EP1779125A2 - Methods and apparatus for improving the accuracy and reach of electronic media exposure measurements systems - Google Patents

Abstract

Methods and apparatus for improving the accuracy and reach of electronic media exposure measurement systems are disclosed. A disclosed method includes processing data representative of locations recorded by an electronic device to enhance at least one of completeness or accuracy of the data, deriving position fixes from the processed data, and modifying at least one of the derived position fixes to align with a known course of travel.

Description

METHODS AND APPARATUS FOR IMPROVING THE

ACCURACY AND REACH OF ELECTRONIC MEDIA

EXPOSURE MEASUREMENT SYSTEMS

RELATED APPLICATIONS

This patent claims benefit of U.S. Provisional Application Serial No. 60/592,554, entitled "Methods and Apparatus for Processing Data Collected by a GPS-enabled Media Measurement System" and filed on July 30, 2004. U.S. Provisional Application Serial Nos. 60/592,554 and U.S. Application Serial Nos. 10/686,872 and 10/318,422 are hereby incorporated by reference in their entireties.

FIELD OF THE DISCLOSURE

[0001] This disclosure relates generally to media exposure measurement systems and, more particularly, to methods and apparatus for improving the accuracy and reach of electronic media exposure measurement

systems.

BACKGROUND

[0002] In the past, media exposure measurement systems for outdoor media relied, for example, on automotive traffic studies (e.g., counting the number of cars traveling down a road on a given day), or claimed recall (e.g., an ability of consumers, through surveys, to remember seeing outdoor advertising) to determine the number of media exposures that were achieved. [0003] More recently, electronic systems for measuring and crediting media exposure have been developed, enabling outdoor advertisers to measure and establish with scientific and verifiable accuracy the reach of their outdoor media sites. FIG. 1 illustrates an example prior art electronic media exposure measurement system 100 that uses satellite positioning system (SPS) (e.g., the U.S. Global Positioning System (GPS) and the European Galileo System (currently under construction)) technology to track motorist and/or pedestrian exposure to outdoor media sites. To track exposure of a participant or a respondent 102, the respondent 102 carries (or wears) an SPS enabled monitoring device 110 (e.g., the Nielsen® personal outdoor device (Npod™)). The device 110 periodically (e.g., every 4 to 5 seconds) acquires and receives a plurality of signals transmitted by a plurality of SPS satellites 105 A-C and uses the plurality of received signals to calculate a current geographic location (i.e., a position fix) for the device 110 and a current time of day. Typically, the device 110 requires the reception of signals from a minimum number of SPS satellites 105A-C (e.g., in the GPS system, the device 110 requires transmitted signals from at least three or four GPS satellites) to determine the current geographic location of the device 110, and, thus, the respondent 102. The device 110 sequentially stores the result of each position fix (e.g., geo- code location data and the time of day and, if desired, the date) for later processing by a computing device 125.

[0004] The sequence of recorded position fix data (e.g., sets of corresponding geo-code location data and time of day and/or date values) are downloaded from the device 110 to a download server 120 on an occasional, periodic, or real time basis. The download server 120 may be either a respondent's personal computer (PC) or a computer associated with the electronic measuring system 100. The download server 120, in turn, provides the downloaded travel path data (i.e., the sequence of recorded position fix data) to the computing device 125. Any of a variety of well-known techniques for downloading data from the device 110 to the download server 120, and transferring the data from the download server 120 to the computing device 125 can be used. For example, the device 110 can be attached to the download server 120 using a universal serial bus (USB) connection and utilize removable storage device drivers executing on the device 110 and the download server 120.

[0005] To determine exposure to a media site 115, the computing device 125 compares the location of each of the position fixes recorded by the device 110 to the location of the media site 115. The location of the media site 115 is available in a database 130 that contains, among other data or information, geo-code location data for a plurality of media sites. In the example system 100 of FIG. I₅ if the respondent's location is 'close enough' to (e.g., within a predetermined distance of) the media site 115, then the media site 115 is credited with a media exposure.

[0006] For a variety of reasons the device 110 may be unable to complete a position fix attempt. For example, the device 110 may not be able to acquire and receive signals from the requisite number of satellites 105 A-C due to, for example, signal attenuation caused by thick foliage, or a structure, either man-made or naturally occurring, that obstructs the path of communication between the SPS satellites 105 A-C and the device 110. Further, a successful position fix may lack accuracy due to multipath distortions caused by nearby objects (e.g., tall buildings in downtown areas) or due to clock (i.e., timing) mismatches or errors. In such circumstances, the sequence of position fixes recorded by the device 110 and subsequently processed by the computing device 125 may contain gaps in the travel path traversed by the respondent 102 or represent a traversed path that does not follow a known course of travel (e.g., street, road, lane, highway, interstate, bridge, sidewalk, pedestrian walkway, trail, tunnel, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 is an example of a known electronic media exposure measurement system.

[0008] FIG. 2 is a schematic illustration of an example manner of implementing an SPS enabled device.

[0009] FIG. 3 is a schematic illustration of an example media exposure computing device constructed in accordance with the teachings of the invention.

[0010] FIG. 4A illustrates an example manner of implementing the travel path processor of FIG. 3.

[0011] FIG. 4B illustrates an example filter configuration used to implement the example processing engine of FIG. 4A. [0012] FIGS. 5 A and 5B are flowcharts representative of example machine readable instructions which may be executed to implement the travel path processor of FIG. 3.

[0013] FIG. 6 A illustrates a portion of an example travel path.

[0014] FIG. 6B illustrates an example deterministic path constructed from the example travel path of FIG. 6A.

[0015] FIG. 6C illustrates an example decision tree constructed from the example travel path of FIG. 6 A.

[0016] FIG. 7A illustrates example recorded travel path data.

[0017] FIGS. 7B and 7C illustrate computation of two data moments using the example travel path data of FIG. 7 A.

[0018] FIG. 8A illustrates example contextual analysis bonuses that may be used in the example street constraint filter of FIG. 4B.

[0019] FIGS. 8B-G illustrate example contextual analysis penalties that may be used in the example street constraint filter of FIG. 4B.

[0020] FIG. 9 is a schematic illustration of an example processor platform that may execute the example machine readable instructions represented by FIGS. 5A and 5B.

DETAILED DESCRIPTION

[0021] Although the example apparatus described herein includes, among other components, software executed on hardware, such apparatus is merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of the disclosed hardware and software components could be embodied exclusively in dedicated hardware, exclusively in software, exclusively in firmware or in some combination of hardware, firmware, and/or software.

[0022] In addition, while the following disclosure is made with respect to example SPS-based electronic media measurement systems, it should be understood that the disclosed apparatus is readily applicable to many other electronic media measurement systems. Accordingly, while the following describes example apparatus, methods, and articles of manufacture, persons of ordinary skill in the art will readily appreciate that the disclosed examples are not the only way to implement such systems.

[0023] In general, the example apparatus, methods, and articles of manufacture described herein may be used to process data specifying a plurality of locations traversed by a respondent. Inaccurate or missing data (e.g., in the sequence of recorded position fixes, or the media site location information) can adversely impact the accuracy of media exposure credits determined by a media exposure computing device. To substantially improve the accuracy and reliability of electronic media exposure measurements, the recorded travel path data may be processed using the example methods and apparatus described herein to overcome the deficiencies discussed above. In the particular examples described herein, the data is processed to alleviate deficiencies present in the data and so that the processed data better represents a path of travel along known courses of travel (e.g., streets, roads, lanes, highways, interstates, bridges, sidewalks, pedestrian walkways, trails, tunnels, etc.). As a result, the examples described herein can be used to improve the accuracy and reach of electronic media measurement systems.

[0024] FIG. 2 illustrates an example SPS enabled device 200 that may be use to implement the monitoring device 110 of FIG. 1. To receive and decode signals (i.e., SPS signals) transmitted by the plurality of SPS satellites 105 A-C, the device 200 includes an SPS signal receiver 205, an SPS signal decoder 210, and an antenna 215. Using any of a variety of techniques, the SPS signal receiver 205 converts radio frequency (RF) analog signals received by the antenna 215 into digital baseband signals (i.e., received signals) suitable for processing and/or decoding by the SPS signal decoder 210. For example, the SPS signal receiver 205 may be implemented using demodulators, down- converters, filters, and/or analog-to-digital (A/D) converters. Using any of a variety of well-known techniques, the SPS signal decoder 210 processes the received signals to determine, if possible (i.e., if a minimum number of SPS satellites 105 A-C are available (e.g., in the GPS system, the SPS signal decoder 210 uses received signals from at least 3 or 4 satellites)), the current location of the device 200 (i.e., to perform a position fix). The SPS signal decoder 210 provides to a processor 220 the current geographic location of the device 200, if determined, as well as the received signals. The processor 220 records into a storage memory 225 both the position fix and the received signals (i.e., pseudorange data). By periodically performing the above methods, the recorded data represents a travel path traversed by the respondent 102 (FIG. 1). [0025] The example device 200 of FIG. 2 further includes an interface 230 to allow the device 200 to communicate with the download server 120 of FIG. 1. The device 200 provides to a media exposure computing device (MECD) 300 (discussed below in connection with FIG. 3) recorded travel path data 305 (i.e., the sequence of position fixes and received signals recorded by the device 200) via the download server 120.

[0026] It will be readily apparent to persons of ordinary skill in the art that the processor 220 of FIG. 2 may monitor and record into the storage memory 225 additional data concerning the operation, status, etc. of the device 200. For example, the processor 220 could monitor battery usage, device power-on and power-off times, software faults, etc.

[0027] To promote consistent and reliable determination of media exposure credits by the MECD 300, the travel path traversed by the respondent 102 will preferably be accurate (i.e., reflect actual locations traversed by the respondent 102), follow one or more known courses of travel (e.g., streets, roads, lanes, highways, interstates, bridges, sidewalks, pedestrians walkways, trails, tunnels, etc.), and contain position fixes that are sufficiently close together. However, as described above, the sequence of position fixes recorded by the device 200 (i.e., the recorded travel path data 305) may not always satisfy these requirements.

[0028] FIG. 3 is a schematic diagram illustrating an example MECD 300 constructed in accordance with the teachings of the invention that can be used to implement the example computing device 125 of FIG.l. To post- process the recorded travel path data 305 and the media site information (contained in the database 130), the MECD 300 of FIG. 3 includes a travel path processor 310 that operates on the recorded travel path data 305 (that contains both determined geographic locations and received signals (i.e., pseudorange data) recorded by the device 200 and provided via the download server 120) to generate enhanced travel path data 315. In the illustrated example, the recorded travel path data 305 and the enhanced travel path data 315 are stored in one or more memories and/or storage devices implemented as part of the MECD 300. It will be readily apparent to persons of ordinary skill in the art that the recorded travel path data 305 and the enhanced travel path data 315 may also be implemented in other ways. For example, using a memory or a storage device attached and configured to communicate with the MECD 300.

[0029] The travel path processor 310 processes the recorded travel path data 305 to enhance the completeness and accuracy of the position fixes. For example, the travel path processor 310 could derive position fixes (e.g., at locations where the device 200 could not determine a geographic location) using the recorded received SPS signals, increase the accuracy of position fixes determined by the device 200, etc. The travel path processor 310 may also include additional algorithms that compensate for other known SPS limitations, such as clock drift and multi-path signal distortions.

[0030] FIG. 4A illustrates an example manner of implementing the example travel path processor 310 of FIG. 3. To process the recorded travel path data 305, the example travel path processor 310 of FIG. 3 includes a processing engine 405 to operate on the recorded travel path data 305. For example, the processing engine 405 could be implemented as one or more filters operating sequentially and/or in parallel on the recorded travel path data 305. In the illustrated example of FIG. 4A₅ the processing engine 405 processes (e.g., applies a set of filters to) a set of data points representative of all or a portion of a travel path transferred into a storage memory 410 by a data transfer unit 415. The processing engine 405 operates on the set of data points, placing intermediate values (e.g., modified and/or additional data points created as outputs of a filter and used as inputs to a subsequent filter), if any, back into the storage memory 410. Final output data points are placed into the enhanced travel path data 315 by the processing engine 405.

[0031] As illustrated in FIGS. 3 and 4A, and discussed below in connection with an example precise ephemeris filter 442 (FIG. 4B), the example processing engine 405 of FIG. 4 A can access data 395 provided by the International Geological Society (IGS) via an Internet connection 390. For example, the data 395 includes data precisely specifying the locations of SPS satellites 105 A-C at known instants in time.

[0032] In the illustrated example of FIG. 4 A, the storage memory 410 contains both recorded received SPS signals, position fixes determined by the device 200, and position fixes derived by the travel path processor 310. The data stored in the storage memory 410 may be stored using any of a variety of suitable techniques. For example, using object-oriented data storage techniques, using an array of data structures, etc.

[0033] The example processing engine 405 may be implemented using any of a variety of techniques. For example, the processing engine 405 could be implemented as software and/or firmware running on a general purpose processing device and/or a specialized processing device (e.g., a digital signal processing device), using hardware, or any combination of software, firmware and/or hardware.

[0034] It will also be readily apparent to persons of ordinary skill in the art that the storage memory 410 may be implemented using any of a variety of techniques. For example, using one or more portions of a memory or a storage device used to implement the recorded travel path data 305, or a separate memory, storage device and/or hardware registers directly associated with the travel path processor 310. Further, it will also be readily apparent to persons of ordinary skill in the art that the data transfer unit 415 could be eliminated. For example, the processing engine 405 could be configured to read the initial data points directly from the recorded travel path data 305.

[0035] FIG. 4B illustrates an example sequence of filters that may be used to implement the example processing engine 405 of FIG. 4 A. In the illustrated example of FIG. 4B, the filters are implemented using object- oriented programming techniques, thereby, facilitating flexibility in the number, type, sequence, configuration, interconnections, etc. of the filters.

[0036] The example filter sequence illustrated in FIG. 4B begins with a NAV Estimate Filter 440 that creates an initial set of derived position fixes using the set of position fixes determined by the device 200. Using any of a variety of well-known techniques, a precise ephemeris filter 442 acquires the precise SPS satellite location data 395 (i.e., the ephemeris data 395) from the IGS via the Internet 390 and uses the ephemeris data 395 to improve the accuracy of pseudorange data (i.e., received SPS signals) recorded by the device 200. For example, the precise ephemeris filter 442 uses each time stamp recorded by the device 200 at each data point in the pseudorange data to interpolate between known positions of the SPS satellites 105 A-C at known times (i.e., the ephemeris data 395) to determine precise satellite locations at the recorded time stamp instant. An elevation filter 444 then calculates, based on the satellite ephemeris data 395 and using standard orbital geometry principles, the angle above the horizon for each of the SPS satellites 105 A-C associated with each pseudorange or position fix data point. To improve the accuracy of position fixes derived from the pseudorange data, the elevation filter 444 discards pseudorange data corresponding to ones of the SPS satellites 105 A-C that are low relative to the horizon.

[0037] Next, a non-simultaneous pseudorange (NSPR) filter 446 locates missing position fix data points (e.g., representing locations where the device 200 was unable to determine a position fix), and derives additional position fixes. In an example, the NSPR filter 446 uses a set of pseudorange data points centered about a missing position fix data point and an interpolated clock drift value computed from the pseudorange data associated with the missing position fix data point and the nearest position fix data points to derive the missing position fix data point.

[0038] A receiver autonomous integrity monitor (RAIM) filter 448 processes the travel path to eliminate errors caused by multipath distortions. Multipath distortions are caused by the reception of an SPS transmit signal that has been reflected off of a plurality of surfaces located between one or more of the SPS satellites 105 A-C and the device 200. Thus, the device 200 receives multiple versions of the SPS transmit signal, each having a different time delay and phase characteristic. In an example where a pseudorange data point contains signals from four or more SPS satellites, the RAIM filter 448 derives a position fix using each permutation of three of the SPS satellites. In particular, if four satellites (i.e., #1, #2, #3 and #4) are available, four position fixes are derived for the following combinations of satellites (#1 #2 #3), (#1 #2 #4), (#1 #3 #4), and (#2 #3 #4). In another example where a pseudorange data point contains signals from three SPS satellites (e.g., the satellites 105A- C)₅ the RAIM filter 448 derives a position fix using each permutation of the three SPS satellites 105 A-C and the last known position of a fourth SPS satellite (not shown). In both of the foregoing examples, the RAIM filter 448 compares the derived position fixes to each other. If the derived position fixes substantially concur, the position fix is included in the travel path. Otherwise, multipath distortion is deemed to have occurred and the position fix is removed from the travel path data.

[0039] After having derived additional or improved the accuracy of existing position fixes, a street constraint filter 450 (discussed below in connection with FIGS. 6A-C₅ 7A-C₅ and 8A-G) aligns each position fix contained in the travel path to correspond with a centerline of a known course of travel. For example, the street constraint filter 450 modifies (i.e., aligns) a derived position fix to a closest point coinciding with a known course of travel (e.g., the centerline of the nearest road, sidewalk, etc.), where the closest point might be determined based on minimum Euclidean distance. However, such modifications may result in a travel path that skips or jumps around in an erratic or un-reasonable fashion (e.g., a travel path that moves back and forth between two sidewalks located on opposite sides of a street). To alleviate this problem, additional processing may be performed by the street constraint filter 450. The street constraint filter 450 may also process the travel path data to ensure consistency of motion. For example, the street constraint filter 450 could determine if travel speed indicates that the respondent 102 is in or on a vehicle and, if so, to ensure that the travel path is consistent with movements permitted by the immediate environment (e.g., bridges, over passes, under passes, one way streets, etc.).

[0040] A gap filter 452 derives additional position fixes such that the enhanced travel path data 315 consists of a sequence of position fixes in which each position fix is no more than a pre-determined distance (e.g., fifty feet) from a preceding and a following position fix. Additional position fixes are derived using any of a variety of standard geometric or trigonometric techniques that account for straight and curved travel paths and that ensure that the additional derived position fixes are aligned with a centerline of a known course of travel. Finally, the National Marine Electronics Association (NMEA) filter 454 outputs the enhanced travel path data 315 using a standard data format (e.g., the well-known NMEA-0183 format).

[0041] It will be readily apparent to persons of ordinary skill in the art that the number, sequence, type, configuration, etc. of the filters used to implement the processing engine 405 of FIG. 4 A could be different from that shown in FIG. 4B. For example, a moving average filter could be used to compute a moving average of a sequence of position fixes to smooth noisy data. In particular, a moving average of each of the last n latitudes and the last n longitudes may be computed, where the latitudes and longitudes correspond to the coordinates of the last n position fixes. In another example, a clock drift interpolation filter models the drift in the clock used by the device 200 and applies time corrections to the pseudorange data. In a further example, a dead reckoning filter uses a previous position fix and an estimated respondent travel direction and velocity to estimate a position fix.

[0042] In yet another example, filters are arranged in two parallel paths. For instance, the travel path data 305 is split into two sets by a data sorting filter. A first set contains data points representing locations of the respondent 102 that occurred inside a geographic region containing large buildings (e.g., a downtown area), and a second set contains data points in more urban areas. Each set of data is then passed through one or more filters, where the filters applied to each set of data could be different or identical. Further, data could be exchanged between the two sets of filters (e.g., the two filter paths could be cross-coupled). A solution selector filter is then applied to combine the outputs of the two paths to create an overall travel path for the respondent 102.

[0043] FIGS. 6 A and 6B illustrate flowcharts representative of example machine readable instructions that may be executed by a processor (e.g., one of the processors 2305A-C of FIG. 9) to implement the example travel path processor 310 of FIG. 3. The machine readable instructions of FIGS. 6A-B and/or the example travel path processor 310 may be executed by a processor, a controller and/or any other suitable processing device. For example, the machine readable instructions of FIGS. 6A-B and/or the example travel path processor 310 may be embodied in coded instructions stored on a tangible medium such as a flash memory, or random access memory (RAM) associated with the processors 2305 A-C shown in the example processor platform 2300 and discussed below in conjunction with FIG. 9. Alternatively, some or all of the example machine readable instructions of FIGS. 6A-B and/or the example travel path processor 310 may be implemented using an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable logic device (FPLD), discrete logic, hardware, software, and/or firmware. Also, some or all of the machine readable instructions of FIGS. 6A-B and/or the example travel path processor 310 may be implemented manually or as combinations of any of the foregoing techniques. Further, although the example machine readable instructions of FIGS. 6A-B are described with reference to the flowcharts of FIGS. 6A-B, persons of ordinary skill in the art will readily appreciate that many other methods of implementing the example travel path processor 310 may be employed. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined.

[0044] The example machine readable instructions of FIG. 5 A begin with the travel path processor 310 reading a configuration file that identifies which filters and filter configuration(s) are to be implemented by the travel path processor 310 (block 604). In an example, the configuration file is an XML file that identifies the types, order, sequence, configurations, interconnections, and number of the filters. However, other types and/or numbers of filters could be used instead.

[0045] The travel path processor 310 next processes the travel path data for each respondent (block 606) using the example machine readable instructions of FIG. 5B (block 608). If the travel path data for all respondents has been processed (block 610), the travel path processor 310 ends execution of the example machine readable instructions of FIG. 5 A. Otherwise, the travel path processor 310 returns to block 606 to process the travel path for the next respondent.

[0046] The example machine readable instructions of FIG. 5B begin with the travel path processor 310 operating each of the filters specified in the filter configuration file (discussed above) (block 660). The travel path processor 310 then operates one of the filters (block 662). If all filters have been operated (block 664), the media site processor 320 ends execution of the example machine readable instructions of FIG. 5B. Otherwise, if not all filters have been operated (block 664), the travel path processor 310 returns to block 660 to operate the next filter.

[0047] Returning to the street constraint filter 450 of FIG. 4B₅ each derived (or determined) position fix within a travel path is aligned (i.e., modified, manipulated, etc.) to correspond with a centerline of a known course of travel so that the resulting enhanced travel path data 315 represents consistent and reasonable travel paths along known courses of travel. The street constraint filter 450 determines, based on historical and future travel, an appropriate and most likely location of a position fix. In an example, the implementation of the street constraint filter 450 uses artificial intelligence (AI) algorithms and techniques (with appropriately chosen penalties and weights) to perform the various travel path manipulations. For instance, each of the position fixes may be mapped to multiple points corresponding to nearby known courses of travel to create a Bayesian tree representing multiple possible travel paths connecting the mapped position fixes. A value may then be applied to each point (e.g., based on the Euclidean distance from the actual position fix to the point). A cost associated with each path is determined by adding up the values for each of the mapped points comprising a path, and the path with the smallest cost is selected.

[0048] In the example of FIGS. 3 and 4B₅ the travel path processor 310 has access to geo-code data specifying the locations of known courses of travel. Further, the travel path processor 310 may use a street map file that defines the geographic or demographic region over which the street constraint filter 450 is to operate. Thus, portions of travel paths that traverse within or across the region will be processed by the street constraint filter 450. In the example of FIGS. 3 and 4B, the street map file is a configurable XML file that defines a simple bounding rectangle defined by four latitude and longitude pairs. The travel path processor 310 uses the bounding rectangle to determine the segments (e.g., 50 foot lengths) of each known course of travel that falls within the region. The travel path processor 310 operates to constrain position fixes to align with a centerline of one of the segments falling within the region. [0049] FIG. 6 A illustrates a portion of an example travel path that includes 20 derived position fixes (shown as circles 1-20). Within the example street constraint filter 450, a travel segment is an ordered set of consecutive data points that are associated with a particular known course of travel. For example, in FIG. 6A, Pine Street has three travel segments associated with it: (1, 2, 3, 4, 5), (13, 14, 15, 16) and (19, 20).

[0050] A deterministic path is constructed by forcing each position fix to be associated with only one segment of a known course of travel. FIG. 6B illustrates an example deterministic path constructed from the example travel path illustrated in FIG. 6A, where each node in the example deterministic path corresponds to one travel segment. If the street constraint filter 450 only considers deterministic paths, there is a substantial chance that the known course of travel that to which a point appears to be closest is not actually the known course of travel along which the respondent 102 was traveling. For example, in the example of FIG. 6 A, position fix 17 could be associated with either 2^nd Street or Pine.

[0051] Instead of relying on deterministic paths, the example street constraint filter 450 constructs a decision tree that includes a plurality of mappings of the position fixes to possible known courses of travel. Thus, a decision tree consists of possible travel paths corresponding to the position fixes, where the complexity of the tree depends upon the amount of ambiguity in the position fixes (e.g., the number or percentage of ambiguous points). Each node in the decision tree represents a travel segment of a candidate travel path (i.e., a candidate segment). FIG. 6C illustrates an example decision tree containing two branches constructed from the example travel path data illustrated in FIG. 6A. The example decision tree of FIG. 6C is relatively small because the travel path data has a relatively low amount of ambiguity.

[0052] By constructing a decision tree, the street constraint filter 450 may employ fuzzy logic by applying a set of rules to determine the probability that each of the candidate travel paths comprising the decision tree was the actual travel path taken by the respondent 102. In particular, each candidate travel path is assigned a score, and the candidate travel path with the highest score is the travel path most likely taken by the respondent 102.

[0053] hi the example street constraint filter 450, it recognized that a current position is most heavily influenced by the nearest neighboring positions. For example, in the example of FIG. 6A, whether position fix 17 should be on Pine or 2^nd is most affected by position fixes 16 and 18. Thus, the example street constraint filter 450 uses a predictor-corrector algorithm. For example, to determine the best known course of travel to map a position fix to, the example street constraint filter 450 iterates through the travel path data until a decision tree of a pre-determined depth (e.g., four) is constructed. The example street constraint filter 450 then determines the score for each branch in the limited depth tree and selects the branch with the highest score. Having made a decision on a position fix (or candidate segment), the example street constraint filter 450 repeats the process for the next position fix (or candidate segment).

[0054] A variety of methods (i.e., metrics) could be used to score each branch of the limited depth decision tree. For example, the proximity of position fixes to a candidate segment, an apparent alignment of position fixes with respect to a candidate segment, etc. FIG. 7A illustrates additional example position fixes. An example metric is based on data moments, such as, for example, data moments taken about candidate segments. FIGS. 7B and 7C illustrate two moments of the example position fixes of FIG. 7A taken about 1^st and 2^nd, respectively. Candidate segments having a smaller average distance or moment are rated higher than those with a higher average distance or moment. In the example street constraint filter 450, the data moment is used as the initial score assigned to a candidate segment (i.e., node of the decision tree).

[0055] Another example metric is a dot product, which measures how well a candidate segment aligns with the corresponding position fixes. The dot product of the candidate segment and the position fixes determines an angle between the position fixes and the candidate segment. In this example, if the angle is close to 0 or 180 degrees the travel segment (i.e., decision tree node) is rated higher (i.e., receives a bonus), and if the angle is close to 90 or 270 degrees the travel segment is penalized.

[0056] Yet another example metric utilizes contextual analysis based on candidate segments. For instance, consider a candidate segment s[n]. FIG. 8A lists some example contextual analysis bonuses that are awarded to the candidate segment s[n]. In particular, if s[n] has more than five consecutive points (i.e., position fixes), the candidate segment s[n] is awarded a 40% bonus (i.e., increases its score by 40%). If the score of a previous candidate segment s[n-l] is greater than a pre-determined amount (e.g., 60), the candidate segment s[n] is awarded a 10% bonus.

[0057] FIGS. 8B-G illustrate example candidate segment configurations that each result in a 15% contextual analysis penalty. For example, as illustrated in FIG. 8C, if the candidate segments s[n] and s[n+l] are not connected, a penalty of 15% is applied to the candidate segment s[n].

[0058] Returning to FIG. 3, to determine if exposure of the respondent 102 to the media site 115 has occurred, the MECD 300 of FIG. 3 includes a passage processor 328. The passage processor 328 of the illustrated example of FIG. 3 uses the enhanced travel path data 315, the media site location information contained in the database 130 to determine if the respondent 102 passed the media site 115 (FIG. 1) in such a way that the respondent 102 had an opportunity to see the media site 115. For the media site 115 to be credited with media exposure in the illustrated example of FIG. 3, the respondent 102 must traverse 'close enough' to (e.g., within a predetermined distance of) the media site 115. Each exposure credited to the media site 115 is recorded by the passage processor 328 in the database 130.

[0059] FIG. 9 illustrates the example processor system 2300 capable of implementing the methods and apparatus disclosed herein. The processor system 2300 includes one or more processors 2305A-C having associated system memory. The system memory may include one or more of a random access memory (RAM) 2315 and a read only memory (ROM) 2317.

[0060] The plurality of processors 2305A-C₅ in the example of FIG. 9, are coupled to an input/output controller hub (ICH) 2325 to which other peripherals or devices are interfaced. In the illustrated example, the peripherals interfaced to the ICH 2325 include an input device 2327, a mass storage device 2340 (e.g., hard disk drive), a universal serial bus (USB) 2345, a USB device 2350, a network port 2355, which is further coupled to a network 2360, and/or a removable storage device drive 2357. The removable storage device drive 2357 may include associated removable storage media 2358, such as magnetic or optical media. One or more peripherals may implement the providing of recorded position fix data 305 by the download server 120. The mass storage device 2340 may be used to store the example machine readable instructions illustrated in FIGS. 5A and 5B.

[0061] The example processor system 2300 of FIG. 9 also includes a video graphics adapter card 2320, which is a peripheral coupled to a memory controller hub (MCH) 2310 and further coupled to a display device 2322.

[0062] The example processor system 2300 may be, for example, a conventional desktop personal computer, a notebook computer, a workstation, a network server, or any other computing device. The processors 2305A-C may be any type of processing unit, such as a microprocessor from the Intel^® Pentium^® family of microprocessors, the Intel^® Itanium^® family of microprocessors, the Intel XScale^® family of processors, the AMD^® Athlon™ family of processors, and/or the AMD^® Opteron™ family or processors. The processors 2305A-C may execute the example machine readable instructions of FIGS. 5A and 5B to implement the travel path processor 310.

[0063] The memories 2315 and 2317, which form some or all of the system memory, may be any suitable memory or memory devices and may be sized to fit the storage demands of the system 2300. Additionally, the mass storage device 2340 may be, for example, any magnetic or optical media that is readable by the processors 2305 A-C. The system memory may be used to store the recorded travel path data 305, the enhanced travel path data 315, and/or the database 130. The system memory may also be used to store the example machine readable instructions illustrated in FIGS. 5A and 5B.

[0064] The input device 2327 may be implemented by a keyboard, a mouse, a touch screen, a track pad or any other device that enables a user to provide information to the processors 2305A-C.

[0065] The display device 2322 may be, for example, a liquid crystal display (LCD) monitor, a cathode ray tube (CRT) monitor, or any other suitable device that acts as an interface between the processors 2305 A-C and a user via the video graphics adapter 2320. The video graphics adapter 2320 is any device used to interface the display device 2322 to the MCH 2310. Such cards are presently commercially available from, for example, Creative Labs and other like vendors.

[0066] The removable storage device drive 2357 may be, for example, an optical drive, such as a compact disk-recordable (CD-R) drive, a compact disk-rewritable (CD-RW) drive, a digital versatile disk (DVD) drive or any other optical drive. It may alternatively be, for example, a magnetic media drive. The removable storage media 2358 is complementary to the removable storage device drive 2357, inasmuch as the media 2358 is selected to operate with the drive 2357. For example, if the removable storage device drive 2357 is an optical drive, the removable storage media 2358 may be a CD-R disk, a CD-RW disk, a DVD disk or any other suitable optical disk. On the other hand, if the removable storage device drive 2357 is a magnetic media device, the removable storage media 2358 may be, for example, a diskette, or any other suitable magnetic storage media. The removable storage media 2358 may also be used for providing the recorded position fix by the download server 120 or for storing the database 130.

[0067] The example processor system 2300 also includes the network port 2355 (e.g., a processor peripheral), such as, for example, an Ethernet card or any other card that may be wired or wireless. The network port 2355 provides network connectivity between the processors 2305A-C and the network 2360, which may be a local area network (LAN), a wide area network (WAN), the Internet, or any other suitable network. The network port 2355 and the network 2360 may also be used for providing the recorded ■ position fix by the download server 120

[0068] Of course, persons of ordinary skill in the art will recognize that the order, size, and proportions of the memory illustrated in the example systems may vary. Additionally, although this patent discloses example systems including, among other components, software or firmware executed on hardware, it should be noted that such systems are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of these hardware and software components could be embodied exclusively in hardware, exclusively in software, exclusively in firmware or in some combination of hardware, firmware and/or software. Accordingly, persons of ordinary skill in the art will readily appreciate that the above described examples are not the only way to implement such systems.

[0069] At least some of the above described example methods, machine readable instructions, and/or apparatus are implemented by one or more software and/or firmware programs running on a computer processor. However, dedicated hardware implementations including, but not limited to, application specific integrated circuits, programmable logic arrays and other hardware devices can likewise be constructed to implement some or all of the example methods and/or apparatus described herein, either in whole or in part. Furthermore, alternative software implementations including, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the example methods and/or apparatus described herein.

[0070] It should also be noted that the example software and/or firmware implementations described herein are optionally stored on a tangible storage medium, such as: a magnetic medium (e.g., a disk or tape); a magneto- optical or optical medium such as a disk; or a solid state medium such as a memory card or other package that houses one or more read-only (non¬ volatile) memories, random access memories, or other re- writable (volatile) memories; or a signal containing computer instructions. A digital file attachment to e-mail or other self-contained information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium. Accordingly, the example software and/or firmware described herein can be stored on a tangible storage medium or distribution medium such as those described above or equivalents and successor media.

[0071] To the extent the above specification describes example components and functions with reference to particular standards and protocols, it sis understood that the teachings of the disclosure are not limited to such standards and protocols. For instance, each of the standards for Internet and other packet switched network transmission (e.g., Transmission Control Protocol (TCPyiP, User Datagram Protocol (UDP)/IP, HyperText Markup Language (HTML), HyperText Transfer Protocol (HTTP)); and inter¬ computer and inter-device communications (e.g., USB) represent examples of the current state of the art. Such standards are periodically superseded by faster or more efficient equivalents having the same general functionality. Accordingly, replacement standards and protocols having the same functions are equivalents which are contemplated by the teachings of the disclosure are intended to be included within the scope of the accompanying claims.

[0072] The teachings of the disclosure contemplate one or more machine readable mediums containing instructions, or receiving and executing instructions from a propagated signal so that, for example, a device connected to a network environment can send or receive voice, video or data, and communicate over the network using the instructions. Such a device can be implemented by any electronic device that provides voice, video or data communication, such as a telephone, a cordless telephone, a mobile phone, a cellular telephone, a Personal Digital Assistant (PDA), a set-top box, a computer, and/or a server. [0073] Although certain example methods, apparatus and articles of manufacture have been described 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 appended claims either literally or under the doctrine of equivalents.

Claims

What Is Claimed Is:

1. A method comprising: processing data representative of locations recorded by an electronic device to enhance at least one of completeness or accuracy of the data; deriving position fixes from the processed data; and modifying at least one of the derived position fixes to align with a known course of travel.

2. A method as defined in claim 1 , wherein processing the data comprises filtering the data.

3. A method as defined in claim 2, wherein filtering the data comprises using a plurality of filters.

4. A method as defined in claim 1, wherein processing the data includes at least one of deriving a precise satellite position, calculating a satellite elevation, setting an initial position fix based on an estimated position fix contained in the data, or performing receiver autonomous integrity management.

5. A method as defined in claim 1, wherein deriving the position fixes from the processed data comprises filtering the processed data.

6. A method as defined in claim 1 , wherein deriving the position fixes from the processed data includes at least one of using a derived precise satellite position and a received signal to derive a position fix, using a plurality of received signals and an interpolated clock drift to derive a position fix, using a known position and respondent travel velocity to derive a position fix, or removing a determined or a derived position fix if multipath interference is

detected.

7. A method as defined in claim 1 , wherein modifying the at least one of the derived position fixes to align with a known course of travel comprises using an artificial intelligence technique.

8. A method as defined in claim 1 , wherein modifying the at least one of the derived position fixes to align with a known course of travel comprises constructing a decision tree representative of possible travel paths, assigning likelihood values to nodes of the decision tree, and selecting a travel path that corresponds to a branch of the decision tree having the highest combined likelihood.

9. A method as defined in claim 8, wherein the likelihood values are based on at least one of a data moment, a data line dot product, or a contextual analysis.

10. An apparatus, comprising a processor coupled to a memory and programmed to: process data representative of locations recorded by an electronic device to enhance at least one of a completeness or an accuracy of the data; derive position fixes from the processed data; and modify at least one of derived position fixes to align with a known course of travel.

11. An apparatus as defined in claim 10, wherein the processor is programmed to process the data by filtering the data using at least one filter.

12. An apparatus as defined in claim 10, wherein the processor is programmed to process the data by using at least one of a derived precise satellite position, a calculated satellite elevation, an estimated position fix contained in the data, or a result of receiver autonomous integrity management.

13. An apparatus as defined in claim 10, wherein the processor is programmed to derive the position fixes from the processed data by using at least one of a derived precise satellite position and a received signal to derive a position fix, using a plurality of received signals and an interpolated clock drift to derive a position fix, using a known position and respondent travel velocity to derive a position fix, or removing a determined or a derived position fix if multipath interference is detected.

14. An apparatus as defined in claim 10₅ wherein the processor is programmed to modify the at least one of the derived position fixes to align with a known course of travel by using an artificial intelligence technique.

15. An apparatus as defined in claim 10, wherein the processor is programmed to modify the at least one of the derived position fixes to align with a known course of travel by constructing a decision tree representative of possible travel paths, assigning likelihood values to nodes of the decision tree, and selecting a travel path that corresponds to a branch of the decision tree having the highest combined likelihood.

16. An apparatus as defined in claim 15, wherein the likelihood values are based on at least one of a data moment, a data line dot product, or a contextual analysis.

17. A machine readable medium having instructions stored thereon that, when executed, cause a machine to: process data representative of locations recorded by an electronic device to enhance at least one of a completeness or an accuracy of the data; derive position fixes from the processed data; and modify at least one of the derived position fixes to align with a known course of travel.

18. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to process the data by filtering the data using at least one filter.

19. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to process the data by using at least one of a derived precise satellite position, a calculated satellite elevation, an estimated position fix contained in the data, or a result of receiver autonomous integrity management.

20. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to derive the position fixes from the processed data by using at least one of a derived precise satellite position and a received signal to derive a position fix, using a plurality of received signals and an interpolated clock drift to derive a position fix, using a known position and respondent travel velocity to derive a position fix, or removing a determined or a derived position fix if multipath interference is

detected.

21. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to modify the at least one of the derived position fixes to align with a known course of travel by using an artificial intelligence technique.

22. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to modify the at least one of the derived position fixes to align with a known course of travel by constructing a decision tree representative of possible travel paths, assigning likelihood values to nodes of the decision tree, and selecting a travel path that corresponds to a branch of the decision tree having the highest combined likelihood.

23. A machine readable medium as defined in claim 22, wherein the likelihood values are based on at least one of a data moment, a data line dot product, or a contextual analysis.

24. An apparatus comprising a file reader configured to read recorded data representative of respondent locations; a memory configured to store data; and a processing engine configured to apply filters that: process the recorded data to enhance at least one of a completeness or an accuracy of the data, derive position fixes from the processed data, and modify at least one of the derived position fixes to align with a

known course of travel.