Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Note Regarding Prior Final Rejection
Following applicants “REQUEST FOR RECONSIDERATION OF WITHDRAWL OF FINAL REJECTION UNDER MPEP 707.07(c) AND (d)” filed on 01/06/2026, the examiner agrees a withdrawal of the final rejection due to the erroneous omission of claim 28 from the rejection and the LEWIS reference was erroneously misattributed art to the rejections for claims 48-50 and 52.
The previous final filed 12/17/2025 is withdrawn. Nonetheless, the necessary corrections do not render the current application as warranting a second non-final or allowance. However, a second final is warranted.
A rejection for claim 28 was appropriately added. For claims 48-50 and 52, the LEWIS reference was removed and the MASON is restored to the recite the quoted text in consistency with the 103 rejections section of the non-final filed 07/17/2025 (Non-final, Pgs. 43, 44-45, and 48). The headers for the 103 sections were appropriately modified to reflect the corrected group of claims.
Given the corrections by the examiner regarding the MASON reference for claims 48-50 and 52, the arguments previously presented by the applicant regarding the MASON reference are still valid for claims 48-50 and 52.
Regarding applicant argument that the applicant never argued that the WITTE reference is non-analogous art, the examiner agrees. Applicant’s argument on Pg. 23 line 25 – Pg 24 line 4 discuses WITTE as non-obvious and was erroneously cited as non-analogous. The argument has been corrected accordingly, please see arguments section below.
Response to Claims
Claims 23-52 are pending
Claims 23, 35, 48, and 52 are amended
Response to Arguments
Re the Claims:
Given the applicant’s amendment of claims 23 and 35 to recite the requirement that the implementation have the vehicles function within a mining environment, the MASON reference has been overcome for claims 23-47 and has been replaced by LEWIS.
The section 101 Rejections:
The applicant's arguments have been fully considered and are not persuasive. The application of the "identifying", "grouping", separating", "determining", "forming", selecting", "choosing", and "directing" is directly related to a general improvement in the navigation and dispatch art. Merely narrowing the area of application towards the field of "material delivery systems" does not provide a novel limitation.
However, under the new guidelines, the directing step is an active step. As such the 101 is withdrawn.
Re the section 103 Rejections:
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the applicant argues that the ROWLEY and MASON references do not recognize the problems from inaccurate position and/or location data. For art to be analogous, it is not required to address the same issues as the invention (MPEP 2141.01(a)(I)(1), “(1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem)”). The current application belongs in the navigation and dispatch arts wherein the prior art resides. Due to the ROWLEY and MASON reference not teaching positional inaccuracies, the JUNG reference was implemented.
Regarding the argument that “Jung discloses a different solution entirely, namely the moving trajectory concept” for claim 23, the examiner respectfully disagrees. Nonetheless, JUNG teaches the “grouping” and “separating“ concepts for multiple different vehicles by introducing the concept of classifying and clustering of trajectories (JUNG, ¶ 0084). ROWLEY discloses the “determining” and “forming” via use of collecting data for each individualized user (ROWLEY, ¶ 0058). A person having ordinary skill in the art would recognize that individualized travel information of ROWLEY can be clustered with the information of similarly moving classes of JUNG to yield the result of the aggregating route data.
Likewise, the examiner respectfully disagrees with the applicant’s argument regarding that “determining”, “forming”, and “selecting” first requires the ROWLEY reference to perform “grouping” and “separating” on the same grounds stated in the previous paragraph. The applicant’s interpretation of the currently claimed language overly-narrows the claims text beyond the broadest reasonable interpretation as the aggregation of trip data can be handled separately from the processing of routes. If the applicant desires to have these steps be done sequentially, the office recommends stating such explicitly.
Furthermore, claims 23-34 does not have a requirement for accounting for positional inaccuracies. If the applicant intends to view the claims in light of positional inaccuracies, the office recommends amending the claims to implement this limitation accordingly.
Given all the reasons above, the 35 U.S.C. 103 rejection is sustained. Please see 103 below.
Claims 23-29, 35-42, 48-50, and 52:
Regarding the argument that “Jung discloses a different solution entirely, namely the moving trajectory concept”, please see section “Re the section 103 Rejections”.
Applicant’s arguments with respect to claims 23-29, 35-42 regarding the MASON reference have been considered but are moot in view of the new ground(s) of rejection as necessitated by applicant's amendments.
However, the applicant’s arguments are still sustained for claims 48-50 and 52 given they were not appropriately amended to include the mining environment. Regarding the applicant’s argument for claims 48-50 and 52 that MASON “does not recognize problems associated with inaccurate position/location data”, the examiner respectfully disagrees. The MASON reference merely teaches the dispatching of vehicle fleets and the office does not claim that MASON is capable of inaccurate position/location data. As discussed in page 45 of the office action from date July 27, 2025, JUNG teaches the use of clustering to account and minimize for being “judged as a different path due to error of a sensor or error in correction” (JUNG, ¶ 0101). Broadest reasonable interpretation would read errors in sensors creating different paths as positional inaccuracies.
Given all the reasons above, the 35 U.S.C. 103 rejection is sustained. Please see 103 below.
Claims 27 and 40:
With regards to the applicant’s argument that WITTE “teaches nothing about inaccurate position/location data, much less problems stemming from such inaccurate data” within claims 40 have been considered but are moot in view of the new ground(s) of rejection as necessitated by applicant's amendments.
Furthermore, for claim 27, the amended claims set do not reflect the requirement for the accounting of positional inaccuracies within the dependence chain for claim 27. The office recommends amending the claims to implement this limitation accordingly.
In response to applicant's argument that WITTE is non-obvious art, it has been held that "the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…." In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). In this case, WITTE teaches connection of nodes for generating routes. The prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed within the current application. Merely being able to generate multiple routes (even for traffic distribution) within WITTE does not discount using the current route. Please see MPEP 2143.01(I).
In regards to the lack of teaching “determining”, “forming”, and “selecting”, please see section “Re the section 103 Rejections” above.
Given all the reasons above, the 35 U.S.C. 103 rejection is sustained. Please see 103 below.
Claims 30 and 43:
In regards to applicant’s arguments that KUZNETSOV does not trach the issues related to the “Kuznetsov does not describe problems relating to inaccurate position/location data” with respect to claims 43 have been considered but are moot in view of the new ground(s) of rejection as necessitated by applicant's amendments.
Furthermore, for claim 30, the amended claims set do not reflect the requirement for the accounting of positional inaccuracies within the dependence chain for claim 30. The office recommends amending the claims to implement this limitation accordingly.
In regards to the lack of teaching “determining”, “forming”, and “selecting”, please see section “Re the section 103 Rejections” above.
Claims 34 and 47:
In regards to the lack of teaching “determining”, “forming”, and “selecting”, please see section “Re the section 103 Rejections” above.
Claim 51:
In regards to the lack of teaching “determining”, “forming”, and “selecting”, please see section “Re the section 103 Rejections” above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 23-26, 28, 29, 35-39, 41, 42 are rejected under 35 U.S.C. 103 as being unpatentable over ROWLEY (US 20170138754 A1) in view of JUNG (US 20160178377 A1) in further view of LEWIS (US 20130054133 A1).
Regarding claim 23:
ROWLEY discloses:
at least one of a plurality of starting locations to at least one of a plurality of ending locations, the vehicles traveling on roadways between the starting locations and the ending locations, comprising: (see at least ROWLEY, ¶ 0029, “Each travel route may have a number of identifying characteristics. By way of example, route 12 has a start point 12a, an end point 12b, and a stop point 12c. The start point and end point for a trip may be inferred by identifying locations for which there are long periods of immobility connected by a period or periods of mobility (e.g., with short periods of immobility representing stops during a trip or stoplights). Stop points may be inferred from shorter periods of inactivity (i.e., on the order of several minutes, or long enough to avoid counting a stop at a stoplight as a false positive, but short enough to prevent a stop for coffee or day care drop-off from being a false negative) that have activity on each side.”)
identifying as Trips pathways previously followed by the vehicles between various starting and ending locations in a mining environment, wherein a pathway comprises a defined sequence of road segments; (see at least ROWLEY, ¶ 0027, “The learning period may be a definite or an indefinite period. For example, the device may be programmed to capture data for one week and then stop capturing data. Alternatively, data may be gathered until a recurring pattern begins to show in the data. In such an approach, a minimum collection period, such as one week, may be set so that the system does not stop gathering data simply because, by coincidence, two days in a row involved similar routes. Where the period is indefinite, the system may, for example, continue gathering data so as to update the information continuously, so that predictions of future routes may be made more readily. Such an approach may therefore allow the predicted routes to change as the user changes, such as when the user begins stopping at a day care center every weekday, and thus adds a detour to the user's previous path to work.”; ¶ 0029; ¶ 0033, “Each travel route represents a trip at a certain time, such as a daily morning commute over the course of a week or more. Thus, by example, travel route 12 represents a morning commute on a Monday morning, and shows a trip from the user's home to the user's office with a stop in the middle of the commute. That stop may represent, for example, the retrieval of a cup of espresso needed by the user to get the week started on the right foot. Travel route 14 represents the Tuesday morning commute, with no stops, as the user anticipates the amount of work still needed to be completed for the week. The Wednesday travel route 16 is similar to the Tuesday travel route 14. The Thursday travel route 18 also involves a stop, but this time to drop off clothes at a cleaner. Finally, the Friday travel route 20 involves a slight detour and stop for the user to follow through on a weekly doughnut pick-up for the user's office staff-a savvy management technique.”; ¶ 0034, “Travel routes 22, 24 are trips on the weekend. Travel route 22 shows a trip to swimming lessons for the user's young child, and is a weekly trip, at least for a dozen lessons or so. Travel route 24 is a trip to a football game, and typically occurs on a Sunday but only when the team is “home.” However, from week to week, the end point for the trip varies as the user selects a different parking area for each game. In addition to the routes shown in FIG. 1, other routes may also be gathered, such as those taken during the evening commute.”; ¶ 0036, “In FIG. 2, travel route data is gathered at step 30, in a manner like that discussed above. The data is then correlated, at step 32, with particular events. The events may typically be calendar events, and more particularly, certain times and days of the week. For example, all trips that happen during certain hours on a Monday morning may be correlated with a “morning commute” event. The same is true of trips that happen on other weekdays. Trips on Saturdays and Sundays could also be grouped (although weekend trips may be ignored under an assumption that the trips will be too unpredictable, or traffic too light, for the process to provide substantial assistance).”; ¶ 0056, “Route generator 78 may receive assistance from other components in producing suggested routes. In particular, real-time traffic module 70 may provide the route generator 78 with information that reflects traffic speed on various routes. For example, real-time traffic module 70 may obtain information from traffic service 56 and reformat it in a manner that is usable by route generator 78. Real-time traffic module may also aggregate traffic data from multiple different sources-combining data and selecting the best data when there is overlap. Moreover, the real-time traffic data may include data relating to non-automotive modes of transportation, such as rail, bus, and ferry speeds and schedules (adjusted, e.g., to reflect problems and shut-downs if necessary).”; ¶ 0059, “In generating a suggested route for a user, route generator 78 may gather the navigation point information for a particular user, may use that information and mapping information to identify potential routes (as combinations of route segments), and may then use real-time traffic information to select which actual route or routes are the best routes for the user. Route generator may then transmit the suggested route information through interface 52 to devices 62 or 80. For devices such as cellular telephones, the information may best be transmitted as an html document or other fully-formatted document that will not require much processing by the device 62. The returned information may also include HTML code, XML messages, WAP code, Java applets, xhtml, plain text, voiceXML, VoxML, VXML, etc., that causes the device 62 to generate a display of information.”; ¶ 0071, “For each segment, the real-time speed of the segment may be identified (step 138). The speed may be accessed from, for example, services that track traffic speed using in-road sensors or cameras. The speed may generally be a composite of the average speed across all lanes of traffic, and perhaps along multiple locations of a common roadway (so that temporary bottlenecks do not caused inaccurate readings). The speed may also be an assumed real-time speed, for example on roadways for which there is no actual real-time speed data. Also, speeds may be assumed for mass transit systems, such as light rail, that have closely analyzed and predictable speeds. Such systems may also provide data that reflects actual real-time speeds.”; ¶ 0079, “When a user wishes to have a suggested route generated, they may have a device generate a request (step 166) in a manner like that described above, and the device may transmit the request to a server (step 168). The server may receive the request (step 170) and use it to compute a suggested route or routes. In doing so, the server may access one or more of real-time traffic information, mapping information, and navigation point data (step 172). The server may also access profile information and other information specific to the user. By applying the real-time traffic data to possible routes through the navigation points, the system can compute one or more optimum routes (step 174) for the user.”)
determining a Common Route for each Trip in the Collection of Trips based on a number of times the road segments were followed by the vehicles; (see at least ROWLEY, ¶ 0037, “At step 34, common paths for a particular event are identified. For example, if the user took the identical path to work on two successive Mondays, the paths would be fully common. The determination of commonality may also be determined only for characteristic points along the path, i.e., the start point, the end point, and stop points.”; ¶ 0041, “Once commonality is found for one event or subgroup of events, a determination may be made of whether additional events remain (step 38). If they do, commonality determinations may proceed as just described for those additional events. For example, when one weekday is finished, other weekdays may be analyzed (if they are not a second level of the first event). Or once all weekdays are finished, weekend days may be analyzed. Alternatively, commonality for only a single event (e.g., Monday morning commute) can be computed if that is all that is required.”; ¶ 0056, “Route generator 78 may receive assistance from other components in producing suggested routes. In particular, real-time traffic module 70 may provide the route generator 78 with information that reflects traffic speed on various routes. For example, real-time traffic module 70 may obtain information from traffic service 56 and reformat it in a manner that is usable by route generator 78. Real-time traffic module may also aggregate traffic data from multiple different sources-combining data and selecting the best data when there is overlap. Moreover, the real-time traffic data may include data relating to non-automotive modes of transportation, such as rail, bus, and ferry speeds and schedules (adjusted, e.g., to reflect problems and shut-downs if necessary).”)
forming Current Routes by combining Common Routes based on the road segments that are common to the Common Routes; (see at least ROWLEY, ¶ 0033; ¶ 0034; ¶ 0035, “Upon gathering travel routes over a particular time period-typically more than one week-expected travel routes for the future can be generated. FIG. 2 shows a flow chart of a process for producing expected navigation points from past trip routes. In the example, a trip route is a route the user has previously taken, and is similar to the routes discussed above with FIG. 1. Navigation points are stopping points along a trip route, and would include, for example, the start and end points for the trip, along with any necessary stop points along the trip. The computed navigation points may be combined, as explained below, with information about real-time traffic flow, to generate a trip route that hits each of the navigation points in a predicted minimum elapsed time. In this manner, a user's actions may be monitored, and the user may be provided with effective travel planning for future trips.”)
selecting from among the Current Routes a Current Route between a defined starting location and a defined ending location, the Current Route representing the most commonly followed route previously taken by the vehicles between the defined starting location and the defined ending location; (see at least ROWLEY, ¶ 0037; ¶ 0038, “The presence or absence of commonality may be determined at various levels of granularity. For example, if several instances of an event have been collected, a point may be considered “common” if it is common to a particular percentage of the instances. As one example, if a system has been gathering data for a month, and a point is common to three out of four trips on a Monday morning, then it could be considered common, and the fourth trip could be considered to be non-representative of the user's actual travels (i.e., a “lark”).”; ¶ 0041; ¶ 0043, “Using the analyzed information, the expected navigation points for each expected trip may be established (step 44). Such points may be, for example, the points that were computed to have a sufficiently high level of commonality. For example, where the start point, end point, and a particular intermediate point were all the same for a particular day of the week across multiple weeks, those points may be assigned as the navigation points for that day. Common navigation points from one day to the next may also be used, particularly if enough data has not been collected to determine whether there is commonality from week to week.”; ¶ 0056)
choosing a travel route to be taken by the vehicle based on the selected Current Route; and (see at least ROWLEY, ¶ 0030, “Also, start, end, and stop points can be entered manually. For example, a user may press a key on a navigation device when they arrive at an important point along a route so as to identify that point as a stop point through which they would like to travel. Thus, for example, when training a navigation device, the person could press the key when passing a coffee shop if they want to always pass the coffee shop, regardless of whether they stop at the shop during the training period. Such manual stop point entry can have the benefit of allowing for faster training of a navigation device, and can also allow a user to provide preferences explicitly. As another example, a user may prefer to take a particular bridge to work regardless of what real-time traffic data may indicate because the user knows how to “beat” the traffic for the route.”; ¶ 0057, “Route generator 78 may also use mapping data 72, which may be stored internally to NIS 51 or obtained from external sources (or both). Mapping data generally represents the routes that may be taken, and may be stored and accessed in any appropriate manner. Mapping data may be, for example, the same or similar data to that which is used to provide driving directions in typical applications. The mapping data may serve as an “underlay” for the real-time traffic information, so that a suggested route can be generated on a map using the traffic information that correlates with locations on the map.”; ¶ 0058, “Navigation point generator 76 may operate to receive information about a particular user's travel history, and produce expected route points for future travel, for example, using the methods described above with respect to FIGS. 1 and 2. Navigation point generator 76 stores information it receives from, and generates relating to, users in user data 74. User data 74 may also include other information about a user, such as the user's identifying information, addressing information for the user's devices 62, 80, information about the user's needs and other profile information about the user. Such profile information may also include information about the user's preferences. For example, perhaps the user prefers side streets over freeways, so that the system will provide the user with a suggested route that uses side streets (in some circumstances, if the disparity in commuting time between the two paths is not above some threshold).”)
directing the vehicle to follow the chosen travel route between the defined starting location and the defined ending location. (see at least ROWLEY, ¶ 0090, “Communication between display 204 and navigation computer 202 may occur through user interface 216, which may be a single interface or multiple interfaces, and may take any appropriate form. The user interface may provide cues to a user via speaker 220, such as by providing aural driving directions in a conventional manner. The user interface 216 may also generate graphical information on display 204 for the user to review. The user may provide feedback or other input through control buttons 224, or through touching screen 222, or by other appropriate input techniques. The control buttons 224 may be “customized” by displaying changing labels above the buttons, so that input and output can be coordinated and controlled via software.”)
ROWLEY does not disclose, but JUNG teaches:
grouping into Groups of Trips those Trips having common pairs of starting and ending locations; (see at least JUNG, ¶ 0077, “The path controller 130 in accordance with the present invention identifies moving trajectories reaching respective points of interest. Here, the moving trajectory means a trajectory through which a moving object, for example, a person or a vehicle, habitually moves from a specific point of interest to another point of interest.”; ¶ 0078, “These moving trajectories may be known through points of interest and moving behavior information of a user or a vehicle. That is, the path controller 130 in accordance with the present invention includes a moving trajectory identification unit 131 to identify moving trajectories using the points of interest and the moving behavior information of the user or the vehicle.”; ¶ 0084, “In order to cluster the moving trajectories into the moving sections, the path controller 130 in accordance with the present invention may further include a moving trajectory classification unit 132 and a moving section clustering unit 133. The moving trajectory classification unit 132 may classify the moving trajectories identified by the above-described moving trajectory identification unit 131 into moving trajectories having the same start point and the same end point between the respective moving sections. FIG. 3 illustrates these classified moving trajectories. As exemplarily shown in FIG. 3, there is a plurality of classified moving trajectories. When trajectory classification has been carried out, the moving section clustering unit 133 in accordance with the present invention detects degrees of similarity of moving trajectories in the same class and primarily clusters the moving trajectories into at least one moving section cluster C.sub.n. FIG. 3 illustrates primarily clustered moving section clusters.”)
separating into different Collections of Trips those Groups of Trips having Trips that followed different pathways between the common pairs of starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data of ROWLEY to include the clustering of paths ("moving trajectories") with common trajectories within JUNG with the motivation of improving the ability to discern similarity between a plurality of routes travelling between the same two locations.
ROWLEY in view of JUNG does not disclose, but LEWIS teaches:
A method of directing the movement of a vehicle of a material delivery system, the material delivery system including a plurality of vehicles carrying material from (see at least LEWIS, ¶ 0001, “This disclosure is related to systems and methods for providing automated guidance directions to operators of heavy equipment, and specifically, to a system and method for providing guidance maneuvering assistance to heavy equipment operators in proximity with other heavy equipment, hazards, or geographical features.”; ¶ 0035, “FIG. 2 is an illustration of an open pit mining environment where systems and methods according to embodiments of the invention are implemented. In the environment of FIG. 2, a plurality of mine haul trucks 205a-c operate on a mine haul route network 210. Mine haul trucks 205a-c perform hauling tasks, for example, by moving material between a shovel site 225 a crusher site 220 and a dump or stockpile site 215.”)
in a mining environment, (see at least LEWIS, ¶ 0011, “Embodiments of the invention provide for using GPS and other geolocation technology to guide operators of mine haul trucks into position at a mining facility. Specifically, embodiments of the invention use position tracking and guidance systems to assist an operator of a mining vehicle, or to control directly an autonomous vehicle, in positioning a vehicle at a predetermined location with respect to another mining vehicle or a particular geographical feature.”; ¶ 0013, “In another implementation, the present invention includes a method for navigating a first heavy equipment to a target destination. The method includes retrieving a location of the target destination from a distributed objects database. The location of the target destination is at least partially determined by a position of a second heavy equipment. The method includes using a position sensor to identify a current position and orientation of the first heavy equipment, and calculating a path from the current position of the first heavy equipment to the location of the target destination. The calculated path is selected to avoid hazards. The method includes monitoring a progress of the first heavy equipment along the calculated path using the position sensor, and, when the first heavy equipment deviates from the calculated path, outputting a message to an operator of at least one of the first heavy equipment and the second heavy equipment.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data to include the clustering of paths ("moving trajectories") with common trajectories within ROWLEY in view of JUNG to be placed in a mining environment with route planning for the vehicles as within LEWIS to effectively create a route planning system which accounts for fleets of mining vehicles.
Regarding claim 24:
ROWLEY in view of JUNG in further view of LEWIS discloses the limitations within claim 23 and ROWLEY further discloses:
the chosen travel route is the selected Current Route. (see at least ROWLEY, ¶ 0058, “Navigation point generator 76 may operate to receive information about a particular user's travel history, and produce expected route points for future travel, for example, using the methods described above with respect to FIGS. 1 and 2. Navigation point generator 76 stores information it receives from, and generates relating to, users in user data 74. User data 74 may also include other information about a user, such as the user's identifying information, addressing information for the user's devices 62, 80, information about the user's needs and other profile information about the user. Such profile information may also include information about the user's preferences. For example, perhaps the user prefers side streets over freeways, so that the system will provide the user with a suggested route that uses side streets (in some circumstances, if the disparity in commuting time between the two paths is not above some threshold).”)
Regarding claim 25:
ROWLEY in view of JUNG in further view of LEWIS teaches the limitations of claim 23 and ROWLEY further discloses:
the chosen travel route comprises a portion of the selected Current Route. (see at least ROWLEY, ¶ 0033, “Each travel route represents a trip at a certain time, such as a daily morning commute over the course of a week or more. Thus, by example, travel route 12 represents a morning commute on a Monday morning, and shows a trip from the user's home to the user's office with a stop in the middle of the commute. That stop may represent, for example, the retrieval of a cup of espresso needed by the user to get the week started on the right foot. Travel route 14 represents the Tuesday morning commute, with no stops, as the user anticipates the amount of work still needed to be completed for the week. The Wednesday travel route 16 is similar to the Tuesday travel route 14. The Thursday travel route 18 also involves a stop, but this time to drop off clothes at a cleaner. Finally, the Friday travel route 20 involves a slight detour and stop for the user to follow through on a weekly doughnut pick-up for the user's office staff-a savvy management technique.”; ¶ 0059, “In generating a suggested route for a user, route generator 78 may gather the navigation point information for a particular user, may use that information and mapping information to identify potential routes (as combinations of route segments), and may then use real-time traffic information to select which actual route or routes are the best routes for the user. Route generator may then transmit the suggested route information through interface 52 to devices 62 or 80. For devices such as cellular telephones, the information may best be transmitted as an html document or other fully-formatted document that will not require much processing by the device 62. The returned information may also include HTML code, XML messages, WAP code, Java applets, xhtml, plain text, voiceXML, VoxML, VXML, etc., that causes the device 62 to generate a display of information.”; ¶ 0064, “Where the device allows for search in addition to navigation, the position of the device and/or the stop points along a route may be used as a location input for a “local search” such as Google Local Search. Thus, for example, where a user is taking a long trip, they may search for a restaurant that is one hour ahead on the map by identifying a location such as a stop point on the map and then conducting a local search. In this manner, the user can have a local search without knowing additional properties about the locale in which the search is to be conducted.”; ¶ 0066, “The route generation may be accomplished in the system 50 by NIS 51, by devices 62, 80, or by cooperation of NIS 51 and devices 62, 80. For example, device 62 may be programmed with code that, when executed, analyzes results from a request and then generates stop points on an expected route. Also, the device 62 may be programmed with code that, when executed, generates a map with a suggested route. The NIS 51 may also take on the task of suggested route generation, so that the devices 62, 80 need only receive the information, store it, and present it.”; ¶ 0074, “In addition, deviations for each route may be computed (step 146). In particular, data about the speeds for a particular route or segment will vary from day-to-day, so that the average speed is not a completely satisfactory measure of the segment's speed. Thus, where two routes have similar average times, the route having a smaller deviation may be preferred over the other route (particularly if the user is risk-averse).”)
Regarding claim 26:
ROWLEY in view of JUNG in further view of LEWIS discloses the limitations within claim 23 and ROWLEY further discloses:
dividing each road segment into a plurality of snap points. (see at least ROWLEY, ¶ 0010, “In another embodiment, a navigation system is provided. The system comprises a location generator for a moving vehicle that produces data indicative of a plurality of vehicle locations, a navigation point generator that analyzes the data indicative of a plurality of vehicle locations and generates one or more expected navigation points for the vehicle, and means for generating one or more optimized routes through the one or more expected navigation points.”; ¶ 0029, “Each travel route may have a number of identifying characteristics. By way of example, route 12 has a start point 12a, an end point 12b, and a stop point 12c. The start point and end point for a trip may be inferred by identifying locations for which there are long periods of immobility connected by a period or periods of mobility (e.g., with short periods of immobility representing stops during a trip or stoplights). Stop points may be inferred from shorter periods of inactivity (i.e., on the order of several minutes, or long enough to avoid counting a stop at a stoplight as a false positive, but short enough to prevent a stop for coffee or day care drop-off from being a false negative) that have activity on each side.”; ¶ 0030, “Also, start, end, and stop points can be entered manually. For example, a user may press a key on a navigation device when they arrive at an important point along a route so as to identify that point as a stop point through which they would like to travel. Thus, for example, when training a navigation device, the person could press the key when passing a coffee shop if they want to always pass the coffee shop, regardless of whether they stop at the shop during the training period. Such manual stop point entry can have the benefit of allowing for faster training of a navigation device, and can also allow a user to provide preferences explicitly. As another example, a user may prefer to take a particular bridge to work regardless of what real-time traffic data may indicate because the user knows how to “beat” the traffic for the route.”; ¶ 0044, “Route information between navigation points may also be used or discarded as necessary. For example, navigation points can be computed merely for start, end, and stop points, as it could be assumed that the user simply wants to get to or through those points, wants to do so quickly, and does not care which particular route to use. As such, the other information is not relevant and can be discarded to save on storage space. The other information can also be used to provide more accurate selections of navigation points, indicating for example whether a literal stop by the user should be considered a “stop” navigation point.”)
Regarding claim 28:
ROWLEY in view of JUNG in further view of LEWIS discloses the limitations within claim 26 and ROWLEY further discloses:
sensing positions of the vehicles on the roadways and producing sensed vehicle position data related thereto; and (see at least ROWLEY, ¶ 0024, “Layered on top of travel zone 10 are a number of travel routes 12-24 which in the figure each represent a route that has been traveled by a user. Data for the routes 12-24 may be gathered in any appropriate manner. For example, global positioning system (GPS) data may be collected, such as by a cellular telephone, personal digital assistant (PDA), automotive navigation device, or other such device.”; ¶ 0025, “The data may be collected, for example, by sampling position data at set intervals such as every fifteen seconds, or at another interval. In addition, the position data may be recorded at set location intervals, with the elapsed time between positions representing the rate of change in position. The data may also be generated from triangulation in a cellular telephone system or other appropriate method.”)
correlating the sensed vehicle position data with the snap points. (see at least ROWLEY, ¶ 0029, “Each travel route may have a number of identifying characteristics. By way of example, route 12 has a start point 12a, an end point 12b, and a stop point 12c. The start point and end point for a trip may be inferred by identifying locations for which there are long periods of immobility connected by a period or periods of mobility (e.g., with short periods of immobility representing stops during a trip or stoplights). Stop points may be inferred from shorter periods of inactivity (i.e., on the order of several minutes, or long enough to avoid counting a stop at a stoplight as a false positive, but short enough to prevent a stop for coffee or day care drop-off from being a false negative) that have activity on each side.”)
Regarding claim 29:
ROWLEY in view of JUNG in further view of LEWIS discloses the limitations within claim 23 and ROWLEY further discloses:
said separating further comprises performing a clustering analysis on the Groups of Trips. (see at least ROWLEY, ¶ 0036, “In FIG. 2, travel route data is gathered at step 30, in a manner like that discussed above. The data is then correlated, at step 32, with particular events. The events may typically be calendar events, and more particularly, certain times and days of the week. For example, all trips that happen during certain hours on a Monday morning may be correlated with a “morning commute” event. The same is true of trips that happen on other weekdays. Trips on Saturdays and Sundays could also be grouped (although weekend trips may be ignored under an assumption that the trips will be too unpredictable, or traffic too light, for the process to provide substantial assistance).”)
EXAMINERS NOTE: BRI of clustering analysis is correlating trips with particular events.
Regarding claim 35:
ROWLEY discloses:
from at least one of a plurality of starting locations to at least one of a plurality of ending locations, the vehicles traveling on roadways between the starting locations and the ending locations, comprising: (see at least ROWLEY, ¶ 0029)
retrieving from a database historical position data relating to movement of the vehicles between the starting and ending locations (see at least ROWLEY, ¶ 0058)
using the retrieved data from the database to identify as Trips pathways previously followed by the vehicles between various starting and ending locations, wherein a pathway comprises a defined sequence of road segments; (see at least ROWLEY, ¶ 0027; ¶ 0028, “Each of the travel routes 12-24 represents a particular path taken by the user. The pictured travel routes 12-24 may be a subset of all routes taken by the user during the learning period, and may be selected as all trips during a particular time of day, such as during the morning commute time. Routes for other times may be handled together as a distinct group. In addition, routes that are unlike any others, and that are not repeated, may be treated as a lark and may be discarded, or simply saved in memory (e.g., as part of a “recently visited” list), but not used to predict future routes.”; ¶ 0029; ¶ 0033; ¶ 0034; ¶ 0036; ¶ 0051; ¶ 0056; ¶ 0058; ¶ 0059, “In generating a suggested route for a user, route generator 78 may gather the navigation point information for a particular user, may use that information and mapping information to identify potential routes (as combinations of route segments), and may then use real-time traffic information to select which actual route or routes are the best routes for the user. Route generator may then transmit the suggested route information through interface 52 to devices 62 or 80. For devices such as cellular telephones, the information may best be transmitted as an html document or other fully-formatted document that will not require much processing by the device 62. The returned information may also include HTML code, XML messages, WAP code, Java applets, xhtml, plain text, voiceXML, VoxML, VXML, etc., that causes the device 62 to generate a display of information.”; ¶ 0078, “The server may then receive the transmitted location information (step 160) and may generate navigation points using the information, as described above (step 162). With the navigation points generated, the system may save the new information and wait for a request (step 164).”; ¶ 0079)
determining a Common Route for each Collection of Trips based on a number of times each road segment was traversed by the vehicles; (see at least ROWLEY, ¶ 0037; ¶ 0041; ¶ 0056)
forming Current Routes by combining Common Routes based on the road segments that are common to the Common Routes; (see at least ROWLEY, ¶ 0033; ¶ 0034; ¶ 0035)
defining a starting location and an ending location for a subject vehicle; (see at least ROWLEY, ¶ 0029; ¶ 0030, “Also, start, end, and stop points can be entered manually. For example, a user may press a key on a navigation device when they arrive at an important point along a route so as to identify that point as a stop point through which they would like to travel. Thus, for example, when training a navigation device, the person could press the key when passing a coffee shop if they want to always pass the coffee shop, regardless of whether they stop at the shop during the training period. Such manual stop point entry can have the benefit of allowing for faster training of a navigation device, and can also allow a user to provide preferences explicitly. As another example, a user may prefer to take a particular bridge to work regardless of what real-time traffic data may indicate because the user knows how to “beat” the traffic for the route.”; ¶ 0031, “The start point and end point may also be adjusted to simplify a travel route. For example, it may be impractical to provide more than one route near each end of a travel route. That is because there may be only one logical path out of a user's residential neighborhood (and no traffic in the neighborhood). The user might also be required to weave through a large parking lot at work-another area in which navigational advice is not helpful. Thus, the start point and end point, for purposes of computing a suggested route, may be shifted so as to bypass such problems at each end of a trip. However, as shown to the user, one or both points may be kept in their original location so that the user gets end-to-end directions. The user simply will not know that some of the directions were decided statically, i.e., without reference to current traffic conditions.”)
selecting from among the Current Routes a Current Route between the defined starting location and the defined ending location, the Current Route representing the most commonly followed route previously taken by the vehicles between the defined starting location and the defined ending location (see at least ROWLEY, ¶ 0037; ¶ 0041; ¶ 0043; ¶ 0056)
choosing a travel route to be taken by the subject vehicle based on the selected Current Route; (see at least ROWLEY, ¶ 0030; ¶ 0057; ¶ 0058)
directing the subject vehicle to follow the chosen travel route between the defined starting location and the defined ending location; and (see at least ROWLEY, ¶ 0030; ¶ 0057; ¶ 0058)
ROWLEY does not disclose, but JUNG teaches:
the historical position data including inaccuracies; (see at least JUNG, ¶ 0007, “However, in such a conventional navigation terminal, error in position information received from the GPS may be generated, a position information collection cycle may not be proper for a moving path, and geographic information received from a geographic information system (GIS) may be incorrect.”; ¶ 0108, “In this manner, one optimized moving path may be extracted using paths in clusters and road information (in/out, position information) including the respective paths. Here, each cluster may include one moving path including the same moving trajectories and such a moving path may be filled with moving trajectories in which roads unknown due to sensing error of the GPS sensor 110 are completely corrected.”; ¶ 0152, “In this manner, one optimized moving path may be extracted using paths in clusters and road information (in/out, position information) including the respective paths. Here, each cluster may include one moving path including the same moving trajectories and such a moving path may be filled with moving trajectories in which roads unknown due to sensing error of the GPS sensor 110 are completely corrected.”)
grouping into Groups of Trips those Trips having common pairs of starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
separating into different Collections of Trips those Groups of Trips having Trips that followed different pathways between the common pairs of starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
notwithstanding the inaccuracies in the historical position data; (see at least JUNG, ¶ 0007; ¶ 0108; ¶ 0152)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data of ROWLEY to include the clustering of paths ("moving trajectories") with common trajectories and GPS error prevention within JUNG with the motivation of improving the ability to discern similarity between a plurality of routes travelling between the same two locations with minimized duplicated location data by being resilient to GPS errors that are widely known in the art (¶ 0007).
ROWLEY in view of JUNG does not disclose, however LEWIS teaches:
the material delivery system including a plurality of vehicles carrying material (see at least LEWIS, ¶ 0003, “Within a mining environment there may be many other vehicles such as shovels, dozers, bucket wheel excavators, or other equipment that are each similarly difficult to control. Because the vehicles are so large, they can have large blind spots, large turning radii, and slow braking capabilities, making navigating the vehicles to a given destination extremely difficult. In many cases, though, by accurately positioning these vehicles in proximity to other vehicles or geographical features of the mine, the mine's efficiency can be greatly improved. Additionally, through accurate navigation, dangers of injury or property damage resulting from a collision can be mitigated.”)
in a mining environment, (see at least LEWIS, ¶ 0001, “This disclosure is related to systems and methods for providing automated guidance directions to operators of heavy equipment, and specifically, to a system and method for providing guidance maneuvering assistance to heavy equipment operators in proximity with other heavy equipment, hazards, or geographical features.”; ¶ 0035, “FIG. 2 is an illustration of an open pit mining environment where systems and methods according to embodiments of the invention are implemented. In the environment of FIG. 2, a plurality of mine haul trucks 205a-c operate on a mine haul route network 210. Mine haul trucks 205a-c perform hauling tasks, for example, by moving material between a shovel site 225 a crusher site 220 and a dump or stockpile site 215.”)
repeating said defining, said selecting, said choosing, and said directing for additional vehicles of the material delivery system. (see at least LEWIS, ¶ 0011, “Embodiments of the invention provide for using GPS and other geolocation technology to guide operators of mine haul trucks into position at a mining facility. Specifically, embodiments of the invention use position tracking and guidance systems to assist an operator of a mining vehicle, or to control directly an autonomous vehicle, in positioning a vehicle at a predetermined location with respect to another mining vehicle or a particular geographical feature.”; ¶ 0103, “Returning to step 804, if the vehicle is autonomously controlled, and multiple filtered targets were generated in step 802, the system moves to step 814. In step 814, a supervisor (e.g., an individual and/or automated system) reviews the list of targets generated in step 802 and selects the most appropriate target for the autonomous vehicle. The supervisor can be a human, or, in some cases, an automated mine supervisor system. The automated system evaluates the target listing in view of the other activities of other vehicles operating in the mine and automatically selects the most appropriate target. If only a single target was generated, though, the system selects that target and moves to step 812 (in some implementations, this single target would also require approval by a supervisor). In step 812, the position and heading of the selected target is determined and the target is displayed to the vehicle operator for confirmation. The system may also then verify that any parameters associated with the selected target fall with acceptable, predefined ranges.”; ¶ 0120, “In one implementation, the potential paths are generated to minimize the build-up of ruts within the area between the vehicle and the target. As such, the system may be configured to inject a certain amount of `dither` into a particular path to ensure that vehicles traveling on the same path do not create destructive or dangerous ruts. In one implementation, the position of each path through the mine environment can be adjusted based upon changing roadway conditions. As more and more vehicles utilize a particular roadway, for example, the position of the path defined through that roadway can be adjusted to avoid too many vehicles driving in the same area. Accordingly, in particular congested areas, the system can generate `anti-rut` paths that are each configured to prevent multiple vehicles using the identical (or similar) paths to prevent rutting.”; ¶ 0147, “After identifying the various features, a navigation aid (e.g., navigation aid 322 of FIG. 3) accesses the features and uses them to guide mine haul trucks into position according to methods described above (see, for example, FIG. 6-12). Additionally, the central application can assign one of the two reverse points 1350 and 1355 to a given truck as part of a haul truck dispatch system. For example, when the central application is aware that a first truck is located at a first loading envelope 1330, a second truck is given a second reverse point 1355 as a waypoint to which to navigate. Reverse points 1350 and 1355 are generally assigned to trucks heading to shovel 1305 in an alternating sequential manner depending on expected time of arrival.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data to include the clustering of paths ("moving trajectories") with common trajectories and GPS error correction within ROWLEY in view of JUNG to be placed in a mining environment with route planning for the vehicles as within LEWIS to effectively create a route planning system which accounts for fleets of mining vehicles.
Regarding claim 36:
ROWLEY in view of JUNG in further view of LEWIS teaches the limitations of claim 35 and ROWLEY further discloses:
the historical position data (see at least ROWLEY, ¶ 0058, “Navigation point generator 76 may operate to receive information about a particular user's travel history, and produce expected route points for future travel, for example, using the methods described above with respect to FIGS. 1 and 2. Navigation point generator 76 stores information it receives from, and generates relating to, users in user data 74. User data 74 may also include other information about a user, such as the user's identifying information, addressing information for the user's devices 62, 80, information about the user's needs and other profile information about the user. Such profile information may also include information about the user's preferences. For example, perhaps the user prefers side streets over freeways, so that the system will provide the user with a suggested route that uses side streets (in some circumstances, if the disparity in commuting time between the two paths is not above some threshold).”)
wherein the Current Route represents the most commonly followed route previously taken by the vehicles between the defined starting location and the defined ending location (see at least ROWLEY, ¶ 0037, “At step 34, common paths for a particular event are identified. For example, if the user took the identical path to work on two successive Mondays, the paths would be fully common. The determination of commonality may also be determined only for characteristic points along the path, i.e., the start point, the end point, and stop points.”; ¶ 0038, “The presence or absence of commonality may be determined at various levels of granularity. For example, if several instances of an event have been collected, a point may be considered “common” if it is common to a particular percentage of the instances. As one example, if a system has been gathering data for a month, and a point is common to three out of four trips on a Monday morning, then it could be considered common, and the fourth trip could be considered to be non-representative of the user's actual travels (i.e., a “lark”).”; ¶ 0043, “Using the analyzed information, the expected navigation points for each expected trip may be established (step 44). Such points may be, for example, the points that were computed to have a sufficiently high level of commonality. For example, where the start point, end point, and a particular intermediate point were all the same for a particular day of the week across multiple weeks, those points may be assigned as the navigation points for that day. Common navigation points from one day to the next may also be used, particularly if enough data has not been collected to determine whether there is commonality from week to week.”)
ROWLEY does not disclose, but JUNG teaches:
includes position errors (see at least JUNG, ¶ 0007, “However, in such a conventional navigation terminal, error in position information received from the GPS may be generated, a position information collection cycle may not be proper for a moving path, and geographic information received from a geographic information system (GIS) may be incorrect.”)
notwithstanding the position errors in the historical position data. (see at least JUNG, ¶ 0101, “Since, as a result of primary clustering, the same path may be judged as a different path due to error of a sensor or error in correction, such clustering into the moving path is carried out so as to minimize such error.”; ¶ 0108, “In this manner, one optimized moving path may be extracted using paths in clusters and road information (in/out, position information) including the respective paths. Here, each cluster may include one moving path including the same moving trajectories and such a moving path may be filled with moving trajectories in which roads unknown due to sensing error of the GPS sensor 110 are completely corrected.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data of ROWLEY to include the clustering of paths ("moving trajectories") with common trajectories and GPS error prevention within JUNG with the motivation of improving the ability to discern similarity between a plurality of routes travelling between the same two locations with minimized duplicated location data by being resilient to GPS errors that are widely known in the art (¶ 0007).
Regarding claim 37:
With regards to claim 37, this claim is substantially similar to claim 24 and is therefore rejected using the same references and rationale.
Regarding claim 38:
With regards to claim 38, this claim is substantially similar to claim 25 and is therefore rejected using the same references and rationale.
Regarding claim 39:
With regards to claim 39, this claim is substantially similar to claim 26 and is therefore rejected using the same references and rationale.
Regarding claim 41:
With regards to claim 41, this claim is substantially similar to claim 28 and is therefore rejected using the same references and rationale.
Regarding claim 42:
With regards to claim 42, this claim is substantially similar to claim 29 and is therefore rejected using the same references and rationale.
Claims 27, 40 are rejected under 35 U.S.C. 103 as being unpatentable over ROWLEY (US20170138754A1) in view of JUNG (US 20160178377 A1) in further view of LEWIS (US 20130054133 A1) in further view of WITTE (US 20160223348 A1).
Regarding claim 27:
ROWLEY in view of JUNG in further view of LEWIS disclose the limitations within claim 26 and ROWLEY further discloses:
said forming Current Routes by combining Common Routes further comprises (see at least ROWLEY, ¶ 0033; ¶ 0034; ¶ 0035)
ROWLEY does not disclose, but WITTE teaches:
forming Current Routes by combining Common Routes having greater than about 90% shared snap points. (see at least WITTE, ¶ 0091, “The term “segment” as used herein takes its usual meaning in the art. A segment may be a navigable link that connects two nodes, or any portion thereof. While embodiments of the present invention are described with reference to road segments, it should be realized that the invention may also be applicable to other navigable segments, such as segments of a path, river, canal, cycle path, tow path, railway line, or the like. For ease of reference these are commonly referred to as a road segment, but any reference to a “road segment” may be replaced by a reference to a “navigable segment” or any specific type or types of such segments.”; ¶ 0156, “In some embodiments, a specific algorithm is used to determine a number, in embodiments a predetermined number, of routes between the current location and the destination, and, subsequently, the current capacity data relating to the segments of the routes is used to determine a relative flow value for each route. In other preferred embodiments, the steps of generating the subset of possible routes and obtaining the flow values for the routes using current capacity data for segments of the road network are carried out concurrently. For example, an algorithm can determine a first optimum route between a first location and a second location, e.g. the fastest, shortest, cheapest, most ecological, etc., and then determine any alternate routes within a predetermined threshold of the optimum route, e.g. any routes within 10%, 20%, etc. of the determined fastest, shortest, cheapest, most ecological route. In these embodiments the route determination and relative flow values may be determined concurrently by the same algorithm. It has been found that an algorithm based on the Ford-Fulkerson algorithm may be used to determine the plurality of routes between a first and second location, e.g. a current location and destination, for use in the present invention, together with a relative flow value for each route. FIG. 6 shows three routes computed using an algorithm based on the Ford-Fulkerson algorithm according to an embodiment of the invention; the algorithm further computing the flow values (and thus probability distribution) for each route. The flow values of the subset of routes, and indeed other routes considered on obtaining the subset of routes, are based on the current capacity data for road segments (also indicated in FIG. 6).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the generation of routes for the driver within ROWLEY in view of JUNG in further view of LEWIS with the optimum route segment algorithm of WITTE to yield an effective route planner capable of modification to account for traffic, travel times, and possible detours.
Regarding claim 40:
With regards to claim 40, this claim is substantially similar to claim 27 and is therefore rejected using the same references and rationale.
Claims 30,43 are rejected under 35 U.S.C. 103 as being unpatentable over ROWLEY (US20170138754A1) in view of JUNG (US20160178377A1) in further view of LEWIS (US 20130054133 A1) in further view of KUZNETSOV (US20080300778A1).
Regarding claim 30:
ROWLEY in view of JUNG in further view of LEWIS disclose the limitations within claim 23 and do not disclose, but KUZNETSOV teaches:
omitting at least one terminal road segment from at least one of the plurality of Trips. (see at least KUZNETSOV, ¶ 0026, “Occasionally these regular trips may have small aberrations (for instance, a driver stopping to buy coffee or fill up a gas tank). Location of a coffee shop and a gas station may be added into the system as an origin or destination if the ignition is turned off. However, these tiny detours may be detected and removed since such locations possibly may not be that important.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the generation of routes for the driver within ROWLEY in view of JUNG in further view of LEWIS with the filtering of small aberrations in the routes within KUZNETSOV to yield a more effective route planner that removes erroneous stops and detours that as outliers would otherwise bias the clustering of routes as within JUNG.
Regarding claim 43:
With regards to claim 43, this claim is substantially similar to claim 30 and is therefore rejected using the same references and rationale.
Claims 31-33 and 44-46 are rejected under 35 U.S.C. 103 as being unpatentable over ROWLEY (US20170138754A1) in view of JUNG (US20160178377A1) in further view of LEWIS (US 20130054133 A1) in further view of DUAN (US20160033292A1).
Regarding claim 31:
ROWLEY in view of JUNG in further view of LEWIS disclose the limitations within claim 23 and do not disclose, but DUAN teaches:
suturing a gap in at least one of the plurality of Trips. (see at least DUAN, ¶ 0005, “It has been proposed to generate a road network using various methods, such as a grid-based method. Afterwards, the original GPS data collected by the vehicle can be mapped to the road network, so as to infer a trajectory pattern using Welch test. However, the process per se of generating a road network has a high computational complexity and a time cost. In many cases such as location data missing for some segments, low location data precision, and complex segments existing in the route result in generation of the road network to be error-prone. Additionally, the precision of the road network likely exceeds the precision requirement on the trajectory pattern in applications such as fleet management, which causes wastes of computational resources.”; ¶ 0071, “As an example, refer to FIG. 6, in which the main direction of the coverage area 610 is supposed to be 620. It is seen that the main direction 620 is apparently deviated from the actual direction of the route 600 in the coverage area 610, which can be caused by a larger location sensing error of the points in the coverage area 610. At this point, within the search direction determined based on the main direction 620, regardless of how to expand the search distance, the successive coverage area for the coverage area 610 cannot be found. Therefore, in one embodiment, an upper limit threshold of the search distance can be set. When the search distance through once or multiple times of expansion reaches or exceeds the upper limit threshold, the search for the current coverage area will be stopped. Thus, the current coverage area is determined as a wrong coverage area. In this case, the operation can return to a previous coverage area connected to the current coverage area to search again, so as to “skip” the wrong coverage error.”; ¶ 0072, “In FIG. 6, a previous coverage area 630 for the current coverage area 610 is illustrated. Then, the previous search distance for the previous coverage area is expanded. Only exemplarily, suppose the previous search distance for the previous search area 630 is an error distance of location sensing. In this case, the previous search distance for the previous coverage area 630 can be expanded to 2× error distance. Then, the candidate coverage areas for the previous coverage area 630 are searched again with the expanded previous search distance. Consequently, another candidate coverage area for the coverage area 640 as the coverage area 630 can be found. In this way, the error coverage area 610 can be skipped to guarantee that the global connection can proceed in a correct direction.”; ¶ 0074, “In particular, in the new search, besides the coverage area 640, the coverage area 610 can be still determined as a candidate coverage area for the coverage area 630. However, since the coverage area 610 per se does not have any successive coverage area, the connection algorithm (e.g., Dijkstra algorithm or A-Star algorithm) will finally select the coverage area 640 to be connected to the coverage area 630. In this way, the error coverage area 610 can be skipped. Alternatively, in another embodiment, the error coverage area 610 can be directly flagged, such that in the search again with respect to the coverage area 630, the error coverage area 610 will not be selected as the candidate coverage area.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the generation of routes for the driver within ROWLEY in view of JUNG in further view of LEWIS to provide the detection and algorithm-based correction of path errors by DUAN with the motivation of reducing the number of errors in the path data (DUAN, ¶ 0005).
Regarding claim 32:
ROWLEY in view of JUNG in further view of LEWIS in further view of DUAN disclose the limitations within claim 31 and DUAN further teaches:
said suturing the cap comprises using a shortest path algorithm. (see at least DUAN, ¶ 0005, “It has been proposed to generate a road network using various methods, such as a grid-based method. Afterwards, the original GPS data collected by the vehicle can be mapped to the road network, so as to infer a trajectory pattern using Welch test. However, the process per se of generating a road network has a high computational complexity and a time cost. In many cases such as location data missing for some segments, low location data precision, and complex segments existing in the route result in generation of the road network to be error-prone. Additionally, the precision of the road network likely exceeds the precision requirement on the trajectory pattern in applications such as fleet management, which causes wastes of computational resources.”; ¶ 0071, “As an example, refer to FIG. 6, in which the main direction of the coverage area 610 is supposed to be 620. It is seen that the main direction 620 is apparently deviated from the actual direction of the route 600 in the coverage area 610, which can be caused by a larger location sensing error of the points in the coverage area 610. At this point, within the search direction determined based on the main direction 620, regardless of how to expand the search distance, the successive coverage area for the coverage area 610 cannot be found. Therefore, in one embodiment, an upper limit threshold of the search distance can be set. When the search distance through once or multiple times of expansion reaches or exceeds the upper limit threshold, the search for the current coverage area will be stopped. Thus, the current coverage area is determined as a wrong coverage area. In this case, the operation can return to a previous coverage area connected to the current coverage area to search again, so as to “skip” the wrong coverage error.”; ¶ 0072, “In FIG. 6, a previous coverage area 630 for the current coverage area 610 is illustrated. Then, the previous search distance for the previous coverage area is expanded. Only exemplarily, suppose the previous search distance for the previous search area 630 is an error distance of location sensing. In this case, the previous search distance for the previous coverage area 630 can be expanded to 2× error distance. Then, the candidate coverage areas for the previous coverage area 630 are searched again with the expanded previous search distance. Consequently, another candidate coverage area for the coverage area 640 as the coverage area 630 can be found. In this way, the error coverage area 610 can be skipped to guarantee that the global connection can proceed in a correct direction.”; ¶ 0074, “In particular, in the new search, besides the coverage area 640, the coverage area 610 can be still determined as a candidate coverage area for the coverage area 630. However, since the coverage area 610 per se does not have any successive coverage area, the connection algorithm (e.g., Dijkstra algorithm or A-Star algorithm) will finally select the coverage area 640 to be connected to the coverage area 630. In this way, the error coverage area 610 can be skipped. Alternatively, in another embodiment, the error coverage area 610 can be directly flagged, such that in the search again with respect to the coverage area 630, the error coverage area 610 will not be selected as the candidate coverage area.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the generation of routes for the driver within ROWLEY in view of JUNG in further view of LEWIS to provide the detection and algorithm-based correction of errors by DUAN with the motivation of reducing the number of errors in the path data (DUAN, ¶ 0005).
Regarding claim 33:
ROWLEY in view of JUNG in further view of LEWIS disclose the limitations within claim 32 and DUAN further teaches:
said using the shortest path algorithm comprises using Dijkstra's algorithm. (see at least DUAN, ¶ 0005, “It has been proposed to generate a road network using various methods, such as a grid-based method. Afterwards, the original GPS data collected by the vehicle can be mapped to the road network, so as to infer a trajectory pattern using Welch test. However, the process per se of generating a road network has a high computational complexity and a time cost. In many cases such as location data missing for some segments, low location data precision, and complex segments existing in the route result in generation of the road network to be error-prone. Additionally, the precision of the road network likely exceeds the precision requirement on the trajectory pattern in applications such as fleet management, which causes wastes of computational resources.”; ¶ 0072, “In FIG. 6, a previous coverage area 630 for the current coverage area 610 is illustrated. Then, the previous search distance for the previous coverage area is expanded. Only exemplarily, suppose the previous search distance for the previous search area 630 is an error distance of location sensing. In this case, the previous search distance for the previous coverage area 630 can be expanded to 2× error distance. Then, the candidate coverage areas for the previous coverage area 630 are searched again with the expanded previous search distance. Consequently, another candidate coverage area for the coverage area 640 as the coverage area 630 can be found. In this way, the error coverage area 610 can be skipped to guarantee that the global connection can proceed in a correct direction.”; ¶ 0074, “In particular, in the new search, besides the coverage area 640, the coverage area 610 can be still determined as a candidate coverage area for the coverage area 630. However, since the coverage area 610 per se does not have any successive coverage area, the connection algorithm (e.g., Dijkstra algorithm or A-Star algorithm) will finally select the coverage area 640 to be connected to the coverage area 630. In this way, the error coverage area 610 can be skipped. Alternatively, in another embodiment, the error coverage area 610 can be directly flagged, such that in the search again with respect to the coverage area 630, the error coverage area 610 will not be selected as the candidate coverage area.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the generation of routes for the driver within ROWLEY in view of JUNG in further view of LEWIS to provide the detection and algorithm-based correction of path errors by DUAN with the motivation of reducing the number of errors in the path data (DUAN, ¶ 0005).
Regarding claim 44:
With regards to claim 44, this claim is substantially similar to claim 31 and is therefore rejected using the same references and rationale.
Regarding claim 45:
With regards to claim 45, this claim is substantially similar to claim 32 and is therefore rejected using the same references and rationale.
Regarding claim 46:
With regards to claim 46, this claim is substantially similar to claim 33 and is therefore rejected using the same references and rationale.
Claims 34, 47 are rejected under 35 U.S.C. 103 as being unpatentable over ROWLEY (US20170138754A1) in view of JUNG (US20160178377A1) in further view of LEWIS (US 20130054133 A1) in further view of YOSHIZUMI (US20130238242A1).
Regarding claim 34:
ROWLEY in view of JUNG in further view of LEWIS disclose the limitations within claim 23 and do not disclose, but YOSHIZUMI teaches:
removing untraveled routes from the Current Routes. (see at least YOSHIZUMI, ¶ 0019, “In another characteristic of the present invention, by searching for new routes using the actual routes at the end of an iteration, routes with a high utility value are preferentially searched for as alternate routes. At this time, any route having a minimum cost greater than the best solution at the time, and any unused route added in the previous iteration is removed as an unpromising route.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the generation of routes for the driver within ROWLEY in view of JUNG in further view of LEWIS to provide the aforementioned limitations taught by YOSHIZUMI with the motivation of only presenting promising routes to the user.
Regarding claim 47:
With regards to claim 47, this claim is substantially similar to claim 34 and is therefore rejected using the same references and rationale.
Claims 48-50 and 52 are rejected under 35 U.S.C. 103 as being unpatentable over ROWLEY (US20170138754A1) in view of JUNG (US20160178377A1) in further view of MASON (US20160334236A1).
Regarding Claim 48:
ROWLEY discloses:
the defined starting location being selected from among the starting locations, the defined ending location being selected from among the ending locations, (see at least ROWLEY, ¶ 0029; ¶ 0030; ¶ 0031)
comprising: a database, said database storing historical position data relating to movement of the vehicles of the material delivery system; (see at least ROWLEY, ¶ 0058)
a processing system operatively associated with said database, said processing system being configured to: (see at least ROWLEY, ¶ 0058; ¶ 0079)
retrieve from said database the historical position data of the vehicles; (see at least ROWLEY, ¶ 0058)
use the retrieved historical position data of the vehicles to identify as Trips pathways previously followed by the vehicles between various starting and ending locations, wherein a pathway comprises a defined sequence of road segments; (see at least ROWLEY, ¶ 0027; ¶ 0028; ¶ 0029; ¶ 0033; ¶ 0034; ¶ 0036; ¶ 0051; ¶ 0056; ¶ 0058; ¶ 0059; ¶ 0078; ¶ 0079)
determine a Common Route for each Collection of Trips based on a number of times each road segment was traversed by the vehicles; (see at least ROWLEY, ¶ 0037; ¶ 0041; ¶ 0056)
form Current Routes by combining Common Routes based on the road segments that are common to the Common Routes; (see at least ROWLEY, ¶ 0033; ¶ 0034; ¶ 0035)
select from among the Current Routes a Current Route between a defined starting location and a defined ending location, the Current Route representing the most commonly followed route taken by the vehicles between the defined starting location and the defined ending location (see at least ROWLEY, ¶ 0037; ¶ 0038; ¶ 0041; ¶ 0056)
choose a travel route to be taken by a vehicle based on the selected Current Route; and (see at least ROWLEY, ¶ 0030; ¶ 0057; ¶ 0058)
a director system operatively associated with said processing system, said director system directing the vehicle to follow the chosen travel route between the defined starting location and the defined ending location. (see at least ROWLEY, ¶ 0030; ¶ 0047; ¶ 0057; ¶ 0058)
ROWLEY does not disclose, but JUNG teaches:
group into Groups of Trips those Trips having common pairs of starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
separate into different Collections of Trips those Groups of Trips having Trips that followed different pathways between the common pairs of starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
the historical position data including inaccuracies; (see at least JUNG, ¶ 0007, “However, in such a conventional navigation terminal, error in position information received from the GPS may be generated, a position information collection cycle may not be proper for a moving path, and geographic information received from a geographic information system (GIS) may be incorrect.”; ¶ 0108, “In this manner, one optimized moving path may be extracted using paths in clusters and road information (in/out, position information) including the respective paths. Here, each cluster may include one moving path including the same moving trajectories and such a moving path may be filled with moving trajectories in which roads unknown due to sensing error of the GPS sensor 110 are completely corrected.”; ¶ 0152, “In this manner, one optimized moving path may be extracted using paths in clusters and road information (in/out, position information) including the respective paths. Here, each cluster may include one moving path including the same moving trajectories and such a moving path may be filled with moving trajectories in which roads unknown due to sensing error of the GPS sensor 110 are completely corrected.”)
notwithstanding inaccuracies in the historical position data; (see at least JUNG, ¶ 0007; ¶ 0108; ¶ 0152)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data of ROWLEY to include the clustering of paths ("moving trajectories") with common trajectories and GPS error prevention within JUNG with the motivation of improving the ability to discern similarity between a plurality of routes travelling between the same two locations with minimized duplicated location data by being resilient to GPS errors that are widely known in the art (¶ 0007).
ROWLEY in view of JUNG does not disclose, but MASON teaches:
A system for directing a vehicle in a material delivery system from a defined starting location to a defined ending location, the material delivery system including a plurality of vehicles traveling on roadways between a plurality of starting locations and a plurality of ending locations, (see at least MASON, ¶ 0002, “Route selection or optimization has applications in vehicle routing, printed wire circuit layout, chip design and layout, and biological activities. Trucking and other vehicle fleets, for instance, select vehicle routes to deliver goods to various destinations. Similarly, transportation companies route vehicles to pick up and drop off passengers. In addition to land-based vehicles, route selection can also be used for ship, airplane, and rail transport route scheduling”; ¶ 0035, “The management devices 135 can be computing devices used by dispatchers, fleet managers, administrators, or other users to manage different aspects of the vehicle management system 150. For example, a user of a management device 135 can access the vehicle management system 150 to generate routes, dispatch vehicles and drivers, define access paths, select access paths, update site details information for a site, and perform other individual vehicle or fleet management functions. With the management devices 135, users can access and monitor vehicle information obtained from one or more of the in-vehicle devices 105 by the vehicle management system 150. Such vehicle status information can include data on vehicle routes used, stops, speed, vehicle feature usage (such as power takeoff device usage), driver behavior and performance, vehicle emissions, vehicle maintenance, energy usage, and the like. In some embodiments, the management devices 135 are in fixed locations, such as at a dispatch center. The management devices 135 can also be used by administrators in the field, and may include mobile devices, laptops, tablets, smartphones, personal digital assistants (PDAs), desktops, or the like.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the path planning of ROWLEY in view of JUNG to include the delivery of goods as seen in MASON to effectively create a route planning system which accounts for fleets of delivery vehicles.
Regarding claim 49:
ROWLEY in view of JUNG in further view of MASON teaches the limitations of claim 48 and ROWLEY further discloses:
the position location system producing the historical position data, the historical position data (see at least ROWLEY, ¶ 0058)
a network operatively connected to said position location system and said database, said database receiving the historical position data from said position location system via said network, and (see at least ROWLEY, ¶ 0048; ¶ 0050)
wherein the Current Route represents the most commonly followed route previously taken by the vehicles between the defined starting location and the defined ending location (see at least ROWLEY, ¶ 0037; ¶ 0038; ¶ 0043)
ROWLEY does not disclose, but JUNG teaches:
including position inaccuracies; and (see at least JUNG, ¶ 0007)
notwithstanding the position errors in the historical position data. (see at least JUNG, ¶ 0101; ¶ 0108)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data of ROWLEY to include the clustering of paths ("moving trajectories") with common trajectories and GPS error prevention within JUNG with the motivation of improving the ability to discern similarity between a plurality of routes travelling between the same two locations with minimized duplicated location data by being resilient to GPS errors that are widely known in the art (¶ 0007).
ROWLEY in view of JUNG does not disclose, but MASON teaches:
a position location system operatively associated with the plurality of vehicles, (see at least MASON, ¶ 0002; ¶ 0035)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the path planning of ROWLEY in view of JUNG to include the delivery of goods as seen in MASON to effectively create a route planning system which accounts for fleets of delivery vehicles.
Regarding claim 50:
ROWLEY in view of JUNG in further view of MASON teaches the limitations of claim 49 and ROWLEY further discloses:
a display system operatively associated with said processing system, said processing system further being configured to display on said display system the selected Current Route. (see at least ROWLEY, ¶ 0047, “FIG. 3 is a schematic diagram of a system 50 to receive route requests and to generate suggested routes. The system 50 includes a device 62, shown as a cellular telephone handset for communicating with a user, but could take any appropriate form, such as a personal digital assistant, a personal computer, or a voice-driven communication device. The device may include appropriate input and output structures, including a display screen which may have a touch-sensitive surface, data entry keys, clickable data entry wheels, speakers, and a microphone including for voice recognition.”; ¶ 0090, “Communication between display 204 and navigation computer 202 may occur through user interface 216, which may be a single interface or multiple interfaces, and may take any appropriate form. The user interface may provide cues to a user via speaker 220, such as by providing aural driving directions in a conventional manner. The user interface 216 may also generate graphical information on display 204 for the user to review. The user may provide feedback or other input through control buttons 224, or through touching screen 222, or by other appropriate input techniques. The control buttons 224 may be “customized” by displaying changing labels above the buttons, so that input and output can be coordinated and controlled via software.”)
Regarding claim 52:
ROWLEY discloses:
A non-transitory computer-readable storage medium having computer-executable instructions embodied thereon that, when executed by at least one computer processor, cause the computer processor to: (see at least ROWLEY, ¶ 0058)
retrieve from a database historical position data relating to the (see at least ROWLEY, ¶ 0058)
from at least one of a plurality of starting locations to at least one of a plurality of ending locations, (see at least ROWLEY, ¶ 0029)
the vehicles traveling on roadways between the starting locations and the ending locations; (see at least ROWLEY, ¶ 0058)
use the retrieved historical position data to identify as Trips pathways previously followed by the vehicles between various starting and ending locations, wherein a pathway comprises a defined sequence of road segments; (see at least ROWLEY, ¶ 0027; ¶ 0029; ¶ 0033; ¶ 0034; ¶ 0036; ¶ 0056; ¶ 0058; ¶ 0059; ¶ 0071; ¶ 0079)
determine a Common Route for each Collection of Trips based on a number of times each road segment was traversed by the vehicles; (see at least ROWLEY, ¶ 0037; ¶ 0041; ¶ 0056)
form Current Routes by combining Common Routes based on the road segments that are common to the Common Routes; (see at least ROWLEY, ¶ 0033; ¶ 0034; ¶ 0035)
select from among the Current Routes a Current Route between a defined starting location and a defined ending location, the Current Route representing the most commonly followed route taken by the vehicles between the defined starting location and the defined ending location; (see at least ROWLEY, ¶ 0037; ¶ 0038; ¶ 0041; ¶ 0043; ¶ 0056)
choose a travel route to be taken by a vehicle based on the selected Current Route; and (see at least ROWLEY, ¶ 0030; ¶ 0057; ¶ 0058)
direct the vehicle to follow the chosen travel route between the defined starting location and the defined ending location. (see at least ROWLEY, ¶ 0030; ¶ 0057; ¶ 0080)
ROWLEY does not disclose, but JUNG teaches:
the historical position data including position inaccuracies, (see at least JUNG, ¶ 0007, “However, in such a conventional navigation terminal, error in position information received from the GPS may be generated, a position information collection cycle may not be proper for a moving path, and geographic information received from a geographic information system (GIS) may be incorrect.”; ¶ 0108, “In this manner, one optimized moving path may be extracted using paths in clusters and road information (in/out, position information) including the respective paths. Here, each cluster may include one moving path including the same moving trajectories and such a moving path may be filled with moving trajectories in which roads unknown due to sensing error of the GPS sensor 110 are completely corrected.”; ¶ 0152, “In this manner, one optimized moving path may be extracted using paths in clusters and road information (in/out, position information) including the respective paths. Here, each cluster may include one moving path including the same moving trajectories and such a moving path may be filled with moving trajectories in which roads unknown due to sensing error of the GPS sensor 110 are completely corrected.”)
group into Groups of Trips those Trips having common pairs of starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
separate into different Collections of Trips those Groups of Trips having Trips that followed different pathways between the common pairs of starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
notwithstanding the position inaccuracies in the historical position data; ; (see at least JUNG, ¶ 0007; ¶ 0108; ¶ 0152)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data of ROWLEY to include the clustering of paths ("moving trajectories") with common trajectories and GPS error prevention within JUNG with the motivation of improving the ability to discern similarity between a plurality of routes travelling between the same two locations with minimized duplicated location data by being resilient to GPS errors that are widely known in the art (¶ 0007).
ROWLEY in view of JUNG does not disclose, but MASON teaches:
movement of vehicles in a material delivery system, (see at least MASON, ¶ 0035, “The management devices 135 can be computing devices used by dispatchers, fleet managers, administrators, or other users to manage different aspects of the vehicle management system 150. For example, a user of a management device 135 can access the vehicle management system 150 to generate routes, dispatch vehicles and drivers, define access paths, select access paths, update site details information for a site, and perform other individual vehicle or fleet management functions. With the management devices 135, users can access and monitor vehicle information obtained from one or more of the in-vehicle devices 105 by the vehicle management system 150. Such vehicle status information can include data on vehicle routes used, stops, speed, vehicle feature usage (such as power takeoff device usage), driver behavior and performance, vehicle emissions, vehicle maintenance, energy usage, and the like. In some embodiments, the management devices 135 are in fixed locations, such as at a dispatch center. The management devices 135 can also be used by administrators in the field, and may include mobile devices, laptops, tablets, smartphones, personal digital assistants (PDAs), desktops, or the like.”)
the vehicles of the material delivery system carrying material (see at least MASON, ¶ 0002, “Route selection or optimization has applications in vehicle routing, printed wire circuit layout, chip design and layout, and biological activities. Trucking and other vehicle fleets, for instance, select vehicle routes to deliver goods to various destinations. Similarly, transportation companies route vehicles to pick up and drop off passengers. In addition to land-based vehicles, route selection can also be used for ship, airplane, and rail transport route scheduling”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the path planning of ROWLEY in view of JUNG to include the delivery of goods as seen in MASON to effectively create a route planning system which accounts for fleets of delivery vehicles.
Claims 51 is rejected under 35 U.S.C. 103 as being unpatentable over ROWLEY (US20170138754A1) in view of JUNG (US20160178377A1).
Regarding claim 51:
ROWLEY discloses:
generating a database of historical position data relating to the movement of the vehicles between the starting locations and the ending locations, (see at least ROWLEY, ¶ 0058)
using the database of historical position data to identify as Trips pathways previously followed by the vehicles between various starting and ending locations, wherein a pathway comprises a defined sequence of road segments; (see at least ROWLEY, ¶ 0027; ¶ 0029; ¶ 0033; ¶ 0034; ¶ 0036; ¶ 0058; ¶ 0059; ¶ 0071; ¶ 0078; ¶ 0079)
determining a Common Route for each Collection of Trips based on a number of times each road segment was traversed by the vehicles; (see at least ROWLEY, ¶ 0037; ¶ 0041; ¶ 0056)
identifying as similar Common Routes based on road segments that are common to the Common Routes; combining similar Common Routes to form Current Routes; (see at least ROWLEY, ¶ 0033; ¶ 0034; ¶0035)
defining a starting location and an ending location; (see at least ROWLEY, ¶ 0029; ¶ 0030; ¶ 0031)
selecting from among the Current Routes that Current Route between the defined starting location and defined ending location, the selected Current Route representing the most commonly followed route taken by the vehicles between the defined starting location and the defined ending location (see at least ROWLEY, ¶ 0037; ¶ 0038; ¶ 0041; ¶ 0043; ¶ 0056
displaying the selected Current Route on a display system. (see at least ROWLEY, ¶ 0059)
ROWLEY does not disclose, but JUNG teaches:
the historical position data including position inaccuracies; (see at least JUNG, ¶ 0007)
grouping into Groups of Trips those Trips having common pairs of starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
separating into different Collections of Trips those Groups of Trips having Trips that followed different pathways between the starting and ending locations; (see at least JUNG, ¶ 0077; ¶ 0078; ¶ 0084)
notwithstanding the position inaccuracies in the historical position data; and (see at least JUNG, ¶ 0101; ¶ 0108)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the collection of various travel paths, collection of start/stop points for the determination of route paths, and generation of routes/maps based on the collected data of ROWLEY to include the clustering of paths ("moving trajectories") with common trajectories and GPS error prevention within JUNG with the motivation of improving the ability to discern similarity between a plurality of routes travelling between the same two locations with minimized duplicated location data by being resilient to GPS errors that are widely known in the art (¶ 0007).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
VAN LATUM (US 20170023372 A1)
¶ 0008, “In one implementation, the present invention is a method including accessing a road network database to identify a tire cost for a plurality of edges in a mining road network, identifying least-cost paths between each of a plurality of nodes on the mining road network using the tire cost for each of the plurality of edges, the nodes including a loading area and a dumping area for a plurality of haul trucks, accessing a distributed objects database to identify constraints for the nodes on the mining road network, determining a production plan using the constraints for the nodes on the mining road network by maximizing a function of the form αf′(x)-βg′(x), where f′(x) is a normalized measure of productivity, g′(x) is a normalized measure of impact on tire conditioning, and α and β, are nonnegative constants with α+β=1, subject to constraints on resource capacities and production requirements, with the material flow rates between each of the nodes on the mining road network specifying the production plan, determining a task assignment for each of the plurality of haul trucks using the production plan as guide, and transmitting the task assignment for each of the plurality of haul trucks.”
¶ 0064, “In some cases, the objects defined within distributed objects database 116 change over time. Because the mining environment is constantly being modified by the mining operations, nearly all objects within distributed objects database 116 can change over time. Accordingly, to ensure that database 116 contains up-to-date information, the contents may be periodically refreshed via a connection to a central computer system that monitors the position and status of objects within the mining environment. Accordingly, whether distributed objects database 116 is based in vehicle 102, dispatch system 100, or a combination of both, distributed objects database 116 is configured to be constantly and routinely updated. Updates to distributed objects database 116 are distributed efficiently and the database reflects the known objects within the mining environment at any point in time.”
MUCKELL (US 20100179849 A1)
¶ 0025, “FIG. 3 is a supply chain network 60 created based on trip clustering. Given that a large database of trips are generated, it is difficult to visually explore patterns in the trips and hence, `similar` trips are determined to cluster the trips. This makes it convenient for a user to segregate or classify the trips. As used herein, the term `similar trips` refers to trips that have their respective start and end locations spatially close. Different global positioning coordinates may refer to a same location.”
¶ 0029, “In order to generate a model for historical vehicle movement, variables need to be associated with appropriate geographical locations and routes. Exemplary variables include cargo status and frequency information. It should be noted that other variables related to temporal information such as, extent of time collaboration or load sharing that may occur, also may be employed. Furthermore, location of distribution centers where trucks may be physically loaded and unloaded may be critical. As used herein, cargo status is defined as the ratio of full trips to total trips, and is recorded as a Boolean, specifically, 1=cargo_status, 0=no cargo_status. A value of 0 indicates that vehicles that traveled along that route were empty. Similarly, if cargo_status=1, then all the vehicles traveled full. A mean cargo_status of 0.5 would indicate half of the vehicles traveling that route as empty and half were full. Knowledge of cargo status is crucial in assessing backhaul opportunities i.e. matching between empty trips and full trips occurring in the same direction. A similar process occurs for route frequency, except that the frequency for each route is initialized based on number of trips clustered together. The frequency of these trips weights a backhaul opportunity in determining likelihood that a collaborative match may occur within temporal restraints. In the model, each road segment is embedded with cargo status and frequency information for each direction of travel. To determine cargo status and frequency at specific route segments, routes that overlap need to be combined.”
RENZ (US 20150081217 A1)
¶ 0017, “According to a further variant the graphical manipulation of the course of the selected road segment on the screen may comprise collectively displacing all shape points associated with the selected road segment. Collectively displacing all shape points of a route segment may mean that all shape points of the selected route segment are subjected to substantially the same singular displacement operation applied to the road segment as a whole. The singular displacement operation may comprise at least one of a road segment translation, road segment rotation, a road segment stretching or road segment compression on the screen. A collective displacement may be indicated by grouping together (manually by the user) all shape points of the shape point sequence. Such a collective displacement of the displayed road segment may be advantageous in case the geographic position rather the shape of the segment is incorrectly or improperly reflected by the digitized data.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAFAEL VELASQUEZ VANEGAS whose telephone number is (571)272-6999. The examiner can normally be reached M-F 8 - 4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, VIVEK KOPPIKAR can be reached at (571) 272-5109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAFAEL VELASQUEZ VANEGAS/Patent Examiner, Art Unit 3667
/VIVEK D KOPPIKAR/Supervisory Patent Examiner
Art Unit 3667
January 16, 2026