, audible and/or textual features. Once the particular definitions are implemented for each template, the present technique renders multiple versions of the movie or commercials advertisement in accordance with the above preference. Thereafter, the disclosed systems provide an output, whereby the multiple movie versions distributed to various locales across the network. Ultimately, each of the different network locales receives an personalized advertisement having content similar to the content of the original ad, however, such a personalized may include various scenery, images, sounds, objects, texts and so forth, tailored to be personalized and specific to various users, for example, such as those located specific regions or those having a personal preference, relation, or liking, respectively, to certain features the ad may include. Thus, in accordance with the present technique, advertisers are better adapted to reach and target a consumer audiences for promoting information associated with their products and/or services. 
         [0011]    Further, because of their personalized nature, the above described systems are adapted to generate and distribute a very large amount of videos whose quality meets acceptable viewing standards. Accordingly, the present technique utilizes a video quality verification module adapted to inspect the personalized videos as part of the rendering process of the videos. Thus, as part of the verification module, the present technique utilizes various algorithms adapted to inspect and detect defects and/or artifact with the personalized videos before those are distributed and become available to the users of the network. Accordingly, the verification module is further adapted to rectify any of the detected defects and artifacts so that such videos are rendered in compliance with acceptable standards for viewing. 
         [0012]    More particularly, the present technique utilizes a copy of a correctly defined ad video, such as one chosen to meet certain quality criteria considered to be pristine, i.e., good quality video as prescribed for good and acceptable viewing purposes. According to the present technique, such pristine video can be determined amongst a plurality of videos based on certain statistical methods, such as maximal likelihood and similar probabilistic methods. Further, the chosen pristine video provides a base to which other generated videos are compared. In accordance with exemplary embodiments of the present technique, such comparison may be carried out using hash functions, as well as visual diff functions for determining whether a particular generated personalized ad is valid for viewing and distribution. Should the verification module determine that there exists some defect with the personalized video, such that the video does not comply with or is otherwise considered defective in accordance standards, the module directs the system to re-render the video so that it ultimately complies with acceptable viewing criteria. 
         [0013]    According to a still further aspect of the present invention there is provided a method, comprising: obtaining information on one or more users of a network from a third party; obtaining a digital video, which is adapted to be provided to users over a network and includes one or more parameters, by a video generator; modifying the one or more parameters based on the information relating to one or more users of the network; creating a plurality of versions of the digital video based on the modification, by a rendering engine, wherein each of the plurality of versions comprises a broadcast quality digital video having a different modification adapted to be associated with a criterion in the information that relates to at least one of the one or more users of the network; determining a real time assessment of rendering resources available to render the plurality of broadcast digital videos; and coordinating allocation of the rendering resources to render the plurality of digital videos based on the real time assessment to the at least one of the one of more users of the network. 
         [0014]    In an embodiment the obtained information on the one or more users is received from a Customer Relational Management. 
         [0015]    In an embodiment two or more users are grouped into a segment based on common information between multiple users, such that the different modification of the digital video can be sent to the segment 
         [0016]    In an embodiment the source of the CRM data comes from third part account. 
         [0017]    In an embodiment the obtained information on the one or more users is received from a third party. 
         [0018]    In an embodiment the obtained information on the one or more users is obtained from public resources. 
         [0019]    In an embodiment modifying comprises partial rendering the video to obtain the plurality of versions of the broadcast quality digital videos. 
         [0020]    In an embodiment each one of the plurality of versions of the digital videos are encoded, by an encoder. 
         [0021]    In an embodiment the method further comprises creating a video template incorporating the obtained parameters, and modifying the template to create the plurality of versions of the digital video. 
         [0022]    In an embodiment the digital video comprises a commercial video appearing over a network. 
         [0023]    In an embodiment the parameters comprise, text data, visual data, image data, audible data, voice data, animation data, look and feel data or any combination thereof. 
         [0024]    In an embodiment the information comprises at least one of: what have you bought, how much have you spent, what do you like to buy, how many times to you buy, 
         [0025]    In an embodiment the method further comprising: assembling a user profile based on the information. 
         [0026]    In an embodiment the method further comprising: displaying an ad to the user that is based on the user profile 
         [0027]    Other aspects of the invention may include a system arranged to execute the aforementioned method. These, additional, and/or other aspects and/or advantages of the embodiments of the present invention are set forth in the detailed description which follows; possibly inferable from the detailed description; and/or learnable by practice of the embodiments of the present invention. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0028]    For a better understanding of embodiments of the invention and to show how the same may be carried into effect, reference will now be made, purely by way of example, to the accompanying drawings in which like numerals designate corresponding elements or sections throughout. 
           [0029]    In the accompanying drawings: 
           [0030]      FIG. 1  illustrates a network, in accordance with an embodiment of the present technique. 
           [0031]      FIG. 2  is a block diagram of a digital item, in accordance with an exemplary embodiment of the present technique. 
           [0032]      FIG. 3  is a depiction of a video scene, in accordance with and exemplary embodiment of the present technique. 
           [0033]      FIG. 4  is a depiction of another video scene, in accordance with an exemplary embodiment of the present technique. 
           [0034]      FIG. 5  is a block diagram of a system for providing personalized video over a network, in accordance with an embodiment of the present technique. 
           [0035]      FIG. 6  illustrates a system for rendering personalized video, in accordance with an embodiment of the present technique. 
           [0036]      FIG. 7  is a block diagram describing a process, in accordance with an embodiment of the present technique. 
           [0037]      FIG. 8  illustrates a system for rendering personalized video, in accordance with a further embodiment of the present technique. 
           [0038]      FIG. 9  is a block diagram describing a process, in accordance with a further embodiment of the present technique. 
       
    
    
     DETAILED DESCRIPTION 
       [0039]    With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of the preferred embodiments of the present invention only, and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the invention. In this regard, no attempt is made to show structural details of the invention in more detail than is necessary for a fundamental understanding of the invention, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice. 
         [0040]    Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is applicable to other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting. 
         [0041]    Turning now to the Figures,  FIG. 1  illustrates a network  10 , in accordance with an exemplary embodiment of the present technique. The network  10  is an exemplary embodiment of a platform on which personalized dynamic videos are created, processed, rendered, stored and provided to multiple users having access to the network  10 . Accordingly, network  10  is a communications network adapted for connecting various nodes, such as servers, computer systems and end users, as well as for facilitating the transfer of data between the nodes and end users. Furthermore the network  10  may be formed of various dedicated computer system and/or servers, some of which may be functioning as a computer cluster and/or computing cloud for providing and distributing personalized videos in accordance with exemplary embodiments of the present technique. 
         [0042]    More specifically,  FIG. 1  illustrates, nodes/endpoints/end users  12  and  14 , as well as, servers  16 , and computing cloud system (CCS)  18 . The user  12  and/or  14  may be client computers such as a home or office personal computer (PC), a remote client, a thin client or other type of computer and/or processing interface adapted for general data processing and for connecting to the network  10 . Although not illustrated by  FIG. 1 , the client computers may further be coupled and/or connected to other peripheral devices, such as monitors, keyboards, mice, printers, routers, wireless devices, microphones, speakers, cameras, finger print identifiers, external memory devices, and other devices. The PC  12  may include software platforms and operating systems, such Windows, Linux-Red Hat, and other supporting programs. As will be discussed below, the CCS  18  may be part of a computing cloud having multiple servers, processors and the like, adapted to intake original ad digital video data and to perform considerable amount of processing for ultimately rendering creating multiple versions of the original ads so to conform to the personalized desires and preferences of users having access to such video ads. In certain exemplary embodiments, the cloud  18  may utilize various algorithm and techniques in perform parallel and/or other supercomputing operations for rendering, generating, verifying and correcting multiple versions of videos out of the originally obtained advertisement. 
         [0043]    Thus, users having access to the network  10  may be provided with personalized videos as part of any general browsing or searching of the network  10 . It should be borne in mind that the network  10  may be accessed by a plurality of users, such as the users  12  and  14 , formed of various segments, locations, preferences, and/or other attributes characterizing the personal make-up of the network users. For example, users accessing the network  10  may be dispersed over various geographical regions/segments of the network, or the users may make different demographical, gender and/or other segments. By further example, the users, i.e., users  12  and  14  may have different shopping habits, movie and/or music preferences, and/or other parameters varying in accordance with those users&#39; personal attributes and characteristics. Accordingly, as further described herein, as part of their general browsing through the network for performing any of the above-mentioned network related tasks, the various user segments the network may download any otherwise generic advertisement made up a video personalized in accordance with the aforementioned user attributes. In other words, while, for example, both the users  12  and  14  may access a webpage having an advertisement showing a video of a particular vendor, each of the user will receive a version of the video custom tailored to that user personal, demographic and/or geographical preference and/or location, respectively. 
         [0044]    Returning to  FIG. 1 , the server  16  and CCS  18  may be adapted for storing, routing and/or communicating data within the network  10  and/or other networks to which the server  16  and CCS  18  may be connected. Thus, the server  16  may store information related to material included as part of vendor website, such as those belonging to certain vendors, advertisers, promoters, administrators and so forth. Alternatively, the server  16  may store originally created ads, as well as parameters specifying the manner by which personalized should be rendered. As will be described further below, the server  16  is further adapted to employ various algorithms, such as those based on statistical and probabilistic methods, for verifying large amount of personalized videos to ensure those are properly rendered for complying with certain criteria and in accordance with good quality viewing standards. Thus, the CCS  18  may be formed of multiple processors, servers, and/or other dedicated devices, such as those used for forming, processing, and/or encoding personalized videos, as implemented by the system illustrated and discussed above. 
         [0045]    Further, in an exemplary embodiment, the server  16  may be of the type available by Sun Microsystems, Hewlett Packard, Dell, International Business Machines (IBM), and/or other known server vendors and providers. Accordingly, the server  16  and the CCS  18  may include various hardware devices, such as microprocessors, memory cards, graphic cards, routers, wireless devices and other modules for receiving, transmitting and/or processing data. In addition, the servers may include various software platforms and packages, such as those providing code written in Java, Python, Ruby, and/or other computer languages, for facilitating the everyday operation and use of the server  16  and CCS  18  as part of the network  10 . It should further be borne in mind that the user nodes  12  and  14  and the servers  16  and CCS  18  are exemplary, and that the network  10  may include many other additional user nodes similar to the users  12  and  14 , as well as, multiple other servers similar to those discussed herein. 
         [0046]    Further, the server  16  may be adapted to store data, such as websites, generally accessible to the user  12  and/or  14  via the network  10 . Those skilled in the art will appreciate that each website accessible, for example, to the user may contain multiple web pages which may also be accessible to the users  12  and  14  upon request. For example, the server  16  may store websites of private companies and/or corporations, as well as government and/or other pubic organizations. Hence, the server  16  provides access to the user  12  of web pages provided by the above mentioned private or public entities so that the user, for example, can conduct business and/or manage various tasks through the network  10 . For instance, the user  12  may access the server  16  for downloading a webpage belonging to a vendor through which the user  12  may perform financial transactions such as when purchasing consumer items or the like. By further example, the user  12  may access the server  16  for downloading webpages, such as those associated with various public institutions, through which the users  12  and  14  can provide personal and/or other type of information for conducting everyday personal and/or work-related business and so forth. Accordingly, the users  12  and  14  may generally form communication sessions during which the user  12  and server  16  exchange information through the network  10 . 
         [0047]      FIG. 2  is a block diagram a digital item  20 , in accordance with an exemplary embodiment of the present technique. The digital item/template  20  may generally be part of a structured digital object, a form, or a combination of resources such as videos, audio tracks, images; or metadata, which may further include items such as descriptors and identifiers; and structures for describing the relationships between the resources the digital item/object, may include. 
         [0048]    More specifically and in accordance with an embodiment of the present technique, the digital item  20  forms a digital movie and/or images part of an advertisement, commercial and/or other promotional material accessible to the users  12  and  14  through the network  10 . Accordingly, those skilled in the art will appreciate that the depiction shown in  FIG. 2  is more of a representation of digital components from which such digital items are formed and therefore, the depiction of such elements are exemplary for illustration purposes included herein to provide a general understating of the present technique. 
         [0049]    Thus, the digital item  20  may form a digital movie part of an advertisement or commercial adapted to be displayed, viewed and/or heard on a computer, such as a home, office or any other type computing device, i.e., mobile device or a handheld device etc. Hence, movie advertisements formed by the digital item  20  may be provided over the Internet so that those may be downloadable by the users at various locales, regions and countries at their time of choosing or at designated times, as may be determined by a vendor, advertiser and/or other enterprises wishing to promote and/or disseminate information to the general public. Accordingly, such movie commercials may be made up of data forming substantive information and various contents that can be typically played on an Internet digital player, spanning a time duration that could last seconds, minutes or longer, depending on choice of the various providers making use digital items, such as the item  20 . 
         [0050]    As further illustrated by  FIG. 2 , the digital item  20  is made up of non-dynamic elements  22 , as well as dynamic elements  24 , including dynamic images  26 , dynamic sounds  28  and dynamic texts  30 . The dynamic elements  24  are further made up of dynamic animations elements  32  and dynamic look and feel elements  34 . The non-dynamic elements/data  22  of the digital item  20  are elements that are usually static and are otherwise not personalized to the specific user to which the commercial formed by the item  20  is intended. In other words, the non-dynamic elements/data  22  form those features of an advertisement that would be provided to all users viewing the advertisement, regardless of the region, demographic, other personalized preference to which the various users belong. Thus, non-dynamic elements  22  may be associated with certain objects, features, or other attributes (see below  FIGS. 3 and 4 ) shown in each and every version of an advertisement originating from the item  20 , as provided to the user. For example, in one embodiment the elements  22  may form an image or movie of person whose depiction overtime would be identical regardless of where and how the commercial is shown. By further example, the elements  22  may form portions of landscape, trees, grass, buildings and/or other moving or static elements that would be displayed identically in all personalized video commercials. 
         [0051]    By contrast, dynamic elements  24  are those elements within the commercial or ad intended to be varied and/or modified in accordance with preferences tailored for specific users, for example, located in various regions. Thus, dynamic images element  26  may form certain images or portions thereof of a commercial clip that could be specifically custom made to the user viewing the commercial. For example, the element  26  may form background sceneries, conditions or views, such as oceans, trees, roads, bridges and other scenery typifying the general location where certain users may reside, work, or those locations to which certain users may have some relation or affiliation. By further example, element  26  may emulate weather conditions i.e., sunshine, rain, snow, fall, winter and so forth, normally typifying conditions at a locale of a respective consumer who is viewing the advertisement. Hence, the dynamic image elements  16  may emulate various lighting, brightness, shading, and other visual effects and conditions and features to fit various settings in various regions. Still by further example, dynamic elements  26 - 34  may include features specific to a particular gender, that is, the elements  24  may incorporate in an otherwise generic videos features adapted to appeal to a specific gender in which certain attributes of the video could accentuate certain features to which the targeted gender can relate. For example, a video displaying an automobile commercial can be personalized to appeal to women by using the dynamic images  26 , and or animation element  32  to form an automobile having color usually likeable to women, such as pink, red, etc. Hence, while the aforementioned automobile commercial may originally be created and widely distributed as a generic advertisement having a generic automobile color, i.e., a color not necessarily adapted to target an audience segment, the present technique may create multiple versions of the advertisement, whereby each version is adapted to accentuate a particular feature bound to appeal to certain users who may find such features interesting or at least worth noting. 
         [0052]    Similarly, dynamic sounds element  28  forms an audible portion of the digital item  20  which can be changed and/or tailored in accordance to consumer location and/or other preferences. Thus, the elements  28  can form part of an audible portion adapted for specifying specific locations, such as streets, buildings, parks and/or other landmarks that could be of interest and are specific to the user&#39;s locations and other preferences, such various languages, gender voice, accents, dialects. Thus, for example, dynamic sounds element  28  can be used to alter a certain dialect or an accent of a speaker of generic-made commercial to include a dialect appealing to certain population groups. For example, a commercial, originally having an actor/speaker utilizing a particular accent (i.e., Anglo-American) can be personalized to show that same speaker talk in a different accent, i.e., such that shared by Spanish-American, so as to provide such a group similar informational content, yet more personalized and appealing to that group. 
         [0053]    In addition, dynamic texts element  30  provides a portion of digital item  20  having textual portions which, too, can be changed to fit the location and other attributes with which the viewer is associated. Hence, the dynamic texts element can form any readable or otherwise viewable contexts such as titles and subtitles, product names, service names, street names, maps, satellite photography and the like. Thus, the digital item  20  can be part of a commercial ad advertised, for example, by a chain store or a franchise having multiple stores dispersed in different locations across a country or region, whereby the dynamic text elements  30  are adapted to display, for instance, a general map showing the store location in relation to where the ad or movie commercial is shown. For example, a viewer in Los Angeles, Calif. will view the commercial in which the element  30  is adapted to display a map of the store closest to the viewer located in the LA area. Similarly, a viewer in Miami, Fla. will be able to see the same commercial while being provided with substantive content similar to that of the ad shown in the LA area, however, the commercial provided to the viewer in the Miami area will include a map showing the user where the closest store in that area is located. 
         [0054]    In so doing, the present technique contemplates using the digital item/template  20 , particularly, the dynamic elements  24  to generate multiple versions of commercial videos, each having similar informational content, yet, each generated to custom fit user(s) preferences, such as user locations throughout a given region. 
         [0055]      FIGS. 3 and 4  are depictions of video scenes, in accordance with and exemplary embodiment of the present technique. Hence, for example,  FIG. 3  depicts a commercial scene  40 , such as one provided by a vendor or chain store, for advertising and promoting various products and/or services. As illustrated, the scene  40  includes various items and objects, such as a house  42 , car  44 , person  46  and ladder  47 . Accordingly, the commercial seen  40  may be adapted to promote products, such roofing of the house  42 , ladders (e.g., ladder  47 ), and/or services (e.g., roofing, landscape and other services). Further, the scene  40  also includes a background  48 , for example, indicative of certain weather winter-like condition, i.e. rain, storms and so forth. As further shown, at the bottom of scene  40 , the illustrated embodiment further includes a portion  50 , including a text box  52  and a map portion  54 . Accordingly, text box  52  and map portion  54  are adapted to provide a viewer with location and other details regarding specific stores located within the vicinity of the user. Hence, the scene  40  can be generated and custom fitted to include certain information, such as hours of operation, special products, special sales and discounts and other types of promotional material, all specific to the region in which the commercial movie is displayed, as well as specific to other personalized preferences that would enable vendors to appeal to certain segments of users having access to a network on which personalized videos are accessible. 
         [0056]    In addition, in the illustrated embodiment of scene  40 , the background  48  can be chosen to include weather-like conditions indicative of the conditions observed and experienced in accordance with the where the specific store is advertised. Thus, the winter-like conditions  48  of scene  40  can be tailored to fit places such as those located, for example, in the northeastern portions of the United States, where similar conditions may apply and where a consumer may be experiencing similar whether conditions. Hence, in providing consumers with video contents tailored to consumers&#39; settings, vendors and/or advertisers can target and better appeal to consumer&#39;s preferences and/or locale conditions that are specific to where such consumers are located. 
         [0057]    By further example,  FIG. 4  shows a scene  70  that is almost identical to the scene  40  of  FIG. 3  with exceptions of background  72 , text box  74 , and map portion  76 , all providing similar content and information, yet, specific to a location different from that shown in  FIG. 3 . Accordingly, in the illustrated embodiment of  FIG. 4 , background  72  illustrates a clear, sunny-like day indicative of weather conditions that could otherwise present, for example, in the southwestern portions of the United States. Thus, while the scene  70  may be part of a commercial identical to the one provided by the scene  40 , the scene  70  may include content and other information adapted for appealing to consumers located in the aforementioned part of the U.S., and where corresponding vendor stores and locations are located. Thus, text box  74  and map portion  76  correspondingly provide the consumer located in that part of the country information pertaining to location, store hours, products, special sales and other related material specific to where the consumer is located. 
         [0058]    Still by further example, a chain store promoting certain home and related products can use specially tailored made commercials, such as the scene  40 , for advertising certain winter tools, such as snow plows, shovels and other winter items to those populations located in regions where the scene  40  experiencing a winter like-setting. Thus, to the extent the user of scene  70  is provided with substantive information similar to that provided in scene  40 , the viewers of the sunny scene  70  will not be provided with the winter-like background and related queues but, instead, the provided with corresponding products adapted for summer and sunny weather, in other words, shading fixtures, barbecues, pools, fountains, and the like. 
         [0059]    By further example, while car  44  of  FIG. 3  may be chosen to be that of a particular make, year, and color i.e., Volkswagen Beatle, 2005 flash-green, a favorite among females, the car of  FIG. 4 , may be illustrated in the commercial as a Corvette, or a Jeep, or another type of an automobile favorite among men. Thus, while the sense  40  and  70  may be adapted to promote a product (not necessarily related to the shown cars), nevertheless, each of the aforementioned scenes can appeal in a varied manner, respectively, to men and women. In other aspects, the two houses  42  of the scenes  40  and  70  can be shown such that their overall design and shape varies in accordance with different population groups. For example, the house of  FIG. 3  can be personalized to have Victorian-type architecture, such as that appealable to certain conservative or old fashion population segments. By contrast, the house shown in the scene  70  may be personalized to have modern type architecture, thereby appealing to younger population groups. 
         [0060]      FIG. 5  is a block diagram of a system  100  for providing personalized video over a network, in accordance with an embodiment of the present technique. Generally, the system  100  may be considered as a central computing system such as one forming a portion of a communications network, or computing cluster, a cloud computing structure, or a combination thereof. Accordingly the system  100  is adapted to connect various nodes, such as servers, computer systems and end users, as well as for facilitating the transfer of data between nodes/end users (e.g., users  12  or  14  of  FIG. 1 ). Further, the system  100  may be part of and/or reside in a general network (e.g., network  10  of  FIG. 1 ), including an Internet Network, an Intranet, or other types of local, wide and/or global area communications network, such as those formed of a wire line network, wireless network, satellite network, or a combination thereof. 
         [0061]    As further illustrated, the system  100  includes a feed generator device  110 , a software plug-in device  112 , a rendering engine device  114 , a video verification module  115 , and ad server device  116 . Those skilled in the will appreciate that the term device, as used herein, may encompass one, multiple, and/or an ensemble of devices, formed either as stand-alone or a combination of hardware and/or software platforms, each adapted to store and/or execute various algorithms, routines, and various computational tasks for manipulating and configuring digital elements/templates (e.g.,  FIG. 2 ) to ultimately generate and verify personalized digital videos over the Internet or other networks, as described above by  FIGS. 3 and 4 . 
         [0062]    Accordingly, those skilled in the art will appreciate that the system  100  and the devices  110 - 116  may include, either alone or in combination, various microprocessors, servers, such as those available, for example, by Sun Microsystems, Hewlett Packard, Dell, International Business Machines (IBM), and/or other known processor and server vendors and providers. In addition, the devices  110 - 116  may include, either alone or in combination, hardware devices, memory and storage devices, graphic cards, routers, wireless devices and other modules for receiving, transmitting and/or processing data. 
         [0063]    In addition, the system  100 , particularly, the devices  110 - 116  may be housed and/or run on a computing cloud, such as that available by Amazon.com and/or similar cloud-providing vendors. As such, the system  100  and its various components may be adapted to run various software platforms and packages, such as those providing code written in Java, Python, Ruby on Rails, and/or other computer languages, for facilitating the everyday operation and use of the system  100 . 
         [0064]    In accordance with one embodiment of the present technique, the feed generator  110  may form, for example, an RSS feed generator, adapted to intake information, such as a website URL, and or other related material included as part of a specific website  118  belonging to particular vendor(s). Such vendors having the web site  118  may include firm(s), commercial companies, or any private or public organization interested in providing the general public with information and content regarding its various products, services, as well as any other general and/or specific information adapted to promote or otherwise enhance the company&#39;s image in the public eye. As will be discussed below, such information may include digital content in a form of a digital movie, such as one that can be downloaded and played by an average home or office user upon throughout a communication session, as may happen when a user generally browses the Internet. 
         [0065]    Further, once the system  100  obtains a desired website and URL information via the generator  110 , (that is, from where the URL information is stored) the software plugging device  112  obtains original ads  120  to further process such ads and provide personalized digital video ads adapted to appeal to specific segments, groups, locations, genders and so forth. As mentioned, such movies may include advertising and other promotional material, as promulgated by the vendor of the website  118 . Further, the device  112 , particularly, software plug-ins employed therein are adapted to generally analyze digital items form which the digital movie is formed. In so doing, the device  112  finds and determines the position of dynamic elements, elements  16 - 20  (see  FIG. 2 ) so that those can be modified and/or edited in accordance. Hence, an animator employing the plug-in device  112  can define and mange dynamic texts, visual animations, images, and voice to fit a specific ad that can be personalized and adapted for specific users segment located therein. 
         [0066]    More specifically, the device  112  utilizes the non-dynamic elements in conjunction with dynamic elements of the ad, i.e., movie, whereby dummy texts may be inserted to the ad, via an editing software, ultimately determined by constraints are configured in accordance with a desired implementation. For example, certain portions of the movie including text messages may be designated for dynamic rendering, eventually determined according to the animator&#39;s choice and/or according to specified demands. By further example, the device  112  can be used to insert and configure certain desired images, as well as vary the properties of images existing in an otherwise distributed movie. This may also include designating and configuring dynamic image background features, such opacity, brightness, lighting, object views, text images and other related visual features. 
         [0067]    These and other operations, as performed by the device  112 , create what may be called a master ad that can be provided to core rendering engine device  114 , adapted to compose all variations of the video ads in accordance with the specified regional and/or other types of preferences to where the ad is intended. In so doing, the rendering engine  114  receives personalized parameters  122  which determine the uniqueness of each ad, as well as the extent to which dynamic elements within each video are varied. Accordingly, the engine  114  inserts into the master ad provided by the software plug-ins  112  actual specific data values to create actual videos, where each video is uniquely created to reflect the various changes in an otherwise original ad, modified to create multiple ads that are each adapted to target a certain region, population and so forth. Once the rendering engine  114  creates the multiple ad movies, the engine  114  encodes each of the multiple movies according to certain accepted and usable image formats, such as .JPEG, .GIF, .MPEG, and other image and/or video well known and used in the industry. 
         [0068]    In accordance with exemplary embodiments of the present technique, once the personalized digital video is rendered and encoded, as performed by the rendering engine  114 , the personalized video undergoes a verification process by the video verification module  115 . The verification module  115  is adapted to detect various failures and/or defects that may have occurred during the rendering and/or encoding processed. Such failures may stem, for example, from various multiple software or configuration “bugs,” causing images or texts to be displayed incorrectly, or rendering videos not in par with standard videos such as those available on the market. 
         [0069]    To counter such failures, the verification module  115  employs various algorithms for detecting and rectifying the above-mentioned failures occurring in mass production of personalized videos. Accordingly, in an exemplary embodiment, the present technique utilizes a comparison procedure by which generated personalized videos are compared to a specifically chosen pristine standard video that provides a baseline against which other videos are compared. In so doing, for each set of mass produced personalized videos, a pristine version is chosen for that set by examining a subset of videos in that set and determining which personalized videos in the subset has features matching closest all other videos in the subset of personalized videos. Such determination may further involve, for example, utilizing maximum likelihood and/or Bayesian statistical methods, as well as other averaging methods. 
         [0070]    Hence, the verification module  115  utilizes the aforementioned pristine copy of the personalized video for comparing other similar personalized rendered and encoded videos. More specifically, the comparison performed by the module  115  may be based on employing one or more functions providing a quantitative numerical measure for determining how well the personalized video matches the pristine video of that version of the ad. Hence, in one exemplary embodiment, the module  115  separates each of the personalized videos in to its image frames and corresponding soundtrack. Thereafter, the video verification module  115  applies, for example, a binary hash function (e.g., MD5 hash function) to the soundtrack of each personalized video to determine whether it matches a value provided by applying the hash function to a corresponding portion of soundtrack in the pristine video. Thus, for example, if the hash values of portions of a soundtrack of a personalized video undergoing verification do not match the hash values provided by the corresponding version of the pristine video, the module  115  returns a “FALSE” notification, thereby indicating the sound track of the personalized video undergoing verification may deviate significantly from the soundtrack of the pristine version in that corresponding portion of the video. Accordingly, this provides a further indication that the video undergoing verification may have certain defects, artifacts, or is otherwise corrupt to the extent it may fail to meet required criteria for being acceptable for viewing. 
         [0071]    Similarly, by further example, a visual Diff function can be applied to one or more frames of the personalized video to determine and compare similar frames in the pristine videos of those corresponding frames. Thus, in an exemplary embodiment, the module  115  can employ a comparison algorithm defining that for each frame N in number of frames of the pristine video P, calculate a visual difference of Frame (N, P) versus Frame (N, T), where T indicates the personalized video undergoing verification. Thus, for example, when comparing matching frames of the pristine video to that of the video undergoing verification, then if visual Diff (Frame (FN, P), Frame (FN, T))&gt;0.50, then the module  115  returns a “FALSE” indication, thereby indicating that the personalized video undergoing verification may be corrupt or is otherwise not in par with acceptable viewing standards. Hence, the Diff function, as employed above, evaluates the difference of pixel values between the nth frame of the pristine version and the nth frame of the video version undergoing verification. This may provide a rather standardized and accurate metric for evaluating whether the generated personalized videos meet desired standards. 
         [0072]    In employing Diff functions for evaluating variations in frames between the video undergoing verification and the pristine version, visual differences of two images may be calculated using visual distance algorithm that divides the image, for example, into cells of 8 by 8 pixels. Hence, for each pixel cell, the module  115  can separates each pixel into its red, green and blue (RGB) values for determining a root-mean-square (RMS) for each pixel in the cell. Accordingly, a comparison between RMS values for each pixel in each cell of the verified video and corresponding pixel in the pristine video can be performed such that it is carried throughout all of the pixels in each and every cell of every frame. In so doing, a metric can be obtained having a numerical measure between 0 and 1, such that a value of 0 indicates that there is no difference between the verified video and the pristine video, while a value of 1 indicates no similarity between frames of the video undergoing verification and the pristine video. 
         [0073]    After personalized video files are rendered, encoded and verified, as described above, such files are then provided to targeting server  116  adapted to receive request for the videos from multiple locations or with users segmentation parameters. Accordingly, upon such requests, the server  116  outputs personalized video ads to multiple end users  124 ,  126  and  128 , including home, office, or other users having access to websites, such as the website  118 . In this manner, each of the different users  124 ,  126  and  128  may individually receive a personalized video that accommodates and is made to fit the user&#39;s regional or geographical location and setting. Thus, while outputs  124 - 128  may generally be formed of the same ad (see  FIGS. 3 and 4 ), each sharing similar content information and appearance, those outputs may differ to some extent according to the personalized preferences  122 , defined above. For example, users viewing output  124  in one region may view a video that may be visually identical the output  126  viewed in another region, however, the video  124  may contain textual information (e.g., maps, location address, store names, etc.) indicative of the first region while the output  126  may contain textual information indicative of the second region, yet, different from the first region. As mentioned herein, the varied outs  124 - 126  may contain personalized attributes appealable to various user groups, such as gender, groups, demographic groups, cultural groups, age groups, employment groups, social groups, artistic and academic groups, fraternities, hobby and sport clubs, and/or other associations with which general users having access to the network can relate. Thus, personalized videos discusses herein can provide an otherwise generic advertisement while tweaking certain images, colors, sceneries, and/or voices, i.e., dialects, languages and the like to appeal to certain group segments who may find those particular colors and/or accent appealing. In so doing, the present technique creates a multitude of versions of the same ad, each having its own personalized features for targeting a certain population group, while preserving the overall content conveyed by the originally and previously created and distributed ad. 
         [0074]      FIG. 6  illustrates a system  200  for rendering personalized video, in accordance with an embodiment of the present technique. The system  200  includes various components adapted to receive original webpage data having original ads, as well as components for processing such data for ultimately rendering and encoding personalized videos, such as those described above. Furthermore, because the rendering process of the personalized video can be quite computationally demanding, the system  200  may be implemented over a computing cloud or a computing cluster having servers and/or processors, dedicated for processing voluminous data, and adapted for executing and performing various computational tasks in parallel that could otherwise be too overwhelming for conventional computing systems. To the extent resources of the computing cloud are needed for rendering personalized videos, the amount of devices dedicated to such tasks within the cluster could expand or contract as the needs for using such resources changes with time. Thus, the disclosed computing cloud/cluster may be continually elastic and dynamic for efficiently accommodating the computational tasks at hand. 
         [0075]    It should further be borne in mind that the term computing cloud and/or computing cluster as used herein refers to a consolidated remote data and processing system adapted for allocating and provisioning dynamic and scalable virtualized resources. Accordingly, computing resources located on the cloud may take a form of ease to access remote computing sites provided by a network, such as an Internet, and/or other forms of web-based tools or applications that users can easily attain and use through web browser. In such implementation, the cloud offers a real time emulation of programs and applications as if those were installed locally on the client&#39;s computer. 
         [0076]    Returning to  FIG. 6 , the system  200  includes a feed generator  202  adapted to retrieve original ads through network  204  from webpages  206 . From the acquired webpages  206 , the feed generator  202  is further adapted to generate structured data, such as an XML file, including dynamic ad data that is prone to change as part of creating those portions of the video ad that ultimately become personalized and may appeal to certain segment of the network. Such data is then provided to the orchestrator  208  which, among other things, functions to oversee the entire rendering operations of the personalized videos generated by the system  208 . Hence, the orchestrator  208  obtains from the original ad, those digital elements of the ad defined as static, as well as those elements defined as dynamic, as brought forth above by  FIG. 2 . 
         [0077]    Upon receiving such information, the orchestrator  208  prompts change set manger (CSM)  210  to recognize and identify ongoing computational changes currently occurring or those about to occur in the cloud  200 . Thus, if new ads originating from the webpages  206  are provided to the cluster  200 , as may happen at any given moment, the CSM  210  provides a real time overview and assessment of resources available to the cluster  200  for performing the current required operations, as well as those resources that would be needed to performing processing and creating new personalized videos based on the newly acquired ads  206 . 
         [0078]    For example, the CSM  210  may identify that certain ads, previously acquired from webpages  206 , are no longer running, outdated, or otherwise unavailable for further processing to create personalized video for distribution across the network. Alternatively the CSM  210  may recognize the emergence of new ad videos, as may be provided by one or more vendors, requiring processing and rendering for creating multiple versions of personalized videos. Thus, cessation or emergence of new videos available for processing in accordance with the present technique may be part of commercial and/or promotional campaigns conducted by one or more vendors attempting to commercialize and bring forth products and/or services to various segments of network users. 
         [0079]    Hence, orchestrator  208  obtains the real time assessment of resources available to the cloud  200 , as provided by the CSM  210 , to coordinate further rendering operations, as performed by rendering engine servers  212 , for generating multiple versions of personalized videos. Thus, the orchestrator  208  is adapted to time and/or synchronize the operation of servers  212  in accordance with the needs specified to cloud for processing newly acquired ad videos from the webpages  206 . Hence, rendering servers  212  may operate in parallel and in capacity that could vary at any point of time depending on varying loads experienced by the cloud  200 . Hence, the ability of the computing cloud  200  to tap or release rendering servers  212  and/or other resources at will provides much flexibility for better facilitating a better, efficient and cost effective system for generating multiple versions of personalized videos. Furthermore, the ability of the cloud of the orchestrator to distribute and allocate the rendering operation of large scale data may significantly reduce the amount of rendering time allocated for generating each personalized videos. Thus, by utilizing the cloud  200 , personalized ad movies normally taking hours to render may be rendered in minutes, thereby leading to substantial reduction of processing time and cost. 
         [0080]    In further aspects of the present technique, the computer cloud  200  further includes servers, such as servers  214 , adapted to fore encoding the personalized videos, as rendered by the servers  212 . In so doing, orchestrator  208  prompts servers  214  to encode and videos rendered by the serves  212 . Accordingly, those skilled in the art will appreciate that the personalized videos may be encoded in accordance with a variety of known digital video formats, such as MPEG, JPEG MMV, and/or other known formats. In so doing, orchestrator  208  may coordinate various operations of available encoding servers  214  so as to expand or contract the computing capacity of the cloud  200  with varying needs. Such needs may be specified by the amount of originally acquired videos ads from webpage  206 , as well as by the urgency required to produce and/or make such videos available for distribution across the networks. 
         [0081]    The cloud  200  further includes video verification module  216 , made up of one or more processors such as those found in servers or other computing devices. The video verification module  216  is adapted to detect any failures or defects within the personalized videos, as rendered and encoded by the servers  212  and  214 , respectively. Accordingly, as described above with reference to  FIG. 5 , the video verification module  216  within the cloud  200  is adapted to obtain the pristine video file of every version of the personalized videos, so as to form a baseline comparison copy to which other similar versions of the personalized are compared. Thus, the module  216  utilizes the above described statistical methods for selecting the pristine version, as well as employing the various hash and differential (Diff) function for comparing the other personalized videos to the pristine version. In so doing, the video verification module performs various mathematical and algorithmic operations for producing a comparative platform employing certain criteria (as describe above with reference to module  115  of  FIG. 5 ) to determine whether rendered and encoded personalized videos are in par with acceptable viewing standards for users in a network. 
         [0082]    Furthermore, upon receiving notice form the encoding server  214  that a personalized video has been encoded, the orchestrator  208  instructs the video verification module to commence video verification for that personalized video, in accordance with the operation described above. Accordingly, upon completing the video verification, the module  216  may return to the orchestrator  208  an indication on whether the personalized video can be released and is acceptable for viewing as or within a personalized advertisement. Thus, the module  216  may prove a Boolean value, such “TRUE” for a valid video which can be viewed and/or be accessed on the network, as opposed to a value of “FALSE,” indicating the personalized video is not proper is otherwise too defective for being placed on the network as a personalized ad. 
         [0083]    It should be appreciated that having the video verification module  216  as an integral part of the computing cloud  200  and the video creation process effected by the cloud  200  provides a significant advantage for handling the verification process of a large number of personalized videos. Accordingly, because the present technique ensures that the verification process of each personalized video is embedded within the actual creation process of the personalized video, provides the cloud  200  a real time capacity to inspect frame by frame the personalized video as it is being generated. Such on-the-fly capability circumvents any post processing of the videos that would otherwise require reopening video files so as to decompress, decode while performing considerable amount of computation before any validation of the video may commence. 
         [0084]      FIG. 7  is a block diagram  300  describing a process, in accordance with an embodiment of the present technique. Accordingly, the process  300  describes a method for generating personalized videos utilizing computer resources available within a computing cloud. The process begins at block  302  in whereby one or more feed generators of a computing cloud obtains through one and/or more feed generators of a computing cloud, parameters of a digital video. Such video may generally be provided to users having access to the network and its various applications. Further, the process  300  further includes step  304  whereby the obtained parameters are modified by one and/or more software generators located within the computing cloud. The modification, as obtained at step  304  is based on information relating to segments of the users of the network. Hence, this information may relate to various aspects of segments of network, including information on their personal preferences, geographical location, demographic and/or gender make up, and many other aspects. 
         [0085]    The process  300  further includes step  306 , in which a plurality of versions of the originally obtained video are created based in the modification of the parameters. Step  306  may generally be performed utilizing one or more rendering engines located within the computing cloud. In so doing, the present technique utilizes the cloud to perform a significant amount of rendering operations of numerous digital video frames belonging to various digital video ads. The use of the computing cloud provides a robust platform for performing heavy and laborious computing operations, thereby significantly shortening entire rendering operations. In accordance with other aspects of the present technique, the process  300  further includes step  308  whereby the cloud employs computing operations for determining a quality of one or more personalized videos. As described above, such a quality generally pertains to determining whether the personalized videos comply with acceptable viewing standards. Such a quality may depend and/or be derived from, for example, a numerical metric that defined a threshold or criteria on whether the video is acceptable for viewing. Accordingly, at step  310 , based on the determined quality and its quantitative measure it is determined whether the video is acceptable for viewing. 
         [0086]    In a still further embodiment of the present invention there is provided a unique system and method for sharing user data between different commercial entities such as vendor, advertisers, etc. The sharing of the user data may be used by any entity to provide bespoke products or services to the user. One such product or service may be personalized advertisements. In accordance with this embodiment of the present invention the creation of the personalized advertisement is carried out as in the embodiments above and will not be described again. The difference with this embodiment relates to how the user data is obtained. This will now be described in further detail below. 
         [0087]    The system  800  of  FIG. 8  operates over the cloud  802 . A first entity  804  and a second entity  806  are connected to the cloud  802 . For simplicity only two entities are shown but there could be many more as will be appreciated in due course. Each entity has one of more webpages to which users (U1, U2, and U3) are connected. The users connected to the webpages of the first entity at a first time are the same users connected to the webpages of the second entity at a second time. This scenario is used to describe the operation of this part of the invention. However, in reality users may come and go at random. 
         [0088]    Each user connected to the first entity may have an account with the first entity this means the first entity has user data relating to the user. This user data may relate to personal details or preferences and trends. Generally a user with an account will have a unique identifier, such as a user ID, email address, or any other unique identifier. The user data may include name, address, other contact details and any other relevant personal details. The user data may also include preferences, trends in purchasing, age group, sex and other demographic information. Thus there are essentially two types of user information; personal data and preference data. It will be appreciated that the dividing line between the two types may be fluid and in some cases what would be personal data for one entity may be preference data for another and vice versa. 
         [0089]    The second entity may be a vendor which supports the display of personalized video advertising from an advertiser  810 . Alternatively entity  806  and advertiser  810  may be the same entity and could thus be illustrated by a single box such as box  806 . 
         [0090]    When the user connects to a website or an app that is associated to the first entity for the first time, the first entity will place a marker on the user&#39;s device, such as a cookie. That marker will be associated in the first entity&#39;s database with this user&#39;s profile. Then, when the user connects to a website or an app that is associated with the first entity subsequently, the first entity will be able to identify the user corresponding to the user&#39;s particular profile. 
         [0091]    When the user connects to a website or an app that is associated to the second entity for the first time, the second entity will also place a marker on the user&#39;s device, such as a cookie. That marker will be associated in the second entity&#39;s database with this user&#39;s profile. Then, when the user connects to a website or an app that is associated with the second entity subsequently, the second entity will be able to identify the user corresponding to the user&#39;s particular profile. 
         [0092]    Also connected to the cloud  802  is an intermediary  808 . The intermediary is an entity which provides a service of sharing user data between different entities. The entities establish an account with the intermediary so that the intermediary can share at least a part of the user data with another entity which also has an account with the intermediary. The data shared by the first entity with the intermediary may include all the user data held by the first entity or a sub-set thereof. 
         [0093]    Once the relationship with the intermediary is in place and the user logs onto a website associated to the second entity, the second entity checks not only for the existence of its own marker, but also for the existence of the first entity marker on the user&#39;s browser. It then associates this marker with the user&#39;s profile on the second entity&#39;s database. The second entity then polls the intermediary and requests information about the user by passing on to the intermediary the information in the first entity marker. 
         [0094]    The intermediary provides this information based on one or both cookies. The information may be some or all of the information held by the intermediary. In one embodiment, the information includes the preferences stripped of the personal information. The advertiser then creates a bespoke personalized advertisement for the user, to be used when the user next logs onto the second entity webpages. Next time the user is identified as logging onto the second entity&#39;s webpages the advertiser presents and displays the personalized advertisement. 
         [0095]    Using the above described procedure a number of different videos may be created that are targeted to the same user. The advertiser uses an extensive and well catalogued storage space for storing the videos for a specific user based on a user Id or other identifier. The advertiser can log which videos have been played to the user and play a different one each time. The sequence can vary based on any changes in the user preferences. Also if a new video is prepared for a particular event, such as Christmas this may be shown before others that may be awaiting broadcast from the advertiser. 
         [0096]    It is clear that certain users may have similar or the same preferences. If this is the case the users can be grouped into segments which have a certain preference in common. This could be age, demographic, choice of car, zip code, favorite color or whatever might be a shared preference of interest to the advertiser. Once a segment has been created the same personalized video may be sent to all the users in the segment. In this case the videos may be stored based on the segment and/or the user. 
         [0097]    Referring now to  FIG. 9  the method steps  900  carried out by the system will now be described. In a first step  902  entities subscribe to an intermediary which provides services sharing user data and preferences. In a second step  904  a first entity collects user data from user&#39;s subscribed to his webpages. The first entity then shares some or all of the user date with the intermediary in a step  906 . The intermediary then searches for the user on webpages of any other subscribed entity in a step  908 . The intermediary plants a cookie on the user&#39;s computer in a step  910  when the user is identified. In step  912  a second entity reads the intermediary&#39;s cookie. The second entity plants its own cookie at step  914 . The intermediary and second entity exchange information about the user based on one or both cookies at step  916 . The second entity then creates a personalized video based on the shared information in step  918 . The personalized video advertisement is displayed to the user when they next log on to the webpages of the second entity at step  920 . 
         [0098]    It will be appreciated that there may be many variations to this embodiment and the description above is not intended to limit the scope of the present invention.

Technology Category: h