Source: http://www.google.com.tw/patents/US7831387
Timestamp: 2013-05-24 15:07:01
Document Index: 216085586

Matched Legal Cases: ['Application No. 2', 'Application No. 2', 'arth 1', 'arth 1', 'arth 1', 'arth 1', 'Application No. 200580013912', 'Application No. 200580013512', 'Application No. 1237', 'Application No. 1247', 'Application No. 2', 'Application No. 2', 'Application No. 200580013512', 'Application No. 05726046']

�M�Q US7831387 - Visually-oriented driving directions in digital mapping system - Google �M�Q�j�M �Ϥ� �a�� Play YouTube �s�D Gmail ���ݵw�� ��h »�i���M�Q�j�M | �������� | �n�J�i���M�Q�j�M�M�QDigital mapping techniques are disclosed that provide visually-oriented information to the user, such as driving directions that include visual data points along the way of the driving route, thereby improving the user experience. The user may preview the route associated with the driving directions,...http://www.google.com.tw/patents/US7831387?utm_source=gb-gplus-share�M�Q US7831387 - Visually-oriented driving directions in digital mapping system���}��US7831387 B2�X���������v�ӽЮѽs��11/181,386�o�G���2010�~11��9���ӽФ��2005�~7��13�� �u���v���2004�~3��23����L���}�M�Q��US7920968US20050288859US20070016368WO2007008809A2WO2007008809A3�o��HAndrew R. GoldingJens Eilstrup Rasmussen��M�Q�v�HGoogle Inc.Google, Inc. ���M�Q������701/438340/995.27340/995.24382/104701/467��ڱM�Q������G01C21/34G01C21/32 �X�@����G01C21/3644G01C21/3647G01C21/3602 �ڬw������G01C21/36G6G01C21/36CG01C21/36G5�ѦҤ��m�M�Q�ޥ� (70)�D�M�Q�ޥ� (49)�Q�H�U�M�Q�ޥ� (4)�~���s�����M�Q�ӼЧ� ���M�Q�ӼЧ��M�Q����T�� �ڬw�M�Q��Visually-oriented driving directions in digital mapping systemUS 7831387 B2�K�n Digital mapping techniques are disclosed that provide visually-oriented information to the user, such as driving directions that include visual data points along the way of the driving route, thereby improving the user experience. The user may preview the route associated with the driving directions, where the preview is based on, for example, at least one of satellite images, storefront images, and heuristics and/or business listings.
RELATED APPLICATIONS This application is related to U.S. application Ser. No. 11/088,542, filed Mar. 23, 2005, titled ��Generating and Serving Tiles in a Digital Mapping System,�� which claims the benefit of U.S. Provisional Application Nos. 60/650,840, filed Feb. 7, 2005, and 60/567,946, filed May 3, 2004, and 60/555,501, filed Mar. 23, 2004. In addition, this application is related to U.S. application Ser. No. 11/051,534, filed Feb. 5, 2005, titled ��A Digital Mapping System��, which claims the benefit of U.S. Provisional Application Nos. 60/567,946, filed May 3, 2004, and 60/555,501, filed Mar. 23, 2004. Each of these applications is herein incorporated in its entirety by reference.
Whether vector-based or raster-based, such existing map systems typically provide computer-generated driving directions expressed in abstractions that are convenient for computers, such as ��Stay on Main St. for 1.2 miles, and turn Right onto Center Street.�� Although such directions provide useful information that can be effectively employed to reach an intended destination, they can only be used in a literal sense. Other than the meaning of the words making up the directions, there is no further guidance to the user.
As previously explained, computer-generated driving directions are typically expressed in abstractions that are convenient for computers, such as ��Stay on Main St. for 1.2 miles��, etc. In contrast, human-generated directions tend to be more visually-oriented, such as ��Stay on Main St, you'll pass a big Sears on your left, then turn right at the Dunkin Donuts��. Through the use of satellite-based imagery and/or storefront images, a mapping system configured in accordance with an embodiment of the presenting invention can give more visually-oriented (and hence human-friendly) directions.
Similarly, storefront (street-level) images can be used to identify salient buildings and features, such as stores with large visible logos, stores of unusual colors (e.g., ��the bright purple store��), stores that are easily recognized because their brands are well-known and/or their store architectures and tradedress are distinctive (e.g., ��the McDonalds��). This last category involving well-known brands, trademarks, tradedress, etc. can be identifiable even without image information (e.g., most everyone knows what a McDonalds looks like). Such visual data can be used to identify the target address, such as ��Fred's Shoe Repair is in the middle of the block, just past the bright purple store��.
The user can preview the driving route by a simulated drive-through or ��fly-through�� using the satellite/street-level images, in conjunction with the relevant digital maps. Thus, when the user actually drives to their targeted destination, the visual cues in the directions will remind the user of what to look for, and give that user a greater sense of confidence that he or she is on the right track. Numerous other benefits will be apparent in light of this disclosure.
The location data serving system 120 is the part of the overall system that delivers location data of various forms to the computing device 103. Its functions include, for example, finding the geographic location of a street address, generating and formatting visually-oriented driving directions, and searching for location-specific results to a query (e.g., as with the Google Local Search service). Other services may also be provided. In general, when the user enters a search string, it is put into a request by the computing device 103, and sent to the location data serving system 120 via the network 105. The location data serving system 120 then determines what the request is for (e.g., generate driving directions with visual ��fly-through�� of route, or generate visually-oriented driving directions, or generate both visually-oriented driving directions and visual ��fly-through�� of route), and responds with the appropriate data from various sub-systems, such as geo-coders, routing engines, and local search indexes or databases (e.g., including a scored waypoint database), in a format that computing device 103 can use to present the data to the user (e.g., via a browser). Example architecture and functionality of the location data serving system 120 will be discussed in turn with reference to FIGS. 2-4.
In the embodiment shown in FIG. 2, the location data serving system 120 operates in both an off-line capacity, as well as in an on-line capacity. In particular, ��scored waypoints�� are generated by the waypoint identification and scoring module 205 in an off-line process, and stored in the scored waypoint database 210. This off-line process effectively stocks the database 210 with waypoints and their corresponding distinctiveness scores. During on-line operations, requests (e.g., HTTP) for driving directions are received by the driving direction generator 215. The request can be initiated, for example, by a user engaging a web browser of a computing device 103 to access the system. In response to such a client request, the driving direction generator 215 accesses the scored waypoint database 210 and integrates the relevant scored waypoints into the driving directions generated in response to the request. These visually-oriented driving directions are then served to the requestor via the network 105 and web browser of the computing device 103.
In more detail, the satellite image processor 325 is programmed or otherwise configured to recognize navigational features included in the satellite images. Certain navigational features are particularly useful for drivers, because a driver has to attend to such features anyway, including the likes of traffic lights, stop signs, on/off-ramps, traffic circles, city blocks, tunnels, bridges, end of divided highway, and other driver-centric features. These navigational features facilitate human directions such as: ��turn right at the third light��, ��get off at the second off-ramp��, and ��go through 2 traffic circles and then it's on your right.��
The storefront image processor 330 is programmed or otherwise configured to analyze storefront images. In one embodiment, this analysis is earned out at both a coarse level (e.g., width, height, color histograms) and a more refined level (e.g., segmentation into facade, doors, windows, roof; architectural elements such as pillars and balconies; decorative elements such as awnings, signage, neon lights, painted designs). Such analysis can be carried out, for example, using standard image-processing techniques (e.g., computer vision). Standard feature extraction algorithms typically extract high level information from images, such as shapes, colors, etc. Pattern recognition algorithms can then be applied to classify the extracted information so as to ��recognize�� objects in the storefront images.
All of these identified features can then be evaluated for use as waypoints. For instance, waypoints can be proposed based on observed locally distinctive or otherwise salient features, including any measurable dimension or highly noticeable quality (e.g., unusually wide/narrow/tall/short building, building with purple door, orange roof, green neon sign, etc). If a feature is ��locally distinctive��, it is unique within some vicinity, such as the only pink building on the block. The features discussed so far are intrinsic to the building, but extrinsic features can also be used (e.g., the building at the corner, the first building over the railroad tracks, etc).
While the storefront image processor 330 can be used to detect decorative and structural features within the images, the OCR module 335 can be applied to the storefront images to read the storefront signage (if any), using standard OCR algorithms and techniques. This text recognition enables waypoint proposal of visual features such as ��the bar with the neon Karaoke sign in the window.�� In one particular embodiment, the OCR accuracy of module 335 can be improved by identifying what kind of a store is in the storefront image, based on its corresponding category listing (e.g., bars and restaurants) in the yellow pages database 315. Recall that the image registration module 320 has already mapped the images to corresponding listings within the yellow pages database 315, thereby facilitating this context identification for the OCR process. In addition, text related to that yellow pages category can be obtained, for example, by visiting web sites of stores in that category, and adjusting the language model used for OCR module 335, accordingly. This supplemental information from yellow page listings and/or websites enables the OCR module 335 to be informed of the context in which it is operating.
For example, the visual learning module 345 would discover that McDonalds frequently have the same logo (e.g., the word ��McDonalds�� in a particular color and font), a glass storefront, Golden Arches, etc. In this sense, the visual learning module 345 learns a prototype of what the target chain store typically looks like. In addition, the visual learning module 345 identifies the extent to which each branch of the chain store at a given address matches the prototype. If a McDonalds branch at a given address sufficiently matches the common McDonalds' features found in the prototype, that McDonalds branch is a useful waypoint to use in driving directions, because it will be easily recognized by humans as a McDonalds. The combination of these two features (prototyping and comparison to prototype) improves the system's reliability in finding useful waypoints. Note that a given chain-store branch may be actually be a ��bad�� waypoint if, for instance, it doesn't match the prototype well. For example, a McDonalds in an upscale neighborhood may be required to conform to the prevailing architecture (e.g., no large Golden Arches allowed), thus reducing its recognizability as a McDonalds. Likewise, a given chain-store branch may actually be a ��bad�� waypoint if, for instance, that particular branch is not clearly visible from the road (e.g., obscured by foliage or other buildings). The visual learning module 345 will avoid using such branches of the chain store as waypoints, thereby further refining results from the image processors 325 and 330, and the OCR module 335.
Having identified waypoints and their distinctiveness scores, they can be used in several ways when generating driving directions (e.g., using the driving directions generator 215). For instance, turns can be identified using waypoints at or near an intersection (e.g., ��you'll see a McDonalds on your right and a Mobil station on your left�� or ��turn right just past the Home Depot on your right��). In one particular embodiment, the waypoints that are given are on the same side of the street as the turn (e.g., waypoints on the right-hand side for right turns), so drivers can focus on one side of the road at a time. Confirmatory waypoints can be provided, such as ��You'll pass a Safeway on your left, then a large park on your right.�� Also, ��gone too far�� waypoints can be given, such as ��If you see a Tower Records on your right, you've gone too far.�� Waypoints can also be used as early warning indicators, to signal the driver to start watching for a turn, especially if the driver has been on the same road for awhile (��you'll be on Center St. for 20 miles; after you pass the Crabtree Shopping Center on your left, start watching for your left turn��). Waypoints can also be used to identify the target destination (e.g., ��your destination is the three-story white house on the left�� or ��you'll see the Fry's on your right; look for the big red logo��). The entire route can be chosen with the quality of waypoints in mind. For instance, the user can be directed along a somewhat longer route if the longer path affords better waypoints. In one particular embodiment, the user can be offered a choice of maximizing ��ease of following directions�� versus ��shortest distance�� (or other such alternatives).
In all of these uses of waypoints, there is a balance between choosing a waypoint in exactly the desired location with choosing a waypoint with a higher distinctiveness score. For example, it may be better to direct the user as follows: ��Your destination is the third house after the big red building�� compared to: ��Your destination is the house on the left with a brown front door.�� Note also that streets can be used as waypoints (e.g., ��It's the first right-hand turn after you cross Maple St.��). In such an embodiment, the waypoint distinctiveness evaluation module 350 is configured to score the salience of streets (e.g., ��large street with two lanes in each direction�� or ��a boulevard with grassy median��).
In one particular embodiment, businesses are allowed to bid or otherwise pay to be included as a waypoint. The ��cost-per-use�� of a waypoint could then be an additional factor that would be taken into account when scoring each waypoint. Furthermore, incorporating one or more of the scored waypoints into requested driving directions could include selecting waypoints related to a destination of the requestor. For instance, if the user is asking for driving directions to a national park, then waypoints such as camping equipment stores could be selected for integration into the served driving directions. This choice of waypoints has several benefits: (1) the waypoints might be of interest in their own right; (2) the waypoints are more likely to be familiar to the user and thus better waypoints; and (3) advertisers would be likely to pay more for such targeted waypoint usage.
In one particular embodiment, an ��autoplay preview�� feature is provided on the client side. When selected by the user, a request for preview data would be sent to the server side. The client could initially be given the entire route, which can be represented, for example, as a sequence of latitude/longitude pairs of line segments approximating the path. The client can then use simple linear interpolation to follow the route. For instance, the client could display a map (e.g., satellite map or bitmap map) centered initially at the starting point of the route, with a progress marker (e.g., a car icon), and would continuously scroll the display along the desired route. Movement of the progress marker could be carried out using script code (e.g., JavaScript) executing on the client. The client could pre-fetch map tiles (or other map image) in conjunction with moving the progress marker to create an uninterrupted viewing experience.
In one particular configuration, the user is allowed to divert from the original route, so as to explore alternate routes and the surrounding area (��virtual reconnaissance��). Note in this latter case, however, that pre-fetching becomes more difficult. To reduce latency of such real-time image delivery, predictive caching of the relevant map images/storefront images can be used based on the user's current direction and previous use habits. A number of predictive caching and image delivery schemes can be used, as will be apparent in light of this disclosure.
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