• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    method for constructing trajectory analysis in an area, method for determining traversable trajectories in an area and system for modeling traversable trajectories in an area. Techniques for constructing a trajectory analysis in an area are provided. data is received from a mobile device in an area. the data is based on a trajectory that is traversed by the mobile device. a traversable trajectory is then determined from the data received from the mobile device. the traversable trajectory is overlaid on a map and the map, including the traversable trajectory, is packaged for provision or display to requesting devices.
    公开号:BR112012014599A2
    申请号:R112012014599-1
    申请日:2010-05-28
    公开日:2020-09-01
    发明作者:Ezekiel Kruglick
    申请人:Empire Technology Development Llc；
    IPC主号:
    专利说明:

    - 1st “METHOD FOR CONSTRUCTING A TRAJECTORY ANALYSIS IN AN AREA, METHOD FOR DETERMINING TRAJECTORY TRAJECTORIES IN AN AREA AND SYSTEM FOR MODELING TRAJECTORY TRAJECTORIES IN AN AREA” ” 5 Background of the invention “Electronic devices such as computers and mobile devices are becoming increasingly integrated into everyday life. Mobile devices are often provided with applications, such as e-mail, which are becoming increasingly useful. Many people use their mobile devices as one of their primary means of communication.
    The capabilities of mobile devices are not limited to applications such as e-mail. Other applications are becoming more common. Some mobile devices, for example, have mapping applications. With advances in GPS technologies, traveling can be made easier by the use of digital maps that are presented by mapping applications. However, digital maps are often limited to existing roads and often are. unable to dynamically adapt to changing road conditions. More specifically, the ability of conventional mapping applications to provide directions or identify traversable trajectories is tied to existing roads. While mapping applications may be able to pinpoint a user's location using GPS technologies, they are unable to provide mapping capabilities that do not follow existing roads.
    ' For example, consider a situation where a person is walking in a park. When asked for directions to the other side of the park, conventional mapping technologies are likely to provide directions that follow the roads. In other words, these mapping technologies provide directions that are based on existing roads. follow directions
    . based on existing roads will likely take one around the park's surroundings along the lines. roads rather than identifying a traversable path through the park. . 5 While some mapping applications may augment their information with satellite imagery, . satellite do not identify traversable trajectories and directions are still based on existing roads. In addition, it is often difficult for a user to distinguish a traversable path in satellite images because the satellite image is often out of date and in insufficient resolution. Also, it is difficult for a user to distinguish between shadows, impassable water features, elevation changes, and the like in satellite imagery. Indeed, there are many areas such as parks and walkways that are poorly mapped. Attempting to traverse these areas using conventional mapping applications is often unreliable and frustrating.
    Brief Description of the Figures Figure 1 shows an illustrative configuration of a map modeling system; Figure 2 shows an example of trajectories followed by mobile device users; Figure 3 shows an example of location data that is collected from mobile devices and used to determine traversable trajectories in an area of the map shown in Figure 2; 7 Figure 4 shows an example of a traversable path that has been superimposed on the map shown in Figures 2 and 3; Figure 5 is a flow diagram of an illustrative configuration of a method for determining traversable trajectories in an environment.
    Figure 6 shows an exemplary computing device for a map modeling system; and Figure 7 shows an illustrative configuration of a SED
    : 3 . special purpose computing device. Detailed Description of the Invention. In the following detailed description, reference is made to the accompanying drawings, which form a part thereof. In the '5 drawings, similar symbols typically identify similar components, unless the context indicates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not intended to be limiting. Other configurations may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. It will be readily understood that aspects of the present disclosure, as described generally herein, and illustrated in the figures, can be arranged, replaced, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein. The settings disclosed here relate to environmental modeling including, but not limited to, mapping. Location data is collected from location-enabled devices operating in an area. The location data is then analyzed to determine or identify trajectories that the devices have traversed. Location data can be processed to determine traversable trajectories that can be provided to other devices, for example as maps or overlayed on top of existing maps. 7 Over time, location data can be collected | 30 from many different devices and the location data these devices provide can be analyzed and used as mapping data. Specifically, traversable trajectories can be determined from the location data collected. Traversable trajectories can then be overlaid on maps of existing parks (or other areas) to identify traversable trajectories (e.g. to YYYYYYYYYYYYYYYYYYYYYYYYYYYYY
    . runners, walkers, cyclists, vehicles, etc.) through the park or other areas. . Analysis of location data can be used to identify areas that should be avoided, such as ' 5 bushes or other natural development or impediment, walls, construction zones or areas, elevation changes, or the like, when generating traversable trajectories. In addition, the traversable trajectories that are determined from the collected data can be dynamically updated as the environment changes and new location data are collected. For example, location data collected after the completion of a construction project may result in a new traversable trajectory that was not previously present or was not evident from existing location data. | location data collected from | location-enabled devices provide a map | multidimensional from multiple sources of traversable l 20 space. The traversable space map can be | updated with time as location data | additional ones are collected for the traversable space. In addition, analysis of location data can distinguish between location data that is collected, for example, from a device in an automobile versus a device carried by a user who is walking. Some configurations described here provide an automated solution for modeling areas that have complicated characteristics and formats. Traversable trajectories generated from location data 7 can take into account the features and shape of an area, even when information about features and shapes may not be known. The configurations disclosed here may identify traversable trajectories for indoor areas (including specific trajectories for different elevations), outdoor areas, and the like or any combination thereof. EESC
    ; | [ | | . Figure 1 shows an illustrative configuration of a map modeling system 100. The system 100 may include a server 170 which may be configured to receive data from or survey data from devices including mobile devices, for example. example, a mobile device 105 and a mobile device '110. Data received or collected from mobile devices 105 and 110 may include, respectively, location data 120 and location data 125. Depending on whether the system 100 is a | push or pull system (or some hybrid thereof), the server 170 may go on standby and passively receive location data 120 and 125 from the mobile devices 105 and 110 and/or the server 170 may actively search mobile devices 105 and 110 for location data 120 and 125. Location data 120 and 125 that is received or collected from mobile devices 105 and 110 generally includes the specific locations of mobile devices 105 and 110. For example, the locations of mobile devices 105 and 110 can be expressed in terms of latitude, longitude, and/or elevation. Location data 120 and 125 may also include data from other applications and/or sensors, such as accelerometer data, altimeter data, heading data, or the like or any combination thereof. In addition, location data 120 and 125 may also include identifiers such that location data 120 and 125 can be analyzed to identify the trajectories followed by mobile devices 105 and 110.
    ' In one example, identifiers allow server 170 to identify the specific trajectories followed by mobile devices 105, 110.
    Because the trajectories of mobile devices 105 and 110 are not generally known or predetermined, location data 120 and 125 in this context may be referred to as non-predetermined trajectory data. the —————ssess.=—
    : 6 . In contrast to predetermined trajectory data (e.g., data on the location of freeways, - roads, sidewalks, and so on), non-predetermined trajectory data may include data on ' 5 trajectories (e.g. .e.g. through parks, construction areas, etc.), often used but not "mapped" trajectories (eg, in enclosed structures such as buildings), and any trajectories used on foot, bicycle, or even car that are not shown on traditional maps.
    Location data 120 and 125 collected from mobile devices 105 and 110 may include non-predetermined trajectory data.
    In some cases, some of the location data 120 and 125 may correspond to both non-predetermined trajectory data and predetermined trajectory data (such as when a user is crossing a road). This determination can be made by the mobile devices 105 and 110 themselves or by the server 170. However, the presence of location data corresponding to predetermined trajectory data (such as a road) in the location data 120 and 125 collected from of mobile devices 105 and 110 may be incorporated into environmental modeling or mapping data that is generated from location data 120 and 125, including non-predetermined trajectory data.
    For example, predetermined trajectory data can be combined with non-predetermined trajectory data to identify "crosswalks" (or other areas that can identify the intersection of predetermined trajectory data with non-predetermined trajectory data). predetermined) that occur on traversable paths generated from location data 120 and 125. Paths that are generated from undetermined path data can be dynamic and therefore can change over a period of time (unlike a highway). These trajectories are not us ———.»)M=sssmnmm—
    . necessarily predetermined and can be determined by analyzing location data 120 and 125 collected from mobile devices 105 and 110. For example, the location of mobile device 105 can be determined by collecting or receiving data from | location 120. Location data 120 may include, for purposes of example only, GPS data and an identifier.
    The movement of the mobile device 105 can | then be determined by analyzing the location data | 10 120. More specifically, the mobile device 105 may have a built-in GPS device that can record the location of the mobile device 105 at various intervals which may ! be predetermined.
    Over time, the mobile device | 15 105 records multiple locations that can be stored in memory of the mobile device 105 (although the mobile device 105 may transit each location as it is measured rather than recording the location on the device itself). The rate at which | 20 120 location data are recorded may vary.
    By | For example, mobile device 105 may record location data 120 at intervals, which may be defined or determined in terms of distance, time, device measurements, or the like, or any combination thereof.
    For example, the range could be a foot or a meter if the user is walking, or a few hundred feet or meters if the user is driving a car.
    Alternatively, the range can be measured | ' in time.
    The interval can be a few seconds if the user is driving a car, or several or more seconds if the user is walking.
    Therefore, recording location data 120 may be context dependent.
    In yet another example, the interval can be fixed (every 1 second; or every 5 seconds; or every 10 seconds). The amount of logging may be inversely proportional to battery life.
    So, in one example,
    . users can set specifications regarding how much logging will be performed (and therefore how much battery usage will be expanded). In yet another example, the mobile device bearer can set a standard '5 -« logging interval (eg, every 5 seconds). Alternatively, changes in acceleration and/or direction of mobile device 105 may be used to trigger recording of location data 120. The particular context (e.g., walking, driving, cycling, etc.) of mobile devices can be determined from accelerometer data (alone or in combination with GPS data). mobile devices of walking users have (relatively) small acceleration data; user mobile devices that drive have (relatively) large acceleration data; the mobile devices of users who are pedaling have comparably different acceleration data.
    Such mobile device location data is rich enough for three-dimensional (3D) mapping, as the data is derived from GPS, accelerometer, altimeter, and/or other devices or sensors on the mobile device.
    For example, a GPS device can provide X and 7Y dimensions (left, right, up and down), and the accelerometer and/or altimeter device can help determine the Z dimension (height) and/or direction. - The location data 120 can then be collected by the server 170 and evaluated to identify a path 140 traversed by the mobile device 105. Similarly, the location data 125 can be evaluated to determine a path 135 of the mobile device 110. Each case of location data 120, for example, can form a point of the trajectory 130. The points can be connected, for example, by interpolation by the server 170 (or by the device itself) to determine the trajectory 130. Thus, the trajectories 130 and 135 can be traced
    . using GPS technology when users have a trajectory tracking application turned on. Like . mentioned above, to obtain data on the vertical movement of mobile users (thus intensifying the ' 5 tracking to three dimensions, instead of the two dimensions of GPS), accelerometer and altimeter data ' can also be used. Such vertical motion data can be measured during the same intervals as the above mentioned GPS data. Alternatively, the GPS sensor may be able to provide location data in three dimensions (eg, latitude, longitude, and altitude) as well as a time measurement. The inclusion of time measurement can be used, for example, to weight location data 120 and 125 from mobile devices 105 and 110. It is noted that GPS tracking data is well known in the art. Also, the use of accelerometers and altimeters is well known in the art. In one example, location data 120 about the movement of mobile device 105 can be determined from GPS data generated by the mobile device.
    105. Mobile device 105 may use mobile device 105's GPS device, for example, to record GPS data (eg, longitude and latitude). As described earlier, GPS data can be logged at various intervals that can be set, for purposes of | example only, in terms of distance, time, device measurements, or the like or any combination. Accelerometer data can also be recorded with the | 30 longitude and latitude data. The particular context | ' (eg, cycling, walking, driving) can be determined from the accelerometer data and/or the | GPS data as described above. In one example, the particular context can be derived from the GPS device itself since GPS technology allows the determination of speed (distance traveled divided by time). O ——
    ] |
    ! . Location data 120 and 125 may be sent over a network 115 (wired/wireless, WAN/LAN, etc., or any combination thereof) to the server 170| that receives or collects location data 120 and 125.
    ' 5 In other examples, data 120 and 125 may be sent to server 170 via a peer-to-peer network.
    ' For example purposes, in Figure 1, server 170 receives: location data 120 from mobile device 105.
    This location data 120 describes the trajectory 130 10 of the mobile device 105 as well as the trajectory taken by an individual carrying the device | 105. Once the location data 120 is collected, the server 170 can determine a traversable path 131 from the location data 120. The traversable path 131 is based on the data! location 120 (i.e. from traversed path 130). The trajectory traversed 130 by a | individual in the past can take trajectory 131 for use; by other individuals (or the same) in the future.
    | 20 In this example, the traversed path 130 is not ! necessarily identical to traversable path 131.
    | For example, the mobile device may follow a trajectory that encounters significant elevation. Location data from other mobile devices indicates elevation is avoided. As a result, the traversable path 131 determined by the server 170 may differ from the traversed path 130 followed by the mobile device 105. Alternatively, the traversed path 130 may also correspond to the traversable path 131.
    ' In one example, traversable path 131 may be exactly the same trajectory as traversed path 130. In another example, if many traversable paths 130 are available to server 170, then an interpolation can be performed to obtain one (or more ) traversable trajectory(s) (see 131 using techniques such as those that are well SS
    . known in the art. In one example, an interpolation component or module can perform the calculations. mathematicians to determine traversable path 131 from traversed path 130.
    , 5 Server 170 can overwrite any trajectories | non-predetermined traversables 131, 135 and 140 about | : a map 160 already having predetermined trajectories 145 and 150 (eg roads). After superimposing trajectories 131, 135 and 140 over the map 160, the map 160 as a whole can be packaged for delivery or display, to the original mobile device 105 and 110 that provided the location data 120 and 125, or to any other device. computing devices such as computing devices 180 and 185. In one example, a large number of devices may provide location data that allows server 170 to determine a traversable path in a given area. Location data from each device can be weighted according to various heuristics, although this is optional. For example, location data from trusted users may be given more weight than data from new or unverified users. In one example, trusted mobile devices (and/or users thereof) can be determined based on the validity or quality of previous contributions of location data. Mobile devices that contribute location data that result in 'traversable trajectories' are reliable or may become more reliable over time. For example, if the mobile device 110 consistently provides location data 125 to the server 170 compatible with location data provided by other mobile devices, then the mobile device 110 can build up a reputation for being accurate and thus become reliable. The weighting factors can be set according to the desired need. For example, the extension to the so EEEEaaeaeeaA As E O —p——-—-——
    It is how an area has been mapped can influence how weighting factors are set. In an area that is . poorly mapped, to location data from | Reliable sources can be given more weight when '5 determining traversable trajectories in the area compared to mobile devices that are unreliable' or that are less reliable. The weighting factors for location or mobile device data can also be based on a speed associated with the mobile device. Mobile devices that travel closer to the average speed of other mobile devices in the same area can be weighted more heavily as a typical mobile device. Mobile devices that are moving faster (eg, a mobile device carried by a skater) or slower (eg, a mobile device carried by a person moving a piece of furniture), however, may undertake a trajectory unusual in the area and be weighted more lightly.
    Location data weighting factors can also be based on accelerometer data or data from another sensor. For example, sensor data can be used to establish the probability that the mobile device will be carried by a user who is walking, running, or engaged in some other form of travel. The form of travel that is determined from sensor data (and/or location data) can also influence how the device | " mobile is weighted.
    The .mobile device's orientation can also be used to determine how location data is weighted. For example, a change in a mobile device's orientation or a cessation in the mobile device's movement could indicate someone sitting briefly as opposed to a clean walking path. Corresponding location data can be weighted accordingly. For example, the location data associated with the CC turn —s——-———
    pa MM 1 13 . of a sitting path may not be incorporated into the traversable path. At the same time, however, . this information can be used to identify a resting point in the area. Location data can also be weighted by determining how much time has passed since location data was collected.
    ' In other examples, users can provide personal information, home address, phone number, etc., to become trusted or to turn their corresponding devices into trusted devices. As users provide more of this type of personal information, the location data provided by their respective mobile devices becomes more reliable. At the same time, when users' mobile devices providing personal information are consistently wrong about what a traversable path is (based on their traversed path, for example), then the trust status of users and/or their mobile devices may be diminished or denied.
    For example, mobile devices of users who consistently step over or jump over fences may receive or be assigned a reduced or nullified trust status. Alternatively, location data from these users' mobile devices may suggest that they are traversing trajectories that other users avoid or do not take for various reasons (eg crossing, construction, etc.). One way to determine this would be to cross-reference the trajectory traversed by such users against known barriers (eg photographs provided by other users) in a map database. Although these users may traverse trajectories that others do not, there may be reasons to include such trajectories or to selectively display these trajectories on a map.
    As mentioned earlier, as users traverse trajectories, they may take photographs and/or ——n——--
    On the 14 . videos with your mobile devices.
    For example, a user can take a picture of a fence and still . jump over the fence to inform server 170 that the path being traversed has a barrier.
    The '5 photograph, in combination with the location data from the user's mobile device, results in the 'server 170 getting a better understanding of what physical barriers look like and how the barrier relates! with the trajectory.
    In some examples, human administrators having access to server 170 can also rank traversable trajectories based on photo and/or video data taken by users with mobile devices.
    Alternatively, image recognition applications or other applications and modules can be employed to automatically examine photographic and/or video data and rank traversable trajectories without human input.
    Therefore, location data 120 and 125 may also include video and/or photo data.
    Video and/or photo data can be associated with a specific location.
    For example, the server 170, when generating the map 160, may also embed the video and/or photograph data associated with the trajectory being followed in any “map provided to the devices. mobile devices 105 and 110, for example, can be alerted in advance with photo and/or video data that illustrates what users of mobile devices 105 and 110 can expect to see or experience based on their current trajectory.
    Ú In another example, any remote data that is collected from too much location data may be discarded or relied upon, depending on a determination of whether the remote data is reported to be useful.
    Such a utility report can be received from the mobile devices 105, 110 by the server 170. If a user issues a text or writes via an application the —-—-2-2-2s
    | 15 | . mobile that the traversable path 131 is difficult or imprecise, the server 170 can take this report and : . embed it for future use.
    Server 170 can also | provide an alternative trajectory at that point. ' 5 Additionally, data may be discarded if enough mobile devices do not traverse along a ' trajectory corresponding to the location data and the remote data is inconsistent with the location data.
    On the other hand, location data can be a basis for a new trajectory if they prove to be useful.
    The usefulness measure of trajectory data can be defined by an administrator according to a specific need/desire.
    In one example, a traversed path that avoids physical obstacles can be the basis for a traversable path.
    Such obstacles may include structures | man-made (walls, holes, etc.) or they may include flora (trees, rivers, etc.). In some examples, location data 120 and 125 can be used to identify physical obstacles in the area.
    For example, location data 120 and 125 may suggest that certain locations in an area are avoided by users of mobile devices 105 and 110. This allows server 170 to infer or identify avoided locations as physical obstacles, even when server 170 does not. can know the specific nature of physical obstacles.
    In some examples, photo and/or video data of the physical obstacles may be received by the mobile devices 105 and 110. In another example, a less traversable trajectory than the traversable trajectory can be determined.
    The traversable trajectory can be a shortcut of some sort, while the less traversable trajectory can be an even better shortcut, albeit through a construction zone.
    If safety is a concern for map users, map users can choose the traversable path; if time is of the essence,
    | | | | 16 . less traversable path can be followed.
    In another example, the map might be a map of . walk, a traffic map, or just about any other kind of map.
    Data from GPS, accelerometer, and other devices can not only provide relatively accurate 3D motion, but can also indicate what type of vehicle (if any) the mobile device user is using.
    For example, accelerometer data can | indicate whether a faster moving object, such as a car, is making the traversed path (based on relatively large acceleration (=) deceleration data) or whether a slower moving object, such as an individual merely walking, is making the traversed path.
    The vertical movement of cars, for example, is often restricted in the vertical direction.
    As a result, accelerometer data would tend to suggest two-dimensional motion.
    The vertical movement of people walking, in contrast, is rhythmic with a sharp thrust in the background.
    Thus, accelerometer data can be used to distinguish between which type of vehicle, if any, the mobile device user is using.
    In another example, to address potential privacy and tracking concerns, location data 120 and 125 can be anonymized.
    This allows path 130 to be disassociated from mobile device 105 and/or user to device 105. One way to accomplish this is to assign an anonymously generated identification number (anonymous number can also be random) to each mobile device and keep o : Mapping devices to anonymous numbers on a separate, secure server.
    In one example, this could be a secure third-party server.
    With respect to this last example, a mobile user, “*John', with phone number 555-1234, can be assigned an anonymous number 43A54CDE33X445E33, and the trajectory followed by John will be tracked by the number
    : anonymous and not your phone number or a mobile device identification number. In addition, the anonymous number ã can be redefined to remove the ability to determine that John is associated with a given trajectory. Once a trajectory is determined, the associated identifier can be disassociated from the | Í trajectory. Additionally, location data can be sent to multiple servers to be anonymized randomly delayed in time to: 10 assure users that location data cannot be fragmented together to extract the anonymous numbers (or other identifiers) used or associated, for example, with mobile device 105 or mobile device user 105.
    Location data 120 and 125 can be sent from mobile devices 105 and 110 at each set interval to server 170, or it can be batched on mobile devices 105 and 110 to, for example, preserve battery life. . Communications between server 170 and mobile devices 105 and 110 (or other devices/clients) may be dynamic to the extent that any maps using or incorporating location data 120 and 125 remain current to a standard defined by those using or producing such maps. Trade-offs between battery life and information updates may be considered.
    Figure 2 shows an example of trajectories followed by mobile device users. A map 200 illustrated in Figure 2 includes roads 208 (which may already be known and predetermined) and an area 210 (eg, a park or other area that may not have mapped traversable paths). Figure 2 illustrates trajectories 212, 214, and 216 which are examples of trajectories followed by mobile devices. Path 212 starts at point 202 and ends at point
    222. Path 214 starts at point 204 and ends at LL —n—m———-
    . point 226. Path 216 starts at point 206 and ends at point 224. Paths 212, 214, and 216 are . represented by dashed lines. In one example location data or cases of location data ' - (i.e. specific measurements of latitude and longitude) can be collected on each trace on trajectories 212, ' 214, and 216 from each of the mobile devices. Thus, each trace represents an interval, for example, in terms of time and/or distance. Each of trajectories 212, 214, and 216 can be generated by analyzing location data collected from the respective mobile devices associated with trajectories 212, 214, and 216.
    In this example, the area 210 includes a barrier 232. The barrier 232 can be, for example purposes only, a natural barrier (e.g., water, flora, elevation change) or a man-made barrier (e.g. ., construction, physical partition). The location data associated with trajectory 216 suggests that the user encountered barrier 232 and went around barrier 232 to reach point 224. In this example, the location data associated with trajectory 212 overlaps with location data that has already They are known. In an area 230, data from | 25 path location 212 can overlap data | on road 208 that can already be predetermined. Server 170 may be able to determine, particularly as more location data is received from area 230, that area 230 may correspond to a crosswalk. In one example, a user can take a photograph of the crosswalk and provide the photograph to the server 170. In this sense, the traversable trajectories that are generated can be used to identify characteristics of traversable trajectories that are not generally known in advance. A user who wants to cross a busy road, for example, may be able to follow an O ————
    . traversable path to a crossing lane that was previously identified by server 170. . Figure 3 shows an example of location data that is collected from mobile devices and used to determine traversable trajectories. In this example, location data 308 (which may be an example of location data 120 and/or 125) has been collected in relation to area 210 from multiple devices. Location data 308 as illustrated in Figure 3 may be suggestive of a traversable path or traversable paths in area 210. Relatively less dense location data 304, combined with location data 308, may indicate to server 170 that the area 210 includes barrier 232.
    Figure 3 further illustrates that the server 170 may be able to identify a fork 302 in the location data 308 and therefore the traversable path being determined. Location data 308, . which include location data 306 and 310, diverge at bifurcation 302. However, location data 306 and 310 are still dense enough to suggest a traversable trajectory. The density of location data 304, however, suggests that at least one point on trajectory 216 (see Figure 2) may not be included in a traversable trajectory of map 200 because of barrier 232.
    Figures 2 and 3 further illustrate that, when determining traversable paths in area 210, location data from mobile devices can be considered individually or collectively.
    ] Figure 2 demonstrates that individual trajectories can be considered when generating the traversable trajectory. Figure 3 demonstrates that the density of location data 308 from multiple mobile devices in area 210 can be used to identify traversable paths in area 210.
    Figure 4 shows an example of a trajectory
    . traversable path that has been superimposed on the map illustrated in Figures 2 and 3. In this case, a traversable trajectory * 402 was identified on the map 200 after the server 170 has determined at least one traversable trajectory ' 5 for area 210. Traversable trajectory 402 can cover or be superimposed over the map 200 to: illustrate the traversable trajectory 402 for devices requesting the map 200. In some configurations, the map 200 may also be provided with a visual indication of the barrier 232 (e.g., with data from photo and/or video from one of the mobile devices that encountered the barrier). In this example, area 210 can be immediately traversed following trajectory 402, thus avoiding barrier 232. Additionally, map 200 can also identify a crosswalk 404 on road 208 as part of traversable trajectory 402. In one example, the map 200 may be informable by traversable path 402 or other paths | traversables determined by the server 170 of | amenities in area 210. Amenities such as, for purposes | for example only, the locations of toilets, benches, | scenery, or the like can be determined from location data collected by the server 170. Amenities can be determined from photo and/or video data that may be included in the | location data. In addition, sensor data that | may be included in the location data may be used to determine the locations of benches, toilets, or the like. For example, the cessation of movement and change in elevation (eg, such as when a person sits down) when mobile device 105 in area 210 may indicate a bench or other rest area along a traversable trajectory. As additional location data from other mobile devices provides corroboration, traversable trajectories may inform map 200 of other secondary data. THE
    . Secondary data derived from location data can also be used to identify which - traversable trajectories (or other trajectories including known roads) are traversed most frequently.
    This can be used to identify traversable trajectories that are popular or frequently traveled.
    Secondary data can also be used to identify when and/or how traversable trajectories are used.
    For example, the | 10 secondary data can indicate when and where a | crowd is anticipated to be present or when and where a particular area is likely to be crowded.
    In one configuration, secondary data can be displayed on the map 200 independently of traversable trajectories.
    Figure 5 is a flow diagram of an illustrative embodiment of a method for determining a traversable path.
    At block 502, a server receives location data from at least one device operating in an area.
    More generally, the server receives location data from multiple devices operating in the area.
    Location data, as previously recorded, may include one or more GPS data, identification data, video data, photograph or image data, personal information, or the like, or any combination thereof.
    At block 504, the server analyzes the location data and determines a traversable path from the location data received from the mobile devices.
    Determining a traversable trajectory may include Ú performing interpolation on location data to connect instances of location data, identifying trends (e.g. densities) in location data from multiple mobile devices, considering the trusted status of mobile devices, or similar or any combination thereof.
    At block 506, the server overlays path O ———
    . traversable given in block 504 on a map. The server can also provide the traversable path in - another way. The traversable trajectory can be integrated with the map, provided as a separate layer, '5 or similar. For example, an existing road map | may include parks and other areas. The path(s) S determined in block 504 may be the paths traversable in the parks. As a result, the | Traversable trajectories can be superimposed over the parks of the area map that is displayed on the devices.
    In block 508, maps, including trajectories | traversable, are packaged for display on the device. The resulting map can be displayed on any device, including mobile and other non-mobile devices.
    One skilled in the art will appreciate that, for this and other processes and methods disclosed herein, the functions performed in the processes and methods may be implemented in a different order. Additionally, the sketched steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without departing from the essence of the disclosed configurations.
    The present disclosure should not be limited in terms of the particular configurations described in this patent application, which are intended as illustrations of various aspects. Many modifications and variations can be made without deviating from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatus within the scope of the disclosure, in addition to those enumerated here, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims. The present disclosure is to be limited only! by the terms of the appended claims together with the scope false E shoot "O ———"—.
    - full of equivalents to which such claims | have rights.
    It should be understood that this disclosure | . is not limited to particular methods, reagents, compositions of compounds or biological systems, which can, of course, vary.
    It is also to be understood that the terminology used herein is for the purpose of describing particular configurations only, and is not intended to be limiting.
    In an illustrative configuration, any of the operations, processes, etc. described here can be implemented | as computer-readable instructions stored on a computer-readable medium.
    Computer read instructions may be executed by a mobile unit processor, a network element, and/or any other computing device.
    There is little distinction left between hardware and software implementations of systems aspects; The use of hardware and software is usually (but not always, where in certain contexts the choice between hardware and software can become significant) a design choice representing tradeoffs between cost vs. efficiency.
    There are various vehicles by which the processes and/or systems and/or other technologies described here may be executed (e.g., hardware, software, and/or firmware [resident boot program)), and the preferred vehicle will vary with the context in which processes and/or systems and/or other technologies are installed.
    For example, if an implementer determines that speed and accuracy are paramount, the implementer may opt for a primarily hardware and/or firmware vehicle; ' if flexibility is critical, the implementer may opt for a primarily software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware.
    The previous detailed description recorded various configurations of the devices and/or processes via The use
    . block diagrams, flow diagrams, and/or examples.
    To the extent that such block diagrams,
    . flow diagrams, and/or examples contain one or more functions and/or operations, will be understood by those within
    : 5 of the art that each function and/or operation within such block diagrams, flow diagrams, or examples may
    ' be implemented, individually and/or collectively, over a wide range of hardware, software, firmware, OR virtually any combination thereof.
    In one configuration, various portions of the subject matter described here may be implemented via Circuits
    Application-Specific Integrated Systems (ASICs). Field Programmable Gate Arrangements (FPGAS),
    digital signal processors (DSPs), or other integrated formats.
    However, those skilled in the art will recognize that some aspects of the configurations disclosed herein, in whole or in part, may be equivalently implemented on integrated circuits,
    as one or more computer programs running on one or more computers (eg, one or more programs running on one or more computer systems),
    as one or more programs running on one or more processors (eg, as one or more programs running on one or more microprocessors), such as
    | 25 firmware, or virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and/or firmware will be well within the
    | experience of someone of experience in the technique in the light
    " of this disclosure.
    In addition, those skilled in the art will appreciate that the mechanisms of matter in
    ' subject matter described herein are capable of being distributed as a program product in a variety of ways, and that an illustrative subject matter configuration described herein applies irrespective of the particular type of signal carrying medium used to actually effect the distribution.
    Examples of a signal supporting medium include, but are not limited to, the following: a medium o - A
    | 25 . writable type such as a floppy disk, a hard disk drive, a CD, a DVD, a digital tape, a . computer memory, etc.; and a type of transmission medium such as a digital and/or “analog” communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communications link , etc.). Those experienced in the technique will recognize that it is common | within the art to describe devices and/or processes in the manner set forth herein, and thereafter use engineering practices to integrate such devices and/or processes: described in data processing systems. That is, at least a portion of the devices and/or processes! described here can be integrated into a data processing system via a fair amount of experimentation. Those of skill in the art will recognize that a typical data processing system generally includes one or more of a unitary system housing, a video display device, memory such as volatile and non-volatile memory, processors such as microprocessors, and digital signal, computational entities such as operating systems, drivers, graphical user interfaces, and application programs, one or more interacting devices, such as a stand or touch screen, and/or control systems including feedback loops and motors (eg, feedback to detect position and/or speed; control motors í to move and/or adjust components and/or quantities). A typical data processing system may be implemented using any suitable commercially available components, such as those commonly found in computing/data communication and/or network computing/communications systems.
    The subject matter described here sometimes illustrates different components contained within, or connected with, other different components. It should be understood Eb A
    “that such represented architectures are merely exemplary, and that in fact many other architectures . can be implemented which achieve the same functionality.
    In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved.
    Therefore, any two components here combined to achieve a particular functionality can be seen as “associated with each other” such that the desired functionality is achieved, independent of architectures or intermediate components.
    Likewise, any two components so associated can also be seen as being "operablely linked", or "operablely coupled", with each other to achieve the desired functionality, and any two components capable of being so associated can also be seen as being "operablely coupled" to each other to achieve the desired functionality. operably attachable” to each other to achieve the desired functionality.
    Specific examples of operably attachable include but are not limited to physically combinable and/or physically interacting components and/or wirelessly interacting and/or wirelessly interacting components and/or logically interacting or logically interacting components.
    Figure 6 shows an exemplary computing device 600 that is arranged to model an environment, including generating traversable trajectories in an area, in accordance with the present disclosure.
    In a very basic 602 configuration, the computing device 600 generally includes one or more 604 processors and a 606 system memory. A 608 memory bus can be used for communication between the 605 processor and 606 system memory. desired configuration, the processor 605 can be of any type including but not limited to a microprocessor (uP), a microcontroller (UC), a digital signal processor (DSP), or any ———————————— ——
    . combination thereof.
    Processor 605 may include one or more cache levels [internal memory, such as a level one cache 610 and a level two cache 612, a processor core 614, and registers 616. An exemplary processor core 614 may include a unit arithmetic logic r - (ALU), a floating point unit (FPU), a digital signal processing core (DSP Core), or any combination thereof.
    An exemplary memory controller 618 may also be used with processor 604, or in some implementations memory controller 618 may be an internal part of processor 604. Depending on the desired configuration, system memory 606 may be any type including but not limited to limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof.
    System memory 606 may include an operating system 620, one or more applications 622, and program data 624. Application 622 may include an application | mapping 626 that is arranged to generate traversable trajectories from location data collected from devices including mobile devices.
    Program data 624 may include stored location data 628 which may be useful in generating traversable trajectories in a | 25 area.
    In some embodiments, the application 622 can be arranged to operate with program data 624 in the operating system 620 such that traversable trajectories are generated and packaged with existing maps such that the maps identify traversable trajectories in the areas of the map.
    This basic configuration. 602 is illustrated in Figure 6 by those components within the inner dotted line.
    Computing device 600 may have additional features or functionality, and additional interfaces to facilitate communications between the base configuration 602 and any required devices and interfaces.
    For example, a controller
    “ bus/interface 630 can be used to facilitate ; communications between the basic configuration 602 and one or more i . storage devices 632 via a storage interface bus 634. data storage devices 632 may be | 636 removable storage devices, devices | ' 638 non-removable storage, or a combination thereof. Examples of storage devices: removable and non-removable storage include magnetic disk devices such as floppy disk drives and hard disk drives (HDD), disk drives; optical disc such as compact disc (CD) or digital versatile disc (DVD) drives, solid state drives (SSD), and tape drives to name a few. Exemplary computer storage media may include media | non-volatile, removable and non-removable implemented in any method or technology for storage of | information, such as computer-readable instructions, data structures, program modules, or other data.
    System memory 606, removable storage devices 636 and non-removable storage devices 638 are examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash or other memory technology, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other "magnetic storage devices, or any other medium that can be used to store desired information and that can be accessed by the computing device 600. Any computer storage media may be part of the computing device.
    600.
    Computing device 600 may also include an interface bus 640 to facilitate communication; from various interface devices (eg, UU nn—......— to
    Flight: 5 % .2 2722 "222 A/2BÚIA - 2 1252;D" 3%“, ss ani a2“ºjk; : sao "s . ls=ó“.0 “+ 0 "o hu. - =... u 0. 29 o output devices 642, peripheral interfaces 644, and communication devices 646) to the basic r configuration 602 via the bus/interface controller 630. i Exemplary output devices 642 include a ] 5 graphics processing unit 648 and an audio processing unit 650, which can be configured to communicate with various external devices such as a display or speakers via one or more A/V ports 652. Exemplary peripherals 644 include a serial interface controller 654 or a parallel interface controller 656, which can be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device). , touch input device, etc.) or other peripheral devices (eg, printer, scanner, etc.) via one or more 1/O ports
    658. An exemplary communication device 646 includes a network controller 660, which can be arranged to | facilitating communications with one or more computing devices 662 over a network communication link via one or more communication ports 664. The network communication link may be an example of a communication medium. Communication media may typically be configured by computer read instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any means of delivery. of information. A : "modulated data signal" may be a signal that has a = 30 or more of its characteristics defined or altered in : such a way as to encode information in the signal. For purposes of example, and not limitation, communication media may include wired media such as a wired network or direct wired connection, and wireless media such as acoustics, radio frequency (RF), microwave, infrared (IR) ) and other wireless media. The term computer readable media as used herein may include both
    . storage media and communication media.
    Computing device 600 can be implemented | % as a portion of a small form factor portable (or | mobile) electronic device such as a telephone | '5 cell phone, a personal digital assistant (PDA), a personal media player V, a wireless web-viewing device, a personal headset, an application-specific device, or a hybrid device that includes any of the above functions. The computing device 600 can also be implemented as a personal computer including both laptop and non-laptop computer configurations.
    Figure 7 shows an illustrative configuration of a special purpose computing device 700, such as a mobile device. Device 700 includes a processor 710, which may be similar to processor 604.
    The device includes memory 720 which can be used to store a database 740 and a location module 760. Device 700 is a location enabled device and location module 760 is used to identify a location of device 700 and then recording or storing the location data in the database 740 as location data 750, which is an example of location data 120. Location data 750 may include data from any sensor 770, such as a GPS 772 and an altimeter 774. Location module '740 can then transmit location data 750 N 30 to server 170 via network 115. As described herein location data 750 can be used to determine traversable trajectories in an area.
    | Server 170 may also have modules 780 to determine traversable paths in an area. 780 modules (e.g., a location module, a map module), for example, can receive location data from multiple mobile devices operating on one o o ————
    PEQUEERREMENÔA and MM MM
    At the 31st - area, determine traversable trajectories from location data, overlay traversable trajectories: + traversable on a map of the area, or similar, or any combination thereof. Server 170 can also ' ' 5 provide the map, including the trajectories | traversable, for devices that request the map: including traversable trajectories in a way that allows traversable trajectories to be displayed on mobile devices. | 10 With respect to the use of substantially any terms (plural and/or singular here, those skilled in the art may translate from plural to singular and/or singular to plural as appropriate for the context and/or application. various singular/plural permutations may be expressly noted herein for the sake of clarity. It will be understood by those in the art that, in general, the terms used herein, and especially in the appended claims (e.g., bodies of appended claims) are generally intended to be 'open' terms (e.g. the term 'including' shall be interpreted as 'having at least', the term 'includes' ' shall be interpreted as 'includes but not limited to', etc. .) It will be further understood by those in the art that if a specific number of an entered claim citation is intended, that intention will be explicitly cited in the claim, and in the absence of such citation no such intention is intended. it's present. For example, as an aid to understanding, the following appended claims may contain the use of introductory phrases “at least one” and “one | or more” to enter the claim quote. | ! However, the use of such phrases should not be interpreted to imply that the introduction of a claim citation by the indefinite articles "a" or "an" limits any particular claim! containing such citation of claim introduced to '
    ” | 32 t
    : configurations containing only one such citation, even when the same claim includes the introductory z-phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or
    Ú 5 “one” shall be interpreted to mean “at least to one” or “one or more”); the same holds true for the use of definite articles used to introduce claim citations.
    In addition, even if a specific number of an entered claim citation is explicitly cited, those skilled in the art will recognize that such citation should be interpreted to mean at least the cited number (e.g., the simple "two citations" citation). , without other modifiers,
    means at least two citations, or two or more citations). Additionally, in those cases where a convention analogous to "at least one of A, B, and C, etc.) is used, in general such a construction is intended to be in the sense that someone having experience in the art would understand the convention (e.g. ., “a system having at least one of A, B, and C” will include but not be limited to systems that have A alone, B alone, C alone, A and
    B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense that someone skilled in the art understands the convention (e.g., "a system having at least one of A, B, or C" will include but not be limited to systems that have A alone B
    ' alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will additionally
    It is understood by those in the art that virtually any disjunctive word and/or phrase featuring two or more alternative terms, whether in the description, claims, or drawings, is to be understood to contemplate the possibilities of including one of the terms, any of the terms, or both terms.
    For example, the phrase “A or B” will be understood to include the possibilities
    : | 33 - of “A” or “B” or “A and B”.
    In addition, where the features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also described in this way in terms of an individual member or subgroup of members of the Markush group.
    | As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible sub-ranges and combinations of sub-ranges thereof. Any range listed can be easily recognized as sufficiently describing and enabling the same range to be divided into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed here can be readily divided into a lower third, middle third, and upper third, etc. As will also be understood by one skilled in the art, all languages such as "until", "at least", and the like include the quoted number and refer to ranges which may subsequently be divided into sub-ranges as discussed above. Finally, as anyone experienced in the art will understand, a track includes each individual member. So, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to "groups having 1, 2, 3, 4, or 5 cells, and so on.
    From the foregoing, it will be appreciated that various embodiments of the present disclosure have been described herein for purposes of illustration, and that various modifications can be made without departing from the scope and spirit of the present disclosure. Consequently, the various settings disclosed here are not intended to be limiting, with the true scope and spirit
    - being indicated by the following claims. UU')
    权利要求:
    Claims (19)
    [1]
    1. Method to build a trajectory analysis in an area, using a computing device *, characterized by the fact that it comprises: ' 5 - receiving a first set of location data from a first mobile device, the first set of data from location representing a first trajectory in the area traversed by the first mobile device; - determining a location of a physical obstacle based on the first set of location data; —- generate a traversable trajectory following an undetermined, unmapped path based on the first set of location data, where the traversable trajectory deviates from the first traversable trajectory by a first mobile device and avoids the physical obstacle; - superimpose the traversable path on a map; and - packaging the map for provisioning or displaying the map on a device.
    [2]
    2. Method according to claim 1, characterized in that it additionally comprises determining a second trajectory following an undetermined route, which is not mapped, based on the first set of location data, where the second traversable trajectory passes through the obstacle physicist.
    [3]
    Method according to claim 2, characterized. because the second traversable path includes " a construction area.
    and 30
    [4]
    4. Method according to claim 1, characterized in that it further comprises searching a second mobile device for a second set of location data, the second set of location data representing a second trajectory in the area traversed by the second mobile device, the traversable trajectory being additionally based on the second set of location data.
    wings if arc
    [5]
    5. Method according to claim 1, characterized in that receiving a first set of location data additionally comprises: - assigning an anonymous identification number to the first * 5 mobile device; and - tracking the first trajectory of the first mobile device v using the anonymously generated identification number.
    [6]
    A method as claimed in claim 1, wherein the first set of location data includes at least one of accelerometer data or GPS data indicating when the first mobile device is used in at least one of a vehicle or when the first mobile device is used on a person.
    [7]
    7. Method according to claim 1, characterized in that it additionally comprises, when establishing the traversable trajectory, rejecting outside location data, where the outside location data is inconsistent with the first location data set.
    [8]
    8. Method, according to claim 1, characterized in that it additionally comprises at least one of: - classifying the traversable path for an intended purpose; and - receiving at least one of photo data or video data from the first mobile device, being ; ' that the traversable path includes at least one of the photo data or the video data. > 9. Method to determine traversable trajectories in an area, using a computing device, | characterized in that it comprises: - receiving location data from each of a plurality of mobile devices, the location data representing trajectories in an area traversed by the plurality of mobile devices; - determining a location of at least one obstacle based on the location data;
    [9]
    | - generating a traversable trajectory following an undetermined route, which is not mapped, in the location data, where the traversable trajectory deviates from at least one of the traversable trajectory by a plurality of mobile devices and at least one obstacle is avoided in the area; and 4 - provide an area map that includes traversable paths in the area for a requesting device.
    [10]
    10. Method according to claim 9, characterized in that it additionally comprises interpolating the location data of each of the plurality of mobile devices independently to identify the trajectory of each mobile device in the area, with the trajectory of each mobile device contributes to the determination of the traversable path.
    [11]
    11. Method according to claim 10, characterized in that the traversable paths are determined according to a density distribution of the location data received from each of the plurality of mobile devices.
    [12]
    12. Method, according to claim 9, characterized in that it additionally comprises superimposing traversable trajectories on the area map.
    [13]
    13. Method according to claim 9, characterized in that it additionally comprises: - receiving at least one of the photo data or video data from at least one of the mobile devices, at least one of the photo or video data being Video data is embedded in the traversable trajectory on the map.
    [14]
    Method according to claim 9 | characterized in that receiving location data further comprises collecting location data from a plurality of mobile devices, each mobile device recording its location data at intervals.
    [15]
    15. Method, according to claim 9, characterized in that the map is informed by traversable paths.
    [16]
    16. System to model traversable trajectories in a | area, characterized by the fact that it comprises: | - a localization module configured to collect data. 5 location of mobile devices in an area, wherein the mobile devices providing location data are 4 location-enabled to provide the location data; | - a database configured to store the data | data collected from mobile devices, being | that location data identifies trajectories traversed by mobile devices in the area; - a processor configured to determine a location of at least one obstacle based on the location data, and to determine the traversed trajectories following trajectories that are not mapped in the area based on the trajectories of the mobile devices, at least one of the traversable trajectories determined deviates from paths traversed by mobile devices, determined traversable paths avoiding at least one obstacle; and - a map module configured to package a map for display on a device, the map including area and traversable trajectories.
    [17]
    17. System as claimed in claim 16 | characterized by the fact that the processor is still configured to adapt the traversable trajectories with time to the changes that occur in the area. V
    [18]
    18. System, according to claim 16, characterized in that the processor is further configured to determine secondary data from the location data and the maps module packages the secondary data for display on the device.
    [19]
    19. System according to claim 18, characterized in that the secondary data determine at least one of a popularity of traversable trajectories, an anticipated density of people on traversable trajectories, or amenities on traversable trajectories. | | | | | | |
    ND kb s = í 2 o o so =|
    AND . 2 , o / o NX o F rá x E , - “ E / “ 7 * "gives SE / Ss ) x = rá ' Ms s 7 o — Fo 7 NE C S8 in ET e, WE ow 7 “ ss 2 = ” s E 7 N 2 7 bh 2 Gives NF 7
    Y o PE IS SA NR / o e o E—=>
    ES EE TSE) CA to 2 E Eos. 2 ES | =: a38 ==> o Ee o | ==3 o ” —— > Y . = S o o o 2 So > 37 = $S| DE TS os 88 8 o 2 E ao o o DD = | ' TE O o Ss : EN =| Pg 219) TE EN o L 2 as Em & so os So s o 2 & os Ú
    H CD»: : DE >, Ês and 3 on OOo ! ae = SE a E La E a b o q Oo =P IQ sr - [| , : o o ! S oO S& Ss | esenmnrrrre nn (O Vo, ) V sz ! the UN ES N 1 ' 1 1 1 1 ] U 1 | [E ' == , 1 1 in Lu. ro 1 ; 8 1 U 1 l i 1 U 1 i i o ' 1! ' i H oO ) 1 1 H o 1 Sil 1 = |) o ro i PS | s ra 1 à i = 1a I ! ! - 7 1 1 j i / ! J! ' 1 A ! t 1 ' t ' ! 1 ' ' 1 1 ! 1 Í' ! 1 1 z s 1 ' Ss | The ! 2 OT & 1 Ss 1 | Are theses The | Y! 3 | 1 ; ' m—"> Nm > - ARE) o o Q - o
    ) 8 ” o ' 58 g & .=. 3 Bs EN o) " CR
    FEEL A
    ; 417 1 S&. i | << Tr |
    FP L' | [ )
    u
    V 502 Receive Location Data 504 Determine a trajectory from Location Data 506 Overlay the trajectory on a map 508 o Package the map for display on a display | FIG.5
    .
    CO : -o3
    FEEL = o j Es 3008, oLo 3 & dt TIL 7 TATIIAA Vc — — <o| " À - EE ou) 1 i db 3 É 1 Eis 1 Co e o £ o 8 e 5 E So : 1 dae 28 lo ES O 8 ess E qo la 5 + 1 L jo gs | 1 ss o € 8% 13 ã 8 1 3 = ns es ; io — 2 o 88, ff. ! ' ” o, e, oo 8th BE 2338 ! ' SIS EL 3 os 8| IF DES o $ É ' o Sus S|osTO | o2 o 185 ! ' 2/8160 ss t| 285 sEX O iEs ! ' 3/8853 [536s S| E Es is ' ' $18 3 SS! 5 3 2 £| ES ãs ã ' a = 285 £E5 o E| 8 8 1 je5º “LL 7 :
    E T ! pojusSWeneg ! : 055 SIEpAIUTSp ON T
    SE 1 aaa z : ' e , | E re ARA 528 ; o < o IEL said: 2 1'O TIO $ i 1 o! 1] ONSI COL CE ooo E Polã se Ilza o Bl: E EEE ! lol! ão 2 O o o |; os ' -= = [0/02 (Oo Ú Ss 8 o É 3 az 85S s at Ss. o Ss. BS Ss 5 E FB ' 19 18 o 2ailE IN E = & | E 8 s3 is es! |, — E = o3S SB S = ; Do $I5 23 83 | EE 21/98] St FE e | ass | S IS 2)! |O! 539 and 'E | oz z j OE 5 | E: ser = 8 | Only TFRRARF s on Ox (Ol o 1 go Ág PE and 'TS ES i| 2 ! ; o RE í R <c o) | = i. ' ' o : = E És & ml fel ' Ss Sl | i FM | - DB & Ss | E oo S Rs E 2 h 18. [8] 82 FF Silhs E 2 L Bo ses s SÉ si /98 119 | 553 2 ES Es) < ES le FE 1st ses 118 | SÊ 2 ê ss ia <S : Sã Sc ESSES OR Sã 18º 88 | ão E | 28. 2 ! z; 8 Es Ds 2 as o So 18/15 181: PE Rj os | 28 and Parents | 1 2 1 o << — Iê o é Es | = So Lil. —— is 1 ld E EDECE Deo. 18 LL TES: o —— 12 and nene
    , .
    LV Processor 710 Database 740 Data d 700 location data 1750 760 Location module : 170 772 174 "
    L
    类似技术:
    公开号 | 公开日 | 专利标题
    BR112012014599A2|2020-09-01|method for constructing a path analysis in an area, method for determining traversable trajectories in an area and system for modeling traversable trajectories in an area
    US10352718B2|2019-07-16|Discovering points of entry to a location
    US8718922B2|2014-05-06|Variable density depthmap
    CN107843907A|2018-03-27|The method of the processor dutycycle of mobile device is dynamically determined based on geographical position
    Chen et al.2017|ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints
    Gunduz et al.2016|A REVIEW OF RECENT RESEARCH IN INDOOR MODELLING & MAPPING.
    BR102017027622A2|2018-08-14|device in the vehicle and route information display system
    Schroder et al.2011|Giving the ‘right’route directions: The requirements for pedestrian navigation systems
    de Graaff et al.2016|Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas
    Wakamiya et al.2016|Lets not stare at smartphones while walking: memorable route recommendation by detecting effective landmarks
    US9341498B2|2016-05-17|Navigation system with route guidance mechanism and method of operation thereof
    Wenig et al.2017|Pharos: Improving navigation instructions on smartwatches by including global landmarks
    Amirian et al.2016|Landmark-based pedestrian navigation using augmented reality and machine learning
    Comai et al.2016|Maps for easy paths |: enriching maps with accessible paths using MEP traces
    US20150169630A1|2015-06-18|Recommending an outdoor activity using a geographic information system
    Motta et al.2018|Overview of smart white canes: connected smart cane from front end to back end
    US11024054B2|2021-06-01|Method, apparatus, and system for estimating the quality of camera pose data using ground control points of known quality
    US10909714B2|2021-02-02|Method, apparatus, and system for providing a distance marker in an image
    Fang et al.2018|An invisible salient landmark approach to locating pedestrians for predesigned business card route of pedestrian navigation
    US20200249027A1|2020-08-06|Method and apparatus for data consumption reduction based on map-based dynamic location sampling
    US20220053283A1|2022-02-17|Method, apparatus, and system for determining a non-specific location according to an observed mobility pattern derived from non-positioning related sensor data
    US9644988B2|2017-05-09|Navigation system with data gathering mechanism and method of operation thereof
    US20220007131A1|2022-01-06|Radio-Visual Approach for Device Localization based on a Vehicle Location
    US20220007132A1|2022-01-06|Image-Based Approach for Device Localization based on a Vehicle Location
    US20210341300A1|2021-11-04|Method, apparatus, and system for providing a personally relevant navigation route comparison
    同族专利:
    公开号 | 公开日
    US8818711B2|2014-08-26|
    WO2011075187A1|2011-06-23|
    KR101452602B1|2014-10-22|
    CN102667408A|2012-09-12|
    KR101562485B1|2015-10-22|
    JP5496359B2|2014-05-21|
    CN102667408B|2016-11-16|
    JP2013513797A|2013-04-22|
    KR20120060239A|2012-06-11|
    US20110153208A1|2011-06-23|
    KR20140034942A|2014-03-20|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    JP3776741B2|2001-03-29|2006-05-17|株式会社東芝|Map information acquisition method, map information acquisition system, and program|
    JP2003240592A|2002-02-20|2003-08-27|Hitachi Plant Eng & Constr Co Ltd|Method for automatically creating barrier-free map for wheelchair user and navigation system therefor|
    US7133771B1|2002-08-29|2006-11-07|America Online, Inc.|Automated route determination to avoid a particular maneuver|
    EP1491858A1|2003-06-27|2004-12-29|Harman Becker Automotive Systems GmbH|Navigation method and system|
    US7149626B1|2004-06-30|2006-12-12|Navteq North America, Llc|Method of operating a navigation system|
    US7627423B2|2005-03-10|2009-12-01|Wright Ventures, Llc|Route based on distance|
    US20070210937A1|2005-04-21|2007-09-13|Microsoft Corporation|Dynamic rendering of map information|
    KR101213868B1|2005-04-21|2012-12-18|마이크로소프트 코포레이션|Virtual earth|
    JP2007011557A|2005-06-29|2007-01-18|Nissan Motor Co Ltd|Traffic jam detection system, onboard information terminal, information center, and method for detecting traffic jam|
    KR20070031641A|2005-09-15|2007-03-20|주식회사 현대오토넷|Navigation system using radio frequency identification system and method for displaying construction site road|
    US7469827B2|2005-11-17|2008-12-30|Google Inc.|Vehicle information systems and methods|
    US8532678B2|2006-03-08|2013-09-10|Tomtom International B.V.|Portable GPS navigation device|
    US20080059007A1|2006-06-09|2008-03-06|Whittaker William L|System and method for autonomously convoying vehicles|
    JP2007333998A|2006-06-15|2007-12-27|Hitachi Ltd|Automatic map generating device|
    US8588464B2|2007-01-12|2013-11-19|International Business Machines Corporation|Assisting a vision-impaired user with navigation based on a 3D captured image stream|
    US20080201074A1|2007-02-15|2008-08-21|Garmin Ltd.|System and method for creating and sharing navigation routes with electronic devices|
    JP2010531430A|2007-04-03|2010-09-24|ヒューマン・ネットワーク・ラブズ・インコーポレーテッド|Method and apparatus for obtaining local location and overlaying information|
    JP5064870B2|2007-04-17|2012-10-31|株式会社日立製作所|Digital road map generation method and map generation system|
    US9354077B2|2008-05-20|2016-05-31|Honeywell International Inc.|Navigation system|
    US8335647B2|2008-12-04|2012-12-18|Verizon Patent And Licensing Inc.|Navigation based on popular user-defined paths|KR101336191B1|2005-10-28|2013-12-03|모직스, 인코포레이티드|Rfid receiver|
    JP5134020B2|2007-03-23|2013-01-30|モジクス，インコーポレイティッド|RFID system using distributed exciter network|
    JP6150455B2|2008-04-14|2017-06-21|モジクス， インコーポレイティッド|System and method for estimating and tracking the location of wireless automatic identification tags|
    US20120239290A1|2009-12-02|2012-09-20|Pioneer Corporation|Information processing apparatus, information processing system, information processing method and bicycle map|
    US10031885B2|2010-02-01|2018-07-24|Netmotion Wireless, Inc.|Public wireless network performance management system with mobile device data collection agents|
    JP5471626B2|2010-03-09|2014-04-16|ソニー株式会社|Information processing apparatus, map update method, program, and information processing system|
    WO2011130582A1|2010-04-14|2011-10-20|Mojix, Inc.|Systems and methods for detecting patterns in spatio-temporal data collected using an rfid system|
    US9071370B2|2010-05-20|2015-06-30|CSC Holdings, LLC|System and method for set top box viewing data|
    WO2012076040A1|2010-12-07|2012-06-14|Tomtom International B.V.|Mapping or navigation apparatus and method of operation thereof|
    WO2012095754A2|2011-01-14|2012-07-19|Anagog Ltd.|Predicting that a parking space is about to be vacated|
    US9602316B2|2011-03-07|2017-03-21|Mojix, Inc.|Multiple symbol noncoherent soft output detector|
    US8615359B2|2011-05-23|2013-12-24|Microsoft Corporation|Map navigation with suppression of off-route feedback near route terminus|
    US8788222B2|2011-07-25|2014-07-22|International Business Machines Corporation|Detection of pipeline contaminants|
    US8706325B2|2011-07-27|2014-04-22|International Business Machines Corporation|Evaluating airport runway conditions in real time|
    US8990033B2|2011-07-27|2015-03-24|International Business Machines Corporation|Monitoring operational conditions of a cargo ship through use of sensor grid on intermodal containers|
    US8538667B2|2011-07-28|2013-09-17|International Business Machines Corporation|Evaluating road conditions using a mobile vehicle|
    US9146112B2|2011-10-04|2015-09-29|International Business Machines Corporation|Mobility route optimization|
    US9322657B2|2011-10-04|2016-04-26|International Business Machines Corporation|Mobility route optimization|
    US9207089B2|2011-10-04|2015-12-08|International Business Machines Corporation|Mobility route optimization|
    US9322648B1|2012-06-12|2016-04-26|Trx Systems, Inc.|Reducing elevation error with environmental pressure anomaly compensation|
    US9671224B2|2012-06-12|2017-06-06|Trx Systems, Inc.|Methods resolving the elevation of a tracked personnel or assets|
    US9335175B2|2012-08-15|2016-05-10|Google Inc.|Crowd-sourcing indoor locations|
    US10148709B2|2012-08-31|2018-12-04|Here Global B.V.|Method and apparatus for updating or validating a geographic record based on crowdsourced location data|
    CN102928815B|2012-11-01|2017-11-03|无锡清华信息科学与技术国家实验室物联网技术中心|Method, device and the intelligent communication equipment of a kind of robust user trajectory estimation in indoor positioning|
    US9047703B2|2013-03-13|2015-06-02|Honda Motor Co., Ltd.|Augmented reality heads up displayfor left turn safety cues|
    US10708654B1|2013-03-15|2020-07-07|CSC Holdings, LLC|Optimizing inventory based on predicted viewership|
    US9111156B2|2013-03-15|2015-08-18|Mojix, Inc.|Systems and methods for compressive sensing ranging evaluation|
    US9115997B2|2013-06-11|2015-08-25|Qualcomm Incorporated|Modeling characteristics of a venue|
    US9097520B2|2013-06-12|2015-08-04|Caterpillar Inc.|System and method for mapping a raised contour|
    JP2015049049A|2013-08-29|2015-03-16|アイシン・エィ・ダブリュ株式会社|Communication terminal, communication terminal control method, and computer program|
    GB201419807D0|2014-11-06|2014-12-24|Tomtom Int Bv|Method for estimating the occupancy of a parking lot|
    JP6693422B2|2014-12-09|2020-05-13|ソニー株式会社|Information processing apparatus, control method, and program|
    US9883337B2|2015-04-24|2018-01-30|Mijix, Inc.|Location based services for RFID and sensor networks|
    US10564794B2|2015-09-15|2020-02-18|Xerox Corporation|Method and system for document management considering location, time and social context|
    US10605614B2|2016-10-17|2020-03-31|International Business Machines Corporation|Generation of route network data for movement|
    CN108267146B|2016-12-30|2021-03-26|比亚迪股份有限公司|Path planning device, vehicle-mounted navigation equipment, navigation method and system|
    US10237699B2|2017-02-09|2019-03-19|International Business Machines Corporation|Predicting obstacles in a travel path|
    KR102091180B1|2017-12-19|2020-04-29|전자부품연구원|A global map synthesis system and method using multiple maps and synthetic reference device thereof|
    CN113418522B|2021-08-25|2021-12-14|季华实验室|AGV path planning method, following method, device, equipment and storage medium|
    法律状态:
    2020-09-15| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2020-10-06| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-10-27| B08F| Application dismissed because of non-payment of annual fees [chapter 8.6 patent gazette]|Free format text: REFERENTE A 10A ANUIDADE. |
    2021-01-19| B11B| Dismissal acc. art. 36, par 1 of ipl - no reply within 90 days to fullfil the necessary requirements|
    2021-11-23| B350| Update of information on the portal [chapter 15.35 patent gazette]|
    优先权:
    申请号 | 申请日 | 专利标题
    US12/642,355|US8818711B2|2009-12-18|2009-12-18|3D path analysis for environmental modeling|
    US12/642,355|2009-12-18|
    PCT/US2010/036611|WO2011075187A1|2009-12-18|2010-05-28|3d path analysis for environmental modeling|
    [返回顶部]