HK1181224B - Improved network data transmission system and method - Google Patents

Abstract

A network data transmission system including a locus metrics database, a locus parameters database, a scoring engine and a system controller coupled to the locus metrics database, the locus parameters database and the scoring engine. The locus metrics database and the locus parameters database may be at least partially linked and may be at least partially distributed. In an embodiment, the scoring engine may include a weight function operating on at least some of the locus metrics.

Description

Improved network data transmission system and method

Technical Field

Embodiments of the present invention relate to systems for improving the effectiveness of data transmission in a network, and more particularly, to systems and methods for efficiently transmitting video streams over a network.

Background

It is estimated that by 2013, video traffic will be 90% of all user IP traffic (e.g., internet network traffic) and 64% of mobile user traffic (e.g., telephone network traffic). See, for example, "Cisco: by 2013 Video Willbe 90Percent of All Consumer IP Traffic and 64Percent Mobile ", TechCruch, 6.9.2009, Erick Schonfeld. Since all networks inherently have limited bandwidth, it is very important to distribute video efficiently and effectively. Although video advertisements are used herein as an example of video transmission, it will be appreciated that the systems and methods disclosed herein are generally applicable to efficient, effective distribution of data over a network based on qualitative analysis of the targeted recipients of the data.

Electronic commerce, commonly referred to as "e-commerce," involves the purchase and sale of products or services over electronic systems such as the internet. With the widespread adoption of internet technology, the amount of electronic commerce conducted has grown dramatically. One particularly explosive area of growth in e-commerce is the field of advertising, particularly video advertising over the internet.

Advertising is a common way to cause goods and/or services to be sold by sellers. In traditional media, such as television and print media, advertisements are visible to a wide audience population. Typically only a small percentage of the audience will be interested in purchasing goods or services. Furthermore, with traditional media, the space provided for advertisements is often limited. The amount of resources (e.g., physical space, time, etc.) available for advertising is sometimes referred to in the art as "inventory".

The inherent nature of the internet is that it creates an increasing amount of advertising inventory. This is because web technologies can generate an advertisement message image (referred to as a "show") each time a web page (or other, e.g., hypertext markup language html-based platform) is accessed. Since multiple users may be accessing internet content simultaneously, and since the number of internet users and web pages is constantly increasing, the "inventory" of advertising space on the internet is almost limitless.

Due to the large surplus of inventory, there is competition for advertisers and entities on behalf of advertisers through websites ("publishers"). That is, because many advertisers are represented by advertising agencies, advertising networks, and/or other entities that manage the distribution of advertisements (collectively, "advertising networks"), competition for advertisers extends to these entities. Since most network publishers provide some form of reward amortization method with the advertising network, some competition can be reflected in the profit margin that the network publishers provide to the advertising network. In addition, different websites satisfy different customer groups, have different "click-through" rates, etc., all of which may be used to attract relevant advertisers and advertising networks.

Due to competition, publishers have focused on attracting paid advertisements well by optimizing website content, adjusting the display of advertisements, attracting viewers with demographic characteristics that are pleasing to advertisers, and the like. These and other aspects of adjusting their advertisement "position" have become a relatively inefficient, random process of guesswork and experimentation.

In addition, advertisers desire to place their advertisements on high quality web pages or other advertising spots in order to maximize the value of their advertising expenditures. Furthermore, this has become an arbitrary process based on intuitive and time-consuming feedback.

These and other limitations of the prior art will become apparent to those of skill in the art upon a reading of the following description and a study of the several figures of the drawings.

Disclosure of Invention

To illustrate the combination of elements and acts within the scope of the disclosure of the specification and the drawings, a number of examples are set forth herein. Other combinations of elements and acts and variations thereof are also supported herein, as will be apparent to those of skill in the art.

It is an object of the enumerated embodiments of the present invention to improve network data transmission, and in particular to improve transmission of video stream data over bandwidth-constrained networks.

It is a further object of enumerated embodiments of the present invention to provide methods and systems that allow qualitative analysis of the effectiveness of video data transmission for purposes such as video advertising.

By way of exemplary and non-limiting list, a network data transmission system comprising a location metric database; a location parameter database; a scoring engine; and a system controller connected to the location metric database, the location parameter database, and the scoring engine. In another example, the location metric database and the location parameter database are linked, at least in part. In yet another example, at least one of the location metric database and the location parameter database is distributed, at least in part. In yet another example, the scoring engine includes a weighting function operating on at least some of the location metrics. In yet another example, the weighting function is a weighted sum function. In yet another example, the weighting function is a weighted average function. In yet another example, the weighting function includes a weighting coefficient obtained from the location parameter database. In yet another example, the weighting function is implemented by a neural network. In yet another example, a scoring database is connected to the system controller. In yet another example, at least two of the scoring database, the location metric database, and the location parameter database are linked, at least in part. In another example, at least one of the location metric database and the location parameter database is distributed, at least in part. In yet another example, the report generator is connected to the system controller. In yet another example, the report generator generates an ordered list of ad spots. In yet another example, the ordered list is associated with a demographic profile.

By way of example and not limitation, an enumerated method for transmitting video data over a network includes: obtaining a plurality of location metrics and a plurality of location parameters for a plurality of internet video display devices; generating a plurality of scores associated with the plurality of internet video display devices; ranking at least a subset of the plurality of internet video display devices based on the plurality of scores. In another example, generating the plurality of scores includes a weighting function operating on at least some of the location metrics. In yet another example, the weighting function is at least one of a weighted sum function and a weighted average function. In another example, the weighting function includes a weighting coefficient. In yet another example, the weighting function is implemented by a neural network.

By way of example and not limitation, an enumerated method for forming a quality ranking of advertisement positions includes: generating a plurality of scores for the advertisement locations; and ranking the advertisement positions based on the quality scores. The ranked ad positions may be used by publishers to improve the quality of their ad positions, and may be used by advertisers in selecting their ad positions.

By way of example and not limitation, enumerated video advertisement scoring systems for websites, webpages, and/or other internet locations form one or more advertisement "quality scores" that correlate to their "advertisement quality". Websites may be "ranked" by their quality scores to provide relevant information about video advertising decisions made by, for example, advertisers, networks, and publishers for the websites.

The quality score (e.g., "PQS") may be advantageously used by advertisers and publishers. For example, advertisers can optimize their advertising budget by placing their advertisements to publishers that meet their quality criteria. Publishers, on the other hand, can use the quality scores to increase appeal to advertisers by, for example, changing their content and/or lowering their price.

Furthermore, PQS allows for more efficient and effective distribution of large amounts of data (particularly video data) over networks with limited bandwidth. By way of non-limiting example, the video distribution system may determine that a limited number of videos are effectively distributed in qualitatively satisfactory "locations" or video display devices. This may greatly increase the efficiency of the distribution system and reduce the overall load on the network system.

These and other examples of combinations of elements and acts supported herein, as well as advantages thereof, will become apparent to those of ordinary skill in the art upon reading the following description and studying the various figures of the drawings.

Drawings

Various examples will now be described with reference to the drawings, wherein like elements and/or operations are provided with like reference numerals. These examples are intended to illustrate and not limit the concepts disclosed herein. These drawings include the following figures:

FIG. 1 illustrates an example system that supports an ad placement scoring process;

FIG. 2 is a block diagram of an example computer, computer-controlled device, proxy server, and/or server that may form part of the system of FIG. 1;

FIG. 3 is a block diagram of an exemplary advertisement location scoring system;

FIG. 4 is a state diagram of an exemplary advertisement location scoring system;

FIG. 5 is a flow chart of exemplary scoring data; and

fig. 6 is a table of exemplary metric data obtained from a large number of issuers over time and exemplary normalized values and issuer quality scores (PQS) associated therewith.

Detailed Description

FIG. 1 shows a system 10 that supports an ad placement scoring process, according to a non-limiting example. In this example, system 10 includes one or more operating servers 12, one or more advertiser computers 14, and one or more publisher server systems 16. The system 10 may also include other computers, servers, or computer-controlled systems, such as a proxy server 18. In this example, the operating server 12, advertiser computers 14, publisher server system 16, and proxy server 18 may communicate over a wide area network such as the Internet 20 (also referred to as a "global network" or "wide area network" or "WAN" operating with TCP/IP packet protocols).

As will be appreciated by those skilled in the art, the operations server 12 may be implemented as a single server or as a plurality of servers (such as a server farm and/or virtual servers). Alternatively, the functionality of operating server 12 may be implemented elsewhere in system 10, such as on advertiser computer 14, indicated at 12A, on publisher server system 16, indicated at 12B, on proxy server 18, indicated at 12C, or as part of cloud computing, indicated at 12D, as non-limiting examples. As will be appreciated by those skilled in the art, the process of operating the server 12 may be distributed to these systems within the system 10.

In one embodiment, the operating server provides an intermediary service between advertisers and publishers to facilitate the purchase and sale of advertisements over the Internet. In other embodiments, the operations server provides intermediary services and/or convenience services for client computers and resource server systems to enhance various e-commerce activities.

In the example of FIG. 1, system 10 includes a plurality of advertiser computers 14{ ADV.1, ADV.2, …, ADV.N }. Adv.1 may be, for example, the manufacturer of soft drinks, adv.2 may be the computer manufacturer and adv.n may be, for example, an accounting office. Alternatively, the advertiser may be an advertising agent that acts as an intermediary for purchasing advertisements for the client. Although the individual advertiser computers 14 may be implemented as a single computer, such as a personal computer or computer workstation, they may also represent other computer configurations, such as a cluster of computers on a Local Area Network (LAN).

The issuer server systems 16 may each represent one or more servers, such as a server farm. In the example of FIG. 1, the system 10 includes a plurality of publisher server systems 16{ PUB.1, PUB.2, …, PUB.M }. For example, pub.1 may be an internet portal, pub.2 may be a search engine, and pub.m may be a news website. As previously mentioned, one or more of the issuer server systems 16 may implement some or all of the functionality of the operations server 12.

Proxy server 18 may be a computer, server, or server cluster that acts as an intermediary or proxy between operating servers, advertiser computers, and/or publisher server systems 16. As noted above, some or all of the functionality of the operations server 12 may be implemented on the proxy server 18.

It should also be noted that the system 10 as shown in fig. 1 is merely one example of such a system. By way of non-limiting example, advertiser computers 14 may be generalized to virtually any form of client computer. As another non-limiting example, the publisher server system 16 may be generalized to almost any form of resource server system. Thus, it will be appreciated that while certain embodiments described herein are intended for e-commerce advertising sales and purchases, many other embodiments may be implemented by the system 10 described herein.

Fig. 2 is a simplified block diagram of a computer and/or server 22 suitable for use in the system 10. By way of non-limiting example, computer 22 includes a microprocessor 24, which microprocessor 24 is connected to a memory bus 26 and an input/output (I/O) bus 30. A number of memories and/or other high speed devices, such as RAM32, SRAM 34, and VRAM 36, may be connected to memory bus 26. Attached to I/O bus 30 are various I/O devices such as mass storage 38, network interface 40, and other I/O42. As will be appreciated by those skilled in the art, there are many computer readable media available to the microprocessor 24, such as RAM32, SRAM 34, VRAM 36, and mass storage 38. The network interface 40 and other I/O42 may also include computer readable media such as registers, caches, buffers, and the like. The mass storage 38 may be of various types including a hard disk drive, an optical disk drive, and a flash drive, to name a few.

It should be noted that other computer controlled devices may be within the scope of the system of FIG. 1. For example, a number of devices, such as cell phones, palm top computers (PDAs), network appliances, tablets, and other portable and non-portable devices may acquire information, provide information, or otherwise interact with the system 10. In many cases, these devices support electronic advertising.

It should be noted that the selection of publishers can be enhanced by, for example, classifying publishers according to content. That is, as a number of non-limiting examples, a "publisher" may be a single legal entity, or a subset of that entity, or a portion of a group of entities. For example, a publisher entity may have 1000 publications, 100 of which are for dramatic content, 100 for comedies, etc. A subset of the publications of publisher entities having common subject matter may be considered "publishers". Further, a "publisher" may include a set of publications provided by different agents that conform to a theme such as drama, sports, or entertainment, as non-limiting examples.

It should also be noted that in some cases, the advertising network is substantially transparent to the advertiser, the publisher, or both. That is, for an advertiser, the advertising network may be viewed as one or a collection of publishers, and/or for a publisher, the advertising network may be viewed as one or a collection of advertisers.

As used herein, "internet advertising location" refers to the location or particulars of an advertisement as seen after direct or indirect transmission over the internet to a computer, computer-controlled device, or other "endpoint" or "video display. Typically, a large number of internet advertising locations are referred to as "internet advertising locations". However, in some cases, an "internet ad location" may be a set of "internet ad locations". For example, a website that includes a large number of web pages may be considered an internet ad spot, although each web page may itself be considered an internet ad spot. Alternatively, an "internet ad spot" may also be considered an "internet ad spot" filtered by, for example, one or more demographic characteristics. For example, when filtering for male and female viewers, advertisements on a web page may be viewed as different locations.

A very common internet advertising location is a web page. In such an example, the ad location may be associated not only with the URL of the web page, but also its relative location on the web page, and in proximity to other elements of the web page, for example.

In fig. 3, a block diagram of an exemplary ad placement scoring system 44 includes a scoring system controller 46, a metrics database 48, a parameters database 50, a scoring engine 52, a scoring database 54, and a report generator 56. It should be noted that various elements of scoring system 44 may be real and/or virtual, and that some or all of the elements may comprise computer-implemented processes.

For purposes of illustrating examples, the advertisement position scoring system will be described with respect to video advertisements that are visible via the internet, it being understood that other forms of communication media (such as non-commercial communications) are alternate examples of "advertisements" as used herein, whether or not for advertising purposes.

Thus, in this example, the video advertisement may be associated with a website or web page or a particular location on a web page. Typically, a video advertisement includes a "play" button that, when activated by a mouse click, will begin playing the video advertisement (which is also referred to as a "click"). Also, video advertisements can typically be played to the end or stopped before the end. The number of video ads played is also referred to as "play-through" and may be measured, for example, in terms of percentage (e.g., video completion rate or "VCR") or in seconds. In some cases, the video advertisement may include links to other resources to provide additional information, content, the ability to order products, or feedback that may improve the video advertisement process, as non-limiting examples.

Websites, objects implanted therein, web servers, and other internet resources often have the ability to monitor website activity, including the display and/or interaction with advertisements. Data obtained from such monitoring functions may provide metrics that may be used to analyze the performance of the advertisement. For example, one common metric is "exposure," which in this example is the number of times a web page including a particular advertisement has been displayed on the web page over a period of time. Another common metric is "click-through rate," which is the percentage of the amount of clicks to the amount of impressions over a period of time. Another common metric is "browsing rate" or video-completion rate (VCR), which is the average rate (usually expressed in percentage) of browsing over a period of time. These and other metrics, well known to those skilled in the art, may be obtained from ad spots and accumulated for data storage and analysis.

As described above, an "ad location" may have other uses besides advertising, such as communication, training, or entertainment. Nonetheless, metrics associated with ad placement are useful for data storage and analysis. Further, the "ad location" may be displayed in other locations besides the web page. As a non-limiting example, the advertising location may be displayed on the screen of a mobile phone or on the screen of a tablet computer. An "endpoint," e.g., a computer-controlled device that displays advertisements to a user, is also a useful metric for analytical purposes.

In the example of FIG. 3, metrics obtained from different ad spots may be stored in metrics database 48 for parallel and/or subsequent analysis. By way of non-limiting example, metric database 48 may be located and/or distributed, and metric database 48 may be found, in part or in whole, at various locations in the example system of FIG. 1. Scoring system controller 46 may be in two-way communication, indicated at 49, with metric database 48.

Parameter database 50 can also be seen in the example of fig. 3. Parameter database 50 may include additional information related to internet advertising locations. For example, the database 50 may include demographic information, such as age range or gender, endpoints, etc. of the audience, which may be obtained simultaneously or over time from the ad location or elsewhere. As another example, the parameter database may include weight factors for the metrics of the metrics database 48. By way of non-limiting example, parameter database 50 may be located at one location and/or distributed, and may be found partially or wholly at multiple locations in the example system of FIG. 1. Scoring system controller 46 may be in two-way communication, indicated at 51, with parameter database 50. Further, metric database 48 and parameter database 50 may be integrated as a unified real and/or virtual database or may be linked as a real and/or virtual database.

In this example, scoring system 44 also includes a scoring engine 52 that may be used to generate a score associated with the internet advertisement location. In this example, scoring engine 52 operates on one or more metrics obtained from metrics database 48 to derive a score that may characterize an ad placement. If the score thus obtained is directly related to the desirability of an advertisement at that location, the score may be considered a "quality score" for that advertisement location. By providing a normalized quality score for an ad spot, comparisons can be made to make ad decisions and/or to make improvements to the "quality" of the ad spot. In this example, scoring engine 52 is in two-way communication with scoring system controller 46, as shown at 53.

The scores obtained by the scoring engine 52 may be stored in a scoring database 54, in this example, the scoring database 54 is in two-way communication with the scoring system controller 46, as shown at 55. The scoring database 54 may be located at one location and/or distributed, and may be found in part or in whole in multiple locations in the example system of fig. 1. Further, scoring database 54, metrics database 48, and parameters database 50 may be integrated into a joint real and/or virtual database, or may be linked as real and/or virtual databases. "database" herein refers to any ordered store of data for its systematic retrieval. For example, the database may be a flat database, a table, a relational database, and the like.

In this example, report generator 56 is connected to scoring system controller 46 for bi-directional communication, shown at 57. For example, report generator 56 may be used to generate reports derived from data in score database 54 or other locations. For example, the report generator 56 may generate an ordered quality list or "quality ordering" of advertisement locations. The score associated with a particular ad slot may provide an indication of the desirability or "quality" of that ad slot.

In fig. 4, a state diagram of an exemplary ad placement scoring process 58 includes a central control process 60, a metrics process 62, a parameters process 64, a scoring database update process 66, and a reporting process 68. In this example, the central control 60 may perform a metrics process 62, such as retrieving stored metrics from the metrics database 48 (see FIG. 3). Also, as an example, the central control 60 may perform a parameter process 64, such as storing weights and/or demographic parameters in, for example, the parameter database 50. Central control 60 may also perform a scoring database update process 66 and/or perform a reporting process 68, e.g., on scoring engine 52 and/or report generator 56, respectively, of fig. 3.

The example score update process 66 of fig. 4 is illustrated in more detail in fig. 5. Process 66 begins at 70 and in a computer-implemented action or "operation" 72, a determination is made as to whether the update process is complete. If so, as shown at 74, the process 66 is complete and process control returns to the central control 60 (see FIG. 4). If not, the next location parameter and metric is retrieved in operation 74. Operation 78 then generates one or more location scores that are stored, for example, in a scoring database (see fig. 3).

Generating a mass fraction

As a non-limiting example, a weighting function may be used to generate the quality score. A weighting function is a mathematical method used when performing, for example, a summation, integration or averaging, in order to give some elements that are more important or have a greater impact on the result than other elements in the same set. In this example, the elements in the set are selected from metrics associated with ad positions, and the weights are constants or functions associated with ad positions, and in some examples, functions associated with relevant demographic characteristics. As used herein, a "quality score" may be referred to as an issuer quality score or "PQS".

One type of weighting function is a weighted sum given by equation 1 as follows:equation 1

Where m (i) is the ith metric of the n selected location-associated metrics, and f (i) is a weighting function associated with metrics m (i). As described above, the weighting function may be a constant stored in, for example, an array, table, or other data structure in parameter database 50. Alternatively, f (i) may be a function of several constants and/or variables, including demographic variables, which may also be stored, for example, in the parameter database 50.

Another form of weighting function is a weighted average. A weighted average or "weighted average" is typically used in statistics to compensate for existing deviations. Weighted averages are similar to arithmetic means ("means" of the most common type), with some measures contributing more than others, except that the measures act equally on the final average. The concept of weighted averages plays an important role in descriptive statistics and also appears in more general form in a number of other mathematical domains. As is well known to those skilled in the art, there are other forms of weighted averages, including weighted geometric averages and weighted harmonic averages.

Once the raw mass scores are obtained, they can be normalized for easier comparison by analysts. For example, if the raw quality scores are in the range of 0 to 1, then these scores may be normalized to the range of 0 to 100 by multiplying by 100. The normalized scores are easier for the human brain to remember and compare.

Given a sufficiently large scoring database 66, the artificial neural network may also be trained to provide a quality score. Artificial Neural Networks (ANN), also commonly referred to simply as "neural networks," are computational models that simulate structural and/or functional aspects of biological neural networks. Neural networks include a set of interconnected artificial neurons, and process information computationally using a connectionism approach. In many cases, neural networks are adaptive systems that change their structure based on external or internal information flowing through the network during a learning phase. Most neural networks are non-linear statistical data modeling tools that can be used to model complex relationships between inputs and outputs or to discover patterns in data.

To be properly "trained," many examples should be applied to the neural net during the training phase. For a particular ad location, the location metric and location parameter are applied to the input values of the neural network and the quality score as stored in the scoring database 54 is applied to the output values. The neural network then internally adjusts the "weights" of its neurons so that the output values are a weighted function of the input values. After a number of examples, the neural net learns how to generate the correct quality score based on any set of input values.

The advantage of trained neural networks is that it is not necessary to know how to get the correct answer. In fact, more metrics can be input to the neural network than can be conventionally processed through human-assisted computations. This has the advantage of increased robustness and may cause the neural network to "find" transfer function relationships that are not considered by the designer. Once properly trained, the neural network can operate without any human interaction regarding the selection of weights for the weighting functions.

For new systems, e.g. systems where the scoring database has not been started, it is preferred to start with a simple weighted function scoring engine, where the operator selects several metrics that need to be done and assigns weight constants to these metrics based on expert knowledge and (to some extent) human intuition. These weights are all scores, and the sum of these weights is "1". Since the scoring database is populated with data and additional experience is accumulated, the weight constants may be adjusted by changing the weights, and/or additional metrics may be added. Furthermore, weighting functions may be selectively assigned and different sets of weights may be associated with different demographics or different "demographics". For example, one set of weights may be associated with a male viewer's ad position, and another set of weights may be associated with the same ad position of a female viewer.

Accordingly, scoring engine 52 may become more sophisticated and accurate with increased human intervention. However, in certain instances, interrelationships between a number of potential metrics and parameters may limit the sophistication of scoring engine 52. In this case, scoring engine 54 may augment or be replaced by a neural network if a sufficiently large scoring database 54 has been established.

It should be noted that the examples listed above for scoring engine 52 are not exhaustive of the potential techniques. The scoring engine may also be implemented using expert system technology, for example. Further, the scoring engine performance may be an interactive process with other inputs, processes, and systems.

Example 1-Uniform metrics

The following example illustrates the generation of a PQS by executing a weighting function, such as by scoring engine 52. Suppose that for a particular ad location, such as on a web page, two types of metrics can be tracked: 1) click rate of 5%; and 2) a 75% browsing rate. Further, it is also assumed that the Click Through Rate (CTR) is weighted 0.6 and the browsing rate (VCR) is weighted 0.4, i.e., the click through rate is weighted more heavily than the browsing rate in this example. Using equation 1, the PQS for an ad position as a weighted sum is:

Q=0.6（5）+0.4（75）=3+30=33

since the unit of the metric in this example is a percentage (i.e., the metric is uniform), no normalization is required.

Continuing with the same example, assume that the weights given above are a click rate of 0.4 and a browse rate of 0.6 for "female" in the demographic data and for "male" in the demographic data. Then, applying equation 1 for ad placement as a weighted sum for "men" in the demographic data, we get: q' =0.4 (5) +0.6 (75) =2+45=47

Thus, it can be seen that for a given ad position, the PQS is 33 for females, but 47 for males. Thus, advertisements for males are more effective at the advertisement location than advertisements for females.

Example 2-non-uniformity metric

Another example of obtaining an issuer quality score may refer to the table of fig. 6. In this non-limiting example, three metrics are used: video completion rate ("VCR"), click-through rate ("CTR"), and inventory cost ("cost").

As described above, a VCR corresponds to the average percentage of video that is played. For example, in general, if 30 seconds of video is played for 27 seconds, its VCR is 90%. A high VCR may be considered satisfactory by advertisers since it means that their information or branding is effectively communicated to the consumer.

CTR is the percentage of time that the video is "selected" while being played. For example, if a video is playing on a web page, it may be selected by "clicking" on the video by activating a pointing device, such as a mouse. Typically, clicking on a video advertisement displayed on a web page opens the advertiser's web page.

Cost is inventory cost and is typically measured in a cost per thousand impressions ("CPM"). The cost is related to the "Reach", e.g., the number of impressions made by the advertiser.

It should be noted that the measurement ranges and/or units for the three exemplary metrics of VCR, CTR, and cost are non-uniform. For example, VCR may range from 0 to 100%, CTR may range from 0 to 5%, and cost may range from $ 0 to $ 30. Since PQS preferably reflects a complex of metrics, some form of normalization of the metric data may be desirable. One skilled in the art will appreciate that there are a variety of normalization techniques that can be used. For example, a linear scale transformation may be used to normalize the non-uniformity metric data.

As a non-limiting example, assume that the data of the metric has a range or scale from A to B, and it is to be converted or "normalized" to a scale of 1 to 10, where A is transformed to 1 and B is transformed to 10. In this example, the intermediate point between a and B is transformed to an intermediate value between 1 and 10 or 5.5, since a linear transformation algorithm is used. According to the above rule, the following (linear) equation can be applied to any number x on the A-B scale: (equation 1) y =1+ (x-a) (10-1)/(B-a).

It should be noted that if x = a, then y =1+0=1, as needed, and if x = B, then y =1+ (B-a) × (10-1)/(B-a) =1+10-1=10, as needed. Even if A > B, the equation is still applicable.

It should also be noted that equation 1 above can be generalized to the following case: the final scale is between any two numbers, not necessarily between 1 and 10, and can be replaced in the equation by C and D, respectively. The case of x = a may be transformed into y = C, and x = B may be transformed into y = C + (D-C) = D.

In the table example of fig. 6, the metrics measured for a number of hypothetical consumers during the month of april are shown. The first column of the table represents the issuer, the second column the number of presentations provided, the third column is "empty inventory", and the fourth, fifth, and sixth columns are the issuer's VCR, CTR, and cost, respectively, measured during april.

The seventh, eighth, and ninth columns of fig. 6 include normalized values for the metrics VCR, CTR, and cost. By normalizing these metrics, a number of different issuer quality scores (PQS) may be obtained as shown in the tenth, eleventh, and twelfth columns of the table. For example, these different PQS scores may be weighted to reflect advertiser preferences.

For example, if an advertiser is interested in "brand promotion," e.g., has a better brand awareness, the VCR may be weighted more heavily than the CTR. Alternatively, if interaction and Reach (Reach) is more important to the advertiser, the CTR or cost is weighted more heavily.

Different issuer mass scores may also be provided with "cutoff" values. For example, the VCR PQS threshold may be 6, the CTR PQS threshold may be 1.3, and the Reach threshold may be 1.5. That is, in this example, any publisher that does not meet the threshold for the required PQS cannot run any advertisement.

It will be appreciated that the PQS value is a useful tool for deciding which publisher an advertisement should be placed with. Since the PQS value may be generated on a real-time basis, the decision as to where the advertisement should be delivered may change dynamically. However, in many instances, it has been found that the PQS value (or at least the use of a new PQS value) should be updated at intervals that may eventually balance short term anomalies. For example, the PQS value may be updated every 1 minute, every 5 minutes, every 15 minutes, every 30 minutes, every 60 minutes, or every 120 minutes. The PQS value may also be updated daily, weekly, monthly, or at longer intervals, or within seconds or fractions of a second.

Example 3-iterative update of Scoring database

In an exemplary embodiment, the scoring database may be updated periodically, for example, every 15 minutes. In this example, central control 60 activates process 66 to perform a scoring database update process every 15 minutes to obtain the current metric from metric database 48 and parameter database 50.

To prevent the quality score from varying widely with each update, the latest metrics and/or parameters may be averaged together with the historical metrics and/or parameters. For example, the metric applied to the scoring database update process may be an average of the metric and the parameter during a time "window" that moves forward in steps of 15 minutes. The window may be selected to have a sufficient length of time to eliminate any short term peaks or dips in the quality fraction, but not so long as to underestimate or exaggerate the current quality level. For example, the window may be 1 day to 5 days in length.

It should be noted that the order information of the second, third, etc. may be obtained from the iterative set of metric data. For example, a velocity (e.g., a velocity of a metric change) and an acceleration (e.g., an acceleration of a metric change) may be calculated and input to the scoring database update process.

Example 4 optimized Transmission of video stream data

It will be appreciated that the above-described systems and methods allow for qualitative analysis by potential targets or "ad spots" (e.g., web pages, mobile devices, etc.) in order to increase the efficiency and effectiveness of video stream data. As a non-limiting example, the advertising network may dynamically adjust the transmission of video advertisements to one or more web pages based on their current PQS.

For example, if the advertising network has 1000 advertisements to be served and selects two web pages to serve those advertisements, and if the two web pages have PQS of 4 and 5, respectively, it may be decided to serve all advertisements on the web page with the higher PQS, or to divide the advertisements in two in a ratio of 4:5, or to serve or divide in other ways based on additional rules. Further, if the PQS of one or both web pages changes before 1000 ads are served, the distribution ratio may be adjusted to reflect the new condition.

Since these advertisements are delivered more efficiently, fewer video advertisements will need to be delivered to provide similar results. Thus, the overall load on the network will be reduced because fewer video ads need to be transmitted over the network for a particular ad campaign. Alternatively, the processes and systems disclosed herein may result in a reduced need to increase the number of delivered video advertisements in order to realize the benefits of a larger reach, thereby reducing the amount of potential future load on the network.

Industrial applicability

Embodiments disclosed herein include systems and methods for more efficient transmission of large amounts of data, particularly video data, over limited bandwidth networks, such as the internet or telephone networks. The technical effect is to reduce the overall network traffic level and increase the efficiency and effectiveness of video data transmission.

Although various examples have been described using specific terms and devices, such description is for illustrative purposes only. The words used are words of description rather than limitation. It is to be understood that changes and modifications may be made by one skilled in the art without departing from the spirit or scope of any of the examples described herein. Moreover, it should be understood that aspects of various other examples may be interchanged both in whole or in part. Accordingly, it is intended that the claims subsequently presented herein be interpreted in accordance with their true spirit and scope and not as limiting or prohibited.

Claims (18)

1. A network data transmission system, the system comprising:

a location metric database for storing one or more publisher location metrics relating to internet publisher advertising locations, the publisher location metrics comprising: i) a display amount comprising a number of times that advertisements from a plurality of advertisers are displayed on a web page over a period of time; ii) the relative location of the advertisement on the web page and proximity to other elements of the web page; iii) click through rate, which comprises the percentage of click through volume to show volume over a period of time; iv) a browsing rate comprising a percentage of the viewed amount of video advertisements over a period of time; and v) an endpoint comprising a type of computer controlled device by which the advertisement is displayed to a viewer;

a location parameter database for storing one or more location parameters, the location parameters comprising: i) demographic information about the audience, and ii) weighting factors for metrics stored in the location metrics database;

a scoring engine connected to the location metric database and the location parameter database to generate a quality score associated with the internet publisher advertisement location, wherein the internet publisher advertisement location is scored by the scoring engine using the publisher location metric and location parameter;

a scoring system controller bi-directionally connected to the location metric database, the location parameter database, and the scoring engine; and

a scoring database bi-directionally connected to the scoring system controller and periodically updated, wherein the most recent metrics and/or parameters are averaged along with historical metrics and/or parameters during the forward moving time window of step;

wherein the quality score represents the desirability of the Internet publisher's advertising location for the publisher to compare to make advertising decisions or to refine the publisher's web page to improve the quality score.

2. A network data transmission system as claimed in claim 1 wherein the location metric database and the location parameter database are linked at least in part.

3. A network data transmission system as claimed in claim 1 wherein at least one of the location metric database and the location parameter database is at least partially distributed.

4. A network data transmission system as claimed in any one of claims 1 to 3 wherein the scoring engine comprises a weighting function operating on at least some of the location metrics.

5. A network data transmission system as claimed in claim 4 wherein the weighting function is a weighted sum function.

6. A network data transmission system as claimed in claim 4 wherein the weighting function is a weighted average function.

7. A network data transmission system as claimed in claim 4 wherein the weighting function comprises a weighting factor derived from the location parameter database.

8. The network data transmission system of any one of claims 5 to 7, wherein the weighting function is implemented by a neural network.

9. A network data transmission system as claimed in claim 1 wherein at least two of the score database, the location metric database and the location parameter database are linked at least in part.

10. A network data transmission system as claimed in claim 1 wherein at least one of the score database, the location metric database and the location parameter database is distributed at least in part.

11. The network data transmission system of claim 1 further comprising a report generator connected to the system controller.

12. The network data transmission system of claim 11, wherein the report generator generates an ordered list of advertisement positions.

13. A network data transmission system as claimed in claim 12 wherein the ordered list is associated with a demographic profile.

14. A method for transmitting video data over a network, the method comprising:

a plurality of location metrics and a plurality of location parameters are obtained for a plurality of internet publisher advertisement locations,

the location metrics include one or more publisher location metrics for each internet publisher advertising location, the publisher location metrics including: i) a presentation amount comprising a number of times an advertisement is displayed on a web page over a period of time; ii) the relative location of the advertisement on the web page and proximity to other elements of the web page; iii) click through rate, which comprises the percentage of click through volume to show volume over a period of time; iv) a browsing rate comprising a percentage of the viewed amount of video advertisements over a period of time; and v) an endpoint comprising a type of computer controlled device by which the advertisement is displayed to a viewer; the location parameters include one or more location parameters for each internet publisher advertising location, the location parameters including: a) demographic information about the audience, and b) a weight factor for the issuer location metric;

storing the location metrics and location parameters in at least one database;

invoking a scoring engine connected to said at least one database for generating a plurality of quality scores associated with said plurality of internet publisher advertisement locations from said publisher location metrics and location parameters stored in said at least one database;

periodically storing the plurality of scores in a scoring database, wherein the most recent metrics and/or parameters are averaged with the historical metrics and/or parameters during the forward-moving time window of step;

wherein the plurality of internet publisher ad locations are scored by the scoring engine and the quality scores associated with the plurality of internet publisher ad locations represent desirability of the plurality of internet publisher ad locations for comparison to make an advertising decision or improvement to one or more of the plurality of internet publisher ad locations to improve one or more of the quality scores;

ranking at least a subset of the plurality of internet publisher advertisement locations based on the plurality of quality scores; and

distributing the video over the network based at least in part on the ranking.

15. The method for transmitting video data over a network according to claim 14, wherein generating the plurality of scores comprises a weighting function operating on at least some of the location metrics.

16. The method for transmitting video data over a network as recited in claim 15 wherein the weighting function is at least one of a weighted sum function and a weighted average function.

17. A method for transmitting video data over a network as defined in claim 15, wherein the weighting function comprises a weighting coefficient.

18. A method for transmitting video data over a network as recited in claim 16 wherein the weighting function is implemented by a neural network.