Source: http://www.google.de/patents/US8661111
Timestamp: 2018-01-17 05:01:37
Document Index: 53169223

Matched Legal Cases: ['application No. 60', 'application No. 60', 'Application No. 00', 'Application No. 00980233', 'art 2', 'Application No. 2001', 'Application No. 2001']

Patent US8661111 - System and method for estimating prevalence of digital content on the world ... - Google Patentsuche
The present invention is a system, method and computer program product for tracking and measuring digital content that is distributed on a computer network such as the Internet. The system collects online advertisement data, analyzes the data, and uses the data to calculate measurements of the prevalence...http://www.google.de/patents/US8661111?utm_source=gb-gplus-sharePatent US8661111 - System and method for estimating prevalence of digital content on the world-wide-web
Veröffentlichungsnummer US8661111 B1
Anmeldenummer US 09/695,216
Prioritätsdatum 12. Jan. 2000
Auch veröffentlicht unter CA2396565A1, EP1252735A2, EP1252735A4, EP1252735B1, US9514479, US20050235030, US20140122224, WO2001052462A2, WO2001052462A3
Veröffentlichungsnummer 09695216, 695216, US 8661111 B1, US 8661111B1, US-B1-8661111, US8661111 B1, US8661111B1
Erfinder Gregory J. Lauckhart, Craig B. Horman, Christa Korol, James T. Bartot
Patentzitate (308), Nichtpatentzitate (281), Referenziert von (5), Klassifizierungen (11), Juristische Ereignisse (8)
US 8661111 B1
The present invention is a system, method and computer program product for tracking and measuring digital content that is distributed on a computer network such as the Internet. The system collects online advertisement data, analyzes the data, and uses the data to calculate measurements of the prevalence of those advertisements. The system processes raw traffic data by cleansing and summarizing the traffic data prior to storing the processed data in a database. An advertisement sampling system uses site selection and definition criteria and a probe map to retrieve Web pages from the Internet, extract advertisements from those Web pages, classify each advertisement, and store the data in a database. A statistical summarization system accesses the processed raw traffic data and the advertisement data in the database to calculate advertising prevalence statistics including the advertising frequency, impressions, and spending.
1. A system for estimating a number of times content has been accessed via a network, the system comprising:
an estimating device to determine an estimate of a number of times that a webpage has been accessed at a web server;
a prober to repeatedly send requests to the web server for the webpage and, in response, receive content files; and
a statistical summarization system including a processor to determine a number of times that a first content object is included in the content files received in response to the requests, determine a total number of the requests, and estimate a number of times that the first content object has been accessed by visitors of the webpage by:
determining a rotation rate for the first content object by dividing the number of times that the first content object was included in the content files received in response to the requests by the total number of the requests; and
determining the number of times that the first content object has been accessed by visitors by multiplying the estimate of the number of times that the webpage has been accessed by the rotation rate.
2. The system of claim 1, wherein the estimating device is to receive the estimate of the number of times that the webpage has been accessed from at least one proxy cache server.
3. The system of claim 1, further comprising a sampling device that includes:
an extractor to locate a fragment of the webpage that includes the first content object; and
a classifier to perform a structural analysis of the fragment to classify the content.
4. A system as defined in claim 1, wherein at least a portion of the estimate of the number of times that the webpage has been accessed is received from a proxy.
5. A system as defined in claim 1, wherein the system comprises an advertising prevalence system.
6. A system as defined in claim 1, wherein at least a portion of the estimate of the number of times that the webpage has been accessed is received from at least one panelist computer.
7. A system as defined in claim 1, wherein the content object is an advertisement.
8. A method of estimating a number of times content has been accessed via a network, the method comprising:
repeatedly sending requests for a webpage and, in response, receiving content files;
determining a number of times that a first content object is included in the content files received in response to the requests; and
estimating, with a processor, a number of times that the first content object has been accessed by visitors of the webpage by:
determining a rotation rate for the first content object by dividing the number of times that the first content object was included in the content files received in response to the requests by a total number of the requests; and
determining the number of times that the first content object has been accessed by visitors by multiplying an estimate of the number of times that the webpage has been accessed by the rotation rate.
9. A method as defined in claim 8, wherein at least a portion of the estimate of the number of times that the webpage has been accessed is received from a proxy.
10. A method as defined in claim 8, wherein the method is performed by an advertising prevalence system.
11. A method as defined in claim 8, wherein at least a portion of the estimate of the number of times that the webpage has been accessed is received from at least one panelist computer.
12. A method as defined in claim 8, wherein the content object is an advertisement.
13. A storage device or storage disk storing instructions that, when executed, cause a machine to at least:
repeatedly sending requests for a webpage;
determine a number of times that a first content object is included in content files received in response to the requests; and
estimate a number of times that the first content object has been accessed by visitors of the webpage by:
14. A storage device or storage disk as defined in claim 13, wherein at least a portion of the estimate of the number of times that the webpage has been accessed is received from a proxy.
15. A storage device or storage disk as defined in claim 13, wherein the instructions stored on the storage device or storage disk are to be executed by an advertising prevalence system.
16. A storage device or storage disk as defined in claim 13, wherein at least a portion of the estimate of the number of times that the webpage has been accessed is received from at least one panelist computer.
17. A storage device or storage disk as defined in claim 13, wherein the content object is an advertisement.
The present application claims priority from, and incorporates by reference, the provisional patent application No. 60/175,665, filed in the United States Patent and Trademark Office on Jan. 12, 2000, and provisional patent application No. 60/231,195, filed in the United States Patent and Trademark Office on Sep. 7, 2000.
The advertisement sampling system, also known as the “prober” or “Cloudprober”, uses a robust methodology that continually seek out the most significant and influential Web sites to probe (i.e., monitor). Moreover, the site selection and definition performed by the present invention dictates the Web pages that comprise each Web site to ensure that complete, singularly branded entities are reported as such. The advertisement sampling system uses intelligent agent technology to retrieve Web pages at various frequencies to obtain a representative sample. This allows the Cloudprober to accurately assess how frequently each advertisement appears in the traffic data. After the Cloudprober fetches a Web page, the advertisement sampling system extracts the advertisements from the Web page. In the preferred embodiment, the advertisement extractor, also known as the “extractor”, invokes an automatic advertisement detection (“AAD”) process, a heuristic extraction process, to automatically extract all of the advertisements from the Web page.
FIG. 7D is a flow diagram that describes, in greater detail, the process of probing the Internet to gather sample data from FIG. 7A.
2. “Client-Side Panel Collection” retrieves sample data from each panelist via a client-side mechanism and transfers that data to a collection repository. The client-side mechanism may monitor the browser location bar, user browser, a client-side proxy, or TCP/IP stack hooks.
The advertisement sampling system 220 uses the anonymous traffic data to determine which URLs to include in the sample retrieved from the Web server 112. The advertisement sampling system 220 contacts the Web server 112 through the Internet 100 to retrieve a URL 114, 116, 118 and extract the advertisements therein along with the accompanying characteristics that describe the advertisements. The advertisement sampling system 220 stores these advertisement characteristics in the database 200. The advertisement sampling system 220, for example, Online Media Network Intelligent Agent Collection (“OMNIAC”), or the Cloudprober, repeatedly probes prominent Web sites, extracts advertisements from each Web page returned by the probe and classifies the advertisements in each Web page by type, technology and advertiser.
The probe mapping system 320 generates a probe map, i.e., the URLs 114, 116, 118 that the advertisement sampling system 220 will visit. This probe map assists the advertisement sampling system 220 with the measurement of the rotation of advertisements on individual Web sites. The preferred embodiment of the present invention continuously fetches various Web pages in the probe map. In an alternative embodiment, the present invention visits each URL in the probe map approximately every 6 minutes. Another embodiment can vary the fetching rate by considering several factors including the amount of traffic that visits the Web site as a whole and the individual Web page in question, the number of advertisements historically seen on the Web page, and the similarity of the historically observed ad rotation to other sampled pages.
The Web page retrieval system 322 uses the probe map generated by the probe mapping system 320 to determine which Web pages it needs to sample and the frequency of the sampling. For each URL in the probe map generated by the probe mapping system 320, the Web page retrieval system 322 fetches a Web page, extracts each advertisement from the Web page, and stores the advertisement's attributes in the database 200. The data retrieved from each URL in the probe map is used to calculate the frequency with which each advertisement is shown on a particular Web site.
The structural classifier 328 parses each advertisement and stores the structural components in the database 200 and passes those components to the statistical summarization system 230. Each advertisement fragment extracted by the advertisement extractor 326 is analyzed by the structural classifier 328. The process performed by the structural classifier 328 comprises duplicate fragment elimination, structural fragment analysis, duplicate advertisement detection. The structural classifier 328 performs duplicate fragment elimination by comparing the current advertisement fragment to other fragments in the database 200. Two advertisement fragments are duplicates if the fragments are identical (e.g., each fragment has the exact same HTML content). If the structural classifier 328 determines that the current fragment is a duplicate of a fragment in the database, the advertisement sampling system 220 logs another observation of the fragment and continues processing fragments.
The structural classifier 328 performs structural fragment analysis on the XML representation of the Web page by determining the “physical type” of the fragment (i.e., the HTML source code used to construct the advertisement). Physical types that the present invention recognizes include banner, form, single link, and embedded content. Banner advertisement fragments include a single HTML link having one or two enclosed images and no FORM or IFRAME tag. Form advertisement fragments include a single HTML form having no IFRAME tag. Single link advertisement fragments include a link with textual, but no IMG, FORM, or IFRAME tags. Embedded content advertisement fragments reference an external entity using an IFRAME tag. After performing this analysis, the structural classifier 328 updates the advertisement fragment in the database. For a banner advertisement fragment, the structural classifier 328 stores the link and image URL's in the database 200. A form advertisement fragment requires the creation of a URL by simulating a user submission that sets each HTML control to its default value. The structural classifier 328 stores this URL and the “form signature” (i.e., a string that uniquely describes the content of all controls in the form) in the database 200. For a single text advertisement fragment, the structural classifier 328 stores the URL for the link and all text contained within the link in the database 200. For embedded content advertisement fragments, the structural classifier 328 stores the URL associated with the external reference in the database 200. This URL is loaded by the system, and the referenced document is loaded. Once the loaded document has been structurally analyzed, the original fragment inherits any attributes that result from analysis of the new fragment.
The statistical summarization system 230 calculates the advertisement statistics for each unique advertisement in the database 200. The present invention calculates, for each Web site, the advertising impressions (i.e., the number of times a human being views an advertisement). The present invention calculates the advertising impressions, I, using the formula I=T×R, where T is the traffic going to the site, and R is the rotation of advertisements on that site. The present invention also calculates the spending, S, using the formula S=I×RC, where I is the advertising impressions for a Web site, and RC is the rate code for the Web site. Most advertising buys are complicated deals with volume purchasing discounts so our numbers do not necessarily represent the actual cost of the total buy.
The structural classifier 328 performs automated advertisable assignment to determine what the advertisement is advertising. This process includes assigning “advertisables” (i.e., attributes describing each “thing” that the advertisement is advertising) to each advertisement fragment. In another embodiment of the present invention, the advertisement sampling system 220 uses an extensible set of heuristics to assign advertisables to each advertisement. In the preferred embodiment, however, the only automatic method employed is location classification. Location classification relies on the destination URL in order to assign a set of advertisables to an advertisement. A media editor 264 uses the user interface 240 to maintain the set of classified locations. For example, the first time a media editor observes an advertisement in which the click-thru URL is www.honda.com, he can enter this URL as pertaining to the advertiser “Honda Motors”. Any subsequent advertisement that includes the same click-thru URL will also be recognized as a Honda advertisement. A classified location comprises a host, URL path prefix, and set of advertisables. Location classification assigns a classified location advertisable to an advertisement if the host in the destination URL matches the host of the classified location and the path prefix in the classified location matches the beginning of the path in the destination URL.
The first step in the process is to normalize the results from the traffic analysis system 210. The traffic analysis system 210 provides the traffic received by each Web page in the traffic data sample. FIG. 4A depicts the exemplary traffic received at each Web page 411-416, 421-424 in the Internet 100 with the label “Traffic=.” The probe map generated by the probe mapping system 320 includes an entry for each Web page 411-416, 421-424. The probe map also includes an “area” that each Web page 411-416, 421-424 consumes in the probe map. FIG. 4A depicts the exemplary area that each Web page 411-416, 421-424 consumes in the probe map with the label “Area=”. The normalized results are calculated by dividing the area that a Web page consumes in the probe map by the sum of the area for each Web page in the traffic sample. In FIG. 4A, the normalized value, or chance, for Web page P1 411 is the area for Web page P1 (i.e., 15) divided by the sum of the area for Web page P1, P2, P3, P4, P5, P6, Q1, Q2, Q3, and Q4 (i.e., 120). The normalized value is, therefore, 0.125, or 12.5%. In addition to the normalized value, the system also determines the scale by dividing the traffic for a Web page by the area for the Web page. In FIG. 4A, the scale for Web page P1 411 is the traffic for Web page P1 (i.e., 150) divided by the area for Web page P1 (i.e., 15), therefore, the scale for Web page P1 is 10. Table 1 summarizes the scale and chance values for the remaining Web page in FIG. 4A.
FIG. 4B depicts the exemplary Web page fetches at each Web page 411-416, 421-424 in the Internet 100 with the label “Fetches=”. FIG. 4B also depicts the exemplary number of views of each advertisement on a Web page 411-416, 421-424 with a label such as “A1 Views=” to indicate the number of views of advertisement A1, “A2 Views=” to indicate the number of views of advertisement A2, etc.
FIG. 4C depicts the exemplary Web page weighted fetches at each Web page 411-416, 421-424 in the Internet 100 with the label “Fetches=”. FIG. 4C also depicts the exemplary number of views of each advertisement on a Web page 411-416, 421-424 with a label such as “A1 Views=” to indicate the number of views of advertisement A1, “A2 Views=” to indicate the number of views of advertisement A2, etc. The next step in the calculation process is to calculate the Scaled Fetches for each Web site 410, 420 by summing the product of the observed fetches from FIG. 4B and the scale from FIG. 4A, for each Web page 411-416, 421-424 in the Web site. Next, the calculation computes the Traffic for each Web site 410, 420 by summing the traffic from FIG. 4A for each Web page 411-416, 421-424 in the Web site. The rate card, or CPM, is a value assigned by the media editor 264 for each Web site 410, 420. Table 2 summarizes the Scaled Fetches, Traffic, and CPM for FIGS. 4A through 4C.
The next step in the calculation process is to compute the Scaled Observations for each advertisement on each Web site 410, 420 by summing the product of the advertisement views from FIG. 4B and the scale from FIG. 4A, for each Web page 411-416, 421-424 in the Web site 410, 420. The final step in the calculation is to compute the advertising prevalence statistics (i.e., Frequency, Impressions, and Spending) for each advertisement in each Web site 410, 420. Frequency is computed by dividing the scaled observations by the scaled fetches for each advertisement in each Web site 410, 420. Impressions is computed by multiplying the Frequency by the Traffic from Table 2 above for each advertisement in each Web site 410, 420. Spending is computed by multiplying the Impressions by the CPM from Table 2 above for each advertisement in each Web site 410, 420. Table 3 summarizes the Scaled Observations, Frequency, Impressions, and Spending for Web site P 410 using the data in FIGS. 4A through 4C. Table 4 summarizes the Scaled Observations, Frequency, Impressions, and Spending for Web site Q 420 using the data in FIGS. 4A through 4C.
Scaled Observations Frequency Impression Spending
Scaled Observations Frequency Impressions Spending
AS 1.5 0.03 1.78 0.09
For instance, in FIG. 4D, the sample site tree has five probes URLs 431-435, P1˜5, placed on five main branches off a main page and 14 secondary branches. The number on each page is the sample traffic going to that page. Probe P1 on the home page, “www.testsite.com”, measures the rotation, R, to be applied to the traffic going to that main page, with traffic of 88 page views. Branch A has a single probe, P2, placed on the top-level page of that branch with a probing URL “www.testsite.com/A/”. The rotation of this single probing URL is estimated as RA and is applied to the traffic for that entire stem, a total of 21 page views. Branch C has a probe, P3, on a heavily trafficked secondary branch page, with a probing URL “www.testsite.com/C/third.html”. The rotation, Rc, of this page is applied to all the secondary branch pages on that stem and also up one level in the tree, across a total of 25 page views. Branch E receives a large portion of the traffic for the site, a total of 61 page views, and therefore is assigned two probes, P4 and P5. These are on two secondary branch pages, “www.testsite.com/E/first.html” and “www.testsite.com/E/third.html”. The rotation of each is applied the traffic to those individual pages. For the remaining 18 page views on that branch (ten page views from two secondary pages and eight from the top level page of that branch) a weighted rotation is calculated, RE ((13×RE1)+(30×RE3))/(13+30). The analysis of stem rotation results in advertising impressions for over 96% of the site. The impressions for the final two branches, B and D, are calculated with an average rotation from adjacent branches, weighted by traffic,
R B =R D=((21×R A)+(25×Rc)+(61×R E))÷(21+25+61).
FIG. 5 illustrates a database structure that the advertising prevalence system 130 may use to store information retrieved by the traffic sampling system 120 and the Web page retrieval system 322. The preferred embodiment segments the database 200 into partitions. Each partition can perform functions similar to an independent database such as the database 200. In addition, a partitioned database simplifies the administration of the data in the partition. Even though the preferred embodiment uses database partitions, the present invention contemplates consolidation of these partitions into a single database, as well as making each partition an independent database and distributing each database to a separate general purpose computer workstation or server. The partitions for the database 200 of the present invention include sampling records 510, probing definitions 520, advertising support data 530, and advertising summary 540. The preferred embodiment of the present invention uses a relational database management system, such as the Oracle8i product by Oracle Corporation, to create and manage the database and partitions. Even though the preferred embodiment uses a relational database, the present invention contemplates the use of other database architectures such as an object-oriented database management system.
If the site definition for “somesite” includes the inclusive URL prefix “com.somesite/” and the exclusive URL prefix “com.somesite/foo/bar”, the application of this site definition to the above sample URLs listed above yields a system that includes URL 1, 2, and 4. URL 3 is not part of the site definition due to the explicit exclusion of “com.somesite/foo/bar”. URL 5 is not part of the site definition because it was never included in the inclusive URL prefix “com.somesite/”. The user interface 240 populates the site definition 522 area in database 200. The probe mapping system 320 accesses the data in the site definition 522 area to determine which URLs to probe. The statistical summarization system 230 accesses the data in the site definition 522 area to determine traffic levels to sites by summing traffic to URLs included in a site.
The advertisement extraction rule definition 526 area describes Extensible Markup Language (“XML”) tags, typically representing a normalized HTML document, that indicate those portions of the content that the system considers to be advertisements. The system defines an extraction rule in terms of “XML structure” and “XML features”. “XML structure” refers to the positioning of various XML nodes relative to others XML nodes. For example, an anchor (“A”) node containing an image (“IMG”) node is likely an advertisement. After using this structural detection process to match the advertisement content, the system examines the features of the content to determine if the content is an advertisement. To continue the previous example, if the image node contains a link (“href”) feature that contains the sub-string “adserver”, it is very likely an advertisement. Features may match based on a simple sub-string, as in the example, or a more complicated regular expression. Another form of extraction rule may point to a specific node in an XML structure using some form of XML path specification, such as a “Xpointer”. The media editor 264 populates the advertisement extraction rule definition 526 area in the database 200. The advertisement extractor 526 of the advertisement sampling system 220 accesses the data in the advertisement extraction rule definition 526 area to determine which portions of each probed page represent an advertisement.
2. A “location classification” assigns an advertisable to a location prefix that the system uses to match the destination of the advertisement. For example, a media editor 264 creates a location classification by specifying that the location “com.honda” indicates an advertisement for Honda. An advertisement that points to “com.honda.www/cars”, therefore, associates the advertisement with Honda.
The database objects comprising the “core schema” are most frequently used by various components of the OMNIAC system. Code bases that rely on this schema include implementation of the back-end processes that pull advertisements from the Web. Additionally, database schemas utilized by other components associated with OMNIAC are composed of some or all of the tables in the core schema. The core schema is conceptually composed of four sub-schemas including advertising, advertisements, probing, and sites. The advertising sub-schema holds information about “advertiseable” entities along with which entities each advertisement is advertising. The advertisements sub-schema describes the advertisements that the system has located and analyzed. The probing sub-schema defines “when”, “where”, and “how” for the probing process. The sites sub-schema describes Web sites, including structural site definitions and rate card information.
The Advertising Decomposition schema contains a few tables in addition to AD_INFO.
ADV_IMPLICATION is a cache of advertisable implications derived from the hierarchy in ADVERTISABLE. This is used to speed operation of the analysis module. AD_INFO_FLATTENED is a more readily queried version of AD_INFO containing
advertisement/advertisable pairs for each of the fields in AD_INFO that reference ADVERTISABLE. Finally, AD_TECHNOLOGY2 describes advertisement technologies understood by the analysis module that are presentable to the user in the front end.
The Advertisement View Summarization sub-schema covers the single table 5 PLACEMENT_SUMMARY. PLACEMENT_SUMMARY is keyed off of day, advertisement, and slot, and contains, in the CNT field, the number of times an advertisement was seen in a slot on a particular day.
The third sub-schema in the Analysis schema is Slot Statistics. This sub-schema describes advertisement behavior in the context of slots in addition to information about the slots themselves. A slot is a location on the Web in which advertisements rotate, currently defined in terms of the location ID (a reference to AD WEB LOCATION.ID) and extraction rule ID (a reference to EXTRACTION RULE.ID).
The presentation tier 620 retains the programs that manage the interface between the advertising prevalence system 130 and the client 140, account manager 260, operator 262, and media editor 264. In FIG. 6, the presentation tier 620 includes the TCP/IP interface 622, the Web front end 624, and the user interface 626. A suitable implementation of the presentation tier 620 may use Java servlets to interact with the client 140, account manager 260, operator 262, and media editor 264 of the present invention via the hypertext transfer protocol (“HTTP”). The Java servlets run within a request/response server that handles request messages from the client 140, account manager 260, operator 262, and media editor 264 and return response messages to the client 140, account manager 260, operator 262, and media editor 264. A Java servlet is a Java program that runs within a Web server environment. A Java servlet takes a request as input, parses the data, performs logic operations, and issues a response back to the client 140, account manager 260, operator 262, and media editor 264. The Java runtime platform pools the Java servlets to simultaneously service many requests. A TCP/IP interface 622 that uses Java servlets that functions as a Web server that communicates with the client 140, account manager 260, operator 262, and media editor 264 using the HTTP protocol. The TCP/IP interface 622 accepts HTTP requests from the client 140, account manager 260, operator 262, and media editor 264 and passes the information in the request to the visit object 642 in the business logic tier 640. Visit object 642 passes result information returned from the business logic tier 640 to the TCP/IP interface 622. The TCP/IP interface 622 sends these results back to the client 140, account manager 260, operator 262, and media editor 264 in an HTTP response. The TCP/IP interface 622 exchanges data with the Internet 100 via the TCP/IP network adapter 614.
The infrastructure objects partition 630 retains the programs that perform administrative and system functions on behalf of the business logic tier 640. The infrastructure objects partition 630 includes the operating system 636, and an object oriented software program component for the database management system (“DBMS”) interface 632, administrator interface 634, and Java runtime platform 638.
After the traffic analysis application 652 processes a URL 114, 116, 118 identified by the traffic sampling system 120, the visit object 642 invokes a method in the advertising sampling application 654 to retrieve the URL 114, 116, 118 from the Web site 110. The advertising sampling application 654 processes the retrieved Web page by extracting embedded advertisements and classifying those advertisements. The advertising sampling application 654 stores the data retrieved by the Web page retrieval system 322 and processed by the Web browser emulation environment 324, advertisement extractor 326, and the structural classifier 328 in the advertising sampling data 664 state and the database 200. FIGS. 7A, 7C, 7D, and 7E describe, in greater detail, the process that the advertising sampling application 654 follows for each URL 114, 116, 118 identified by the traffic sampling system 120. Even though FIG. 6 depicts the central processor 616 as controlling the advertising sampling application 654, a person skilled in the art will realize that the processing performed by the advertising sampling application 654 can be distributed to a separate system configured similarly to the advertising prevalence system 130.
After the traffic analysis application 652 and the advertisement sampling system 654 process the URL 114, 116, 118 identified by the traffic sampling system 120, the visit object 642 invokes a method in the statistical summarization application 656 to compute summary statistics for the data. The statistical summarization application 656 computes the advertising impression, spending, and valuation statistics for each advertisement embedded in URL 114, 116, 118. The statistical summarization application 656 stores the statistical data in the statistical summarization data 666 state and the database 200. FIG. 7F describes, in greater detail, the process that the statistical summarization application 656 follows for each URL 114, 116, 118 identified by the traffic sampling system 120. Even though FIG. 6 depicts the central processor 616 as controlling the statistical summarization application 656, a person skilled in the art realizes that the function performed by the statistical summarization application 656 can be distributed to a separate system configured similarly to the advertising prevalence system 130.
FIG. 7A is a flow diagram of a process in the advertising prevalence system 130 that measures the value of online advertisements by tracking and comparing online advertising activity across all major industries, channels, advertising formats, and types. Process 700 begins, at step 710, by sampling traffic data from the Internet 100. FIG. 7B describes step 710 in greater detail. Step 720 uses the sampled traffic data from step 710 to perform site selection, and define and refine site definitions for the advertising prevalence system 130. Step 730 uses the result of the site selection and definition process to generate a probe map based on the sampled traffic data. FIG. 7C describes step 730 in greater detail. Step 740 uses the probe map from step 730 to visit the Internet 100 to gather sample data from the probe sites identified in step 730. FIG. 7D describes step 740 in greater detail. For each URL retrieved in step 740, step 750 extracts the advertisements from the URL, step 760 classifies each advertisement, and step 770 calculates the statistics for each advertisement. FIGS. 7E and 7F describe, respectively, steps 760 and 770 in greater detail. Finally, process 700 performs data integrity checks in step 780 to verify the integrity of the data and analysis results in the system.
FIG. 7B is a flow diagram that describes, in greater detail, the process of sampling traffic data from FIG. 7A, step 710. Process 710 begins in step 711 by gathering data from a Web traffic monitor such as the traffic sampling system 120. Process 710 strips the user information from the data retrieved by the Web traffic monitor in step 712 to cleanse the data and guarantee the anonymity of the sample. For each URL in the cleansed sample, step 713 measures the number of Web page views observed in the traffic data. Step 714 completes process 710 by statistically extrapolating the measured number of Web page views in the sample to whole universe of the Internet 100.
FIG. 7C is a flow diagram that describes, in greater detail, the process of generating a probe map based on sampled traffic data from FIG. 7A, step 730. Process 730 begins in step 731 by analyzing a subset of the sample traffic data that falls within eligible site definitions. Following the analysis in step 731, step 732 builds an initial probe map based on the sample traffic data. Step 733 analyzes the historic advertisement measurement results in the database 200 for the URLs in the initial probe map. Step 734 uses these historic results as well as system parameters to optimize the sampling plan. Step 735 completes process 730 by monitoring the sample results and adjusting the system as necessary.
FIG. 7D is a flow diagram that describes, in greater detail, the process of probing the Internet to gather sample data from FIG. 7A, step 740. Process 740 begins in step 741 by fetching a Web page from the Internet 100. The Web page from step 741 is passed to a Web browser emulation environment in step 742 to simulate the display of that Web page in a browser. This simulation allows the advertising prevalence system 130 to detect advertisements embedded in the Web page. These advertisements may be embedded in JavaScript code, Java applet or servlet code, or common gateway interface code such as a Perl script. In addition, the simulation in step 742 allows the advertising prevalence system 130 to detect dynamic and interactive advertisements in the Web page. After the simulation in step 742, step 743 extracts the advertisement data from the Web page and step 744 stores the advertisement data in the database 200. Step 745 determines whether process 740 needs to fetch another Web page to gather more sample data. In the preferred embodiment, process 740 continuously samples Web pages from the Internet 100. A person skilled in the art realizes that the functionality performed by step 745 can be associated with a scheduling system that will schedule the probing of the Internet 100 to gather the sample advertising data.
US4044376 13. Aug. 1976 23. Aug. 1977 Control Data Corporation TV monitor
US5878213 * 15. Febr. 1996 2. März 1999 International Business Machines Corporation Methods, systems and computer program products for the synchronization of time coherent caching system
US5878426 * 23. Dez. 1996 2. März 1999 Unisys Corporation Statistical database query using random sampling of records
US6084875 29. Okt. 1997 4. Juli 2000 Ericsson Inc. Routing of internet traffic and related internet service provider services
US6108782 24. Juni 1997 22. Aug. 2000 3Com Corporation Distributed remote monitoring (dRMON) for networks
US6141686 23. Juni 1998 31. Okt. 2000 Deterministic Networks, Inc. Client-side application-classifier gathering network-traffic statistics and application and user names using extensible-service provider plugin for policy-based network control
US6167402 * 27. Apr. 1998 26. Dez. 2000 Sun Microsystems, Inc. High performance message store
1 "An updated quick look at Viola WWW," retrieved from http,//www.w3.org.History/19921103-hypertextlhypertext/Viola/Review.html, on May 24, 2006, 2 pages.
2 "Basic HTTP as defined in 1992: A protocol for :networked information," retrieved from http://www.w3.org/Protocols!HTfp/HTTP2.html, on May 24, 2006, 2 pages.
3 "CGI: Common Gateway Interface," retrieved from http://www.w3.org/CGII, on May 24, 2006, 2 pages.
4 "Change History for httpd," retrieved from http,//www.w3.org/Daemon/Features.html, on May 24, 2006, 15 pages.
5 "Computer Privacy Digest V8#024," Computer Privacy Digest Moderator, Mar. 17, 1996, hltp://web.archive.org/web/20000829051834/itu.rdg.ac.uklmisc/Mailing—Lists/cpd/00000002.htm, 19 pages.
6 "Computers and the Internet," Earth Station 9, retrieved from http:/www.earthstalion9.com/counters.htm, on May 24, 2006, 2 pages.
7 "Demonstrations --nENC92," retrieved from http://www.w3.org/Conferences/JENC92/Demonstrations.htrnl, on May 24, 2006, 1 page.
8 "First International Conference on the World-Wide-Web," May 25-27, 1994, CERN, retrieved from http://www94.web.cem.ch/WWW94/, on May 24, 2006, 2 pages.
9 "Gwstat v1.1-generate graphs of httpd server traffic," public release 1994, retrieved from http://www.bubl.ae.uk///archive/internet/www/servers/gwstat6.htm on Jun. 12, 2006, 1 page.
10 "Gwstat v1.1—generate graphs of httpd server traffic," public release 1994, retrieved from http://www.bubl.ae.uk///archive/internet/www/servers/gwstat6.htm on Jun. 12, 2006, 1 page.
11 "Horace's Java: Page View Timer Demo," Page View Timer Applet, 1995-2005 by Iwv.Software, Last Modified Aug. 7, 1999, 2 pages.
12 "Hot Java Read Me and link to the software on SunSITE," SunSite Software Information and Technology, retrieved from http://www.ibiblio.org/hotjava/, on May 24, 2006, 1 page.
13 "HyperText Transfer Protocol Design Issues," retrieved from http://www.wj.orgfProtocols/DesignsIssues.html, on May 24, 2006, 3 pages.
14 "Logging Control in W3C httpd," retrieved from http://www.w3.org/Daemon/User/Config/Logging.html, on May 24, 2006, 3 pages.
15 "NetScape History," retrieved from http://www.holgennetzger.de/Netscape—History.html, on May 24, 2006, 4 pages.
16 "Older History of Changes . . ." Counter was re-released Aug 7, 1995, retrieved from http://www.ualberta.ca/GEO/Counter.History .html, on May 24, 2006, 8 pages.
17 "Real Audio Server Adminstration and Content Creation Guide," Version 3.0, Progressive Networks, Inc., 1995-1996, 292 pages.
18 "Real Server Administration and Content Creation Guide," Version 4.0, Progressive Networks, Inc., 1995-1997, 366 pages.
19 "tcpdump-dump traffic on a network," retrieved from http://www.tcpdump.org/tcpdump-man.html on Jun. 12, 2006, 26 pages.
20 "tcpdump—dump traffic on a network," retrieved from http://www.tcpdump.org/tcpdump—man.html on Jun. 12, 2006, 26 pages.
21 "tcpsl\ce—Linux Command—Unix Command," retrieved from ,http://linux.about.com/library /cmd/blemdl8tcpslice.htm, on Jun. 12, 2006, 3 pages.
22 "tcpslice(S): extract pieces of/glue together . . . ,—Linux man page," retrieved from http://www.die.net/doc/linux/man/man8/tcpslice.8.html, on Jun. 12, 2006, 3 pages.
23 "The World Wide Web project," retrieved from http://www.w3.org/History/19921103-hypertextlhypertext/WWW/TheProjecthtml, on May 24, 2006, 1 page.
24 "They Could Be Monitoring Your Every Web Move," GCN, Apr. 29, 1996, http:www.gcn.com, 3 pages.
25 "Using Norton pcANYWHERE for DOS," Symantec Corporation, 1994, 30 pages.
26 "Welcome to Netscape," 1994, Mosaic Communications Corporation, retrieved from http://www.hnehosting.com/rnirrors/Origin—of—a—Browser/mcom.1 0.1994/homeJwelcome.htrnl, on May 24, 2006, 2 pages.
27 "WWW Talk 1991 Archives," Last Modified Dec. 13, 1991, 1 page, retrieved from http://ksi.cpsu,ucalgary.ca/archives/WWW-TALK/www-talk-12991.index.html, on May 24, 2006.
28 (2.4.02)—EXCALXBUR BBS, "2.4-Windows Based Only Internet BBS Software—The Official BBS FAQ," retrieved from http://www.sysopworld.comlbbsfaqlch02.2.4.htm, on May 24, 2006, 23 pages.
29 1996 Java World editorial calendar, Feb. 26, 1996, 2 pages.
30 Aaron E. Walsh, "Foundations of Java Programming for the World Wide Web," IDG Books Worldwide, Inc., 1996, 915 pages.
31 Aaron E. Walsh, "JAVA for Dummies," IDG Books Worldwide, Inc., 1996, 389 pages.
32 Aaron Johnson, "Implementation Guide," HBX on Demand Web Analytics, WebSideStory, Inc., 2006, 15 pages.
33 Abrams et al., "Multimedia Traffic Analysis Using CHITRA95," ACM Multimedia 95-Electronic Proceedings, Nov. 5-9, 1995, 17 pages.
34 Abrams et al., "Multimedia Traffic Analysis Using CHITRA95," ACM Multimedia 95—Electronic Proceedings, Nov. 5-9, 1995, 17 pages.
35 Anderson et al., "Next Generation Intrusion Detection Expert System (NIDES); Software Users Manual; Beta Update Release," SRI International, Dec. 1, 1994, 316 pages.
36 Ann Lynworth, "Tracking Home Page Hits: Reporting on User Access," Dr. Dobb's Journal, 1995, 11 pages.
37 Applet Demos, Sep. 30, 1995-Nov. 22, 1995 form. java: 1 class, 4kByte, 1995, 2 pages.
38 Applets at Kassel, "Applets and Applications: Department of Engineering Measurement," Last Modified Dec. 12, 1996, retrieved from http://web.archive.orgfweb/19970802020436/http://www.uni-kassel.de/fb16/ipm/mVjavaljavae.html, on May 24, 2006, 6 pages.
39 Applets at Kassel, "Applets and Applications: Department of Engineering Measurement," retrieved from java@mt.e-technik.uni-kassel.de, last modified Aug. 27, 1996, 5 pages.
40 Applets in Kassel, "Applets and Applikationen: Fachgebiet Me8technik," retrieved from java@mt.e-technik.uni-kassel.de, last modified Aug. 27, 1996, 5 pages.
41 Ariel Poler, "Improving WWW Marketing Through User Information and Non-Intrusive Communications," Proceedings of the Second WWW Conference, 1994, 4 pages.
42 Bamshad Mobasher, " Introduction," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 2 pages.
43 Bamshad Mobasher, "A Taxonomy of Web Mining," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
44 Bamshad Mobasher, "About This Document," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survev/nodel.html, on Apr. 18, 2006, 1 page.
45 Bamshad Mobasher, "Agent Based Approach," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 2 pages.
46 Bamshad Mobasher, "Analysis of Discovered Patterns," 1997, retrieved from http://maya.cs.depaul.edu-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
47 Bamshad Mobasher, "Analysis of Mined Knowledge," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
48 Bamshad Mobasher, "Association Rules," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
49 Bamshad Mobasher, "Clustering and Classification," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
50 Bamshad Mobasher, "Conclusion," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
51 Bamshad Mobasher, "Data and Knowledge Querying," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
52 Bamshad Mobasher, "Data Cleaning," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
53 Bamshad Mobasher, "Data Pre-Processing," 1997, retrieved from http://maya.cs.depaul.edu/-mobasherlwebminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
54 Bamshad Mobasher, "Database Approach," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 2 pages.
55 Bamshad Mobasher, "Discovery Techniques in Web Transactions," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
56 Bamshad Mobasher, "OLAP Techniques," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
57 Bamshad Mobasher, "Path Analysis," 1997, retrieved from http:/maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
58 Bamshad Mobasher, "Pattern Analysis Tools," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
59 Bamshad Mobasher, "Pattern Discovery from Web Transactions," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
60 Bamshad Mobasher, "Pattern Discovery Tools," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
61 Bamshad Mobasher, "Preprocessing Tasks," 1997, retrieved from http)://maya.cs.depaul.edu/-mobasher/webminer/survey/node/html, on Apr. 18, 2006, 1 page.
62 Bamshad Mobasher, "References," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survev/nodel/html, on Apr. 18, 2006, 6 pages.
63 Bamshad Mobasher, "Research Directions," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nidel.html, on Apr. 18, 2006, 1 page.
64 Bamshad Mobasher, "Sequential Patterns," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
65 Bamshad Mobasher, "The Mining Process," 1997, retrieved from http://rnaya.cs.depaul.edu/.mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
66 Bamshad Mobasher, "Transaction Identification," 1997, retrieved from http://maya.cs.depaut.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 2 pages.
67 Bamshad Mobasher, "Usability Analysis," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
68 Bamshad Mobasher, "Visualization Techniques," 1997, retrieved from http://maya.cs.depaul/edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
69 Bamshad Mobasher, "Web Content Mining," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 1 page.
70 Bamshad Mobasher, "Web Usage Mining Architecture ," 1997, retrieved from http://maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 3 pages.
71 Bamshad Mobasher, "Web Usage Mining," 1997, retrieved from http:/maya.cs.depaul.edu/-mobasher/webminer/survey/nodel.html, on Apr. 18, 2006, 2 pages.
72 Berners-Lee et al., "Hypertext Transfer Protocol—HTTP/1.0," Network Working Group, RFC 1945, May 1996, 57 pages.
73 Berners-Lee et al., "Uniform Resource Locators," Network Working Group, RFC 1738, Dec. 1994, 23 pages.
74 Berners-Lee et al., "World Wide Web: Proposal for a HyperText Project," Nov. 12, 1990, 7 pages.
75 Bernstein et al., "Architectures for Volatile Hypertext," Hypertext 91 Proceedings, Dec. 1991, 18 pages.
76 Bestavros et al., "Application-Level Document Caching in the Internet," In Proceedings of the Second Intl. Workshop on Services in Distributed and Networked Environments, 1995, 8 pages.
77 Bieber et al.,"Backtracking in a Multiple Window Hypertext Environment," Institute for Integrated Systems Research, ECHT '94 proceedings, Sep. 1994, 9 pages.
78 Blythe et al., "Lynx Users Guide Version 2.3," retrieved from http://www.cse.unsw.edu.aulhelp/docflynx!lynx-help/Lynx-users-guide.html on Jun. 12, 2006, 12 pages.
79 Blythe et al., "Lynx Users Guide Version 2.3," retrieved from http://www.cse.unsw.edu.aulhelp/docflynx!lynx—help/Lynx—users—guide.html on Jun. 12, 2006, 12 pages.
80 Braun et al.," Applied Network Reseach: 1994 Annual Status Report," Applied Network Research San Diego Supercomputer Center and University of California, Apr. 19, 1995, 15 pages.
81 Brian Wilson, "OPERA," Index DOT Html/Css, retrieved from http://www.blooberry.com/indexdotlhistory/opera.htm, on May 24, 2006, 3 pages.
82 Browser History timeline: Overview, "Browser Timelines," retrieved from http://www.blooberry;com/indexdotlhistorylbrowsers6.htm, on May 24, 2006, 3 pages.
83 Carmel et al., "Browsing in Hypertext: A Congnitive Study," IEEE Transactions on Systems, Man and Cybernetics, vol. 22, No. 5, Sep./Oct. 1992, 23 pages.
84 Catledge et al., "Characterizing Browsing Strategies in the World Wide Web," the Third International WWW Conference, 1995, 9 pages.
85 Catledge et al., "Characterizing Browsing Strategies in the World-Wide-Web," Computer Networks and ISDN Systems, Proceedings of the Third International World-Wide-Web Conference, Apr. 10-14, 1995, 10 pages.
86 CERN Computer News Letter-CER.N-CNL-1991-204, IT Division, vol. XXVI, Issue No. 3, Last Updated Jul. 6, 2000, retrieved from http://ref.web.cem.ch/ref/CERN/CNUI99.1/204/, on May 24, 2006, 2 pages.
87 Changes to wwwstat: httpd logfile analysis package, 1994, 1996 Regents of the University of California, retrieved from http://ftp.ics.uci.edu/pub/websoft1wwwstat/Changes, on Jun. 12, 2006, 4 pages.
88 Chankhunyhod et al., "A Hierarchical Internet Object Cache," Computer Science Department, Universities of S. California and Colordao, 1995, 11 pages.
89 Chen et al., "Supporting Advertisement on Electronic Publications," IBM Technical Disclosure Bulletin, vol. 39, No. 8, Aug. 1996, 2 pages.
90 Claffy et al., "Traffic Characteristics of the T1 NSFNET Backbone," National Science Foundation, Joint Study Agreement with IBM, 1993, 11 pages.
91 Cooley et al., "Web Mining: Information and Pattern Discovery on the World Wide Web," Department of Computer Science, University of Minnesota, 1997, 2 pages.
92 Cornell et al., "Core JAVA," The Sunsoft Press, 1996, 655 pages.
93 Cornell et al., "Core JAVA," The Sunsoft Press, Second Edition, 1997, 829 pages.
94 Counter, by java@mt.e-technik uni-kassel.de, 1996, 33 pages.
95 Cove et al., "Online Text Retrieval Via Browsing," Informatio Processing and Management: An International Journal, vol. 24, No. 1, 1988, 10 pages.
96 Crazy Counter, (GIF89a) Last Modified Mar. 8, 1996, 2 pages.
97 Crazy Counter, GIF89a version, Last Modified Mar. 8, 1996, 2 pages.
98 CrazyCounter.Java, 1996 Mark Boyns boyns@sdsu.edU, 5 pages.
99 Crovella et al., "Explaining World Wide Web Traffic Self-Similarity," Computer Science Department, Boston University, Technical Report, Oct. 12, 1995, 19 pages.
100 Cunha et al., "Characteristics of WWW Client Based Traces," Computer Science Department, Boston University, Jul. 18, 1995, 18 pages.
101 Cunha et al., "Characteristics of WWW Client-Based Traces," Computer Science Department, Boston University, Jul. 18, 1995, 18 pages.
102 Dan Connolly, "A Little History of the World Wide Web (1945-1995)," Webmaster, 1995, 6 pages, retrieved from http://www.w3.org/History.html, on May 24, 2006.
103 Dave Raggett, "HTML 3.2 Reference Specification," Jan. 14, 1997, 49 pages.
104 David Beckett, "Combined Log System," Computing Laboratory, University of Kent, 1995, retrieved from http: www.hensa.ac.uk/parallel/www/djbl.html, on May 24, 2006, 8 pages
105 David Flanagan, "Deluxe Edition-Java in a Nutshell-Java 1.1," O'Reilly & Associates, Feb. 1996, 629 pages.
106 David Flanagan, "Deluxe Edition—Java in a Nutshell—Java 1.1," O'Reilly & Associates, Feb. 1996, 629 pages.
107 David Flanagan, "Java in a Nutshell," O'Reilly & Associates, Feb. 1996, 449 pages.
108 David Flanagan, "JavaScript: The Definitive Guide, Beta Edition," O'Reilly & Associates, Feb. 1996, 471 pages.
109 Davis et al., "Instant JAVA Applets," Ziff-Davis Press, 1996, 234 pages.
110 Dean et al., "Security Flaws in the HotJava Web Browser," Department of Computer Science, Princeton University, Nov. 3, 1995, 8 pages.
111 December et al., "HTML and CGI Unleashed," sams.net, 1995, 838 pages.
112 Diane Hackborn, "Java Enhanced Communication Tools," Gamelan Earthweb, Jun. 11, 1996, 7 pages.
113 * E. Gabber et al. Consistent, Yet Anonymouse, Web Acess with LPWA. Communications of the ACM. Feb. 1999/vol. 42, No. 2.
114 Email from Paul Barber, May 3, 1996, 2 pages.
115 Eugene Eric Kim, "Master CGI Programming for the Internet," CGI Developer's Guide, sams.net, 1996, 514 pages.
116 European Patent Office, Communication under 71(3) EPC, regarding Notice of Intent to Grant and claims for grant, issued in EP Application No. 00 980 233.1-2416, issued Jun. 25, 2009, 13 pages.
117 European Patent Office, Supplementary European Search Report, issued in EP Application No. 00980233.1, issued Jun. 25, 2009, 4 pages.
118 Feliciano et al., "Lamprey: Tracking Users on the World Wide Web," Section on Medical Informatics, Stanford University, 1996, 5 pages.
119 File://P:P Drive Files\Clients \Nielsen-Netratings\Settled Cases\Sane Solutions, LLC\ . . . Dec. 21, 2006, 2 pages.
120 Franklin M K et al, "Auditable metering with lightweight security", Published by the author at Financial Cryptography, International Conference, Feb. 24, 1997, 13 pages.
121 Fuller et al., "Measuring User Motivation From Server Log Files," Usability Research, retrieved from http://'#WW.micro.soft.com/usability/webconf/fuller/fuller.htm, on Oct. 30, 2000, 15 pages.
122 Fuller et al., "Measuring User Motivation From Server Log Files," Usability Research, retrieved from http://'#WW.micro•soft.com/usability/webconf/fuller/fuller.htm, on Oct. 30, 2000, 15 pages.
123 Gary Marchionni, "Information Seeking Strategies of Novices Using a Full-Text Electronic Encyclopedia," Journal of the American Society for Information Science, vol. 40, No. 1, Jan. 1989, 16 pages.
124 Gary Perlman, "Asynchronous Design/Evaluation Methods for Hypertext Environment," Hypertext '89 Proceedings, Nov. 1989, 21 pages.
125 Gerald M. O'Connell, "A New Pitch," Moden Media, May 1995, 8 pages.
126 Girdley et al., "Web Programming with Java," sams.net, 1996, 501 pages.
127 Girdley et al., "Web Programming with JAVA," sams.net, 1996, 9 pages.
128 Glenn Fleishman, "Web Log Analysis: Who's Doing What, When? Part 2," Web Developers Magazine, vol. 2, No. 2, May/Jun. 1996, 4 pages.
129 Google Groups : comp.lang.javascript, retrieved from http://groups-beta.google.com/group/comp.lang.javascriptlbrowse-threadlthread/4874a9f9c... on Jun. 8, 2005, 4 pages.
130 Google Groups : comp.lang.javascript, retrieved from http://groups-beta.google.com/group/comp.lang.javascriptlbrowse—threadlthread/4874a9f9c... on Jun. 8, 2005, 4 pages.
131 Google Groups, "Topic in comp—infosystems.www.authoring.html" retrieved from http://groups.google.com/group/comp.infosystems.www.authoring.html/browse—thread/thread.dfbla837f29e165e/a0e0e6a131c6102d?q=acces . . . , on May 24, 2006, 3 pages.
132 Google Search for Garrett Casey Counter, Google Groups: misc.entrepreneurs, Jul. 2, 1996, 3 pages.
133 Google Search for Garrett Casey, Google Groups: comp.infosystems.www.authoring.cgi, Sep. 19, 1996, 2 pages.
134 Google Search, "Access Counters," Google Groups: comp.infosystems.www.authoring.html, Jun. 13, 1995, 3 pages.
135 Google Search, "java applet hit counter," Can u make a hit counter in a java applet?, retrieved from http://groups.google.comlgroup/comp.lang.javalbrowse-thread/thread/f2a41d0cb5c8eee4/c8 . . . on May 2, 2006, 6 pages.
136 Google Search, "java applet hit counter," Can u make a hit counter in a java applet?, retrieved from http://groups.google.comlgroup/comp.lang.javalbrowse—thread/thread/f2a41d0cb5c8eee4/c8 . . . on May 2, 2006, 6 pages.
137 Google Search, "java applet web monitoring," InfoTicker Java Applet, retrieved from http://groups.google.com/groilp/comp.infosysterns.www.announcelbrowse-threadlthreadlcc. . . on May 2, 2006.
138 Google Search, "java applet web monitoring," InfoTicker Java Applet, retrieved from http://groups.google.com/groilp/comp.infosysterns.www.announcelbrowse—threadlthreadlcc. . . on May 2, 2006.
139 Google Search, "Java Project," retrieved from http://groups.google.com/group/comp.lang.java.programmerlbrowse—threadlthread/5430a3 . . . , on Apr. 12, 2006, 3 pages.
140 Google Search: cgi git counter, "Hit Counter," Googie Groups: comp.infosystems.www.users, Mar. 29, 1995, 2 pages.
141 Google Search: cgi hit counter, "WWW hit counter: does this work?" Google Groups: comp.larig.perl, Dec. 7, 1994, 7 pages.
142 Google Search: fwgstat, "Looking for a GN Log," Google Groups: comp.infosystems.gopher, Oct. 7, 1993, 3 pages.
143 Google Search: fwgstat, "Single Program to Summarize Logs of Several Kinds of Server?" Google Groups: comp.infosystems.gopher, Jan. 6, 1994, 10 pages.
144 Google Search: getsites, "Announcing getsites 1.5, a Web Log Analyzer," Google Groups: comp.infosystems.www, Nov. 3, 1993, 2 pages.
145 Google Search: getstats, "Announcing getstats 1.0, a web log analyzer," Google Groups: comp.infosystems.www, Feb. 1, 1994, 3 pages.
146 Google Search: hit counter, "Access Counters . . ." Google Groups: comp.infosystems.www.authoring.html, Jul. 3, 1995, 7 pages.
147 Google Search: javascript counter script, "Simple Event Counter," Apr. 17, 1996, 2 pages.
148 Google Search: javascript exploit, "Netscape 2.0 Javascript Security Concerns," Google Groups: comp.sys.mac.misc, Mar. 1, 1996, 3 pages.
149 Google Search: Javascript, "JavaScript in Netscape 2.0 Shouldn't let me do this," Google Groups: comp.society.privacy Feb. 23, 1996, 3 pages.
150 Google Search: wusage 1.0, "WUSAGE 1.0: Server Usage Graphs, Top Ten . . ." Google Groups: comp.infosystems.www, Jan. 31, 1994, 2 pages.
151 Google Search: wwwstat, "Announcing wwwstat-0.1—an access log summary program," Google Groups: comp.infosystems.www, Jan. 15, 1994, 8 pages.
152 Google Search: xferstats, "uutraf—UUCP statistics for HoneyDanBer," Google Groups: alt.sources, Nov. 5, 1989, 10 pages.
153 Gordon McComb,"JavaScript Sourcebook-Create Interactive JavaScript Programs for the World Wide Web," Wiley Computer Publishing, 1996, 741 pages.
154 Gordon McComb,"JavaScript Sourcebook—Create Interactive JavaScript Programs for the World Wide Web," Wiley Computer Publishing, 1996, 741 pages.
155 Hallman et al., "Extended Log File Format: WJC Working Draft WD-logfile-960323," retrieved from http://www.wJ.org/pubfWWWffR/WD-logfile-960323 html, on May 24, 2006, 6 pages.
156 Hansen et al., "Automated System Monitoring and Notification with Swatch," LISA, Nov. 1-5, 1993, 9 pages.
157 Henrik Frystyk Nielsen, Classic HTTP Documents, retrieved from http://www. w3.org/Protocols/Classic.html, on May 24, 2006, 1 page.
158 HighBeam Research, "Harris Computer Systems and Webster Network Strategies Announce Alliance to Offer WWW Monitoring Capabilities Through Firewalls," Mar. 4, 1996, Business Wire, 5 pages.
159 Hilbert et al., "Agents for Collecting Application Usage Data Over the Internet," Department of Information and Computer Science, University of California, 1998, 9 pages.
160 Holman et al., "Instant JavaScript," Hewlett-Packard Professional Books, 1996, 418 pages. IDG Books Worldwide, Inc.
161 Ibiblio history, the public's library and digital archive, retrieved from http://www.ibiblio.org/history/, on May 24, 2006, 10 pages.
162 Image of Compact Disc, Unica Corporation, Documents Responsive to NetRatings, Inc. v. WebSideStory Inc. Subpeona, Civil Action No. 06-CV-878 (LTS) (AJP), Videotaped Deposition of Gregory Doench dated Jun. 26, 2006, 1 page.
163 Image of Compact Disc, Unica Corporation, Documents Responsive to NetRatings, Inc. v. WebSideStory Inc. Subpoena, Civil Action No. 06-CV-878 (LT S) (AJP), 1 page.
164 Information Operations Timeline, retrieved from http://www.j fsc.ndu.edu/schools programs/jciwsliw/iotimeline.asp, on May 24, 2006, 9 pages.
165 James Staten, "Navigator Tricks Raise Concerns," MacWeek, vol. 10, No. 11, Mar. 18, 1996, 2 pages.
166 Japanese Patent Office, "Notice of Reasons for Rejection," issued in Japanese Patent Application No. 2001-552567 on Oct. 20, 2011 (5 pages.).
167 Japanese Patent Office, "Notice of Reasons for Rejection," issued in Japanese Patent Application No. 2001-552567 on Sep. 6, 2010 (8 pages).
168 Javapage, JAVA, retrieved from http://web.archive.org/web/20040712072256/http://www.groton.k12.ct.us/WWW/fsr/student/Spring02/W . . . , on May 24, 2006, 4 pages.
169 JavaWorld, Apr. 1996, http://www.javaworld.com, 1 page.
170 JavaWorld, Mar. 8, 1996, http://www.javaworld.com, 1 page.
171 Jeffrey C. Mogul, "Efficient Use of Workstations for Passive Monitoring of Local Area Networks," WRL Research Report 90/5, Wester Research Laboratory, Jul. 1990, 29 pages.
172 John E. Tolle, "Performance Measurement and Evaluation of Online Information Systems," Proceedings of the 1985 ACM Computer Science Conference, Mar. 12-14, 1985, 8 pages.
173 John Houston, "A Vision of Advertising Technology—How it Will Work," Modem Media, Aug. 1994, 6 pages.
174 John Nardone, "The Modem Media Internet Research and Involvement Scale (IRIS)," Modem Media, Feb. 1995, 3 pages.
175 John Rodley, "Writing JAVA Applets," Internet Programming Series, Coriolis Group Books, 1996, 437 pages.
176 John Thomson, Jr., "Changes in NSF Policy Become Law—The internet is Commercialized," Commercialization of the Internet, retrieved from http://johntlwmson.org/j561/NSFlaw-9.html, on May 24, 2006, 1 page.
177 John Thomson, Jr., "Creation of the NSFnet," Commercialization of the Internet, retrieved from http://johntlwmson.org/j56I/NSFcreate-4.html, on May 24, 2006, 2 pages.
178 John Thomson, Jr., "Criticism of the NSF grows," Commercialization of the Internet, retrieved from http://johntlwmson.org/j56I/NSFcriticism-6.html, on May 24, 2006, 1 page.
179 John Thomson, Jr., "The Beginning of Commercialization fo the NSFnet," Commercialization of the Internet, retrieved from http://johntlwmson.org/j561/NSFcomm-5.html, on May 24, 2006, 1 page.
180 John Thomson, Jr., "The NSF Starts to Change Their Policy," Commercialization of the Internet, retrieved from http://johntlwmson.org/j56I/NSFpolicy-7.html, on May 24, 2006, 1 page.
181 Johnson et al., "Automatic Touring in a Hypertext System," Wayne State Univeristy, Computer Science Department, IEEE, 1993, 7 pages.
182 Jon Finke, "Monitoring Usage of Workstations with a Relational Database," LISA, Sep. 19-23, 1994, 10 pages.
183 Joseph A. Bank, "Java Security," Dec. 8, 1995, http://net.ai.mit.edu/˜jbank/javapaper/javapapre.html, 11 pages.
184 Justin A. Boyan, "The Anonymizer Home Page," Sep. 1, 1995, retrieved from http://web.archive.org/web/19990208003332m 1/anonymizer.cs.cmu.edu:8080/, on May 24, 2006, 1 page.
185 Kamba et al., "The Krakatoa Chronicle-An Interactive, Personalized, Newspaper on the Web," 1995, 15 pages.
186 Kamba et al., "The Krakatoa Chronicle-An Interactive, Personalized, Newspaper on the Web," In Proceedings of the Fourth International World Wide Web Conference, 1995, 15 pages.
187 Kamba et al., "The Krakatoa Chronicle—An Interactive, Personalized, Newspaper on the Web," In Proceedings of the Fourth International World Wide Web Conference, 1995, 15 pages.
188 Kraut et al., "HomeNet: A Field Trial of Residential Internet Services," retrieved from from http://www.acm.org/sigchi/chi96/proceedings/papers/Kraut/rek-txthtm, 1996, 20 pages.
189 Kraut et al., "HomeNet: A Field Trial of Residential Internet Services," retrieved from from http://www.acm.org/sigchi/chi96/proceedings/papers/Kraut/rek—txthtm, 1996, 20 pages.
190 Leah Haran, "PC-Meter Tracks Computer Uses," Advertising Age, Oct. 2, 1995, 4 pages.
191 Lee et al., "Intelligent Agetns for Matching Information Providers and Consumers on the World-Wide-Web," Department of Computer Science, The University of Hong Kong, Proceedings of the Thirtieth Annual Hawaii International Conference on System Sciences, 1997, 11 pages.
192 Louis Desjardins, "Activity Monitor 1.1 User's Guide," 1994, 5 pages.
193 Marc Wandschneider, "ActiveX Controls Framework: Sample Code for Authoring Non-MFC Controls," Microsoft Corporation, Apr. 1996, 15 pages.
194 Marcia Bates, "The Design of Browsing and Berrypicking Techniques for the Online Search Interface," Graduate School of Library and Information Science, University of California at Los Angeles, 1989, 19 pages.
195 Martha K. Lobow, "An Engineer's Guide to Autocad," Industrial Engineering, vol. 19, No. 8, Aug. 1987, 6 pages.
196 Matt's Script Archive: Book'em Dano: Readme, retrieved from file://P:\p. Drive Files\CLIENTS\NielsenNetRatings\Settled Cases\Sane Solutions, LLC\. . . , on Dec. 21, 2006, 3 pages.
197 McCanne et al., "The BSD Packet Filter: A New Architecture for User-Leverl Packet Capture," Lawrence Berkeley Laboratory, Dec. 19, 1992, 11 pages.
198 McGraw et al., "Untangling the Woven Web: Testing Web-based Software," Reliable Software Technologies Corporation, Apr. 1, 1996, 9 pages.
199 McKenzie et al., "An Empirical Analysis of Web Page Revisitation," Department of Computer Science, University of Canterbury, 2001, 7 pages.
200 Michael A. Abrams, "A Tool to Aid in Model Development and Validation," Systems Engineering-Retail Divison, NCR Corporation, 1986, 10 pages.
201 Michael A. Abrams, "A Tool to Aid in Model Development and Validation," Systems Engineering—Retail Divison, NCR Corporation, 1986, 10 pages.
202 Michael Grobe, "An Early History of Lynx: Multidimensional Collaboration," Apr. 10, 1997, retrieved from http://people.cc.ku.edu/grobelearly-lynx.html, on May 24, 2006, 8 pages.
203 Michael McGee, "Web Pages: A Programmer's Perspective," Jun. 10, 1996, http://www.dfpug.com/loseblattsammlung/migration/whitepapers/webpages.htm, 14 pages.
204 Mosaic Communications Changes Name to "Netscape Communications Corporation" Netscape Communications News Release, retrieved from http://www.holgermetzger.de/netscape/NetscapeCommunicationsNewsRelease.htm, on May 24, 2006, 2 pages.
205 NCSA Mosaic History, "In the beginning there was NCSA Mosaic . . . ," [retrieved from http://www.nesa.uiuc.edu/news/mosaichisotyr/ on Jun. 12, 2006] Jun. 1993, 15 pages.
206 Net. Genesis and Devra Hall, "Build a Web Site: A Programmer's Guide to Creating, Building and Maintaining a Web Presence," Prima Online, 1995, 721 pages.
207 NetAcuity IP Intelligence Whitepaper, "The Role of IP Intelligence in the Online World," Digital Envoy, 2002-2003, 10 pages.
208 Ohn A. Pew, "Instant JAVA," The Sunsoft Press, 1996, 370 pages.
209 Ousterhout et al., "A Trace Driven Analysis of the UNIX 4.2 BSD File System," Computer Science Division Electrical Engineering and Computer Sciences, University of California, Apr. 25, 1985, 30 pages.
210 Patent Cooperation Treaty, "International Preliminary Examination Report," issued by the International Preliminary Examining Authority in connection with PCT application No. PCT/US2000/029352, mailed Jul. 9, 2002 (3 pages).
211 Patent Cooperation Treaty, "International Search Report," issued by the International Searching Authority in connection with PCT application No. PCT/US2000/029352, mailed Apr. 18, 2001 (1 page).
212 Patent Cooperation Treaty, "Written Opinion," issued by the International Preliminary Examining Authority in connection with PCT application No. PCT/US2000/029352, mailed May 8, 2002 (4 pages).
213 Patrick Naughton, "JAVA Handbook: The Authoritative Guide to the Java Revolutionary," 1996, 446 pages.
214 Paul Pallab, "Marketing on the Internet," Journal of Consumer Marketing, vol. 13, No. 4, 1996, 17 pages.
215 Perrone et al. "Enterprise Application Integration", Chapter 38, Building JAVA Enterprise Systems With J2EE, Jun. 2000, pp. 1355-1372.
216 Perrone et al. "Enterprise Application Platforms", Chapter 34, Building JAVA Enterprise Systems With J2EE, Jun. 2000, pp. 1191-1206.
217 Perrone et al. "Modeling Components with Enterprise JavaBeans", Chapter 36, Building JAVA Enterprise Systems With J2EE, Jun. 2000, pp. 1231-1314.
218 Peter Brueggeman, "Monitoring CDROM Usage," UCSD Scripps Institution of Oceanography Library, LASERDISK PROFESSIONAL, 2(6):44-48, Nov. 1989, 5 pages.
219 Peter G. Neumann (Moderator), "Forum on Risks to the Public in Computers and Related Systems," ACM Committee on Computers and Public Policy, vol. 17, Issue 79, Feb. 23, 1996, 11 pages.
220 Peter Kent et al., "Official Netscape Java Script Book-The Nonprogrammer's Guide to Interactive Web Pages," 1996, 512 pages.
221 Peter Kent et al., "Official Netscape Java Script Book—The Nonprogrammer's Guide to Interactive Web Pages," 1996, 512 pages.
222 Pitkow et al., "A Simple Yet Robust Caching Algorithm Based on Dynamic Access Patterns," Graphics, Visualization & Usability Center College of Computing, Georgia Institute of Technology, 1994, 8 pages.
223 Pitkow et al., "Results from the First World-Wide-Web User Survey," Journal of Computer Networks and ISDN Systems, vol. 27, No. 2, GVU Technical Report, 1994, 15 pages.
224 Pitkow et al., "Results From the Third WWW User Survey," Graphics, Visualization & Usability (GVU) Center, The World Wide Web Journal, vol. 1, No. 1, 1995, 10 pages.
225 Pitkow et al., "Using the Web as a Survey Tool: Results from the Secon WWW User Survey," Graphics, Visualization & Usability Center, College of Computing, Georgia Institute of Technology, 1994, 12 pages.
226 Prakash Baskaran, "Gamelan Who's Who More Infor," http://web.archive.org/web/19961027024909/www.gamelan.com/person.cgi?id=PB3, 1996, 1 page.
227 Presentation of Www to Online Publishing 93, "World Wide Web," retrieved from http://www.w3.org./Talks/OnlinePublishing93/Overview.html, on May 24, 2006, 1 page.
228 Prof. Dr. rer. oat. Wolf-Jfirgen Becker, "Department of Engineering Measurement," UNI Kassel, FB16, Engineering Measurement, Last Modified Sep. 20, 1996, webmaster@mt.e-technik.uni-lwssel.de, 1 page.
229 RealNetworks, "Real System 5.0 Security Features Whitepaper," 1997, 10 pages.
230 RealServer Administration Guide Version 5.0, RealNetworks, Inc., 1995-1997, 262 pages.
231 Resonate, "Network Traffic Management, Database Traffic Management," 2005, 2 pages.
232 Reynolds "Building the Object Model", Chapter 3, Beginning E-Commerce With Visual Basic, ASP, SQL Server 7.0 and MTS, 2000 Wrox Press, pp. 41-84.
233 Reynolds "Starting Our Site", Chapter 2, Beginning E-Commerce With Visual Basic, ASP, SQL Server 7.0 and MTS, 2000 Wrox Press, pp. 21-39.
234 Richard A. McGrath, "The Tail-less Mouse," Computer Graphics World, vol. 11, Oct. 1988, 5 pages.
235 Rick Knoblaugh, "IOMON-Protected mode I/O port monitor," Programmer's Journal, vol. 9, No. 5, 1991, 6 pages.
236 Rick Knoblaugh, "IOMON—Protected mode I/O port monitor," Programmer's Journal, vol. 9, No. 5, 1991, 6 pages.
237 RISKS-LIST: Risks-Forum Digest, Monday Mar. 4, 1996 vol. 17 : Issue 83, 11 pages.
238 Roy Fielding, "wwwstat: HTTPd Logfile Analysis Software," Department of Information and Computer Science, University of California, Feb. 15, 2001, 3 pages.
239 Roy Fielding, "wwwstat—swnmarize www server (httpd) access statistics," University of California, retrieved from http://ftp.ics.uci.edu/pub/websoft1wwwstat/wwwstat.html, on Jun. 12, 2006, 11 pages.
240 Ryoji Koike, "Coming of the era of Post banner Internet advertising strategy change," INTERNET magazine, Japan, IMPRESS Inc., Sep. 1, 1999, No. 56 (5 pages).
241 Saul Greenberg, "Using Unix: Collected Traces of 168 Users," Advanced Technologies, The Alberta Research Council, 1988, 14 pages.
242 Scott A. Gile, "Reporting Application Usage in a LAN Environment," 1990, pp. 147-159.
243 Scott Weston, "Netscape 2.0b2 Allows for Invasion of Privacy," Dec. 2, 1995, retrieved from http://www.tbtf.com/resource/b2-privacv-bug.html, on May 24, 2006, 2 pages.
244 Screen Shots of Windows NT 3:1, retrieved from http://www.cs.und.edulhcil/muiseum/systems1winnt31src.html, on Jun. 7, 2005, 4 pages.
245 Sharon Zillmer, "How to Make Your Web Ads Pay Off," Marketing Magazine, vol. 101, Jun. 10, 1996, 4 pages.
246 Shinji Tanaka, The Development of Multimedia Business (the 23rd), Business Communication, Japan, Business Communication Corporation, Feb. 1, 1998, vol. 35, No. 2 p. 116-123-Cited in Japanese Patent Office "Notice of Reasons for Rejection" issued on Sep. 6, 2010, Translation of Japanese Notice cited as NPL 2 in information Disclosure Statement filed on Feb. 28, 2013.
247 Shinji Tanaka, The Development of Multimedia Business (the 23rd), Business Communication, Japan, Business Communication Corporation, Feb. 1, 1998, vol. 35, No. 2 p. 116-123—Cited in Japanese Patent Office "Notice of Reasons for Rejection" issued on Sep. 6, 2010, Translation of Japanese Notice cited as NPL 2 in information Disclosure Statement filed on Feb. 28, 2013.
248 Shishir Gindavaram, "CGI Programming on the World Wide Web," O'Reilly and Associates Inc., Mar. 1996, 456 pages.
249 Siocih et al., "Computer Analysis of User Interfaces Based on Repetition in Transcripts of User Session," ACM Transactions of Information Systems, vol. 9, No. 4, Oct. 1991, 27 pages.
250 Steven Glassman, "A Caching Relay for the World Wide Web," Systems Research Center, Digital Equipment Corporation, 1994, 10 pages.
251 Sukavirilya et al., "A Second Generation User Interface Design Environment: The Model and the Runtime Architecture," Sep. 1992, Graphics, Visualization & Usability Center, Georgia Institute of Technology, 10 pages.
252 Sun and Netscape Announce Javascript, "Javascript Announcement.txt," Dec. 4, 1995, 8 pages.
253 Tapley et al., "The Official Gamelan Java Directory," EarthWeb, Ziff-Davis Press, 1996, 6 pages.
254 Tcptrace—Official Homepage, http://www.tcptrace.org/, 1 page.
255 The HomeNet Project, "Homenet: A Field Trial of Residential Internet Services," Carnegie Mellon University, Pittsburgh, Pa 15213, Apr., 1995, http://homene.hcii.cs.cmu.edu/progresslreport 1.html, 14 pages.
256 The Third International World-Wide Web Conference, Technology, Tools and Applications, Apr. 0-14, 1995, Darmstat, Germany, retreived from http://www.igd.fhg.de/archive/1995— www95/, on May 24, 2006, 1 page.
257 Tim Berners-Lee, "Information Management: A Proposal," CERN, Mar. 1989, May 1990, retrieved from http://www.w3.org/History/1989/proposal.html, on May 24, 2006, 14 pages.
258 Tim Berners-Lee, "Plenary at WWW Geneva 94," slides from first International World Wide Web Conference, retrieved from http://www.w3orgffalksiWWW94Tim/, on May 24, 2006, 4 pages.
259 Tim Berners-Lee, "The World Wide Web Browser," http://www.w3.org/People/Bemers-Lee/WorldWideWeb.btml , May 24, 2006, 4 pages.
260 Timothy Jason Shepard, "TCP Packet Trace Analysis," Laboratory for Computer Science, Massachusetts Institute of Technology, Feb. 1991, 70 pages.
261 Tom Dellecave, Jr., "The Net Effect," A Supplement to Sales and Marketing Management, Mar. 1996, 9 pages.
262 Tony Sanders, "plesux log file summaries," retrieved from http://ksi.cpsu.ucalgary.ca/archives/WWW-TALK/www-talk-1993q2.messages/563.html, on May 24, 2006, 4 pages.
263 Tracking Web Advertising "Advertising Models and Associated Software . . . ," Google Search, May 15, 1996, 5 pages.
264 Tracking Web Advertising "Audience Tracking System for Electronic Newspapers," Telegrafix Communications, Inc., May 3, 1995, 3 pages.
265 Tracking Web Advertising "Goldmail Revolutionizes Internet Advertising," Google Search, Jul. 7, 1996, 4 pages.
266 Tracking Web Advertising "Goldmail thinks consumers should get PAID for reading Advertising!" Google Search, Jul. 7, 1996, 4 pages.
267 Tracking Web Advertising "Study Faults Online Ads for Kids," FTC Probes, Rueters, Mar. 28, 1996, 3 pages.
268 Tradewinds: A monthly round-up of Internet coverage in trade and industry magazines, A publication of Baker Library, Harvard Business School, vol. 2, No. 5, May 1995, 11 pages.
269 Uni Kassel, FB16, Messtechnik Homepage. Fachgebiet Mefltechnik, Univ.-Prof. Dr. rer. nat. Wolf-Jiirgen Becker, Last Modified Sep. 20, 1996, 1 page.
270 Viktor Mayer-Schonberger, "The Internet and Privacy Legislation: Cookies for a Treat?" retrieved from http://web.arcbive—org/web/19990203034203/www. wvjolt. wvu.edu/wvjolVcurrentlissue 1 article/mayer/ . . . , on May 24, 2006, 7 pages.
271 W3 Project—Assorted Design Notes, "Working Notes," retrieved from http://www.w3.org/History/1994/WWW/WorkingNotes/Overview.html, on May 24, 2006, 2 pages.
272 Web Authoring FAQ, 1996-2005 by the Web Design Group, retrieved from file://P:\P Drive Files\CLIENTS \Nielsen-NetRatings\Settled Cases\Sane Solutions, LLC\ . . . , on Dec. 21, 2006, 59 pages.
273 WebSideStory and AIG, screenshots, Jun. 22, 2005, 10 pages.
274 Weiler et al., "Usability Lab Tools: One year Later," CHI '94, Celebrating Independence, Conference Companion, 1994, p. 330.
275 World Wide Web for C5 Presentation, retrieved from http://www.w3.org/Talks/C5—17—May 24, 2006, 1 page.
276 World-Wide Web Servers, "W3 servers," retrieved from http://www.w3.org/History/19921103-hypertext/hypertext/DataSources/WWW /Servers.html, on May 24, 2006, 2 pages.
277 Wu et al., "Virtual Proxy Servers for WWW and Intelligent Agents on the Internet," IEEE Computer Society, Proceedings of The Thirtieth Annual Hawaii International Conference on System Sciences, 1997, 10 pages.
278 WWW Homepage Access Counter and Clock!, retrieved from http://www.muquit.com/muquillsoftware/Coun!/Count.html, on May 24, 2006, 32 pages.
279 Yahoo! Search, Web Prpgramming > Access Counters in the Yahoo! Directory, http://dir.yahoo.com/Computers and Intemeulntemet/W orld Wide-Web/ProgrammingAccess-Counters/ May 24, 2006, 3 pages.
280 Yahoo! Search, Web Prpgramming > Access Counters in the Yahoo! Directory, http://dir.yahoo.com/Computers and Intemeulntemet/W orld Wide—Web/ProgrammingAccess—Counters/ May 24, 2006, 3 pages.
281 Yermo Lamers, "webthreads Announcement.txt," Oct. 8, 1996, 5 pages.
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