KR20070083528A - Methods and apparatus for improving the accuracy and reach of electronic media exposure measurement 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

This patent is incorporated by reference in US Provisional Serial No. 60 / 592,554, filed Jul. 30, 2004, entitled "Methods and Apparatus for Processing Data Collected by GPS-Equipped Media Measurement System"; Priority is given to U.S. Provisional Serial Numbers 60 / 592,554 and U.S. Application Serial Numbers 10 / 686,872 and 10 / 318,422.

TECHNICAL FIELD The present invention generally relates to a medium exposure measurement system and, more particularly, to a method and apparatus for improving the accuracy and range of an electronic medium exposure measurement apparatus.

In the past, a media exposure measurement system for outdoor media has, for example, determined car traffic research (e.g., counting the number of cars passing the road on a given day), or determining the number of media exposures achieved. Depends on the memory required to do so (eg, the consumer's ability to remember seeing external advertisements through research).

More recently, electronic systems have been developed for measuring and crediting media exposure that allow outdoor advertisers to measure the range of external media sites and set them with scientifically verifiable accuracy. 1 shows a satellite positioning system (SPS) (e.g., a US satellite navigation system (GPS) and a European Galileo system (currently in construction) to track motorists and / or pedestrians exposed to external media sites. Exemplary prior art electronic media exposure measurement system 100 using the technique). To track the exposure of the participant or respondent 102, the respondent 102 carries (or wears) an SPS mounted monitoring device 110 (eg, a Nielson® Personal Outdoor Device (Ppod ^™ )). Device 110 acquires and receives a plurality of signals transmitted by a plurality of SPS satellites 105A-C periodically (eg, every 4 to 5 seconds), and the current geographic location for device 110. A plurality of received signals is used to calculate a location (eg, a location point) and a current time. Typically, device 110 is configured to determine the current geographic location of device 110, that is, responder 102, with a minimum number of SPS satellites 105A-C (eg, in a GPS system, device 110). Requires signal reception from) (requires signals received from at least three or four GPS satellites). Device 110 sequentially stores the results of each location point (eg, geocode location data and time, and, if necessary, a date) for later processing by computing device 125.

The recorded sequence of positioning point data (e.g., the corresponding geocode position data and the set of time and / or date values) is sometimes downloaded from the device 110 to the download server 120, periodically or in real time. The download server 120 may be a computer associated with the respondent's personal computer (PC) or the electronic measurement system 100. The download server 120 then provides travel route data (ie, a sequence of recorded location data) downloaded to the computing device 125. Any of a variety of known techniques may be used for downloading from device 110 to download server 120 and for transferring from download server 120 to computing device 125. For example, device 110 may be attached to download server 120 using a universal serial bus (USB) connection and utilize removable storage device drivers running on device 110 and download server 120. Can be.

To determine exposure to media site 115, computing device 125 compares the locations of each positioning point recorded by device 110 with the location of media site 115. The location of media site 115 is available in database 130 including geocode location data for a plurality of media sites, among other data or information. In the example system 100 of FIG. 1, if the location of the respondent is “sufficiently close” to the media site 115 (eg, within a distance of the media device 115), the media site 115 may be configured to provide media. It is considered an exposure.

For various reasons, the device 110 may not complete the location point attempt. For example, device 110 may be constructed from a mandatory or naturally occurring, for example, thick branch or structure by a human being that interferes with the communication path between SPS satellites 105A-C and device 110. It may not be possible to obtain and receive a signal from satellites 105A-C. In addition, a successful location determination may lack accuracy due to multipath distortions or clock (ie, timing) matching errors or errors caused by nearby objects (eg, large buildings in a downtown area). In such a situation, the sequence of location points recorded by the device 110 and sequentially processed by the computing device 125 may include a defect in the travel route traveled by the responder 102, or a known travel course (eg, For example, it may indicate a wrong route that does not follow a driveway, sidewalk, lane, highway, interstate highway, bridge, sidewalk, pedestrian walkway, train track, tunnel, etc.

In order to solve the above problem, the present invention discloses methods and apparatuses for improving the accuracy and range of an electronic medium exposure measurement system. The disclosed method includes processing data that is an indication of locations recorded by the electronic device to improve at least one of completeness or accuracy of the data; Deriving positioning points from the processed data; And modifying at least one of the derived location points to align along a known travel course.

1 is an example of a known electronic medium exposure measurement system.

2 is a schematic diagram of an example method of implementing an apparatus equipped with an SPS.

3 is a schematic diagram of an exemplary media exposure computing device constructed in accordance with the techniques of this disclosure.

4A illustrates an example method of implementing the travel route processor of FIG. 3.

4B illustrates an example filter configuration used to implement the example processing engine of FIG. 4A.

5A and 5B are flow diagrams illustrating machine readable instructions that may be executed to implement the exemplary travel route processor of FIG. 3.

6A illustrates the location of an exemplary travel route.

6B illustrates an example decision path constructed from the example travel path of FIG. 6A.

6C illustrates an example decision tree constructed from the example travel route of FIG. 6A.

7A shows an example of recorded travel route data.

7B and 7C show the calculation of two data moments using the exemplary travel route data of FIG. 7A.

FIG. 8A illustrates an example context analysis bonus that may be used in the example road limit filter of FIG. 4B.

8B-8G illustrate exemplary contextual analysis penalties that may be used in the example road limit filter of FIG. 4B.

9 is a schematic diagram of an example processor platform capable of performing the example machine readable instructions represented in FIGS. 5A and 5B.

The example apparatus described herein includes software running on hardware rather than other components, but such apparatus is merely exemplary and should not be considered as limiting. For example, it is understood that some or all of the disclosed hardware and software components may be implemented exclusively in dedicated hardware, exclusively in software, exclusively in firmware, or some combination of hardware, firmware and / or software.

In addition, while the following description is made with respect to exemplary SPS-based electronic media measurement systems, it should be understood that the disclosed apparatus can be readily applied to many other electronic media measurement systems. Thus, while the following describes exemplary devices, methods, and articles of manufacture, one of ordinary skill in the art can readily appreciate that the disclosed examples are not the only way to implement such a system. There will be.

In general, the example devices, methods, and articles of manufacture described herein can be used to process data specifying a plurality of locations through which respondents pass. Inaccurate or incorrect data (eg, in a sequence of recorded location points, or media site location information) may adversely affect the media exposure credit determined by the media exposure computing device. In order to substantially improve the accuracy and reliability of electronic media exposure measurements, the recorded travel route data may be processed using the example methods and apparatuses described herein to overcome the deficiencies described above. In the specific examples described herein, the data is processed to reduce defects present in the data so that the processed data can be used for known travel courses (eg, driveways, sidewalks, lanes, highways, interstates, bridges, sidewalks, Better represent travel routes along pedestrian walkways, train tracks, tunnels, etc. As a result, the examples described herein can be used to improve the accuracy and range of electronic media measurement systems.

2 illustrates an example SPS mounting device 200 that may be used to implement the monitoring device 110 of FIG. 1. In order to receive and decode signals (i.e., SPS signals) transmitted by the plurality of satellites 105A-C, the apparatus 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 is a digital baseband signal suitable for processing and / or decoding by the SPS signal decoder 210 the radio frequency (RF) analog signals received by the antenna 215. (Ie received signals). For example, the SPS signal receiver 205 may be implemented using demodulators, down-converters, filters and / or analog-to-digital converters. Using any of a variety of known techniques, the SPS signal decoder 210 may be used if possible (ie, if a minimum number of SPS satellites 105A-C are available (eg, in an GPS system). 210 uses signals received from at least three or four satellites)), and processes the received signals to determine the current location of the device 200 (ie, to perform a positioning point). The SPS signal decoder 210 provides the processor 220 with the received signals, as well as the current geographic location of the device 200 if determined. The processor 220 writes both the location point and the received signal (ie, pseudorange data) to the storage memory 225. By periodically performing the above-described methods, the recorded data indicates the travel route passed by the responder 102 (FIG. 1).

The example device 200 of FIG. 2 further includes an interface 230 that allows the device 200 to communicate with the download server of FIG. 1. The device 200 stores the travel route data 305 (ie, the sequence of location points recorded by the device 200 and the received signals) recorded via the download server 120. 300, discussed below in connection with FIG. 3.

It will be apparent to one of ordinary skill in the art that the processor 220 of FIG. 2 can monitor additional data about the operation, status, etc. of the device 200 and write to the storage memory 225. For example, the processor 220 may monitor battery usage, the number of times the device is powered on and off, software errors, and the like.

In order to make consistent and reliable determination of media exposure credit by the MECD 300, the travel route traversed by the respondent 102 is preferably accurate (ie reflects the actual locations traversed by the responder 102). Follow one or more known travel courses (e.g., driveways, sidewalks, lanes, highways, interstates, bridges, sidewalks, pedestrian passages, train tracks, tunnels, etc.), and include location points close enough to each other. do. However, as noted above, the sequence of positioning points recorded by the apparatus 200 (eg, recorded travel route data 305) may not always meet these needs.

3 is a schematic diagram illustrating an exemplary MECD 300 constructed in accordance with the teachings of the present invention that may be used to implement the exemplary computing device 125 of FIG. 1. For post-processing of recorded travel route data 305 and media site information (included in database 130), MECD 300 of FIG. 3 records recorded travel route to generate enhanced travel route data 315. FIG. Travel route processor 310 operating on data 305 (including both determined geographic locations and received signals (ie, pseudorange data) recorded by device 200 and provided by download server 120). ). In the example shown, the recorded travel route data 305 and enhanced travel route data 315 are stored in one or more memories and / or storage devices implemented as part of the MECD 300. The recorded travel route data 305 and enhanced travel route data 315 may also be implemented in other ways, for example, using a memory or storage device attached to the MECD 300 and configured to communicate with the MECD 300. It will be apparent to those skilled in the art.

The travel route processor 310 processes the recorded travel route data 305 to improve the completeness and accuracy of the location point. For example, the travel route processor 310 may record (eg, at locations where the device 200 may not be able to determine its geographic location) to improve the accuracy of the location point determined by the device 200 or the like. The received SPS signals can then be used to derive the positioning point. Travel path processor 310 may also include additional algorithms that compensate for other known SPS constraints, such as clock drift and multipath signal distortions.

4A illustrates an example method of implementing the example travel route processor 310 of FIG. 3. To process the recorded travel route data 305, the example travel route processor 310 of FIG. 3 includes a processing engine 405 operating on the recorded travel route data 305. For example, the processing engine 405 may be implemented with one or more filters operating in series and / or in parallel on the recorded travel route data. As in the example shown in FIG. 4A, the processing engine 405 processes a set of data points that are an indication of all or part of the travel route sent by the data transfer unit 415 to the storage memory 410 (eg, For example, applying a set of filters to a set of data points). The processing engine 405 sets the data points to place intermediate values (e.g., modified and / or additional data points generated as the output of the filter and used as input of the next filter) back to the storage memory 410. It works on The final output data points are located in the travel route data 315 enhanced by the processing engine 405.

As shown in FIGS. 3 and 4A and discussed below in connection with an exemplary trajectory filter 442 (FIG. 4B), the exemplary processing engine 405 of FIG. 4A is connected to the International Map Society through an Internet connection 390. Data 305 provided by the International Geological Society (IGS). For example, data 395 includes data that accurately specifies the locations of SPS satellites 105A-C at a known moment.

In the example shown in FIG. 4A, the storage memory 410 stores all of the received SPS signals, the location points determined by the device 200, and the location points derived by the travel route processor 300. It includes. Data stored in storage memory 410 may be stored using any of a variety of suitable techniques. For example, the data may be stored using an object-oriented data storage technique, using an array of data structures, or the like.

Example processing engine 405 may be implemented using any of a variety of techniques. For example, the processing engine 405 is software and / or firmware operating on a general purpose processing device and / or a dedicated processing device (eg, digital signal processing device), using hardware, or using software, firmware and / or Or as any combination of hardware.

It will also be apparent to those skilled 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 the memory or storage device used to implement the recorded travel route data 305, or separate memory, the storage device and / or hardware may be coupled with the travel route processor 310. Directly related data can be recorded. In addition, the data transfer unit 415 can be eliminated. It will also be apparent to those skilled in the art. For example, the processing engine 405 may be configured to read initial data points directly from the recorded travel route data 305.

4B illustrates a sequence of example filters that can be used to implement the example processing engine 405 of FIG. 4A. In the example shown in FIG. 4B, the filters are implemented using object oriented programming techniques, thereby implementing flexibility in the number, type, sequence, configuration, interconnection, etc. of the filters.

The example filter sequence shown in FIG. 4B begins with a NAV prediction filter 440 that generates an initial set of location points derived using the set of location points determined by the apparatus 200. Using any of a variety of known techniques, the precision orbital filter 442 obtains the correct SPS satellite position data 305 (ie, the orbital data 395) from the IGS via the Internet 390, and the apparatus 200. Orbital data 395 is used to improve the accuracy of the pseudorange data (ie, received SPS signals) recorded by < RTI ID = 0.0 > For example, the precision orbital filter 442 is located between known locations of the SPS satellites 105A-C (ie, orbital data 395) at a known time to determine accurate satellite locations at the recorded time stamp instant. Each time stamp recorded by the device 200 is used at each data point of pseudorange data for publication. The altitude filter 444 then calculates the angle to the horizon for the SPS satellites 105A-C associated with each pseudorange or location data point based on satellite orbital data 395 and using standard orbital geometry theories. Calculate To improve the accuracy of the location points derived from the pseudorange data, the altitude filter 444 discards the pseudorange data corresponding to the SPS satellites 105A-C that are relatively low on the horizon.

Next, a non-simultaneous pseudorange (NSPR) filter 446 establishes an error positioning point data point (e.g., indicating the position when the device 200 cannot determine the positioning point). ), To derive additional positioning points. In this example, NSPR filter 446 is centered on the wrong location data point and pseudoranges associated with the wrong location data point and the nearest location data point to derive the error location data point. Use the inserted clock drift value calculated from the data.

Receiver autonomous conservation monitor (RAIM) filter 448 processes the travel path to remove errors resulting from multipath distortion. Multipath distortions are caused by the reception of an SPS transmission signal reflected from a plurality of surfaces located between one or more SPS satellites 105A-C and device 200. Thus, the device 200 receives multiple versions of SPS transmission signals having different time delay and phase characteristics. In an example where pseudorange data points include signals from four or more SPS satellites, the RAIM filter 448 uses three permutations of the SPS satellites to derive the location point. Specifically, if four satellites (# 1, # 2, # 3 and # 4) are available, the four positioning points are the next combination of satellites (# 1, # 2, # 3), (# 1, # 2, # 4), (# 1, # 3, # 4) and (# 2, # 3, # 4). In other cases where pseudorange data points include a signal from three SPS satellites (eg, satellites 105A-C), the RAIM filter 448 may be configured to include three SPS satellites 105A-C. The last known location of each permutation and the fourth SPS satellite (not shown) can be used to derive the positioning point. In the two previous examples, the RAIM filter 448 compares the derived position points with each other. If the derived attract decisions are substantially consistent, the location point is included in the travel route. Otherwise, the multipath distortion is assumed to have occurred and the positioning point is removed from the travel path data.

Or after further deriving or improving the accuracy of existing positioning points, the road limit filter 450 (hereinafter discussed in connection with FIGS. 6A-6C, 7A-7C and 8A-8G) is included in each travel path. Align the location points to correspond to the centerline of the known travel course. For example, road limit filter 450 may modify (ie, align) the derived location point to the nearest point that matches a known travel course (centerline of the nearest road, sidewalk, etc.), where The point may be determined based on the minimum Euclidean distance. However, such modifications may result in passing or jumping in a faulty or irrational manner (eg, the route travels back and forth between two sidewalks located on either side of the road). In order to alleviate this problem, further processing may be performed by the road limit filter 450. The road limit filter 450 may also process the travel route data to ensure consistency of movement. For example, the road limit filter 450 may determine whether the travel speed is for the respondent 102 to be in the car, and if so, the environment in which the travel route is encountered (eg, over a bridge, over a gate, under a gate, one-way road). It may be determined that it should be guaranteed to match the movement allowed by, for example.

The spacing filter 452 derives additional location points such that the enhanced travel path data 315 consists of a sequence of location points that are no more than a predetermined distance (eg, 5 feet) from the previous location point. Additional location points are derived using any of a variety of standard geometric or trigonometric techniques that take into account straight and curved travel paths and ensure that the additional derived location points are aligned along the centerline of the known travel course. Finally, International Marine Electronics Association (NMEA) filter 454 outputs enhanced travel route data 315 using standard data formats (eg, the known MNEA-0813 format).

It will be apparent to those skilled in the art that the number, sequence format, configuration, etc. of the filters used to implement the processing engine 405 of FIG. 4A may differ from that shown in FIG. 4B. For example, a moving average filter can be used to calculate the moving average of the sequence of positioning points to smooth the noise data. Specifically, the moving average of each of the last n latitudes and last n longitudes can be calculated, where the latitude and longitude correspond to the coordinates of the last n positioning points. In another example, a clock drift insertion filter designs drift in the clock used by the device 200 and applies time correction to the pseudorange data. In another example, the final evaluation filter uses the previous location point and the evaluated responder travel direction and speed to evaluate the location point.

In another example, the filters are arranged in two parallel paths. For example, the travel route data 305 is divided into two sets by a data alignment filter. The first set includes points of data representing locations of the respondents 102 that occurred within a geographic area (eg, a downtown area) that includes a large building, and the second set includes data points in more rural areas. Include them. Each set of data then passes through one or more filters, where the filters applied to each data set may be different or the same. In addition, data may be exchanged between two sets of filters (eg, two filter paths may be crosslinked). A solution selection filter is then applied to combine the outputs of the two routes to create the full travel route of the responder 102.

6A and 6B are exemplary machine readable that may be performed by a processor (eg, one of the processors 2305A-C of FIG. 9) to implement the example travel route processor 310 of FIG. 3. Show flow diagrams representing instructions. The machine readable instructions and / or example travel route processor 310 of FIGS. 6A-B may be executed by a processor, a controller and / or any other suitable processing apparatus. For example, the machine readable instructions and / or example travel route processor 310 of FIGS. 6A-B are shown as an example processor platform 2300 and the processors 2305A discussed below in connection with FIG. 9. -C) can be implemented with stored coded instructions stored on a type of medium, such as flash memory or random access memory (RAM). Optionally, some or all of the machine readable instructions and / or example travel path processor 310 of FIGS. 6A-B may be an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable logic device. (FPLD), discrete logic, hardware, software and / or firmware. In addition, some or all of the machine readable instructions and / or example travel route processor 310 of FIGS. 6A-B may be implemented manually or using any combination of the foregoing techniques. In addition, although the machine readable instructions of FIGS. 6A-B are described with reference to the flowcharts of FIGS. 6A-B, those of ordinary skill in the art will appreciate that there are many other ways of implementing the exemplary travel route processor 310. It will be readily appreciated that they may be employed. For example, the order of execution of the blocks may be changed, and some of the described blocks may be changed, removed, or combined.

The example machine readable instructions of FIG. 5A begin with the travel route processor 310 reading a configuration file that identifies which filters or filter configuration (s) should be implemented by the travel route processor 310 (block 604). For example, the configuration file is an XML file that identifies the format, order, sequence, form, interconnect, and number of filters. However, other types and / or numbers of filters may be used instead.

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

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

Returning to the road limit filter 450 of FIG. 4B, each derived (or determined) location point within the travel route is known such that the enhanced travel route data 315 results are consistent along the known travel course and represent a reasonable travel route. Aligned to correspond to the center line of the travel course (ie, modified, manipulated, etc.). The road limit filter 450 determines the location of an appropriate and most similar positioning point based on past or future travel. For example, the implementation of road restriction filter 450 uses artificial intelligence (AI) algorithms and techniques (with appropriately selected penalties and points) to perform various travel route manipulations. For example, each of the location points may be mapped to a plurality of points that are close to a known travel course to create a tree of Bayesian representing a plurality of possible travel routes connecting the mapped location points. have. The value can then be applied to each point (eg, based on the Euclidean distance from the actual positioning point to that point). The cost associated with each path is determined by adding a value for each mapped point that includes the path, and the path with the lowest cost is selected.

In the example of FIGS. 3 and 4B, the travel route processor 310 accesses geocode data specifying the locations of a known travel course. In addition, the travel route processor 310 may use a road map file that defines the geographic statistical area in which the road restriction filter 450 will operate. Thus, some of the travel routes crossing or crossing the area will be processed by the road limit filter 450. In the example of Figures 3 and 4B, the road map file is a configurable XML file that defines a simple rectangular boundary defined by four longitude and latitude pairs. The travel route processor 310 uses a rectangular boundary to determine if segments of each known travel course (eg 50 strides) are within that area. The travel path processor 310 operates to limit the location points to align with the centerline of one of the segments within that area.

6A illustrates a portion of an exemplary travel route that includes 20 derived location points (shown in circles 1-20). Within the exemplary road limit filter 450, the travel segment is an ordered set of consecutive data points associated with a particular known travel course. For example, in FIG. 6A, Pine Street identifies three travel segments (1,2,3,4,5), (13,14,15,16) and (19,20) associated with it. Have

The decision path may be constructed by ensuring that each location is related to only one segment of the known travel course. FIG. 6B shows an example decision route constructed from the example travel route shown in FIG. 6A, where each node in the example travel route corresponds to one travel segment. If the road limit filter 450 only considers decision paths, there is a substantial possibility that the travel route known as the nearest appearing point may not actually be the known travel route traveled by the responder 102. For example, in the example of FIG. 6A, location point 17 may be associated with either Second Avenue or Fine Street.

Instead of relying on decision paths, the exemplary road limit filter 450 constructs a decision tree that includes a plurality of mapping of location points to possible known travel courses. Thus, the decision tree consists of possible travel paths corresponding to the location points, where the complexity of the tree depends on the amount of ambiguity of location points (eg, the number or percentage of ambiguous points). Each node in the decision tree represents a travel segment (ie, candidate segment) of the candidate travel path. FIG. 6C illustrates an example decision tree comprising two branches constructed from the example travel route shown in FIG. 6A. The example decision tree of FIG. 6C is relatively small because the travel route data has a relatively small amount of ambiguity.

By constructing the decision tree, the road limit filter 450 fuzzy logic by applying a set of rules to determine the likelihood that each candidate travel route included in the decision tree is an actual travel route taken by the responder. logic) can be employed. Specifically, each candidate travel route is assigned a score, and the candidate travel route with the highest score is the closest travel route taken by the responder 102.

In the exemplary road limit filter 450, it is recognized that the current location may be most affected by the nearest neighboring location. For example, in the example of FIG. 6A, whether location point 17 is on Fine Street or on Second Avenue is most affected by location points 16 and 18. Thus, the exemplary road limit filter 450 uses a prediction-correction algorithm. For example, to determine the best known travel route that maps to a location decision point, the exemplary road limit filter 450 filters the travel route data until a decision tree (eg 4) of a predetermined depth is constructed. Repeat through. Exemplary road limit filter 450 then determines the score for each branch in the tree of limited depth and selects the branch with the highest score. Once the determination of the location point (or candidate segment) is made, the exemplary road limit filter 450 repeats the processing for the next location point (or candidate segment).

To score each branch of the decision tree of limited depth, such as various methods (ie, measurements), for example, the proximity of the candidate and location points, the external arrangement of the location points for the candidate segments, and the like. Can be used. 7A shows exemplary additional location points. Exemplary measurements are based on data moments, such as, for example, data moments taken with respect to candidate segments. 7B and 7C show two moments of the example positioning point of FIG. 7A taken for First Avenue and Second Avenue, respectively. Candidate segments with a small average distance or moment have a higher ratio compared to candidate segments with a high average distance or moment. In the example road limit filter 450, the data moment is used as the initial score assigned to the candidate segment (eg, node of the decision tree).

Another exemplary measure is an inner product that measures how well the candidate segments are aligned with their location points. The dot product of the candidate segment and the positioning points determines the angle between the positioning points and the candidate segment. In this example, if the angle is close to 0 or 180 degrees, the travel segment (i.e. the decision tree node) is proportionately higher (i.e. receiving a bonus), and if the angle is 90 degrees close to 270 degrees The travel segment is penalized.

Another example measure uses contextual analysis based on candidate segments. For example, consider candidate segment s [n]. 8A lists some example contextual analysis bonuses given to candidate segment s [n]. Specifically, if s [n] has more than five consecutive points (ie positioning points), the candidate segment s [n] receives a 40% bonus (ie its score is 40 Increases%). If the score of the previous candidate segment s [n-1] is greater than the predetermined amount (eg 60), then the candidate segment s [n] receives a 10% bonus.

8B-G illustrate exemplary candidate segment configurations, each resulting in a 15% contextual analysis penalty. For example, as shown in FIG. 8C, if candidate segments s [n] and s [n-1] are not connected, a 15% penalty applies to candidate segments s [n].

Returning to FIG. 3, the MECD 300 of FIG. 3 includes a pass processor 328 to determine whether exposure of the responder 102 has occurred to the media site 115. In the illustrated example of FIG. 3, the pass processor 328 improves travel route data to determine if the responder has passed through the media site 115 so that the responder 102 (FIG. 1) has a chance to see the media site 113. 315, the media site location information stored in the database 130 is used. For the media site 115 with media exposure potential shown in FIG. 3, the responder 102 must traverse “close enough” to the media site 115 (eg, within a certain distance of the media site). Each exposure to media site 115 is recorded by pass processor 328 in database 130.

9 illustrates an example processor system 2300 that may implement the methods and apparatus disclosed herein. Processor system 2300 includes one or more processors 2305A-C associated with system memory. The system memory may include one or more random access memory (RAM) 2315 and read on knee memory (ROM) 2317.

In the example of FIG. 9, the plurality of processors 2305A-C are coupled with an input / output controller hub (ICH) 2325 to which other peripherals or devices interface. In the illustrated embodiment, peripherals interfacing with the ICH 2325 may include an input device 2327, a mass storage device 2340 (eg, a hard disk drive), a universal serial bus (USB) 2345, and a USB device 2350. ), A network port 2355 associated with another network 2360, and / or a removable storage drive 2357. Removable storage drive 2357 can include associated removable storage media 2358, such as magnetic or optical media. One or more peripheral devices may implement the provision of location point data 305 recorded by the download server 120. Mass storage device 2340 may be used to store the exemplary machine readable instructions shown in FIGS. 5A and 5B.

The example processor system 2300 of FIG. 9 also includes a video graphics adapter card 2320 coupled to a memory controller hub (MCH) 2310 as a peripheral and also to a display device 2232.

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

The memories 2315 and 2317 forming part or all of the system memory may be any suitable memory or memory device and may be sized to meet the storage needs of the system 2300. In addition, mass storage device 2340 may be any magnetic or optical medium readable by processors 2305A-C, for example. System memory may be used to store recorded travel route data 305, enhanced travel route data 315, and / or database 130. System memory may also be used to store the example machine readable instructions shown in FIGS. 5A and 5B.

Input device 2327 may be implemented with a keyboard, mouse, touch screen, track pad, or any other device that enables a user to provide information to processors 2305A-C.

The display device 2322 may operate as an interface between the processors 2305A-C and users via, for example, a liquid crystal display (LCD) monitor, a cathode ray tube (CRT) monitor, or a video graphics adapter 2320. May be another suitable device. Video graphics adapter 2320 is any device used to interface display device 2232 to MCH 2310. Such cards are currently commercially available, for example, from Creative Labs and other similar merchants.

Removable storage drive 2357 can be, for example, a compact disc-writable (CD-R) drive, a compact disc-rewritable (CD-RW) drive, a digital versatile disc (DVD) drive, or any other optical drive. It may be an optical drive such as. Optionally, it can be a magnetic media drive. Removable storage medium 2358 can complement removable storage medium 2357 as long as it can be selected to operate as drive 2357. For example, if removable storage drive 2357 is an optical drive, removable storage medium 2358 can be a CD-R disc, a CD-RW disc, a DVD disc, or any other suitable optical disc. On the other hand, if removable storage drive 2357 is a magnetic media drive, removable storage media 2358 can be, for example, a diskette, or any other suitable magnetic storage medium. Removable storage medium 2358 may also be used to provide location points recorded by download server 120 or to store database 130.

Example processor system 2300 may also include a network port 2355 (eg, processor peripherals), such as, for example, an Ethernet card or any other card that may be wireless or wired. Network port 2355 provides a network connection between processor 2305A-C and network 2360, which may be a local area network (LAN), wide area network (WAN), the Internet, or any other suitable network. Network port 2355 and network 2360 may also be used to provide location points recorded by download server 120.

Of course, it will be appreciated by those skilled in the art that the order, size, and ratio of memories shown in the exemplary systems may vary. In addition, this patent discloses exemplary systems among them, including software or firmware running on hardware, but such systems are merely exemplary and should not be considered as limiting. For example, it is contemplated that some or all of these hardware and software components may be implemented exclusively in hardware, exclusively in software, exclusively in firmware, or in some combination of hardware, firmware and / or software. Thus, it will be apparent to one of ordinary skill in the art that the above examples are not the only way to implement such systems.

At least some of the example methods, machine readable instructions, and / or apparatuses described above 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, may likewise employ some or all of the example methods and / or devices disclosed herein, in whole or in part. It can be configured to implement. In addition, optional software implementations, including but not limited to distributed processing or component / object distributed processing, parallel processing, or virtual machine processing, may be configured to implement the example methods and / or apparatuses disclosed herein. have.

Exemplary software and / or firmware implementations disclosed herein include magnetic media (eg, disks or tapes); Optical media such as magneto-optics or disks; Or other packages containing a solid state medium such as a memory card or one or more read-only (non-volatile) memories, random access memories or other rewritable (volatile) memories; Or on a tangible electronic medium such as signals comprising computer instructions. Attaching a digital file to an email or other self-contained information archive or set of archives is considered a distributed medium equivalent to a tangible storage medium. Thus, the example software and firmware described herein may be stored in tangible storage media or distributed media, such as alternative media described above or equivalent thereto.

For the purposes of extension, the foregoing description illustrates exemplary components and functions with respect to particular standards and protocols, but it should be understood that the disclosed technology is not limited to such standards and protocols. For example, the respective standards for Internet and other packet switched network transmissions (e.g., Transmission Control Protocol (TCP) / IP, User Datagram Protocol (UDP) / IP, Hypertext Writing Language (HTML), Hypertext) Transmission Protocol (HTTP) and communications between a computer and between a device (eg USB) represent an example of the current state of the art. Such standards are periodically replaced by faster, more efficient equivalents with the same general purpose functionality. Accordingly, alternative standards and protocols having the same function are equivalent to the technical spirit disclosed of the present invention, and should be considered to be included within the scope of the appended claims.

The technical idea of the present invention is to receive instructions from a radio signal so that one or more machine-readable media containing instructions, or an apparatus connected with a network environment, can use the instructions to transmit and receive voice, video or data communications with the network. And executing. Such devices may be implemented by electronic devices that provide voice, video or data communications such as telephones, cordless phones, mobile phones, cellular phones, portable personal digital assistants (PDAs), set-top boxes, computers, and / or servers. .

While certain illustrative methods, devices, and articles of manufacture are described herein, the scope of the present invention is not limited thereto. On the contrary, the invention includes all methods, apparatuses and articles of manufacture that are literally included within the scope of the appended claims or are under the equivalent spirit.

Included in this specification

Claims (24)

Processing data indicative of locations recorded by the electronic device to improve at least one of completeness or accuracy of the data; Deriving positioning points from the processed data; And Modifying at least one of the derived location points to align along a known travel course. The method of claim 1, Processing the data comprises filtering the data. The method of claim 2, Filtering the data comprises using a plurality of filters. The method of claim 1, The processing of the data may include deriving an accurate satellite position, calculating satellite altitude, establishing initial positioning points based on expected positioning points included in the data, or performing receiver autonomous conservation management. And at least one of the steps of: The method of claim 1, Deriving location points from the processed data comprises filtering the processed data. The method of claim 1, Deriving the positioning points from the processed data comprises using the derived satellite position and the received signal to derive the positioning point, the plurality of received signals and the published signal to derive the positioning point. At least one of using a clock drift, using a known position and responder travel speed to derive a positioning point, or removing the determined or derived positioning point if multipath interference is observed. How to feature. The method of claim 1, Modifying at least one of the derived location points to align along the known travel course comprising using artificial intelligence techniques. The method of claim 1, Modifying at least one of the derived location points to align along the known travel course comprises constructing a decision tree indicative of possible travel routes, assigning probability values to nodes of the decision tree, And selecting a travel route corresponding to the branch of the decision tree having the highest probability of binding. The method of claim 8, The likelihood values are based on at least one of a data moment, a data line product, or a contextual analysis. Process data indicative of locations recorded by the electronic device to improve at least one of the completeness or accuracy of the data; Derive position points from the processed data; And And a processor coupled to memory programmed to modify at least one of the derived location points to align along a known travel course. The method of claim 10, The processor is programmed to process the data by filtering the data using at least one filter. The method of claim 10, And wherein the processor is programmed to process the data by using at least one of a derived accurate satellite position, a calculated satellite altitude, an expected positioning point included in the data, or a receiver autonomous conservation management result. The method of claim 10, The processor using at least one of the exact satellite position and the received signal derived to derive the positioning point, using the plurality of received signals and published clock drift to derive the positioning point, Program to derive the location points from the processed data by using known location and responder travel speeds to derive a location point, or by removing determined or derived location points if multipath interference is observed. Device characterized in that the. The method of claim 10, And the processor is programmed to modify at least one of the derived location points to align along the known travel course by using artificial intelligence techniques. The method of claim 10, The processor configures the decision tree representing possible travel paths, assigns probability values to nodes of the decision tree, and selects the travel path corresponding to the branch of the decision tree with the highest combining probability. And program at least one of the derived positioning points to align along a course. The method of claim 15, The likelihood value is based on at least one of a data moment, a data line product, or a contextual analysis. When the instructions are executed, the machine Process data indicative of locations recorded by the electronic device to improve at least one of the completeness or accuracy of the data; Derive position points from the processed data; And A machine-readable medium having instructions for modifying at least one of the derived location points to align along a known travel course. The method of claim 17, And when the instructions are executed, the instructions cause the machine to process the data by filtering the data using at least one filter. The method of claim 17, When the instructions are executed, the instructions are generated by the machine using at least one of the exact satellite position derived, the calculated satellite altitude, the predicted satellite decision point included in the data, or the result of receiver autonomous conservation management. Machine-readable medium, characterized in that for processing. The method of claim 17, When the instructions are executed, the instructions are used by the machine to use at least one of the exact satellite position and the received signal derived to derive a positioning point, a plurality of received signals to derive the positioning point and The processed data by using published clock drift, using known positions and responder travel speeds to derive the positioning points, or removing determined or derived positioning points if multipath interference is observed. And derive the positioning points from the machine-readable medium. The method of claim 17, When the instructions are executed, the instructions cause the machine to modify at least one of the derived location points to be aligned along the known travel course by using artificial intelligence. The method of claim 17, When the instructions are executed, the instructions constitute a decision tree representing the possible travel paths for the machine, assigning probability values to the nodes of the decision tree, and traveling corresponding to the branches of the decision tree with the highest joinability. Selecting at least one of the derived location points to align along the known travel course. The method of claim 22, And the likelihood values are based on at least one of a data moment, a data line product, or a contextual analysis. A file reader configured to read recorded data indicating responder locations; A memory configured to store data; And At least one of the derived location points to process the recorded data to derive at least one of the completeness and accuracy of the data, derive location points from the processed data, and align along a known travel course And a processing engine configured to apply filters to modify the filter.

Images