This disclosure relates generally to market research, and, more particularly, to methods and apparatus to characterize households with media meter data.
In recent years, panelist research efforts included installing metering hardware in qualified households that fit one or more demographics of interest. In some cases, the metering hardware is capable of determining whether a media presentation device (such as a television set) is powered on and tuned to a particular station via a hardwired connection from the media presentation device to the meter. In other cases, the metering hardware is capable of determining which household member is exposed to a particular portion of media via one or more button presses on a People Meter by the household member near the television.
Market researchers seek to understand the audience composition and size of media, such as radio programming, television programming and/or Internet media so that advertising prices can be established that are commensurate with audience exposure and demographic makeup (referred to herein collectively as “audience configuration”). As used herein, “media” refers to any sort of content and/or advertisement which is presented or capable of being presented by an information presentation device, such as a television, radio, computer, smart phone or tablet. To determine aspects of audience configuration (e.g., which household member is currently watching a particular portion of media and the corresponding demographics of that household member), the market researchers may perform audience measurement by enlisting any number of consumers as panelists. Panelists are audience members (household members) enlisted to be monitored, who divulge and/or otherwise share their media exposure habits and demographic data to facilitate a market research study. An audience measurement entity typically monitors media exposure habits (e.g., viewing, listening, etc.) of the enlisted audience members via audience measurement system(s), such as a metering device and a People Meter. Audience measurement typically involves determining the identity of the media being displayed on a media presentation device, such as a television.
Some audience measurement systems physically connect to the media presentation device, such as the television, to identify which channel is currently tuned by capturing a channel number, audio signatures and/or codes identifying (directly or indirectly) the programming being displayed. Physical connections between the media presentation device and the audience measurement system may be employed via an audio cable coupling the output of the media presentation device to an audio input of the audience measurement system. Additionally, audience measurement systems prompt and/or accept audience member input to reveal which household member is currently exposed to the media presented by the media presentation device.
As described above, audience measurement entities may employ the audience measurement systems to include a device, such as the People Meter (PM), having a set of inputs (e.g., input buttons) that are each assigned to a corresponding member of a household. The PM is an electronic device that is typically disposed in a media exposure (e.g., viewing) area of a monitored household and is proximate to one or more of the audience members. The PM captures information about the household audience by prompting the audience members to indicate that they are present in the media exposure area (e.g., a living room in which a television set is present) by, for example, pressing their assigned input key on the PM. When a member of the household selects their corresponding input, the PM identifies which household member is present, which includes other demographic information associated with the household member, such as a name, a gender, an age, an income category, etc. However, in the event a visitor is present in the household, the PM includes at least one input (e.g., an input button) for the visitor to select. When the visitor input button is selected, the PM prompts the visitor to enter an age and a gender (e.g., via keyboard, via an interface on the PM, etc.).
The PM may be accompanied by a base metering device (e.g., a base meter) to measure one or more signals associated with the media presentation device. For example, the base meter may monitor a television set to determine an operational status (e.g., whether the television is powered on or powered off, a media device power sensor), and/or to identify media displayed and/or otherwise emitted by the media device (e.g., identify a program being presented by a television set). The PM and the base meter may be separate devices and/or may be integrated into a single unit. The base meter may capture audience measurement data via a cable as described above and/or wirelessly by monitoring audio and/or video output by the monitored media presentation device. Audience measurement data captured by the base meter may include tuning information, signatures, codes (e.g., embedded into or otherwise broadcast with broadcast media), and/or a number of and/or identification of corresponding household members exposed to the media output by the media presentation device (e.g., the television).
Data collected by the PM and/or the base meter may be stored in a memory and transmitted via one or more networks, such as the Internet, to a data store managed by a market research entity such as The Nielsen Company (US), LLC. Typically, such data is aggregated with data collected from a large number of PMs and/or base meters monitoring a large number of panelist households. Such collected and/or aggregated data may be further processed to determine statistics associated with household behavior in one or more geographic regions of interest. Household behavior statistics may include, but are not limited to, a number of minutes a household media device was tuned to a particular station, a number of minutes a household media device was used (e.g., viewed) by a household panelist member and/or one or more visitors, demographics of an audience (which may be statistically projected based on the panelist data) and instances when the media device is on or off. While examples described herein employ the term “minutes,” such as “household tuning minutes,” “exposure minutes,” etc., any other time measurement of interest may be employed without limitation.
To ensure audience measurement systems are properly installed in panelist households, field service personnel have traditionally visited each panelist household, assessed the household media components, physically installed (e.g., connected) the PM and/or base meter to monitor a media presentation device(s) of the household (e.g., a television), and trained the household members how to interact with the PM so that accurate audience information is captured. In the event one or more aspects of the PM and/or base meter installation are inadvertently disrupted (e.g., an audio cable connection from the media device to the base meter is disconnected), then subsequent field service personnel visit(s) may be necessary. In an effort to allow collected household data to be used in a reliable manner (e.g., a manner conforming to accepted statistical sample sizes), a relatively large number of PMs and/or base meters are needed. Each such PM and/or base meter involves one or more installation efforts and installation costs. As such, efforts to increase statistical validity (e.g., by increasing panel size and/or diversity) for a population of interest result in a corresponding increase in money spent to implement panelist households with PMs and/or base meters.
In an effort to increase a sample size of household behavior data and/or reduce a cost associated with configuring panelist households with PMs and/or base meters, example methods, apparatus, systems and/or articles of manufacture disclosed herein employ a media meter (MM) to collect household panelist behavior data. Example MMs disclosed herein are distinguished from traditional PMs and/or base meters that include a physical connection to the media presentation device (e.g., a television). In examples disclosed herein, the MM captures audio without a physical connection to the media device. In some examples, the MM includes one or more microphones to capture ambient audio in a room shared by the media device. In some such examples, the MM captures codes embedded by one or more entities (e.g., final distributor audio codes (FDAC)), and does not include one or more inputs that are to be selected by one or more household panelists to identify which panelist is currently viewing the media device. Rather than collecting audience composition data directly from panelists, example methods, apparatus, systems and/or articles of manufacture disclosed herein apply one or more models to impute which household members are exposed to particular media programming to collected MM data. Such example imputation techniques are described in further detail below and referred to herein as “persons imputation.” Additionally, example methods, apparatus and/or articles of manufacture disclosed herein apply one or more models to impute a number of visitors in each household and corresponding age/demographic characteristics of such visitors. In other words, examples disclosed herein facilitate a manner of determining a probability of household exposure activity, a number of visitors and/or corresponding visitor ages in a stochastic manner that avoids the expense of additional PM device installation in panelist households.
In some examples, a household includes two or more media devices, such as a first television located in a first room and a second television located in a second room. In the event the panelist household includes first and second meters physically connected to the first and second televisions, then the physical connection unambiguously identifies which audio data is originating from which television in the household, even if such audio from the first television propagates to the second room having the second television (and/or vice versa). Circumstances in which media played in one room can be heard and/or otherwise detected in another room (which may also have a media presentation device and accompanying meter) are referred to herein as “spillover.” In the event the panelist household includes first and second MMs located in the first and second rooms, respectively, then spillover audio data “heard” (detected) from the first room may erroneously be credited by the second MM as media presented in the second room (and/or vice versa). Media tuning events logged by a MM as occurring in one room, but actually occurring in a second different room (e.g., due to spillover) are referred to herein as “ambient tuning.” In other words, because the MM includes microphones to collect audio emitted from media devices, the possibility exists that the first MM in the first room is picking-up and/or otherwise detecting audio from the media device in an adjacent (e.g., the second) room. Ambient tuning is distinguished from “real tuning” in that real tuning occurs when the MM properly credits the media presentation device (e.g., television) associated with the room in which the MM is located with a media exposure for media actually presented on that media presentation device. Example methods, apparatus, systems and/or articles of manufacture disclosed herein apply models to identify instances of ambient tuning (e.g., due to spillover) as distinguished from real (legitimate) tuning. Similarly, example methods, apparatus, systems and/or articles of manufacture disclosed herein apply models to identify instances of when a media presentation device is turned on as distinguished from instances of when the media device is powered off. This is important in avoiding crediting of media exposure when no such exposure is occurring. For example, in the event a household member is in a first room with an associated media presentation device in a powered-off state, but the associated meter in that first room is detecting audio from a second media device in a second room, examples disclosed herein identify the occurrence as spillover and do not credit the detection as an actual media exposure.
Turning to
Example households that include a PM collect panelist audience data. As used herein, “panelist audience data” includes both (a) media identification data (e.g., code(s) embedded in or otherwise transmitted with media, signatures, channel tuning data, etc.) and (b) person information identifying the corresponding household member(s) and/or visitors that are currently watching/viewing/listening to and/or otherwise accessing the identified media. On the other hand, MMH households 108 include only a MM to collect media data. As used herein, “media data” and/or “media identifier information” are used interchangeably and refer to information associated with media identification (e.g., codes, signatures, etc.), but does not include person information identifying which household member(s) and/or visitors are currently watching/viewing/listening to and/or otherwise accessing the identified media. As described in further detail below, example methods, apparatus, systems and/or articles of manufacture disclosed herein impute person identifying data to media data collected from MMH household(s) 108.
Although examples disclosed herein refer to code readers and collecting codes, techniques disclosed herein could also be applied to systems that collect signatures and/or channel tuning data to identify media. Audio watermarking is a technique used to identify media such as television broadcasts, radio broadcasts, advertisements (television and/or radio), downloaded media, streaming media, prepackaged media, etc. Existing audio watermarking techniques identify media by embedding one or more audio codes (e.g., one or more watermarks), such as media identifying information and/or an identifier that may be mapped to media identifying information, into an audio and/or video component. In some examples, the audio or video component is selected to have a signal characteristic sufficient to hide the watermark. As used herein, the terms “code” or “watermark” are used interchangeably and are defined to mean any identification information (e.g., an identifier) that may be transmitted with, inserted in, or embedded in the audio or video of media (e.g., a program or advertisement) for the purpose of identifying the media or for another purpose such as tuning (e.g., a packet identifying header). As used herein “media” refers to audio and/or visual (still or moving) content and/or advertisements. To identify watermarked media, the watermark(s) are extracted and used to access a table of reference watermarks that are mapped to media identifying information.
Unlike media monitoring techniques based on codes and/or watermarks included with and/or embedded in the monitored media, fingerprint or signature-based media monitoring techniques generally use one or more inherent characteristics of the monitored media during a monitoring time interval to generate a substantially unique proxy for the media. Such a proxy is referred to as a signature or fingerprint, and can take any form (e.g., a series of digital values, a waveform, etc.) representative of any aspect(s) of the media signal(s) (e.g., the audio and/or video signals forming the media presentation being monitored). A good signature is one that is repeatable when processing the same media presentation, but that is unique relative to other (e.g., different) presentations of other (e.g., different) media. Accordingly, the term “fingerprint” and “signature” are used interchangeably herein and are defined herein to mean a proxy for identifying media that is generated from one or more inherent characteristics of the media.
Signature-based media monitoring generally involves determining (e.g., generating and/or collecting) signature(s) representative of a media signal (e.g., an audio signal and/or a video signal) output by a monitored media device and comparing the monitored signature(s) to one or more references signatures corresponding to known (e.g., reference) media sources. Various comparison criteria, such as a cross-correlation value, a Hamming distance, etc., can be evaluated to determine whether a monitored signature matches a particular reference signature. When a match between the monitored signature and one of the reference signatures is found, the monitored media can be identified as corresponding to the particular reference media represented by the reference signature that with matched the monitored signature. Because attributes, such as an identifier of the media, a presentation time, a broadcast channel, etc., are collected for the reference signature, these attributes may then be associated with the monitored media whose monitored signature matched the reference signature. Example systems for identifying media based on codes and/or signatures are long known and were first disclosed in Thomas, U.S. Pat. No. 5,481,294, which is hereby incorporated by reference in its entirety.
In operation, the example PM interface 202 acquires people meter data from any and all PMs within the example panelist households 104. In particular, the example PM interface 202 acquires PM data from the PM devices located in the example MMPM households 106 (i.e., households that have both MM devices and PM devices). The PM devices have input (s) (e.g., buttons for each household member to select to identify their respective presence in the audience currently exposed to media). In some examples, the MMPM households 106 are associated with a particular geographic area of focus, such as nationwide (sometimes referred to as a “National People Meter” (NPM)), while in other examples the MMPM households 106 are associated with a subset of a particular geographic area of focus, such as a localized geography of interest (e.g., a city within a nation (e.g., Chicago), and sometimes referred to as “Local People Meter” (LPM)).
For example, in the event an analysis of the Charlotte designated market area (DMA) is desired, then the example PM interface 202 captures data from LPM households within a time zone corresponding to the desired DMA (e.g., the Eastern time zone). In some examples, desired data may be streamed back to one or more storage repositories, from which the example imputation engine 110, the example ambient tuning engine 120 and/or the example on/off detection engine 130 may retrieve the data. The example PM interface 202 of the illustrated examples collects, acquires and/or otherwise captures PM data (panelist audience data) from panelist households 104 (having both PMs and MMs) and records or aggregates the media exposure minutes to respective persons within the household as one or more of the possible audience members (e.g., viewers) of the corresponding media. In other words, the captured panelist audience data is at a persons-level rather than at a household level, which facilitates an ability to generate person probabilities, as described in further detail below.
The example categorizer 206 of
As used herein, categories refer to classifications associated with collected exposure minutes (also known as “viewing minutes”). Categories may include, but are not limited to, a daypart associated with collected exposure minutes (e.g., Monday through Friday from 5:00 AM to 6:00 AM, Sunday from 10:00 PM to 1:00 AM, etc.), a station associated with collected exposure minutes (e.g., WISN, WBBM, etc.), an age/gender associated with collected exposure minutes (e.g., males age 2-5, females age 35-44, etc.), and a genre (e.g., kids programs, home repair programs, music programs, sports programs, etc.) associated with collected exposure minutes. In still other examples, the categorizer 206 categorizes the acquired panelist audience data by education (e.g., 8 years or less, 9 years to high school graduate, some college to Bachelor degree, master's degree or higher, etc.), life stage (e.g., pre-family, young family, older family, post family, retired, etc.) and/or a number of media presentation devices (e.g., television sets in the household. One or more combinations of station/affiliate/genre/demographic attribute(s) may be categorized in different ways based on, for example, variations between data available for one or more age/gender levels. For example, some local markets have ten stations in which a sample size for men age 45-54 may exhibit a data sample size of statistical significance for seven of those ten stations. In other examples, a local market may have relatively fewer stations where the age/gender levels are of sufficient size to support statistical significance. In some such examples, the age/gender groupings are adjusted (e.g., from males age 40-45 to males age 40-50) to increase an available sample size to achieve a desired statistical significance.
To impute panelist audience data (e.g., exposure minutes, which is sometimes referred to herein as “viewing minutes”) to media data, the example PM interface 202 identifies Local People Meter (LPM) data that has been collected within a threshold period of time. On a relative scale, when dealing with, for example, television exposure, an exposure index, which provides an indication of how well LPM data accurately imputes exposure minutes, may be computed in a manner consistent with Equation (1).
In the illustrated example of Equation (1), the exposure index is calculated as the ratio of the number of imputed LPM viewing minutes for each category of interest and the number of actual LPM viewing minutes for each category of interest.
The example exposure index of Equation (1) may be calculated on a manual, automatic, periodic, aperiodic and/or scheduled basis to empirically validate the success and/or accuracy of imputation efforts disclosed herein. Index values closer to one (1) are indicative of a greater degree of accuracy when compared to index values that deviate from one (1). Depending on the type of category associated with the collected exposure minutes, corresponding exposure index values may be affected to a greater or lesser degree based on the age of the collected data.
As described above, collected data that is more recent exhibits an imputation accuracy that is better than an imputation accuracy that can be achieved with relatively older collected data. Nonetheless, some data that is relatively older will still be useful, but such older data is weighted less than data that is more recent to reflect its lower accuracy. The example weighting engine 210 applies a temporal weight, and applies corresponding weight values by a number of days since the date of collection. Relatively greater weight values are applied to data that is relatively more recently collected. In some examples, weight values applied to collected tuning minutes and collected exposure minutes are based on a proportion of a timestamp associated therewith. For instance, a proportionally lower weight may be applied to a portion of collected minutes (e.g., tuning minutes, exposure minutes) when an associated timestamp is relatively older than a more recently collection portion of minutes.
In the illustrated example of
In the illustrated examples of Equation (2) and Equation (3), W1 reflects a relatively lower weighting value than W2, in which W2 is the weighting value associated with the current day exposure minutes value. Additionally, d reflects one of n days of the collected data prior to the current day, EMd reflects exposure minutes for corresponding days prior to the current day, TMd reflects household tuning minutes for corresponding days prior to the current day, EMc reflects exposure minutes for the current day, and TMc reflects household tuning minutes for the current day.
In connection with example data shown in the illustrated example of
However, while the market researcher may have a particular category combination of interest, a corresponding probability value accuracy may be improved when different probability calculation techniques are applied in view of corresponding available sample sizes of households sharing the particular category combination of interest. As described in further detail below, if collected LPM data associated with the category combination of interest (e.g., male, age 45-54, tuned to premium channel during 6:00 PM to 7:00 PM with three household members, one television and the head of household have some college credit or a bachelor's degree) is greater than a threshold value, then a cell probability technique may yield a probability value with acceptable accuracy. As used herein, an acceptable accuracy relates to a sample size that is capable and/or otherwise required to establish results having a statistical significance. However, in the event the collected Local People Meter (LPM) data associated with the category combination of interest falls below the threshold value, then the cell probability technique yields unacceptably low probability value accuracy. Instead, example methods, apparatus, systems and/or articles of manufacture disclosed herein employ independent distribution probability calculations when the collected LPM data associated with the category combination of interest is below a threshold value, such as below a threshold value that is capable of facilitating one or more calculations to yield results having statistical significance.
The example category manager 214 of
In operation, the example category manager 214 of
In the event a market researcher seeks probability information for a male aged 50 watching a premium pay channel between the hours of 6:00 PM and 6:30 PM, the example category fit manager 220 of the illustrated example identifies which previously established category groups already exist that would best fit this desired task. In other words, the specific and/or otherwise unique research desires of the market researcher may not align exactly with existing categorical groups collected by LPM and/or NPM devices. Instead, the example category fit manager 220 identifies that the closest categorical combination of industry standard and/or otherwise expected data is with males age 45-54 between the hours of 6:00 PM and 7:00 PM. The example minutes aggregator 222 of the illustrated example identifies a total number of household tuning minutes in all households associated with the identified closest categorical combination, and also identifies a total number of exposure minutes associated with the males age 45-54 in such households. For example, the minutes aggregator 222 may identify forty-five (45) qualifying households that have males 45-54 (e.g., the household could have more than just the males 45-54) in which a premium pay genre station was tuned between the hours of 6:00 PM to 7:00 PM, three household members with one television set and a head of household having some college credit or bachelor's degree.
Within these forty-five (45) qualifying households, the tuning minutes aggregator 222 may identify two-hundred (200) household tuning minutes total, but only one hundred and two (102) of those minutes were associated with the males 45-54. The example imputation engine 224 of the illustrated example calculates a probability for imputation as the ratio of exposure minutes for the males 45-54 and the total household tuning minutes for all qualifying households in a manner consistent with example Equation (4).
In the illustrated example of Equation (4), the probability of imputation using the examples disclosed above is 0.51 (i.e., 102 exposure minutes divided by 200 tuning minutes, in this example). In some examples, the probability value calculated by the example cell probability engine 216 is retained and/or otherwise imputed to MMH households 108 based on a normal distribution, such as a comparison of the calculated probability value to a random or pseudo-random number. In the event the calculated probability value is greater than the random number, then the household member having the categorical combination of interest is credited as viewing a tuning segment. In other words, the household tuning data is imputed to the MMH household 108 as exposure data for the categorical combination of interest. On the other hand, in the event the calculated probability value is less than the random or pseudo-random number, then the household member having the categorical combination of interest is not credited as viewing the tuning segment. In other words, the household tuning data is not imputed to the MMH household 108.
As discussed above, when the combinations of all categories of interest are represented by a number of households less than a threshold value within the donor pool, the cell probability calculation approach may not exhibit a level of confidence deemed suitable for statistical research. Generally speaking, a number of households in a research geography of interest matching a single one of the categories of interest may be relatively high. However, as additional categories of interest are added, the number of households having an inclusive match for all such categories decreases. In some circumstances, the number of matching households available in the donor pool after performing a logical “AND” of all categories of interest eventually results in a donor pool having a population lower than a threshold value, which may not exhibit statistical confidence when applying the cell probability technique described above. In such examples, the probability engine 212 prevents a traditional cell probability technique from being employed to calculate a probability of whether a household of interest should be credited with exposure behavior for the categorical combination of interest (e.g., whether the male age 45-54 of the household should be credited with captured exposure (tuning) behavior of the household). Instead, the example probability engine 212 invokes the example independent distribution engine 218 when the number of households having the desired combination of categories of interest is below a threshold value. As described in further detail below, instead of using a pool of households that match all categories of interest, households are employed that match some of the categories of interest are used when calculating a probability of viewing.
In operation, the example category qualifier 226 of
Because, in the illustrated example, the donor pool is constructed with only MMPM households 106, the example category qualifier 226 retrieves and/or otherwise obtains a total household tuning minutes value and a total exposure minutes value for the available households meeting the size/gender/age criteria of interest (e.g., dimensions (1), (2) and (3) from above). For example, if the size/gender/age criteria of interest is for a household size of two or more people having a male age 45-54, then the example category qualifier 226 identifies a number of households from that size/gender/age subset.
The example proportion manager 228 of
For example, the proportion manager 228 of the illustrated example selects one or more secondary categories to determine a corresponding number of matching households, household tuning minutes and exposure minutes. Again, and as described above, the temporal units of “minutes” are employed herein as a convenience when discussing example methods, apparatus, systems and/or articles of manufacture disclosed herein, such that one or more additional and/or alternative temporal units (e.g., seconds, days, hours, weeks, etc.) may be considered, without limitation. In the illustrated example of
Generally speaking, the proportion manager 228 of the illustrated example identifies secondary category contributions of household tuning minutes and exposure minutes independently from the household tuning and exposure minutes that may occur for only such households that match all of the desired target combination of categories of interest. After each individual secondary category contribution household tuning minute value and exposure minute value is identified, the example distribution engine 230 calculates a corresponding household tuning proportion and exposure proportion that is based on the key predictor household tuning and exposure minute values. As described in further detail below, the example distribution engine 230 calculates a household tuning proportion and an exposure proportion associated with each of the secondary categories of interest (e.g., the tuned station cagegory 504, the education category 506, the number of sets category 508, the daypart category 510 and the life stage/size category 512). In other words, examples disclosed herein capture, calculate and/or otherwise identify contributory effects of one or more secondary categories of interest by calculating and/or otherwise identifying a separate corresponding tuning proportion and separate corresponding exposure proportion for each one of the secondary categories. As described in further detail below, separate contributory effects of the one or more secondary categories are aggregated to calculate expected tuning minutes and expected exposure minutes.
In the illustrated example of
The example distribution engine 230 of
The example distribution engine 230 of
The example distribution engine 230 of
The example distribution engine 230 of
As described above, each of the target combinations of categories of interest has an independently calculated household tuning proportion value and an independently calculated exposure proportion value. The example distribution engine 230 of
In the example exposure and household tuning minutes discussed above, the resulting independent distribution probability is 0.52. In effect, the resulting independent distribution probability is interpreted as a male 45-54 who lives in a three (3) person household, classified as an older family, with a head of house education of nine (9) years to high school graduate, with two (2) television sets in the household, has a 52% likelihood of watching station WAAA during the daypart of Monday through Friday from 9:00 AM to 12:00 PM.
While an example manner of implementing the imputation engine 110 of
When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example people meter interface 202, the example categorizer 206, the example weighting engine 210, the example media meter interface 204, the example probability engine 212, the example category manager 214, the example cell probability engine 216, the example category fit manager 220, the example minutes aggregator 222, the example imputation engine 224, the example independent distribution engine 218, the example category qualifier 226, the example proportion manager 228, the example distribution engine 230, the example average visitor parameter (AVP) calculator 1102, an example distribution engine 1104, an example random number generator 1106, an example visitor assignor 1108, the example simultaneous tuning monitor 1602, the example crediting manager 1604, the example station comparator 1606, the example tuning type assignor 1608, the example automatic gain control monitor 1610, the example code presence manager 1612, the example modeling engine 1614, the example code stacking manager 1616 and/or, more generally, the example imputation engine 110, the example visitor imputation engine 112, the example ambient tuning engine 120, and/or the example on/off detection engine 130 of
Flowcharts representative of example machine readable instructions for implementing the imputation engine 110, the visitor imputation engine 112, the ambient tuning engine 120 and the on/off detection engine 130 of
As mentioned above, the example processes of
The program 600 of
As described above in connection with
Assuming, for the sake of example, the threshold number of households to match all of the categories of interest is thirty (30), and the pool of data includes that threshold amount of available households (block 704), the example cell probability engine 216 is invoked by the probability engine 212 to calculate a probability value via a cell probability technique (block 706). On the other hand, if the pool of data does not satisfy the threshold amount of thirty households (e.g., has less than 30 households) (block 704), then the example probability engine 212 invokes the example independent distribution engine 218 to calculate a probability value via an independent distribution technique (block 708).
The example imputation engine 224 of
Returning to block 704 of
In addition to key predictors having an influence on the probability of viewing, one or more additional secondary predictors may also influence the probability of viewing. As described above, the market researcher may have a combined set or target combination of categories of interest, but a number of households having all of those combined set of categories of interest does not exceed a threshold value (e.g., thirty (30) households). However, while the combined set of categories of interest may not be represented en masse from the donor pool, sub portions of the combined set or target combination of categories may include a relatively large representation within the donor pool. Example methods, apparatus, systems and/or articles of manufacture disclosed herein identify independent sub portions (subgroups) of the combined set of categories of interest and corresponding households associated with each subgroup of interest, which are applied independently to calculate a household exposure probability.
The example proportion manager 228 identifies a number of households from the key predictors group (e.g., 200 households having a size 2+ and a male age 45-54) that match a subgroup of interest (block 904). From the subgroup of interest, the example proportion manager 228 identifies a number of household tuning minutes and divides that value by the total household tuning minutes to calculate a household tuning proportion associated with the subgroup of interest (block 906). For example, if the subgroup of interest is all households tuned to the same station (e.g., WAAA) (e.g., the tuned station category) and such households reflect 1800 tuning minutes, then the example proportion manager 228 divides 1800 by the total household tuning minutes of 4500 to calculate a tuned station category household tuning proportion of 0.40 (block 906). The example proportion manager 228 also identifies a number of exposure minutes and divides that value by the total exposure minutes to calculate an exposure proportion associated with the subgroup of interest (e.g., the example tuned station category) (block 908). For example, if the subgroup of interest is all households tuned to the same station (e.g., WAAA) (e.g., the household tuned station dimension) and such households reflect 1320 exposure minutes, then the example proportion manager 228 divides 1320 by the total exposure minutes of 3600 to calculate a tuned station category exposure proportion of 0.37 (block 908). If more subgroups of interest from the donor pool are available (block 910), then the example proportion manager 228 selects the next subgroup of interest (block 912) and control returns to block 904.
After category household tuning proportion values and exposure proportion values have been calculated for each subgroup of interest, the example distribution engine 230 calculates the product of all household tuning proportion values and the total household tuning minutes (e.g., 4500 in this example) from the categories of interest (block 914), and calculates the product of all exposure proportion values and the total exposure minutes (e.g., 3600 in this example) from the categories of interest (block 916). A final independent distribution probability may then be calculated as the ratio of the exposure minutes and the household tuning minutes in a manner consistent with example Equation (5). For example, and as described above in connection with
As disclosed above, persons imputation utilizes who is in the household and what the household viewed such that for a given tuning segment, one or more household members may be assigned and/or otherwise associated with exposure. However, panelist households may have visitors that are exposed to media within the household, in which the available visitor information is limited to an age and a gender. As described above, the example PM includes inputs (e.g., buttons) for each household member as well as button(s) for entering age and gender information for any visitors interacting with the media device (e.g., a television). Example methods, apparatus, systems and/or articles of manufacture disclosed herein apply a model to, in view of collected panelist household visitor information, determine a number and corresponding age/gender of visitors for households that do not employ a PM.
Visitor imputation disclosed herein exhibits some similarities to persons imputation, and aspects of
In the illustrated example of
In the illustrated example of Equation 6, HH refers to household, and applying the example data from
In the illustrated example of Equation 7, the households of interest for the example demographic group of males age 25-34 have a viewing probability of 0.70. However, the following analysis of visitors in the same households of interest calculates an average visitor viewing ratio in a manner consistent with example Equation 8.
Applying the example data from
In the illustrated example of Equation 9, the households of interest for the visitors that are reporting male age 25-34 exhibit an average of 1.20 minutes of viewing time for each tuned minute.
The example visitor imputation engine 112 invokes the example categorizer 206 and/or example category qualifier 226 to categorize the acquired PM visitor data based on one or more categories of interest. As described above, for a given category or categories of interest, particular households associated with such categories are identified. Depending on whether a threshold number of households exist in the donor pool of visitor data that match all of the desired categories of interest, the example AVP calculator 1102 will invoke a corresponding AVP calculation technique. For example, if more than a threshold number of households exist that have the desired categories of interest (e.g., 30 households), then the cell category approach may be used to calculate AVP, while the independent category approach may be used to calculate AVP, such as the independent category approach described in connection with example
In the event the threshold number of households exist for a given set of categories of interest, the example AVP calculator 1102 calculates the AVP in a manner consistent with example Equation 8, and shown in
On the other hand, in the event a threshold number of households are not available for the desired categories of interest (e.g., less than 30 households), then the example AVP calculator 1102 calculates the AVP in a manner consistent with Equation 8 after determining expected exposure minutes and expected tuning minutes as category proportions, as described above in connection with
The example AVP calculator 1102 and/or the example distribution engine 230 calculates an exposure proportion for each category of interest 1314 and a tuning proportion for each category of interest 1316. Continuing with the illustrated example of
The example AVP calculator 1102 calculates an expected exposure minutes value (cell 1326) as the product of the total exposure minutes (cell 1310) and any number of calculated exposure proportion values that occur based on the number of categories of interest (e.g., result 1318 and result 1320). The example AVP calculator 1102 also calculates an expected tuning minutes value (cell 1328) as the product of the total tuning minutes (cell 1312) and any number of calculated tuning proportion values that occur based on the number of categories of interest (e.g., result 1322 and result 1324). In a manner consistent with example Equation 8, the example AVP calculator 1102 calculates the AVP value (cell 1330), which is used to determine a number of visitors and associated ages, as described in further detail below.
To determine a number of visitors and corresponding ages, example methods, apparatus, systems and/or articles of manufacture disclosed herein employ a distribution model. While the type of distribution model described below is a Poisson distribution, this distribution is used for example purposes and not limitation. The Poisson distribution is a discrete probability distribution to express the probabilities of given numbers of events when their average rate is known, and applied herein to assign a number of visitors watching a given tuning segment (the previously calculated AVP being the known average rate). Probabilities for the Poisson distribution are defined in a manner consistent with example Equation 10.
In the illustrated example of Equation 10, v reflects a number of visitors, p(v) reflects a probability calculated for “v” visitors, and λd reflects the AVP for a given demographic group of interest (e.g., female age 6-11). The example distribution engine 1104 defines the distribution, such as the example Poisson distribution above, and calculates probability values for a candidate number of visitors of interest, as shown in further detail in
In the illustrated example of
For each demographic group of interest, the example visitor assignor 1108 invokes the random number generator 1106 to generate a random number that, when referenced against the cumulative distribution values, reveals a number of visitors to attribute to that demographic group of interest. For example, if the random number generator produces a value of 0.757000 for the first group 1404 associated with females age 6-11, then this value is associated by the example visitor assignor 1108 to fall within a visitor (v) value of 2. Additionally, if the random number generator produces a value of 0.52700 for the second group 1408 associated with males age 55-64, then this value is associated by the example visitor assignor 1108 to fall within a visitor (v) value of 1. As a result, the first group 1404 is deemed to have two visitors, each having an age somewhere between 6-11, and the second group 1408 is deemed to have one visitor having an age somewhere between the ages of 55-64. The example random number generator 1106 is again employed to randomly assign corresponding ages for each of the two visitors from the first group 1404 between the ages of 6-11, and to randomly assign an age for the visitor from the second group 1408 between the ages of 55-64. While the aforementioned example was performed for a target demographic group of interest of females between the ages of 6-11 and males between the ages of 55-64, the same process may be repeated for all demographic groups of interest to possibly assign other visitors to a given tuning segment.
The program 1500 of
When performing an analysis of a market of interest, the example categorizer 206 categorizes the acquired PM data based on one or more categories of interest (block 1506). As described above, categories of interest may include, but are not limited to an age/gender combination of interest, a particular household size of interest, a particular life stage of interest, a particular viewed station/affiliate/genre of interest, a particular daypart of interest, a number of television sets of interest within the household (e.g., households with one television set, households with 2-3 television sets, households with three or more television sets, etc.), and/or an education level of the head of household. While a relatively large number of MMPM households 106 will have at least one of the aforementioned categories, a substantially smaller number of MMPM households 106 will represent all of the target combination of categories of interest to a market researching during a market study.
If the example visitor imputation engine 112 determines that a threshold number of households associated with a preferred and/or otherwise desired set of characteristics is satisfied (e.g., a threshold of at least 30 households) (block 1508), then the AVP value(s) are calculated by the example AVP calculator 1102 in a manner consistent with
The example distribution engine 1104 defines a distribution model to apply, such as the Poisson distribution (block 1514), and calculates probabilities for any number of visitors (v) of interest in a manner consistent with example Equation 10 (block 1516). For example,
As described above, employing a MM without a PM to characterize household media exposure behavior facilitates substantial cost savings when compared to employing PM devices, which may be physically connected to media devices (e.g., televisions) and require professional installation. For example, a MM may be mailed to a panelist, plugged in to power and function without professional installation and/or without connection to the panelist's electronics (e.g., media electronics such as DVD players, set top boxes, televisions, etc.). Although using MM devices without PMs result in substantial panelist household cost savings, some households have two or more media devices located in rooms in a relative proximity to where sound from a first media device reaches the room in which the second media device is located, and vice versa. In such circumstances, a MM device from the first room may incorrectly credit exposure minutes based on audio spillover associated with the second media device in the second room (and vice versa). When MM devices incorrectly credit exposure minutes, one or more household tuning estimates and/or projections may be overreported/inflated. Example methods, apparatus, systems and/or articles of manufacture disclosed herein distinguish instances of ambient tuning (e.g., due to spillover) from instances of real tuning.
In operation, the example PM interface 202 and the example MM interface 204 collect household tuning data from MMPM households 106 and MMH households 108 within a region of interest 104 (e.g., panelist households within a direct marketing area (DMA)) that comprise an available data pool (e.g., LPM households, NPM households, etc.). The example ambient tuning engine 120 invokes the example simultaneous tuning monitor 1602 to identify whether instances of simultaneous tuning minutes from collected household data are either ambient or real. As used herein, “simultaneous tuning” refers to instances where two or more meters within a household are detecting the same media (e.g., the same television station). To illustrate, assume a first MM proximate to a first television in a first room detects station WAAA, and a second MM proximate to a second television in a second room also detects station WAAA. One possibility that may be true is that both media devices (e.g., televisions) are powered on and tuned to station WAAA. However, another possibility is that the first television is on and tuned to station WAAA while the second television is tuned to another station while muted. Yet another possibility is that the first television is on and tuned to station WAAA while the second television is not powered on. In such circumstances, the second MM device may be detecting audio (e.g., spillover) from the first television and, thus, improperly inflating media exposure (e.g., consumption) metrics associated with the second television and/or household members.
In some examples, the crediting manager 1604 identifies quantities of time (e.g., minutes) where the MM device credited a station, and the example station comparator 1606 determines whether an AP device paired with the MM device is also crediting the same station. If so, then the example tuning type assignor 1608 assigns the corresponding tuning minute as real. On the other hand, if the example crediting manager 1604 identifies minutes where the MM devices credited a station (e.g., embedded codes detected by the MM device, embedded codes passed-on by the MM device and detected by the ambient tuning engine 120 during post-processing, signature post processing, etc.), and the example station comparator 1606 determines that the paired AP device is not tuned to the same station, then the example station comparator 1606 determines whether a separate metering device within the household is tuned to the same station, such as another AP and/or MM device associated with a second television in a second room of the household. If so, then that household tuning minute is deemed and/or otherwise labeled as ambient tuning/spillover, which should be ignored to prevent improper overrepresentation. On the other hand, in the event the example station comparator 1606 determines that no other metering device in the household is also tuned to the same station, then the example tuning type assignor 1608 assigns the minute as non-tuning. The example simultaneous tuning monitor 1602 continues to evaluate each received tuning minute within the pool of data collected from the example panelist households 104.
To develop a stochastic approach to determine the occurrence of spillover in which derived model coefficients are derived for use in MMH households 108, the example ambient tuning engine 120 collects additional predictive variables indicative of the occurrence or non-occurrence of spillover. The predictive variables are applied to a model, such as a regression model, to generate coefficients/parameters that facilitate calculation of a probability that spillover is occurring or not occurring within the MMH households 108. At least three predictive variables indicative of the occurrence or non-occurrence of spillover include automatic gain control (AGC) values, the presence of embedded codes, such as final distributor audio codes (FDACs), and the duration of the collected segment.
Generally speaking, by comparing AGC values between two separate MM devices within a household (e.g., calculating a difference therebetween), an indication of spillover may be evaluated. A MM device placed relatively close to a first television, for example, is more likely to have a low AGC value because of a higher relative volume when compared to an AGC value associated with sound from a television relatively farther away. AGC values are typically established by acoustic gain circuits that apply greater gain (e.g., amplification) when attempting to discern and/or otherwise detect sound energy that has a relatively low volume than when attempting to detect sound energy of a higher volume. Volume may be lower, for example, due to a greater distance from a source of the originating sound energy. Additionally, quantities and/or densities of detected codes per unit of time are additional example predictive variable(s) that may be applied to the model to derive an indication of the likelihood of the occurrence or non-occurrence of spillover. Without limitation, segment duration is another predictive variable useful in the indication of spillover, as described in further detail below.
The example AGC monitor 1610 of
The example code presence manager 1612 of
If none of the meters within the household have collected codes in the collected minutes, then the example simultaneous tuning monitor 1602 of
In the illustrated example of Equation (11), the model has the response (dependent) dependent variable as the ambient or real value to which each simultaneous tuning minute is assigned. Independent variables X1, . . . , Xk may be coded and/or otherwise categorized with model coefficients B1, . . . , Bk. Categories may be represented by any scale, such as AGC values ranging from zero to one hundred having sub-groups therein.
If some of the meters within the household have collected codes in the collected minutes (e.g., collected raw audio with codes embedded therein and subsequently identified during audio data post processing), but others do not, then the example modeling engine 1614 of
As described above, the example code presence manager 1612 of
Each simultaneous tuning minute may be identified as either ambient tuning or real tuning based on the resulting probability value and a threshold established by, for example, a market researcher. For example, if the probability value is greater than or equal to 0.50, then the minute may be designated as ambient tuning. On the other hand, the minute may be designated as real tuning for probability values less than 0.50.
The program 1700 of
If the example station comparator 1606 of
If the example station comparator 1606 determines that the other device in the household is tuned to the same station (block 1812) (e.g., based on the detection of the same codes), then the example tuning type assignor 1608 assigns the corresponding minute as ambient tuning (also referred to herein as spillover) (block 1814). On the other hand, if the example station comparator 1606 determines that the other device in the household is not tuned to the same station (block 1812), then the example tuning type assignor 1608 of the illustrated example assigns the corresponding minute as a non-tuning minute (block 1816).
Returning to
In the illustrated example of
If the example code presence manager 1612 of the illustrated example identifies a first category in which no codes are detected, the example simultaneous tuning monitor 1602 determines a maximum segment duration associated with the MM devices (block 1910), and calculates a difference therebetween (block 1912). The example modeling engine 1614 of
If the example code presence manager 1612 of the illustrated example identifies a second category in which some codes are detected in some minutes, while no codes are detected in other minutes (block 1908), then the example modeling engine 1614 of
If the example code presence manager 1612 identifies a third category in which all codes are detected in all collected minutes (block 1908), then the example code stacking manager 1616 of this example determines whether the detected codes are, themselves, complete (block 1918). As described above, while the example MM devices may detect and/or otherwise capture codes that may have been embedded in media from a media device (e.g., a television), the quality of the detected codes may differ. Such differences may be due to, for example, the MM device collecting audio from a television that is relatively far away from where the MM device is located. In such situations, one or more stacking operations may supplement missing portions of the detected code with accurate code data. The example code stacking manager 1616 of this example identifies a difference between MM devices in the household regarding the number of stacked codes versus unstacked codes detected (block 1920). Additionally, the example simultaneous tuning monitor 1602 calculates an average (e.g., a maximum average) seconds of code per metering device in the household, and a corresponding difference value between each metering device (block 1922). The example modeling engine 1614 of the illustrated example applies the logistic regression fit to the collected data in a manner consistent with example Equation (11) (block 1924). However, in this application of example Equation (11), the model employs (1) the AGC values, (2) the differences between stacked/unstacked embedded codes and (3) the differences between the average number of seconds of code between metering devices (block 1924).
Returning to
As described above, employing a MM to characterize household media viewing behavior may be performed in a stochastic manner rather than by employing a PM to save money that would otherwise be spent on the relatively expensive PM devices. When a MM device is employed to collect audio signal (tuning) data from a household, some of the collected minutes may include codes (e.g., embedded codes collected in the raw audio and passed on to the on/off detection engine 130 for post processing), some of the collected minutes may be analyzed via signature analysis (e.g., analysis of the raw audio collected by the MM device and passed on to the on/off detection engine 130 for audio signature comparison against one or more signature database(s)), and some of the collected minutes may have neither codes nor have corresponding signature matches for media identification.
Minutes that have neither codes nor corresponding signatures that may be used with a reference database are referred to herein as all other tuning (AOT) minutes. In such circumstances with a PM device, the media device (e.g., television) will be detected in an on state (e.g., power status ON based on a power status detector of the PM device), but no station and/or media can be credited with tuning. In other circumstances, a media device may be in a muted state or an off state (e.g., power status OFF), thus no audio is emitted that can be used for crediting. Example methods, systems, apparatus and/or articles of manufacture disclosed herein apply a stochastic manner of determining whether AOT minutes are associated with an off state or an on state, which may be associated with other media device usage separate from media programming (e.g., video game usage, video conferencing, etc.).
In operation, the example PM interface 202 collects minutes from a PM device (e.g., an active/passive people-meter) within the household related to three categories of media device usage. A first category of media device usage associated with some collected minutes is related to a particular station or media, such as media identified by way of codes or signature matching. A second category of media device usage associated with other collected minutes is related to instances of non-programming related usage, such as video game play, video conferencing activity, home picture viewing, etc. A third category of media device usage associated with still other collected minutes is related to instances where the media device is powered off.
The example MM interface 204 also collects minutes from a MM device within the household. As described above, because the MM interface 204 is not physically connected to the media device, it cannot directly verify whether the media device is powered on and, instead, collects only audio-based information via one or more built-in microphones. The example MM interface 204 may collect minutes data that either credits a station or media, or designates the collected minutes as AOU. The example AGC monitor 1610 collects AGC values from each of the example MM interface 204 and the example PM interface 202 for each corresponding minute, and the example modeling engine 1614 prepares a regression model to fit the collected data in a manner consistent with example Equation (13).
In the illustrated example of Equation (13), HUT is indicative of a “household using television” on (e.g., an ON power status), off is indicative of an OFF power status, and the independent variables (X) include AGC values, daypart information and/or a number of minutes since a code reader credit occurred.
The example modeling engine 1614 uses derived coefficients (B) to calculate a probability for each minute as either on (HUT) or off in a manner consistent with example Equation (14).
The program 2200 of
The example modeling engine 1614 prepares a model based on AGC values, day parts and a number of minutes since a last MM device credit in a manner consistent with example Equation (13) (block 2208). The model may include, but is not limited to, a regression model, in which coefficients may be derived after fitting the collected data. The derived model coefficients are used by the example modeling engine 1614 to calculate a probability that any particular minute of interest was associated with either an on state or an off state of the household media device. These derived coefficients may be associated with panelist households within the region of interest 104 having only MM devices 108 (block 2210).
The processor platform 2300 of the illustrated example includes a processor 2312. The processor 2312 of the illustrated example is hardware. For example, the processor 2312 can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer.
The processor 2312 of the illustrated example includes a local memory 2313 (e.g., a cache). The processor 2312 of the illustrated example is in communication with a main memory including a volatile memory 2314 and a non-volatile memory 2316 via a bus 2318. The volatile memory 2314 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory 2316 may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory 2314, 2316 is controlled by a memory controller.
The processor platform 2300 of the illustrated example also includes an interface circuit 2320. The interface circuit 2320 may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface.
In the illustrated example, one or more input devices 2322 are connected to the interface circuit 2320. The input device(s) 2322 permit(s) a user to enter data and commands into the processor 2312. The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system.
One or more output devices 2324 are also connected to the interface circuit 2320 of the illustrated example. The output devices 2324 can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a light emitting diode (LED), a printer and/or speakers). The interface circuit 2320 of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor.
The interface circuit 2320 of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network 2326 (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.).
The processor platform 2300 of the illustrated example also includes one or more mass storage devices 2328 for storing software and/or data. Examples of such mass storage devices 2328 include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives.
The coded instructions 2332 of
From the foregoing, it will be appreciated that the above disclosed methods, apparatus and articles of manufacture allow audience measurement techniques to occur with a substantially larger quantity of households, in which each household has a substantially lower metering equipment cost by employing audio-based code reader devices instead of relatively more expensive people meter devices. Examples disclosed herein permit a determination of behavior probability that can be applied to households that do not have a People Meter device and, instead, employ a media meter that captures audio without a physical connection to a media device (e.g., a television). Such examples allow behavior probability calculations based on utilization of other households that include the People Meter device, in which the calculations reveal behavior probabilities in a stochastic manner that adheres to expectations of statistical significance.
Example methods, systems, apparatus and/or articles of manufacture disclosed herein also facilitate a stochastic manner of determining a probability of ambient tuning within households that do not employ a People Meter device. In some examples disclosed herein, both a panelist audience meter (e.g., a People Meter) and a media meter (e.g., captures audio without a physical connection to a media device) are employed to obtain data associated with media code status and one or more automatic gain control (AGC) values. Based on the obtained code status and AGC values, examples disclosed herein create model coefficients that may be applied to households with only media meters in a manner that determines a probability of ambient tuning that upholds expectations of statistical significance. Additionally, data obtained related to AGC values are disclosed herein to be used with daypart information to calculate model coefficients indicative of whether a media device (e.g., a television) is powered on or powered off.
Additional example methods, systems, apparatus and/or articles of manufacture disclosed herein identify probabilities of a number of visitors in a household and their corresponding ages. In particular, examples disclosed herein calculate an average visitor parameter (AVP) based on exposure minutes and tuning minutes, which are further applied to a Poisson distribution to determine a probability of having a certain number of visitors in a household. Such probabilities are in view of a target demographic of interest having a particular age range, which may be selected based on inputs from a random number generator.
Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.
This application claims the benefit of U.S. Provisional Application Ser. No. 61/839,344, which was filed on Jun. 25, 2013, U.S. Provisional Application Ser. No. 61/844,301, which was filed on Jul. 9, 2013, U.S. Provisional Application Ser. No. 61/986,409, which was filed on Apr. 30, 2014, and U.S. Provisional Application Serial No. 62,007,535, which was filed on Jun. 4, 2014, all of which are hereby incorporated herein by reference in their entireties.
Number | Name | Date | Kind |
---|---|---|---|
3540003 | Murphy | Nov 1970 | A |
3696297 | Otero | Oct 1972 | A |
3818458 | Deese | Jun 1974 | A |
3906454 | Martin | Sep 1975 | A |
4058829 | Thompson | Nov 1977 | A |
4125892 | Fukuda et al. | Nov 1978 | A |
4166290 | Furtman et al. | Aug 1979 | A |
4236209 | Lombardo, Jr. et al. | Nov 1980 | A |
4258386 | Cheung | Mar 1981 | A |
4283709 | Lucero et al. | Aug 1981 | A |
4355372 | Johnson et al. | Oct 1982 | A |
4356545 | West | Oct 1982 | A |
4473824 | Claytor | Sep 1984 | A |
4516216 | Armstrong | May 1985 | A |
4546382 | McKenna et al. | Oct 1985 | A |
4566030 | Nickerson et al. | Jan 1986 | A |
4602279 | Freeman | Jul 1986 | A |
4603232 | Kurland et al. | Jul 1986 | A |
4658290 | McKenna et al. | Apr 1987 | A |
4677552 | Sibley, Jr. | Jun 1987 | A |
4695880 | Johnson et al. | Sep 1987 | A |
4700378 | Brown | Oct 1987 | A |
4706121 | Young | Nov 1987 | A |
4713791 | Saluski | Dec 1987 | A |
4718025 | Minor et al. | Jan 1988 | A |
4725886 | Galumbeck et al. | Feb 1988 | A |
4740912 | Whitaker | Apr 1988 | A |
4745549 | Hashimoto | May 1988 | A |
4745559 | Willis et al. | May 1988 | A |
4751578 | Reiter et al. | Jun 1988 | A |
4757456 | Benghiat | Jul 1988 | A |
4774658 | Lewin | Sep 1988 | A |
4783648 | Homma et al. | Nov 1988 | A |
4792921 | Corwin | Dec 1988 | A |
4817080 | Soha | Mar 1989 | A |
4823290 | Fasack et al. | Apr 1989 | A |
4831582 | Miller et al. | May 1989 | A |
4845658 | Gifford | Jul 1989 | A |
4849879 | Chinnaswamy et al. | Jul 1989 | A |
4868866 | Williams, Jr. | Sep 1989 | A |
4887308 | Dutton | Dec 1989 | A |
4907188 | Suzuki et al. | Mar 1990 | A |
4912466 | Call | Mar 1990 | A |
4912522 | Oates et al. | Mar 1990 | A |
4924488 | Kosich | May 1990 | A |
4930011 | Kiewit | May 1990 | A |
4935870 | Burk, Jr. et al. | Jun 1990 | A |
4954699 | Coffey et al. | Sep 1990 | A |
4958284 | Bishop et al. | Sep 1990 | A |
4961132 | Uehara | Oct 1990 | A |
4972367 | Burke | Nov 1990 | A |
4972504 | Daniel, Jr. et al. | Nov 1990 | A |
4977455 | Young | Dec 1990 | A |
4977594 | Shear | Dec 1990 | A |
4989230 | Gillig et al. | Jan 1991 | A |
5006978 | Neches | Apr 1991 | A |
5007017 | Kobayashi | Apr 1991 | A |
5008929 | Olsen et al. | Apr 1991 | A |
5019963 | Alderson et al. | May 1991 | A |
5023907 | Johnson et al. | Jun 1991 | A |
5023929 | Call | Jun 1991 | A |
5038211 | Hallenbeck | Aug 1991 | A |
5038374 | Kaufman et al. | Aug 1991 | A |
5042027 | Takase et al. | Aug 1991 | A |
5047867 | Strubbe et al. | Sep 1991 | A |
5049873 | Robins et al. | Sep 1991 | A |
5062147 | Pickett et al. | Oct 1991 | A |
5063610 | Alwadish | Nov 1991 | A |
5088108 | Uddenfeldt et al. | Feb 1992 | A |
5101402 | Chiu et al. | Mar 1992 | A |
5109350 | Henwood et al. | Apr 1992 | A |
5140419 | Galumbeck et al. | Aug 1992 | A |
5150116 | West | Sep 1992 | A |
5150414 | Ng | Sep 1992 | A |
5155591 | Wachob | Oct 1992 | A |
5159685 | Kung | Oct 1992 | A |
5161109 | Keating et al. | Nov 1992 | A |
5166866 | Kim et al. | Nov 1992 | A |
5181113 | Chang | Jan 1993 | A |
5204947 | Bernstein et al. | Apr 1993 | A |
5208588 | Nishiyama | May 1993 | A |
5210530 | Kammerer et al. | May 1993 | A |
5212684 | MacNamee et al. | May 1993 | A |
5214792 | Alwadish | May 1993 | A |
5220522 | Wilson et al. | Jun 1993 | A |
5220655 | Tsutsui | Jun 1993 | A |
5223827 | Bell et al. | Jun 1993 | A |
5223924 | Strubbe | Jun 1993 | A |
5226120 | Brown et al. | Jul 1993 | A |
5231593 | Notess | Jul 1993 | A |
5235680 | Bijnagte | Aug 1993 | A |
5237677 | Hirosawa et al. | Aug 1993 | A |
5237681 | Kagan et al. | Aug 1993 | A |
5237684 | Record et al. | Aug 1993 | A |
5239540 | Rovira et al. | Aug 1993 | A |
5241625 | Epard et al. | Aug 1993 | A |
5241671 | Reed et al. | Aug 1993 | A |
5245429 | Virginio et al. | Sep 1993 | A |
5247517 | Ross et al. | Sep 1993 | A |
5247575 | Sprague et al. | Sep 1993 | A |
5247697 | Ban | Sep 1993 | A |
5249260 | Nigawara et al. | Sep 1993 | A |
5251324 | McMullan, Jr. | Oct 1993 | A |
5253346 | Okabayashi et al. | Oct 1993 | A |
5260878 | Luppy | Nov 1993 | A |
5262860 | Fitzpatrick et al. | Nov 1993 | A |
5267314 | Stambler | Nov 1993 | A |
5267351 | Reber et al. | Nov 1993 | A |
5276458 | Sawdon | Jan 1994 | A |
5276789 | Besaw et al. | Jan 1994 | A |
5281962 | Vanden Heuvel et al. | Jan 1994 | A |
5283639 | Esch et al. | Feb 1994 | A |
5287363 | Wolf et al. | Feb 1994 | A |
5297249 | Bernstein et al. | Mar 1994 | A |
5299115 | Fields et al. | Mar 1994 | A |
5301350 | Rogan et al. | Apr 1994 | A |
5309243 | Tsai | May 1994 | A |
5315093 | Stewart | May 1994 | A |
5315580 | Phaal | May 1994 | A |
5317140 | Dunthorn | May 1994 | A |
5321831 | Hirose | Jun 1994 | A |
5321838 | Hensley et al. | Jun 1994 | A |
5327237 | Gerdes et al. | Jul 1994 | A |
5327554 | Palazzi, III et al. | Jul 1994 | A |
5331544 | Lu et al. | Jul 1994 | A |
5333302 | Hensley et al. | Jul 1994 | A |
5339239 | Manabe et al. | Aug 1994 | A |
5339412 | Fueki | Aug 1994 | A |
5347632 | Filepp et al. | Sep 1994 | A |
5349662 | Johnson et al. | Sep 1994 | A |
5351278 | Koshishiba et al. | Sep 1994 | A |
5351293 | Michener et al. | Sep 1994 | A |
5355327 | Stent et al. | Oct 1994 | A |
5355484 | Record et al. | Oct 1994 | A |
5359367 | Stockill | Oct 1994 | A |
5361359 | Tajalli et al. | Nov 1994 | A |
5367677 | Stanfill | Nov 1994 | A |
5371846 | Bates | Dec 1994 | A |
5374951 | Welsh | Dec 1994 | A |
5375070 | Hershey et al. | Dec 1994 | A |
5379380 | Mori et al. | Jan 1995 | A |
5388211 | Hornbuckle | Feb 1995 | A |
5388252 | Dreste et al. | Feb 1995 | A |
5388258 | Larsson et al. | Feb 1995 | A |
5390281 | Luciw et al. | Feb 1995 | A |
5398336 | Tantry et al. | Mar 1995 | A |
5406569 | Isozaki | Apr 1995 | A |
5408607 | Nishikawa et al. | Apr 1995 | A |
5410598 | Shear | Apr 1995 | A |
5412778 | Andres | May 1995 | A |
5414809 | Hogan et al. | May 1995 | A |
5418728 | Yada | May 1995 | A |
5461708 | Kahn | Oct 1995 | A |
5481294 | Thomas et al. | Jan 1996 | A |
5483658 | Grube et al. | Jan 1996 | A |
5485897 | Matsumoto et al. | Jan 1996 | A |
5491820 | Belove et al. | Feb 1996 | A |
5495282 | Mostafa et al. | Feb 1996 | A |
5495581 | Tsai | Feb 1996 | A |
5499340 | Barritz | Mar 1996 | A |
5524073 | Stambler | Jun 1996 | A |
5550928 | Lu et al. | Aug 1996 | A |
5555303 | Stambler | Sep 1996 | A |
5557333 | Jungo et al. | Sep 1996 | A |
5560038 | Haddock | Sep 1996 | A |
5568471 | Hershey et al. | Oct 1996 | A |
5572643 | Judson | Nov 1996 | A |
5594911 | Cruz et al. | Jan 1997 | A |
5594934 | Lu et al. | Jan 1997 | A |
5600364 | Hendricks et al. | Feb 1997 | A |
5604867 | Harwood | Feb 1997 | A |
5608445 | Mischler | Mar 1997 | A |
5615264 | Kazmierczak et al. | Mar 1997 | A |
5623652 | Vora et al. | Apr 1997 | A |
5634100 | Capps | May 1997 | A |
5646998 | Stambler | Jul 1997 | A |
5648965 | Thadani et al. | Jul 1997 | A |
5671283 | Michener et al. | Sep 1997 | A |
5673382 | Cannon et al. | Sep 1997 | A |
5696702 | Skinner et al. | Dec 1997 | A |
5706502 | Foley et al. | Jan 1998 | A |
5708709 | Rose | Jan 1998 | A |
5708780 | Levergood et al. | Jan 1998 | A |
5710915 | McElhiney | Jan 1998 | A |
5710918 | Lagarde et al. | Jan 1998 | A |
5712979 | Graber et al. | Jan 1998 | A |
5715453 | Stewart | Feb 1998 | A |
5717860 | Graber et al. | Feb 1998 | A |
5717923 | Dedrick | Feb 1998 | A |
5724521 | Dedrick | Mar 1998 | A |
5732218 | Bland et al. | Mar 1998 | A |
5737619 | Judson | Apr 1998 | A |
5740549 | Reilly et al. | Apr 1998 | A |
5781893 | Felthauser et al. | Jul 1998 | A |
5787253 | McCreery et al. | Jul 1998 | A |
5793302 | Stambler | Aug 1998 | A |
5796952 | Davis et al. | Aug 1998 | A |
5799292 | Hekmatpour | Aug 1998 | A |
5801747 | Bedard | Sep 1998 | A |
5819156 | Belmont | Oct 1998 | A |
5819285 | Damico et al. | Oct 1998 | A |
5823879 | Goldberg et al. | Oct 1998 | A |
5829001 | Li et al. | Oct 1998 | A |
5835923 | Shibata et al. | Nov 1998 | A |
5838919 | Schwaller et al. | Nov 1998 | A |
5841433 | Chaney | Nov 1998 | A |
5848396 | Gerace | Dec 1998 | A |
5857190 | Brown | Jan 1999 | A |
5870546 | Kirsch | Feb 1999 | A |
5872588 | Aras et al. | Feb 1999 | A |
5878224 | Smith | Mar 1999 | A |
5878384 | Johnson et al. | Mar 1999 | A |
5881360 | Fong | Mar 1999 | A |
5892917 | Myerson | Apr 1999 | A |
5926168 | Fan | Jul 1999 | A |
5931912 | Wu et al. | Aug 1999 | A |
5933811 | Angles et al. | Aug 1999 | A |
5935207 | Logue et al. | Aug 1999 | A |
5936541 | Stambler | Aug 1999 | A |
5948061 | Merriman et al. | Sep 1999 | A |
5951643 | Shelton et al. | Sep 1999 | A |
5956483 | Grate et al. | Sep 1999 | A |
5958010 | Agarwal et al. | Sep 1999 | A |
5963914 | Skinner et al. | Oct 1999 | A |
5964839 | Johnson et al. | Oct 1999 | A |
5974148 | Stambler | Oct 1999 | A |
5974299 | Massetti | Oct 1999 | A |
5977964 | Williams et al. | Nov 1999 | A |
5982917 | Clarke et al. | Nov 1999 | A |
5986653 | Phathayakorn et al. | Nov 1999 | A |
5991735 | Gerace | Nov 1999 | A |
5999178 | Hwang et al. | Dec 1999 | A |
6005597 | Barrett et al. | Dec 1999 | A |
6006260 | Barrick, Jr. et al. | Dec 1999 | A |
6014638 | Burge et al. | Jan 2000 | A |
6018619 | Allard et al. | Jan 2000 | A |
6049695 | Cottam | Apr 2000 | A |
6052730 | Felciano et al. | Apr 2000 | A |
6067440 | Diefes | May 2000 | A |
6070145 | Pinsley et al. | May 2000 | A |
6078324 | Phathayakorn et al. | Jun 2000 | A |
6108637 | Blumenau | Aug 2000 | A |
6112238 | Boyd et al. | Aug 2000 | A |
6112240 | Pogue et al. | Aug 2000 | A |
6115608 | Duran et al. | Sep 2000 | A |
6115742 | Franklin et al. | Sep 2000 | A |
6119098 | Guyot et al. | Sep 2000 | A |
6138155 | Davis et al. | Oct 2000 | A |
6167358 | Othmer et al. | Dec 2000 | A |
6173311 | Hassett et al. | Jan 2001 | B1 |
6185586 | Judson | Feb 2001 | B1 |
6216129 | Eldering | Apr 2001 | B1 |
6219786 | Cunningham et al. | Apr 2001 | B1 |
6226677 | Slemmer | May 2001 | B1 |
6237033 | Doeberl et al. | May 2001 | B1 |
6250930 | Mintz | Jun 2001 | B1 |
6256739 | Skopp et al. | Jul 2001 | B1 |
6264560 | Goldberg et al. | Jul 2001 | B1 |
6275854 | Himmel et al. | Aug 2001 | B1 |
6278966 | Howard et al. | Aug 2001 | B1 |
6279036 | Himmel et al. | Aug 2001 | B1 |
6279112 | O'Toole, Jr. et al. | Aug 2001 | B1 |
6286036 | Rhoads | Sep 2001 | B1 |
6298348 | Eldering | Oct 2001 | B1 |
6317787 | Boyd et al. | Nov 2001 | B1 |
6324546 | Ka et al. | Nov 2001 | B1 |
6360261 | Boyd et al. | Mar 2002 | B1 |
6381632 | Lowell | Apr 2002 | B1 |
6393479 | Glommen et al. | May 2002 | B1 |
6397359 | Chandra et al. | May 2002 | B1 |
6408335 | Schwaller et al. | Jun 2002 | B1 |
6418470 | Blumenau | Jul 2002 | B2 |
6434532 | Goldband et al. | Aug 2002 | B2 |
6446261 | Rosser | Sep 2002 | B1 |
6449604 | Hansen et al. | Sep 2002 | B1 |
6457025 | Judson | Sep 2002 | B2 |
6466970 | Lee et al. | Oct 2002 | B1 |
6473407 | Ditmer et al. | Oct 2002 | B1 |
6483813 | Blencowe | Nov 2002 | B1 |
6484316 | Lindberg | Nov 2002 | B1 |
6569095 | Eggers | May 2003 | B2 |
6601100 | Lee et al. | Jul 2003 | B2 |
6606745 | Maggio | Aug 2003 | B2 |
6609239 | Xavier | Aug 2003 | B1 |
6621881 | Srinivasan | Sep 2003 | B2 |
6625648 | Schwaller et al. | Sep 2003 | B1 |
6662195 | Langseth et al. | Dec 2003 | B1 |
6662227 | Boyd et al. | Dec 2003 | B2 |
6671715 | Langseth et al. | Dec 2003 | B1 |
6712702 | Goldberg et al. | Mar 2004 | B2 |
6719660 | Palazzolo | Apr 2004 | B2 |
6735775 | Massetti | May 2004 | B1 |
6766370 | Glommen et al. | Jul 2004 | B2 |
6807558 | Hassett et al. | Oct 2004 | B1 |
6842782 | Malik et al. | Jan 2005 | B1 |
6859833 | Kirsch et al. | Feb 2005 | B2 |
6993590 | Gauthier et al. | Jan 2006 | B1 |
7017143 | Andrew et al. | Mar 2006 | B1 |
7139723 | Conkwright et al. | Nov 2006 | B2 |
7146329 | Conkwright et al. | Dec 2006 | B2 |
7150030 | Eldering et al. | Dec 2006 | B1 |
7181412 | Fulgoni et al. | Feb 2007 | B1 |
7216149 | Briscoe et al. | May 2007 | B1 |
7222071 | Neuhauser et al. | May 2007 | B2 |
7260823 | Schlack et al. | Aug 2007 | B2 |
7260837 | Abraham et al. | Aug 2007 | B2 |
7376722 | Sim et al. | May 2008 | B1 |
7451151 | Horvitz et al. | Nov 2008 | B2 |
7483835 | Neuhauser et al. | Jan 2009 | B2 |
7493655 | Brown | Feb 2009 | B2 |
7757250 | Horvitz et al. | Jul 2010 | B1 |
7953791 | Or Sim et al. | May 2011 | B2 |
7953839 | Sim et al. | May 2011 | B2 |
8046797 | Bentolila et al. | Oct 2011 | B2 |
8112511 | Sim et al. | Feb 2012 | B2 |
8311888 | Ramer et al. | Nov 2012 | B2 |
20010049620 | Blasko | Dec 2001 | A1 |
20020040394 | Shapira | Apr 2002 | A1 |
20020040395 | Davis et al. | Apr 2002 | A1 |
20020049762 | Shah et al. | Apr 2002 | A1 |
20020072966 | Eldering et al. | Jun 2002 | A1 |
20020077787 | Rappaport et al. | Jun 2002 | A1 |
20020078191 | Lorenz | Jun 2002 | A1 |
20020099812 | Davis et al. | Jul 2002 | A1 |
20020099818 | Russell et al. | Jul 2002 | A1 |
20020099819 | Hattori et al. | Jul 2002 | A1 |
20020103664 | Olsson et al. | Aug 2002 | A1 |
20020112048 | Gruyer et al. | Aug 2002 | A1 |
20020124074 | Levy et al. | Sep 2002 | A1 |
20020150054 | Sohraby et al. | Oct 2002 | A1 |
20020161673 | Lee et al. | Oct 2002 | A1 |
20030018969 | Humpleman et al. | Jan 2003 | A1 |
20030046303 | Chen et al. | Mar 2003 | A1 |
20030046385 | Vincent | Mar 2003 | A1 |
20030062223 | Coyle et al. | Apr 2003 | A1 |
20030110485 | Lu et al. | Jun 2003 | A1 |
20030144868 | MacIntyre et al. | Jul 2003 | A1 |
20030149975 | Eldering et al. | Aug 2003 | A1 |
20030163563 | Bean | Aug 2003 | A1 |
20030182387 | Geshwind | Sep 2003 | A1 |
20030187677 | Malireddy et al. | Oct 2003 | A1 |
20030208578 | Taraborelli | Nov 2003 | A1 |
20030231203 | Gallella | Dec 2003 | A1 |
20040024717 | Sneeringer | Feb 2004 | A1 |
20040221033 | Davis et al. | Nov 2004 | A1 |
20050114511 | Davis et al. | May 2005 | A1 |
20060074769 | Looney et al. | Apr 2006 | A1 |
20060075421 | Roberts et al. | Apr 2006 | A1 |
20070011039 | Oddo | Jan 2007 | A1 |
20070022032 | Anderson et al. | Jan 2007 | A1 |
20070174295 | Abraham et al. | Jul 2007 | A1 |
20070276940 | Abraham et al. | Nov 2007 | A1 |
20080120650 | Orihara et al. | May 2008 | A1 |
20080140479 | Mello et al. | Jun 2008 | A1 |
20080256235 | Or Sim et al. | Oct 2008 | A1 |
20080263200 | Or Sim et al. | Oct 2008 | A1 |
20080300965 | Doe | Dec 2008 | A1 |
20090112703 | Brown | Apr 2009 | A1 |
20100228855 | Sim et al. | Sep 2010 | A1 |
20110004682 | Honnold et al. | Jan 2011 | A1 |
20110208860 | Sim et al. | Aug 2011 | A1 |
20110258049 | Ramer et al. | Oct 2011 | A1 |
20120254911 | Doe | Oct 2012 | A1 |
Number | Date | Country |
---|---|---|
0228242 | Jul 1987 | EP |
0228458 | Jul 1987 | EP |
0388658 | Sep 1990 | EP |
0451384 | Oct 1991 | EP |
0632382 | Jan 1995 | EP |
0747841 | Dec 1996 | EP |
2004357311 | Dec 2004 | JP |
2008011365 | Jan 2008 | JP |
20130008978 | Jan 2013 | KR |
9826529 | Jun 1998 | WO |
0111606 | Feb 2001 | WO |
Entry |
---|
Applets at Kassel, “Applets and Applications,” Department of Engineering Measurement, Aug. 27, 1996, retrieved from <http://web.archive.org/web/19970802020436/http://www.uni-kassel.de/fb16/ipm/mt/java/javae.html>, retrieved on May 24, 2006 (5 pages). |
Applet Demos, 1995-1996 (2 pages). |
Arbitron Inc., “Smartplus 8.0: Keeps Getting Smarter So You Can Too,” 2007, retrieved from <http://www.arbitron.com/ad—agencies/smartplus8.html>, retrieved on Sep. 21, 2007 (13 pages). |
Arlitt et al., “Internet Web Servers: Workload Characterization and Performance Implications,” IEEE/ACM Transactions on Networking, vol. 5, No. 5, Oct. 1997 (15 pages). |
Bank, “Java Security,” MIT, Dec. 8, 1995, retrieved from <http://groups.csail.mit.edu/mac/users/jbank/javapaper/javapaper.html,> retrieved on Apr. 30, 2015 (11 pages). |
Becker, “Department of Engineering Measurement,” UNI Kassel, Sep. 20, 1996 (1 page). |
Beckett, “Combined Log System,” Computing Laboratory, University of Kent, retrieved from <http://www.igd.fhg.de/archive/1995—www95/papers/46/comblog.html>, retrieved on May 24, 2006 (8 pages). |
Berners-Lee, “WorldWideWeb: Proposal for a HyperText Project,” Nov. 12, 1990 (7 pages). |
Berners-Lee, “Information Management: A Proposal,” CERN, Mar. 1989, May 1990, retrieved from <http://www.w3.org/History/1989/proposal.html>, retrieved on May 24, 2006 (14 pages). |
Bertot et al., “Web Usage Statistics: Measurement Issues and Analytical Techniques,” Government Information Quarterly, vol. 14, No. 4, 1997 (23 pages). |
Boyan, “Anonymous Surfing, The Anonymizer Homepage,” Anonymizer, Sep. 1, 1995, retrieved from <http://web.archive.org/web/19990208003332m—1/anonymizer.cs.cmu.edu:8080/>, retrieved on May 24, 2006 (1 page). |
Catledge et al., “Characterizing Browsing Strategies in the World-Wide Web,” School of Literature, Communication and Culture, Graphics, Visualization, & Usability Center, Georgia Institute of Technology (9 pages). |
Cern, “Computer News Letter Oct.-Dec. 1991,” Ref. CERN-CNL-1991-204, vol. 26, Issue No. 3, retrieved from <http://ref.web.cern.ch/ref/CERN/CNL/1991/204/>, retrieved on May 24, 2006 (2 pages). |
Cooley et al., “Data Preparation for Mining World Wide Web Browsing Patterns,” Knowledge and Information Systems, vol. 1, 1999 (27 pages). |
Cooley et al., “Web Mining: Information and Pattern Discovery on the World Wide Web,” Department of Computer Science, University of Minnesota, Jul. 16, 1997 (2 pages). |
Cooper, “Design Considerations in Instrumenting and Monitoring Web-Based Information Retrieval Systems,” Journal of the American Society for Information Science, vol. 49, No. 10, 1998 (17 pages). |
Crovella et al., “Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes,” IEEE/ACM Transactions on Networking, vol. 5, No. 6, Dec. 1997 (25 pages). |
Crovella et al., “Explaining World Wide Web Traffic Self-Similarity,” Computer Science Department, Boston University, Technical Report TR-95-015, Oct. 12, 1995 (19 pages). |
Dean et al. (1995) “Security Flaws in the HotJava Web Browser,” Department of Computer Science, Princeton University, Nov. 3, 1995 (8 pages). |
December et al., “HTML and CGI Unleashed,” Sams.net Publishing, 1995 (841 pages.) (NPL in 5 parts). |
E-mail Counter Logs, 1996 (33 pages). |
Fielding, “wwwstat: HTTPd Logfile Analysis Software,” Department of Information and Computer Science, University of California, Irvine, retrieved from <http://ftp.ics.uci.edu/pub/websoft/wwwstat/>, retrieved on May 24, 2006 (3 pages). |
Girdley et al., “Web Programming with Java,” Sams.net Publishing, 1996 (499 pages) (NPL in 2 parts). |
Google Groups, “Access Counters,” retrieved from <http://groups.google.com/group/comp.infosystems.www.authoring.html/browse—thread/thread/dfb1a837f29e165e/a0e0e6a131c6102d?q=access . . . > retrieved on May 24, 2006 (3 pages). |
Google Groups, “Javascript,” retrieved from <http://groups.google.com/group/comp.society.privacy/browse—thread/thread/9b12496aeda7fd78/dd2ebe5f8966fd05?q=javascript&mum=1&hl>, retrieved on May 24, 2006 (3 pages). |
Google Groups, “Javascript Counter Script,” retrieved from <http://groups.google.com/group/comp.databases.oracle/browse—thread/thread/97671e385d1bac94n777a82875e328555?q=javascript+counter+sc . . . >, retrieved on May 24, 2006 (2 pages). |
Google Groups, “Javascript Exploit,” retrieved from <http://groups.google.com/group/comp.sys.mac.misc/browse—thread/thread/f9285c7d4e4354cd/eb94d50669840159?q=javascript+exploit&mum>, retrieved on May 24, 2006 (3 pages). |
Google Groups, “Garrett Casey,” retrieved from <Google Groups: comp.infosystems.www.authoring.cgi> (2 pages). |
Google Groups, “Xferstats,” retrieved from <http://groups.google.com/group/alt.sources/browse—thread/thread/c9e60d434be3ad86/0a180fb213f27e2b?q=xferstats&mum=199&h1=en>, retrieved on May 24, 2006 (10 pages). |
Google Groups, “Cgi Hit Counter,” retrieved from <http://groups.google.com/group/comp.infosystems.www.users/browse—thread/thread/390de231bb3f0097/64bddc80086f124b?q=cgi+hit+counter> retrieved on May 24, 2006 (2 pages). |
Gotta et al., “Performance Evaluation of Web Applications,” Proceeding of the Computer Measurement Group International Conference, 1998 (13 pages). |
Grobe, “An Early History of Lynx: Multidimensional Collaboration,” Academic Computing Services, University of Kansas, Apr. 10, 1997, retrieved from <http://people.cc.ku.edu/˜grobe/early-lynx.html>, retrieved on May 24, 2006 (8 pages). |
Gundavaram, “CGI Programming on the World Wide Web,” o'Reilly & Associates, Inc., 1996 (455 pages) (NPL in 2 parts). |
Hallam-Baker et al., “Extended Log File Format,” W3C, Working Draft WD-logfile-960323, retrieved from <http://www.w3.org/TR/WD-logfile.html>, retrieved on May 24, 2006 (6 pages). |
Hello Net! Counter (1 page). |
Holgermetzger, “A Netscape Timeline,” Mar. 1993 through Mar. 17, 2006, retrieved from <http://www.holgermetzger.de/Netscape—History.html,> retrieved on May 24, 2006 (4 pages). |
Houston, “A Vision of Advertising Technology—How It Will Work and Why Advertisers Must Involve Themselves in the Process,” Modern Media, Aug. 1994, retrieved from <http://www.web.archive.org/web/19961111095749/www.modernmedia.com/clippings/articles/sm . . . >, retrieved on Jun. 8, 2005 (6 pages). |
W3, “Logging Control in W3C httpd,” w3.org, Jul. 1995, retrieved from <http://www.w3.org/Daemon/User/Config/Logging.html,> retrieved on May 24, 2006 (3 pages). |
Intellectual Property Office of the United Kingdom, “Examination Report,” issued in connection with United Kingdom Patent Application No. GB0920943.8, mailed Nov. 10, 2010 (6 pages). |
Intellectual Property Office of the United Kingdom, “Examination Report,” issued in connection with United Kingdom Patent Application No. GB0920943.8, mailed Apr. 11, 2011 (5 pages). |
International Bureau, “International Preliminary Report on Patentability,” issued in connection with International Patent Application No. PCT/US2008/059874, mailed Dec. 10, 2009 (7 pages). |
International Searching Authority, “International Search Report,” issued in connection with International Patent Application No. PCT/US2008/059874, mailed Mar. 2, 2009 (3 pages). |
International Searching Authority, “Written Opinion,” issued in connection with International Patent Application No. PCT/US2008/059874, mailed Mar. 2, 2009 (5 pages). |
International Preliminary Examining Authority, “International Preliminary Examination Report,” issued in connection with International Patent Application No. PCT/AU00/00937, issued Nov. 2, 2001 (3 pages). |
IP Australia, “Examiner's First Report,” issued in connection with Australian Patent Application No. 2008260397, mailed Nov. 17, 2010 (2 pages). |
IP Australia, “Notice of Acceptance,” issued in connection with Australian Patent Application No. 2008260397, mailed Aug. 7, 2012 (2 pages). |
Japan Patent Office, “Notice of Reasons for Rejection,” issued in connection with Japanese Patent Application No. 2001-516087, mailed May 18, 2010 (14 pages). |
Japan Patent Office, “Notice of Reasons for Rejection,” issued in connection with Japanese Patent Application No. 2001-516087, mailed Jan. 25, 2011 (8 pages). |
Lamers, “WebThreads, LLC of Vienna, VA, USA is Pleased to Announce WebThreads™ 1.0.1 Website Interactivity and Visitor Tracking Solution,” Oct. 8, 1996 (5 pages). |
Javaworld, JavaWorld: IDG's Magazine for the Java Community, vol. 1, Issue 1, retrieved from <www.javaworld.com>. Mar. 8, 1996 ( 1 page). |
Javaworld, JavaWorld: IDG's Magazine for the Java Community, vol. 1, Issue 2, retrieved from <www.javaworld.com>. Apr. 1, 1996 ( 1 page). |
Javaworld, JavaWorld Editorial Calendar, Feb. 26, 1996 ( 1 page). |
Johnson, “Implementation Guide, HBX On-Demand Web Analytics,” WebSideStory, Inc., 2006 (12 pages). |
Kamba, “Personalized Online Newspaper,” NEC, vol. 49, No. 7, 1996 (Abstract in English Only) (6 pages). |
Kent et al., “Official Netscape JavaScript Book,” 1996 (508 pages) (NPL in 2 parts). |
Kiesler et al., “Homenet: A Field Trial of Residential Internet Services,” Carnegie Mellon University, Apr. 1995, retrieved from <http://homenet.hcii.cs.cmu.edu/progress/reportl.html> (12 pages). |
Kraut et al., “Homenet: A Field Trial of Residential Internet Services,” Carnegie Mellon University, Apr. 1995, retrieved from <http://www.acm.org/sigchi/chi96/proceedings/papers/Kraut/rek—txt.htm> (14 pages). |
Levine, “Computer Privacy Digest V8#024” Computer Privacy Digest Sun, vol. 8, Issue No. 024, Mar. 17, 1996, retrieved from <http://web.archive.org/web/20000829051834/itu.rdg.ac.uk/misc/Mailing—Lists/cpd/00000002.htm>, retrieved on May 24, 2006 (19 pages). |
Loverso, “Netscape Navigator 2.0 Exposes User's Browsing History,” The Risks Digest, vol. 7, Issue 79, Feb. 1996, retrieved from <http://catless.ncl.ac.uk/Risks/17.79.html>, retrieved on Dec. 11, 2005 (2 pages). |
Lynnworth, “Tracking Home Page Hits,” Dr. Dobbs Journal, Dec. 1, 1995, retrieved from <http://www.drdobbs.com/article/print?articleId=184409739&siteSectionName=web-development>, retrieved on Apr. 30, 2015 (7 pages). |
Mayer-Schonberger, “The Internet and Privacy Legislation: Cookies for a Treat?” West Virginia University, retrieved from <http://web.archive.org/web/19990203034203/www.wvjolt.wvu.edu/wvjolt/current/issue1/articles/mayer/ . . . >, retrieved on May 24, 2006 (7 pages). |
McGraw et al., “Untangling the Woven Web: Testing Web-based Software,” Reliable Software Technologies Corporation, Apr. 1, 1996 (9 pages). |
Consoli, “Nielsen Unveils National TV/Internet Fusion,” Mediaweek, Nov. 1, 2006, retrieved from <http://mediaweek.printthis.clickability.com/pt/cpt?action=cpt&title= . . . >, retrieved on Sep. 21, 2007 (2 pages). |
Microsoft Corporation, “Screen Shots of Windows NT 3:1,” retrieved from <http://www.cs.umd.edu/hcil/muiseum/systems/winnt31src.html>, retrieved on Jun. 7, 2005 (4 pages). |
Montgomery, “Using Clickstream Data to Predict WWW Usage,” Carnegie Mellon University, Aug. 1999 (27 pages). |
Mulvenna et al., “The ‘Soft-Push:’ Mining Internet Data for Marketing Intelligence,” Working Conference: Electronic Commerce in the Framework of Mediterranean Countries Development, Ioannina, Greece (12 pages). |
National Defense University, “Information Operations Timeline,” retrieved from <http://www.jfsc.ndu.edu/schools—programs/jciws/iw/io—timeline.asp>, retrieved on May 24, 2006 (9 pages). |
Naughton, “The JAVA Handbook,” 1996 (446 pages) (NPL in 2 parts). |
Net.Genesis et al., “Build a Web Site: The Programmer's Guide to Creating, Building, and Maintaining Web Presence,” 1995 (721 pages) (NPL in 3 parts). |
Netscape Communication and Sun Microsystems, “Sun and Netscape Announce JavaScript,” Dec. 4, 1995 (8 pages). |
Nardone, “The Modem Media Internet Reach and Involvement Scale (IRIS),” Modem Media, Feb. 1995, retrieved from <http://web.archive.org/web/19961111095728/www.modemmedia.com/clippings/articles/iris>, retrieved on Jun. 8, 2005 (3 pages). |
Naor et al., “Secure Accounting and Auditing on the Web,” Computer Networks and ISDN Systems, vol. 30, 1998 (10 pages). |
Nielsen, “Classic HTTP Documents,” W3C, May 14, 1998, retrieved from <http://www.w3.org/Protocols/Classic.html>, retrieved on May 24, 2006 (1 page). |
O'Connell, “A New Pitch: Advertising on the World Wide Web is a Whole New Ball Game,” Modem Media, May 1995, retrieved from <http://web.archive.org/web/19961111095738/www.modernmedia.com/clippings/articles/ne . . . >, retrieved on Jun. 8, 2005 (8 pages). |
Older Counter.html history, “Older History of Changes,” 1995-1996, retrieved from <http://www.ualberta.ca/GEO/Counter.History.html>, retrieved on May 24, 2006, (8 pages). |
Ostermann, “Tcptrace Homepage,” retrieved from <http://www.tcptrace.org, retrieved on Jun. 12, 2006 (1 page). |
Padmanabhan et al., “Analysis of Web Site Usage Data: How Much Can We Learn About the Consumer from Web Logfiles?,” Center for Digital Economy Research, Stern School of Business, Working Paper Stern #IS-96-18, Dec. 1996 (33 pages). |
Pierrakos et al., “Web Usage Mining as a Tool for Personalization: A Survey,” User Modeling and User-Adapted Interaction, vol. 13, 2003 (62 pages). |
Powell, “Tomorrow's Heavyweight?” Network World, vol. 15, No. 49, Dec. 7, 1998 (1 page). |
Internet Profiles Corporation, “I/PRO is First to Develop a Solution for Measuring Java Applets,” Apr. 12, 1996 (2 pages). |
Realnetworks Inc., “Real System 5.0 Security Features Whitepaper,” 1997 (10 pages). |
Realnetworks Inc., “RealServer Administration Guide Version 5.0,” 1995-1997 (262 pages). |
Rodley, “Writing Java Applets,” 1996 (438 pages) (NPL in 2 parts). |
Schmittlein, “Why Does the NBD Model Work?” (Abstract), Marketing Science at the University of Florida, vol. 4, No. 3, Summer 1985, retrieved from <http://bear.cba.ufl.edu/centers/mks/abstracts/vol4/no3/schmittleinbe.> (1 page). |
Seshan et al., “SPAND: Shared Passive Network Performance Discovery,” Proceedings of the USENIX Symposium on Internet Technologies and Systems, Monterey, California, Dec. 1997 (13 pages). |
Shahabi et al., “Knowledge Discovery from Users Web-Page Navigation,” 1997 retrieved from <http://www.ict.griffith.edu.au/˜vlad/teaching/kdd.d/readings.d/shahabi97knowledge.pdf> (11 pages). |
Siochi et al., “Computer Analysis of User Interfaces Based on Repetition in Transcripts of User Sessions,” ACM Transactions on Information Systems, vol. 9, No. 4, Oct. 1991 (27 pages). |
Staten, “Navigator Tricks Raise Concerns (Privacy Concerns Raised by the Addition of JavaScript and HTTP Cookie to Netscape Communications' Navigator 3.0 Web Browser),” MacWeek, vol. 10, No. 11, Mar. 18, 1996 retrieved from <http://www.dialogclassic.com/main.vmgw>, retrieved on Jun. 7, 2005 (2 pages). |
The Nielsen Company (US), LLC, “Complaint for Patent Infringement,” Case 2:11-cv-00168-RGD-TEM, filed with the United States District Court for the Eastern District of Virginia Alexandra Division and transferred to the Norfolk Division on Mar. 15, 2011 (13 pages). |
United States Patent and Trademark Office, “Final Office Action,” issued in connection with U.S. Appl. No. 12/100,685, mailed Nov. 16, 2009 (9 pages). |
United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 12/100,685, mailed Feb. 12, 2009 (15 pages). |
United States Patent and Trademark Office, “Final Office Action,” issued in connection with U.S. Appl. No. 09/763,338, mailed Jul. 12, 2007 (20 pages). |
United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 12/100,698, mailed Jan. 20, 2010 (18 pages). |
United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 12/100,698, mailed Sep. 25, 2009 (17 pages). |
United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 12/780,890, mailed Oct. 1, 2010 (9 pages). |
United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 13/098,358, mailed Jul. 27, 2011 (10 pages). |
United States Patent and Trademark Office, “Non-Final Office Action,” issued in connection with U.S. Appl. No. 09/763,338, mailed Nov. 21, 2006 (18 pages). |
United States Patent and Trademark Office, “Notice of Allowance,” issued in connection with U.S. Appl. No. 12/100,698, mailed Feb. 25, 2011 (12 pages). |
United States Patent and Trademark Office, “Notice of Allowance,” issued in connection with U.S. Appl. No. 12/100,698, mailed Oct. 20, 2010 (11 pages). |
United States Patent and Trademark Office, “Notice of Allowance,” issued in connection with U.S. Appl. No. 12/780,890, mailed Mar. 22, 2011 (12 pages). |
United States Patent and Trademark Office, “Notice of Allowance,” issued in connection with U.S. Appl. No. 13/098,358, mailed Dec. 13, 2011 (12 pages). |
United States Patent and Trademark Office, “Notice of Allowance,” issued in connection with U.S. Appl. No. 09/763,338, mailed Jan. 2, 2008 (7 pages). |
United States Patent and Trademark Office, “Requirement for Restriction/Election,” issued in connection with U.S. Appl. No. 09/763,338, mailed Aug. 1, 2006 (6 pages). |
European Patent Office, “Office Action,” issued in connection with European Patent Application No. 00949006.1, mailed Apr. 18, 2008 (7 pages). |
Google Groups, “Wwwstat,” retrieved from <http://groups.google.com/group/comp.infosystems.www/browse—thread/thread/784aa9c20470d342/fc0cde0742990875?q=wwwstat&mum=43 . . . >, retrieved on May 24, 2006 (8 pages). |
Google Groups, “Fwgstat,” retrieved from <http://groups.google.com/group/comp.infosystems.gopher/browse—thread/thread/15dc16c0284c02d0/953bf38f2cee15ea?q=fwgstat&mum=34 . . . >, retrieved on May 24, 2006 (3 pages). |
Google Groups, “Fwgstat,” retrieved from <http://groups.google.com/group/comp.infosystems.gopher/browse—thread/thread/4790232128a9157/d1534978ddddf998?q=fwgstat&mum=33 . . . >, retrieved on May 24, 2006 (10 pages). |
Google Groups, “Getsites,” retrieved from <http://groups.google.com/group/comp.infosystems.www/browse—thread/threada36eeb6daea735d8/0fa7adf53e51b894?q=getsites&mum=19 & h . . . >, retrieved on May 24, 2006 (2 pages). |
Google Groups, “Cgi Hit Counter,” retrieved from <http://groups.google.com/group/comp.lang.per1/browse—thread/thread/b4ef428d8c96d525/3e18b779b1dad79e?q=cgi+hit+counter & mum=74 & . . . > retrieved on May 24, 2006 (7 pages). |
Google Groups, “Wusage 1.0,” retrieved from <http://groups.google.com/group/comp.infosystems.www/browse—thread/ thread/a07834d8b38dbc4f/f05bf1df25d47fd3?q=wusage+1.0&mum=1 . . . >, retrieved on May 24, 2006 (2 pages). |
Google Groups, “Getstats,” retrieved from <http://groups.google.com/group/comp.infosystems.www/browse—thread/thread/1009129delaaf6aa/ed924b219923cc7d?q=getstats&mum=1 & h1 . . . >, retrieved on May 24, 2006 (3 pages). |
Ibiblio, “Ibiblio—Ten Years in the Making—Aligning the Sites,” retrieved from <http://www.ibiblio.org/history/> , retrieved on May 24, 2006 (10 pages). |
Javapage, “Java,” retrieved from <http://web.archive.org/web/20040712072256/http://www.groton.k12.ct.us/WWW/fsr/student/Spring02/W . . . >, retrieved on May 24, 2006 (4 pages). |
Mobasher, “Introduction,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node1.html>, Jul. 16, 1997 (2 pages). |
Mobasher, “A Taxonomy of Web Mining,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node2.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Content Mining,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node3.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Agent-Based Approach,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node4.html>, Jul. 16, 1997 (2 pages). |
Mobasher, “DataBase Approach,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node5.html>, Jul. 16, 1997 (2 pages). |
Mobasher, “Web Usage Mining,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node6.html>, Jul. 16, 1997 (2 pages). |
Mobasher, “Pattern Discovery Tools,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node7.html>, Jul. 16, 1997 (2 pages). |
Mobasher, “Pattern Analysis Tool,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node8.html>, Jul. 16, 1997 (2 pages). |
Mobasher, “Pattern Discovery from Web Transactions,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node9.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Preprocessing Tasks,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node10.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Data Cleaning,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node11.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Transaction Identification,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node12.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Discovery Techniques on Web Transactions,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node13.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Path Analysis,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node14.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Association Rules,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node15.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Sequential Patterns,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node16.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Clustering and Classification,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node17.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Analysis of Discovered Patterns,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node18.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Visualization Techniques,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node19.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Olap Techniques,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node20.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Data and Knowledge Querying,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node21.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Usability Analysis,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node22.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Web Usage Mining Architecture,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node23.html>, Jul. 16, 1997 (3 pages). |
Mobasher, “Research Directions,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node24.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Data Pre-Processing for Mining,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node25.html>, Jul. 16, 1997 (1 page). |
Mobasher, “The Mining Process,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node26.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Analysis of Mined Knowledge,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node27.html>, Jul. 16, 1997 (1 page). |
Mobasher, “Conclusion,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node28.html>, Jul. 16, 1997 (1 page). |
Mobasher, “References,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node29.html>, Jul. 16, 1997 (6 pages). |
Mobasher, “About This Document,” retrieved from <http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node30.html>, Jul. 16, 1997 (1 page). |
Muquit, “WWW Homepage Access Counter and Clock!” retrieved from <http://www.muquit.com/muquit/software/Count/Count.html>, retrieved on May 24, 2006 (32 pages). |
Thomson Jr., “Creation of the NSFnet,” Commercialization of the Internet, 2000, retrieved from <http://johnthomson.org/j561/NSFcreate-4.html>, retrieved on May 24, 2006 (2 pages). |
Thomson Jr., “The Beginning of Commercialization of the NSFnet,” Commercialization of the Internet, 2000, retrieved from <http://johnthomson.org/j561/NSFcomm-5.html>, retrieved on May 24, 2006 (1 page). |
Thomson Jr., “Criticism of the NSF Grows,” Commercialization of the Internet, 2000, retrieved from <http://johnthomson.org/j561/NSFcriticism-6.html>, retrieved on May 24, 2006 (1 page). |
Thomson Jr., “The NSF Starts to Change Their Policy,” Commercialization of the Internet, 2000, retrieved from <http://johnthomson.org/j561/NSFpolicy-7.html>, retrieved on May 24, 2006 (1 page). |
Thomson Jr., Changes in NSF Policy Becomes Law—The Internet is Commercialized, Commercialization of the Internet, 2000, retrieved from <http://johnthomson.org/j561/NSFlaw-9.html>, retrieved on May 24, 2006 (1 page). |
W3 C, “HTTP: A Protocol for Network Information: Basic HTTP as Defined in 1992,” retrieved from <http://www.w3.org/Protocols/HTTP/HTTP2.html>, retrieved on May 24, 2006 (2 pages). |
“The World Wide Web Servers: W3 Servers,” w3.org, retrieved from <http://www.w3.org/History/19921103-hypertext/hypertext/DataSources/WWW/Servers.html>, retrieved on May 24, 2006 (2 pages). |
“The World Wide Web Project,” w3.org, retrieved from <http://www.w3.org/History/19921103-hypertext/hypertext/WWW/TheProject.html>, retrieved on May 24, 2006 (1 page). |
“Demonstrations—/JENC92,” w3.org, retrieved from <http://www.w3.org/Conferences/JENC92/Demonstrations.html>, retrieved on May 24, 2006 (1 page). |
BL, “An Updated Quick Look at Viola WWW,” retrieved from <http://www.w3.org/History/19921103-hyptertext/hypertext/Viola/Review.html>, retrieved on May 24, 2006 (2 pages). |
W3C, “HyperText Transfer Protocol Design Issues,” retrieved from <http://www.w3.org/Protocols/DesignIssues.html>, retrieved on May 24, 2006 (3 pages). |
“WorldWideWeb for C5,” Presentation, w3.org, May 17, 1991, retrieved from <http://www.w3.org/Talks/C5—17—May—91.html>, retrieved on May 24, 2006 (1 page). |
W3C, “Change History for httpd,” retrieved from <http://www.w3.org/Daemon/Features.html>, retrieved on May 24, 2006 (15 pages). |
W3C, “A Little History of the Word Wide Web (1945-1995),” retrieved from <http://www.w3.org/History.html>, retrieved on May 24, 2006 (6 pages). |
Wandschneider, “ActiveX Controls Framework: Sample Code for Authoring Non-MFC Controls,” Microsoft Corporation, Apr. 1996, retrieved from <http://msdn.microsoft.com/archive/en-us/dnaractivex/html/msdn—ctrlfmk.asp?frame=true>, retrieved on Mar. 22, 2007 (15 pages). |
Watt, “Management Tools Get with the Web,” Network World, vol. 14, No. 25, Jun. 23, 1997 (1 page). |
Weber, “New Software Helps Advertisers Get Through Tangled Web Pages,” The Wall Street Journal, Oct. 23, 1996 (2 pages). |
Websidestory and Aig, “On Demand Analytics Presentation,” Jun. 22, 2005 (10 pages). |
Weston, “Netscape 2.0b2 allows for invasion of privacy,” TBTF, Dec. 2, 1995, retrieved from <http://www.tbtf.com/resource/b2-privacy-bug.html>, retrieved on May 24, 2006 (2 pages). |
Wilson, “Browser Timelines (Releases Important to HTML and CSS Development=Index DOT Html/Css,” retrieved from <http://www.blooberry.com/indexdot/history/browsers6.htm> retrieved on May 24, 2006 (3 pages). |
Wilson, “Opera (Opera Software) Index DOT Html/Css,” retrieved from <http://www.blooberry.com/indexdot/history/opera.htm> retrieved on May 24, 2006 (3 pages). |
Zaiane et al., “Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs,” IEEE International Forum on Research and Technology Advances in Digital Libraries, Apr. 1998 (12 pages). |
Ziegler, “Startup Net Count Seeks to Tally How Many Web Surfers See Ads,” The Wall Street Journal, Oct. 11, 1996 (2 pages). |
McGrew, “Web Authoring FAQ,” The Web Design Group, 2005, posted to comp.infosystems.www.authoring.html, retrieved from File://P:\P Drive Files\Clients \Nielsen-NetRatings\ Settled Cases\Sane Solutions, LLC\ retrieved on Dec. 21, 2006 (59 pages). |
WwwTalk 1991 Archives, “Messages from Monday Oct. 28, 1991 to Friday, Dec. 13, 1991,” retrieved from <http://ksi.cpsc.ucalgary.ca/archives/WWW-talk/www-talk-1991.index.html>, retrieved on May 24, 2006 (1 page). |
European Patent Office, “Communication Pursuant to Article 94(3) EPC ,” issued in connection with European Patent Application No. 00949006.1, mailed Oct. 7, 2014 (6 pages). |
Screen Shot of ftp.ist.utl.pt, 1999 (1 page). |
Pestana et al., “A Visual History of Web Measurement: Vendor Emergence, Technology Deployment and Seminal Events” (1 page). |
“Third International World-Wide Web Conference—Technology, Tools and Applications,” Apr. 10-14, 1995, retrieved from <http://www.igd.fhg.de/archive/1995—www95/>, retrieved on May 24, 2006 (1 page). |
Sanders, “Plexus Log File Summaries,” email message from sanders@bsdi.com, Jun. 18, 1993, retrieved from <http://ksi.cpsc.ucalgary.ca/archives/WWW-TALK/www-talk-1993q2.messages/563.html> retrieved from May 24, 2006 (4 pages). |
“2.4—Windows Based Only Internet BBS Software—The Official BBS FAQ,” retrieved from <http://www.sysopworld.com/bbsfaq/ch02.2.4.htm>, retrieved on May 24, 2006 (23 pages). |
Java@mt.e-technik.uni-kassel.de, “Counter,” retrieved from <http://web.archive.org/web/19970802020140/www.uni-kassel.de/fb16/ipm/mt/java/counteru.html>, retrieved on May 24, 2006 (1 page). |
“Linear IP Advertising Infrastructure” (1 page). |
“Applet Collection” (5 pages). |
Arbitron Inc., “Respondent-Level Data: Your Key to Reaching the Hispanic Listener,” retrieved from <http://arbitronratings.com/ad—agencies/rld—vs—hispsumm.htm?inframe>, retrieved on Sep. 21, 2007 (1 page). |
Abrams et al., “Multimedia Traffic Analysis Using CHITRA95,” ACM Multimedia 95—Electronic Proceedings, Nov. 5-9, 1995, retrieved from <http://ei.cs.vt.edu/˜succeed/95multimediaAWAFPR/95multimediaAWAFPR.html>, retrieved on Jun. 9, 2006 (17 pages). |
Abrams, “A Tool to Aid in Model Development and Validation,” NCR Corporation, Annual Simulation Symposium, 1986 (10 pages). |
Anderson et al., “Next Generation Intrusion Detection Expert System (NIDES)—Software Users Manual Beta—Update Release,” SRI International, Dec. 1, 1994 (316 pages). |
Barber, e-mail dated May 3, 1996, (2 pages). |
Baskaran, “Gamelan Who's Who More Info,” Earth Web, Inc., 1996 (2 pages). |
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, retrieved from <file://P:\ Drive Files\CLIENTS\Nielsen-NetRatings\SettledCases\Sane Solutions LLC\>, retrieved on Dec. 21, 2006 (19 pages). |
Berners-Lee et al., “Uniform Resource Locators” Network Working Group, Dec. 1994 (23 pages). |
Berners-Lee, “Presentation of WWW to Online Publishing 93—World-Wide Web,” retrieved from <http://www.w3org/Talks/OnlinePublishing93/Overview.html>, retrieved on May 24, 2006 (1 page). |
Berners-Lee et al., “Hypertext Transfer Protocol—HTTP/1.0,” Network Working Group, May 1996, retrieved from <http://www.3.org/Protocols/rfc1945/rfc1945>, retrieved on Mar. 22, 2007 (57 pages). |
Berners-Lee, “The World Wide Web Browser,” retrieved from <http://www.w3.org/People/Berners-Lee/WorldWideWeb.html>, retrieved on May 24, 2006 (4 pages). |
Bernstein et al., “Architectures for Volatile Hypertext,” Hypertext '91 Proceedings, Dec. 1991 (18 pages). |
Bestavros et al., “Application-Level Document Caching in the Internet,” Proceeding of the Second International Workshop on Services in Distributed and Networked Environments (SDNE '95) (8 pages). |
Bieber et al., “Backtracking in a Multiple-window Hypertext Environment,” ECHT '94 Proceedings, Sep. 1994 (9 pages). |
Bl, “Plenary Talk at WWW Geneva 94,” retrieved from <http://www.w3.org/Talks/WWW94Tim/> retrieved on May 24, 2006 (4 pages). |
Bl et al., “W3 Project—Assorted Design Notes—Working Notes,” W3.org, retrieved from <http://www.w3.org/History/1994/WWW/WorkingNotes/Overview.html>, retrieved on May 24, 2006 (2 pages). |
Blackarrow, Inc., “Meeting the Challenges of Video Advertising in an IP ABR Environment,” 2012 (10 pages). |
Blythe et al., “Lynx Users Guide Version 2.3,” retrieved from <http://www.cse.unsw.edu.au/help/doc/lynx/lynx—help/Lynx—users—guide.html>, retrieved on Jun. 12, 2006 (12 pages). |
Boyns, “Crazy Counter,” modified Mar. 8, 1996 (2 pages). |
Boyns, “Crazy Counter (GIF89a),” modified Mar. 8, 1996 (1 page). |
Boyns, “CrazyCounter.java,” 1996 (5 pages). |
Braun et al., “Applied Network Research: 1994 Annual Status Report,” Applied Network Research, San Diego Supercomputer Center and University of California, San Diego, Apr. 19, 1995 (15 pages). |
Brueggeman, “Monitoring CDROM Usage,” UCSD Scripps Institution of Oceanography Library (5 pages). |
Carmel et al., “Browsing in Hypertext: A Cognitive Study,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, No. 5, Sep./Oct. 1992 (23 pages). |
Chankhunthod et al., “A Hierarchical Internet Object Cache,” ATEC '96 Proceedings of the 1996 Annual Conference on USENIX Annual Technical Conference, 1996 (11 pages). |
Chen, “Supporting Advertisement on Electronic Publications,” IBM Technical Disclosure Bulletin, Aug. 1996 (2 pages). |
Claffy et al., “Traffic Characteristics of the T1 NSFNET Backbone,” INFOCOM '93 Proceedings, Twelfth Annual Joint Conference of the IEEE Computer and Communications Societies, Networking: Foundation for the Future, IEEE, 1993 (11 pages). |
Cove et al., “Online Text Retrieval Via Browsing,” Information Processing and Management, vol. 24, No. 1, 1988 (10 pages). |
Cunha et al., “Characteristics of WWW Client-Based Traces,” Computer Science Department, Boston University, Jul. 18, 1995 (19 pages). |
Davis et al., “Instant Java Applets,” Macmillan Computer Publishing USA, 1996 (234 pages). |
Dellecave Jr., “The Net Effect,” Sales & Marketing Management: An Excutive's Guide to Sales and Marketing Technology, Mar. 1996 (9 pages). |
Desjardins, “Activity Monitor 1.1 User's Guide,” 1994 (5 pages). |
Digital Envoy, “NetAcuity IP Intelligence Whitepaper,” Digital Envoy, Inc., 2002-2003 (10 pages). |
Earth Station 9, “Computers and the Internet,” <http://www.earthstation9.com/counters.htm>, retrieved from May 24, 2006 (2 pages). |
Earthweb, Inc., “Java-Enhanced Communication Tools,” Gamelan, The Official Directory for Java, 1996 (7 pages). |
Engagement Marketing Group, “Advanced Advertising Media Project—Phase One: Summary Report—Remaking Video-on-Demand to Deliver Media Value,” May 11, 2011 (50 pages). |
European Patent Office, “Supplementary Partial Search Report,” issued in connection with European Patent Application No. 00949006.1, mailed Apr. 19, 2006 (4 pages). |
European Patent Office, “Supplementary Search Report,” issued in connection with European Patent Application No. 00949006.1, mailed Jul. 12, 2006 (5 pages). |
European Patent Office, “Extended Search Report,” issued in connection with European Patent Application No. 10012280.3, mailed Mar. 17, 2014 (6 pages). |
European Patent Office, “Office Action,” issued in connection with European Patent Application No. 00949006.1, mailed Oct. 7, 2014 (6 pages). |
Feliciano et al., “Lamprey: Tracking Users on the World Wide Web,” Section on Medical Informatics, Stanford University, 1996 (5 pages). |
Fielding, “Wwwstat Manual,” retrieved from <http://ftp.ics.uci.edu/pub/websoft/wwwstat/wwwstat.html>, retrieved on Jun. 12, 2006 (11 pages). |
Finke, “Monitoring Usage of Workstations with a Relational Database,” 1994 LISA—San Diego, California, Sep. 19-23, 1994 (10 pages). |
Fleishman, “Web Log Analysis: Who's Doing What, When? Part 2,” retrieved from <http://www,webdeveloper.com/management/management—log—analysis—2.html> (4 pages). |
Fuller, “Measuring User Motivation From Server Log Files,” Usability Research, retrieved from <http://www.microsoft.com/usability/webconf/fuller/fuller.htm>, Oct. 30, 2000 (15 pages). |
Gellman, “They Could be Monitoring Your Every Web Move,” GCN, retrieved from <http:www.gcn.com/print/15 9/31672-1.html>, retrieved on Apr. 20, 2006 (3 pages). |
Girdley et al., “Web Programming with Java,” 1996 (9 pages). |
Gile, “Reporting Application Usage in a LAN Environment,” ACM SIGUCCS, vol. 18, 1990 (13 pages). |
Gilula et al., “A Direct Approach to Data Fusion,” Journal of Marketing Research, vol. 43, Feb. 2006 (22 pages). |
Goldmail, “GoldMail Thinks Consumers Should Get Paid for Reading Advertising!” Google Groups, Jul. 7, 1996 (4 pages). |
Goldmail, “GoldMail Revolutionizes Internet Advertising!” Google Groups, Jul. 7, 1996 (4 pages). |
Glassman, “A Caching Relay for the World Wide Web,” Systems Research Center, Digital Euipment Corporation, 1994 (10 pages). |
Google Groups, “Java Project” “I am Volunteering My time,” retrieved from <http://groups.google.com/group/comp.lang.java.programmer/browse—thread/thread/5430a3 . . . >, retrieved on Apr. 12, 2006 (3 pages). |
Google Groups, “Counter,” retrieved from <http://groups-beta.google.com/group/comp.lang.javascript/browse—thread/thread/4874a9f9c . . . >, retrieved on Jun. 8, 2005 (4 pages). |
Google Groups, “Can U Make a Hit Counter in a Java Applet?,” retrieved from <http://groups.google.com/group/comp.lang.java/browse—thread/thread/f2a41d0cb5c8eee4/c8 . . . >, retrieved on May 2, 2006 (10 pages). |
Google Groups, “Xferstats,” retrieved from <http://groups.google.com/group/comp.unix.xenix/browse—thread/thread/cb3dlaed2lbc0e3e/8d9ce54693af9e98?q=xferstats&mum=200 . . . >, retrieved on May 24, 2006 (3 pages). |
Google Groups, “Hit Counter” retrieved from <http://groups.google.com/group/comp.infosystems.www.authoring.html/browse—thread/e515dad2e5d1e8cc/0ebdc329e9ec00cc?q=hit+c . . . > retrieved on May 24, 2006 (7 pages). |
Google Groups, “Garrett Casey Counter,” retrieved from <Google Groups: comp.infosystems.www.misc.entrepreneurs> (3 pages). |
Greenberg, “Using Unix: Collected Traces of 168 Users,” Advanced Technologies, The Alberta Research Council (14 pages). |
Hansen et al., “Automated System Monitoring and Notification With Swatch,” Seventh System Administration Conference (LISA ' 93), Monterey California, Nov. 1993 (9 pages). |
Haran, “PC-Meter Tracks Computer Users,” Advertising Age, Oct. 2, 1995, retrieved from <http://adage.com/print/85520>, retrieved on Jun. 2, 2015 (2 pages). |
Highbeam Research, “Harris Computer Systems and Webster Network Strategies Announce Alliance to Offer WWW Monitoring Capabilities Through Firewalls,” Business Wire, Mar. 4, 1996, retrieved from <http://www.highbeam.com> (5 pages). |
Hilbert et al., “Agents for Collecting Application Usage Data Over the Internet,” Department of Information and Computer Science, University of California, Irvine (9 pages). |
“Horace's Java—Page View Timer Demo—Form 1,” Aug. 7, 1999 (2 pages). |
Horak, “IOMON—Protected Mode I/O Port Monitor,” Technical University of Prague, Faculty of Nuclear Science and Physical Engineering (6 pages). |
International Searching Authority, “International Search Report,” issued in connection with International Patent Application No. PCT/US2015/015219, mailed May 22, 2015 (4 pages). |
International Searching Authority, “Written Opinion,” issued in connection with International Patent Application No. PCT/US2015/015219, mailed May 22, 2015 (7 pages). |
Ivler, “Advertising Models and Associated Software . . . ,” Google Groups, May 15, 1996 (5 pages). |
Johnson et al., “Automatic Touring in a Hypertext System,” IEEE Twelfth Annual International Phoenix Conference on Computers and Communications, 1993 (7 pages). |
Kamba et al., “The Krakatoa Chronicle—An Interactive, Personalized, Newspaper on the Web,” Georgia Institute of Technology (15 pages). |
Kugel et al., “Decay Effects in Online Advertising: Quantifying the Impact of Time Since Last Exposure,” Presented at the ARF 50th Annual Convention, New York City, Apr. 26-28, 2004 (19 pages). |
Lee et al., “Intelligent Agents for Matching Information Providers and Consumers on the World-Wide-Web,” Proceedings of the Thirtieth Annual Hawaii International Conference on System Sciences, IEEE, 1997 (11 pages). |
Lubow, “An Engineer's Guide to Autocad,” Industrial Engineering, vol. 19, No. 8, Aug. 1987 (6 pages). |
Long, “Gwstat v1.1—Generate Graphs of HTTPD Server Traffic,” BUBL Administrator, Jul. 14, 1994, retrieved from <http://www.bubl.ac.uk///archive/internet/www/servers/gswtat6.htm>, retrieved on Jun. 12, 2006 (1 page). |
Marchionini, “Information-Seeking Strategies of Novices Using a Full-Text Electronic Encyclopedia,” Journal of the American Society for Information Science, vol. 40, No. 1, 1989 (16 pages). |
McCanne et al., “The BSD Packet Filter: A New Architecture for User-level Packet Capture,” Lawrence Berkeley Laboratory, Berkeley, California, Dec. 19, 1992 (11 pages). |
McGee, “Web Pages: A Programmer's Perspective,” Jun. 10, 1996, retrieved from <http://www.dfpug/loseblattsammlung/migration/whitepapers/webpages.htm>, retrieved on Mar. 22, 2007 (13 pages). |
McGrath, “The Tail-Less Mouse,” Computer Graphics World, vol. 11, Oct. 1988 (5 pages). |
McKenzie et al., “An Empirical Analysis of Web Page Revisitation,” Department of Computer Science, University of Canterbury, 2001 (7 pages). |
Mogul, “Efficient Use of Workstations for Passive Monitoring of Local Area Networks,” WRL Research Report 90/5, Western Research Laboratory, Jul. 1990 (29 pages). |
Mosaic Communications Corporation, “Welcome to Netscape,” retrieved from <http://www.hnehosting.com/mirrors/Origin—of—a—Browser/mcom.10.1994/home/welcome.html>, retrieved on May 24, 2006 (2 pages). |
Mueller, “InfoTicker Java Applet,” Google Groups, May 11, 1996, retrieved from <http://groups.google.com/group/comp.infosystems.www.announce/browse—thread/thread/cc . . . >, retrieved on May 2, 2006 (2 pages). |
NCSA, “In the Beginning There was NCSA Mosaic . . . ” NCSA, retrieved from <http://www.ncsa.uiuc.edu/News/MosaicHistory/>, retrieved on Jun. 12, 2006 (15 pages). |
Netscape, “Mosaic Communications Changes Name to “Netscape Communications Corporation,”” Netscape Communications Corporation, retrieved from <http://www.holgermetzger.de/netscape/NetscapeCommunicationsNewsRelease.htm>, retrieved on May 24, 2006 (2 pages). |
Neumann, “Forum on Risks to the Public in Computers and Related Systems,” Risks-Forum Digest, vol. 17, Issue 83, Mar. 4, 1996 (11 pages). |
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). |
Pallap, “Marketing on the Internet,” Journal of Consumer Marketing, vol. 13, No. 4, Aug. 1996 (17 pages). |
Perlman, “Asynchronous Design/Evaluation Methods for Hypertext Technology Development,” Hypertext '89 Proceedings, Nov. 1989 (21 pages). |
Pew, “Instant Java,” Sun Microsystems, Inc., 1996 (370 pages). |
Philip Morris, “Teenage Attitudes and Behavior Study—Methodology 2006” (9 pages). |
Pitkow et al., “A Simple Yet Robust Caching Algorithm Based on Dynamic Access Patterns,” Proceedings of the Second International WWW Conference, GVU Technical Report; VU-GIT-94-39 (8 pages). |
Pitkow et al., “Results From the First World-Wide Web User Survey,” Journal of Computer Networks and ISDN Systems, vol. 27, No. 2, 1994 (15 pages). |
Pitkow et al., “Using the Web as a Survey Tool: Results From the Second WWW User Survey,” Third International WWW Conference, GVU Technical Report: GVU-GIT-94-40 (12 pages). |
Pitkow et al., “Results From the Third WWW User Survey,” Graphics, Visualization, & Usability Center, Georgia Institute of Technology (10 pages). |
Poler, “Improving WWW Marketing Through User Information and Non-Intrusive Communication,” Internet Profiles Corporation (I/PRO) (4 pages). |
Progressive Networks, Inc., “RealServer Administration and Content Creation Guide Version 4.0,” Progressive Networks, Inc., 1995-1997 (366 pages). |
Progressive Networks, Inc., “RealAudio Server Administration and Content Creation Guide Version 3.0,” Progressive Networks, Inc., 1995-1996 (292 pages). |
Raggett, “HTML 3.2 Reference Specification,” W3C, Jan. 14, 1997, retrieved from <http://www.w3.org/TR/REC-html32>, retrieved from Mar. 22, 2007 (49 pages). |
Regents of the University of California, “Changes to wwwstat: httpd Logfile Analysis Package,” 1994,1996, retrieved from <http://ftp.ics.uci.edu/pub/websoft/wwwstat/Changes>, retrieved on Jun. 12, 2006 (4 pages). |
Resonate, “Network Traffic Mangement, Database Traffic Management,” Resonate, Inc. 2005 (2 pages). |
Reuters, “Study Faults Online Ads for Kids, FTC Probes,” Google Groups, Mar. 28, 1996 (3 pages). |
Shepard, “TCP Packet Trace Analysis,” MIT/LCS/TR-494, MIT Laboratory for Computer Science, Feb. 1991 (70 pages). |
Stengle, “Tying TV Advertising to Action: Leveraging the Second Screen to Raise the Effectiveness & Measurability of TV Advertising,” BlackArrow USA, 2012 (8 pages). |
Sukaviriya et al., “A Second Generation User Interface Design Environment: The Model and the Runtime Architecture,” GIT-GVU-92-24, Graphics, Visualization & Usability Center, Georgia Institute of Technology, Sep. 1992 (10 pages). |
Sunsite, “Hot Java Readme and Link to the Software on SunSITE,” SunSITE Software Information and Technology Exchange, retrieved from http://www.ibiblio.org/hotjava/>, retrieved on May 24, 2006 (1 page). |
Symantec, “Using Norton pcANYWHERE for DOS,” Symantec Corporation, 1994 (30 pages). |
“Tcpslice(8):—Linux Man Page,” retrieved from <http://www.die.net/doc/linux/man/man8/tcpslice.8.html>, retrieved on Jun. 12, 2006 (3 pages). |
Tapley et al., “The Official Gamelan Java Directory,” EarthWeb, 1996 (6 pages). |
“Tcpslice—Linux Command—Unix Command,” tcpslice, 2006, retrieved from <http://linux.about.com/library/cmd/blcmd18—tcpslice.htm>, retrieved on Jun. 12, 2006 (3 pages). |
Thielman, “Comcast Pushes VOD Reruns into the C3 Window,” Adweek, Dec. 2, 2013, retrieved from <http://www.adweek.comnews/television/comcast-pushes-vod-reruns-c3 . . . >, retrieved on Feb. 10, 2014 (1 page). |
Tcpdump.org, “Tcpdump—dump traffic on a network,” retrieved from <http://www.tcpdump.org/tcpdump—man.html>, retrieved on Jun. 12, 2006 (26 pages). |
Tolle, “Performance Measurement and Evaluation of Online Information Systems,” Proceedings of the 1985 ACM Computer Science Conference—Agenda for Computing Research:The Challenge for Creativity, Mar. 12-14, 1985 (8 pages). |
“Tradewinds,” Harvard Business School, vol. 2, No. 5, May 1995 (11 pages). |
User Society, “First International Conference on the World-Wide Web,” May 1994, retrieved from <http://www.94.web.cern.ch/WWW94/>, retrieved on May 24, 2006 (2 pages). |
W3.Org, “CGI—Common Gateway Interface,” W3C, retrieved from <http://www.w3.org/CGI/>, retrieved on May 24, 2006 (2 pages). |
Warren, “Audience Tracking System for Electronic Newspapers” Google Groups, May 3, 1995 (3 pages). |
Weiler et al., “Usability Lab Tools: One Year Later,” CHI'94, Conference Companion, 1994 (p. 330). |
Wright, “Matt's Script Archive: Book'em Dano:Readme,” 1996, retrieved from <file://P:\PDrive Files\CLIENTS\Nielsen-NetRatings\Settled Cases\Sane Solutions, LLC\>, retrieved on Dec. 21, 2006 (2 pages). |
Wu et al., “Virtual Proxy Servers for WWW and Intelligent Agents on the Internet,” Proceedings of the Thirtieth Annual Hawaii International Conference on System Sciences, 1997 (10 pages). |
Yahoo!, “Yahoo! Search Directory > Web Programming > Access Counters,” retrieved from <http://dir.yahoo.com/Computers—and—internet/Internet/World—Wide—Web/Programming/Access—Counters/>, retrieved on May 24, 2006 (5 pages). |
Zeigler, “Startup Net Count Seeks to Tally How Many Web Surfers See Ads,” The Wall Street Journal, Oct. 1996 (2 pages). |
Zillmer, “How to Make Your Web Ads Pay Off,” Marketing Magazine, vol. 101, No. 23, Jun. 10, 1996 (4 pages). |
Number | Date | Country | |
---|---|---|---|
20140380348 A1 | Dec 2014 | US |
Number | Date | Country | |
---|---|---|---|
61839344 | Jun 2013 | US | |
61844301 | Jul 2013 | US | |
61986409 | Apr 2014 | US | |
62007535 | Jun 2014 | US |