DETAILED ACTION
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 .
Claims 1-15 have been presented for examination.
Claims 1-15 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The claimed invention is directed to non-statutory subject matter. The claim(s) 13 does/do not fall within at least one of the four categories of patent eligible subject matter.
Claim 13 is directed to “a computer program product comprising program code” for performing the method of claim 2 when executed by a processor device. As drafted, claim 13 recites only software (program code) and does not recite any physical or tangible article (e.g., it does not recite a non-transitory computer-readable storage medium or other structural manufacture that embodies the program code). Consistent with the guidance in MPEP § 2106.03(I), a computer program per se (software per se), when claimed as a product without structural recitations, is not a statutory “process, machine, manufacture, or composition of matter” under 35 U.S.C. § 101. Accordingly, claim 13 is directed to non-statutory subject matter (software per se) and is rejected. To overcome this rejection, applicant may amend claim 13 to positively recite a statutory category, e.g., a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause performance of the method of claim 2.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a processor device” and “control units” in claims 1-2 and 12-15. See specification [0075], [0172], and [0173].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5, 8, and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Quist (US 8930058 B1), in view of Kanai (US 20230121070 A1).
Regarding Claim 1, Quist discloses a computer system comprising a processor device [Col.2, ll. 49-51] “The embedded Guidance Processor system 120 on board the vehicle includes two integrated computer systems (i.e., a navigational computer and a vehicle control computer).” configured to handle a configuration of a predefined path for at least one vehicle [Col.3, ll. 29-35] “the Control Station computer display preferably displays an icon of the target vehicle together with the path that is currently loaded to the vehicle's Navigation Computer 240 for autonomous control. The Control Station operator may upload to the target Vehicle 210 any path stored in the Control Station 190 computer database, select and set a speed for the target Vehicle 210 to drive” The “predefined path” corresponds to a stored/loaded vehicle route,
the configuration being indicative of one or more positions in the predefined path [Col.6, ll. 46-53] “The spirals are stored as enhanced waypoints 230, which include the following fields relative to an absolute origin for the whole path: x=position (meters north of origin); y=position (meters east of origin); dir=direction (radians clockwise from north); c=curvature (radians/meter of path length); v=along-path speed (meters/second); and, t=time of arrival (seconds).” The “positions” correspond to enhanced waypoint coordinate fields (x,y).
and for each of the one or more positions, the configuration indicates a respective operation to be performed at the respective position of the predefined path [Col.6, ll. 46-53] “The spirals are stored as enhanced waypoints 230, which include the following fields relative to an absolute origin for the whole path: x=position (meters north of origin); y=position (meters east of origin); dir=direction (radians clockwise from north); c=curvature (radians/meter of path length); v=along-path speed (meters/second); and, t=time of arrival (seconds).” The “operation” corresponds to waypoint/segment travel parameters that command longitudinal behavior (e.g., along-path speed v and time t, used to execute a speed/acceleration profile)
each respective operation affecting a longitudinal motion of the at least one vehicle when performing the operation [Col.7, ll. 57-64] “The speed control algorithm uses the brake and throttle calibration data to predict the brake and/or throttle commands, respectively, necessary to achieve a given desired longitudinal acceleration. These predictions are updated or adjusted based on real-time measurements of actual performance, and result in dramatically reduced uncorrected error compared to control algorithms that depend entirely on measured feedback.” Quist teaches that each respective operation affects longitudinal motion, because Quist’s vehicle executes commanded longitudinal acceleration using brake/throttle control.
the processor device is further configured to: obtain the configuration of the predefined path [Col.5, ll. 6-9] “The previously generated and stored Enhanced Waypoints 230 are retrieved and transmitted by the Control Station 190 to the unmanned vehicle's Guidance Processor system 120 in preparation for execution.” “Obtaining the configuration” corresponds to Quist’s retrieving and transmitting the stored enhanced waypoints to the vehicle for execution,
Quist does not appear to teach the full claim limitation regarding “obtain at least one distance for offsetting at least one position of the configuration, for the at least one vehicle, adjust the configuration by offsetting the at least one position of the configuration based on the obtained at least one distance, thereby establishing an adjusted configuration for the at least one vehicle, and trigger the at least one vehicle to operate in the predefined path based on the adjusted configuration.”
However, Kanai teaches equivalent teachings wherein obtain at least one distance for offsetting at least one position of the configuration, for the at least one vehicle [0045] “the map information 251. The map information 251 is information on a series of nodes representing the travel path 60 of the own vehicle. In the map information 251, each node is given a node ID, coordinates indicating a position in a mine, a target speed of the unmanned vehicle 20, and an offset factor for determining an offset amount of each node (described later). The map information 251 is provided in advance with information corresponding to sections required for the unmanned vehicle 20 to travel, and for example, an external control station or the like may set, for each unmanned vehicle 20, nodes of exclusive travel sections that prevent the unmanned vehicle 20 from interfering with another unmanned vehicle 20” [0057] “The offset factor α.sub.i of each node set in advance in the map information 251 is determined as a distance by which the travel path 60 can be offset considering the distance to the road shoulder (corresponding to a road width) on each point.” “at least one distance” corresponds to Kanai’s offset factor (αi) that is explicitly “determined as a distance” by which the travel path can be offset.
adjust the configuration by offsetting the at least one position of the configuration based on the obtained at least one distance [0053] ”a process procedure of the offset amount determination unit 202. The offset amount determination unit 202 first acquires node information (coordinates: (X.sub.i, Y.sub.i), i=1, . . . , N) on the travel path 60 from the map information 251 in the storage device 250 (S901). Next, the offset amount determination unit 202 determines an offset amount (ΔX.sub.i, ΔY.sub.i) of each node using the acquired node information on the travel path 60 and the latest (common) offset information distributed by the offset information distribution unit 203 of the own vehicle or the other vehicle (S902). Then, the offset amount determination unit 202 transmits, as the target track 62, a sequence of coordinate points (X.sub.i+ΔX.sub.i, Y.sub.i+ΔY.sub.i) obtained by adding the offset amount to the coordinates of the original nodes to the autonomous travel control unit 201 (S903).” “Offsetting a position” corresponds to computing an offset amount (ΔXi, ΔYi) and adding it to each node coordinate (Xi, Yi).,
thereby establishing an adjusted configuration for the at least one vehicle [0046] “The offset amount determination unit 202 is adapted to determine an offset amount with respect to each node of the travel path 60 based on the offset information distributed by the own vehicle or the other vehicle and received via the wireless communication device 240 and the map information 251 representing the travel path 60 of the unmanned vehicle 20, and add the determined offset amount to the coordinates of the node, so as to generate a sequence of coordinate points serving as a target track when the unmanned vehicle 20 travels, and then send a target track and a target speed to the autonomous travel control unit 201.” “Adjusted configuration” corresponds to Kanai’s target track 62 (the offset node sequence (Xi+ΔXi, Yi+ΔYi)),
and trigger the at least one vehicle to operate in the predefined path based on the adjusted configuration [0054] “FIG. 10 is a flowchart of a process procedure of the autonomous travel control unit 201. The autonomous travel control unit 201 first acquires a target track and a target speed from the offset amount determination unit 202 (S1001). Next, the autonomous travel control unit 201 acquires an own-vehicle position, an own-vehicle speed, and a steering angle respectively from the position sensor 220, the speed sensor 230, and the steering angle sensor 260 (S1002). Then, the autonomous travel control unit 201 compares the target track with the own-vehicle position to generate a steering angle instruction value so that the own-vehicle position approaches the target track, and also compares the target speed with the own-vehicle speed to generate an acceleration/deceleration instruction value so that the own-vehicle speed approaches the target speed (S1003). Finally, the autonomous travel control unit 201 transmits to the travel drive device 210 the generated steering angle instruction value and acceleration/deceleration instruction value (i.e., travel instruction) (S1004).” “Triggering the vehicle to operate based on the adjusted configuration” corresponds to Kanai’s autonomous travel control unit acquiring the target track and generating steering and acceleration/deceleration instructions to drive along it.
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist and Kanai to incorporate Kanai’s offset-distance based target track generation into Quist’s stored path execution to make the system to obtain at least one distance for offsetting at least one position of the configuration, for the at least one vehicle, adjust the configuration by offsetting the at least one position of the configuration based on the obtained at least one distance, thereby establishing an adjusted configuration for the at least one vehicle, and trigger the at least one vehicle to operate in the predefined path based on the adjusted configuration.
A person that is skilled in the art would have been motivated to combine Quist and Kanai to improve overall system safety [Kanai 0009] “According to the present invention, since a plurality of vehicles operating in a worksite displaces target tracks based on common offset information, it is possible to effectively suppress generation of ruts. In addition, since the target tracks are displaced based on the common offset information when a plurality of unmanned vehicles travels on a transportation path including opposite lanes, it is possible to disperse travel tracks and suppress generation of ruts while maintaining a safe distance between the vehicles when passing each other.”
Regarding Claim 2, The combination of Quist and Kanai teaches a computer-implemented method for handling a configuration of a predefined path for at least one vehicle [Col.2, ll. 49-51] “The embedded Guidance Processor system 120 on board the vehicle includes two integrated computer systems (i.e., a navigational computer and a vehicle control computer).” [Col.3, ll. 29-35] “the Control Station computer display preferably displays an icon of the target vehicle together with the path that is currently loaded to the vehicle's Navigation Computer 240 for autonomous control. The Control Station operator may upload to the target Vehicle 210 any path stored in the Control Station 190 computer database, select and set a speed for the target Vehicle 210 to drive” The “configuration of a predefined path” corresponds to Quist’s path loaded to the Navigation Computer.,
the configuration being indicative of one or more positions in the predefined path [Col.6, ll. 46-53] “The spirals are stored as enhanced waypoints 230, which include the following fields relative to an absolute origin for the whole path: x=position (meters north of origin); y=position (meters east of origin); dir=direction (radians clockwise from north); c=curvature (radians/meter of path length); v=along-path speed (meters/second); and, t=time of arrival (seconds).” The “positions” correspond to Quist’s waypoint coordinate fields x and y.,
and for each of the one or more positions, the configuration indicates a respective operation to be performed at the respective position of the predefined path [Col.6, ll. 46-53] “The spirals are stored as enhanced waypoints 230, which include the following fields relative to an absolute origin for the whole path: x=position (meters north of origin); y=position (meters east of origin); dir=direction (radians clockwise from north); c=curvature (radians/meter of path length); v=along-path speed (meters/second); and, t=time of arrival (seconds).” The “respective operation” at/for each position corresponds to the waypoint/segment’s commanded travel parameters (e.g., v and t).,
each respective operation affecting a longitudinal motion of the at least one vehicle when performing the operation [Col.7, ll. 57-64] “The speed control algorithm uses the brake and throttle calibration data to predict the brake and/or throttle commands, respectively, necessary to achieve a given desired longitudinal acceleration. These predictions are updated or adjusted based on real-time measurements of actual performance, and result in dramatically reduced uncorrected error compared to control algorithms that depend entirely on measured feedback.”, the method comprising:
by a processor device of a computer system, obtaining the configuration of the predefined path [Col.5, ll. 6-9] “The previously generated and stored Enhanced Waypoints 230 are retrieved and transmitted by the Control Station 190 to the unmanned vehicle's Guidance Processor system 120 in preparation for execution.” “Obtaining the configuration” corresponds to retrieving/transmitting the enhanced waypoints to the vehicle.,
Quist does not appear to teach the full claim limitation regarding “by the processor device, obtaining at least one distance to use as an offset for at least one position of the configuration, by the processor device, for the at least one vehicle, adjusting the configuration by offsetting the at least one position of the configuration based on the obtained at least one distance, thereby establishing an adjusted configuration for the at least one vehicle, and by the processor device, triggering the at least one vehicle to operate in the predefined path based on the adjusted configuration.”
However, Kanai teaches equivalent teachings wherein obtaining at least one distance to use as an offset for at least one position of the configuration [0045] “the map information 251. The map information 251 is information on a series of nodes representing the travel path 60 of the own vehicle. In the map information 251, each node is given a node ID, coordinates indicating a position in a mine, a target speed of the unmanned vehicle 20, and an offset factor for determining an offset amount of each node (described later). The map information 251 is provided in advance with information corresponding to sections required for the unmanned vehicle 20 to travel, and for example, an external control station or the like may set, for each unmanned vehicle 20, nodes of exclusive travel sections that prevent the unmanned vehicle 20 from interfering with another unmanned vehicle 20” [0057] “The offset factor α.sub.i of each node set in advance in the map information 251 is determined as a distance by which the travel path 60 can be offset considering the distance to the road shoulder (corresponding to a road width) on each point.” “Obtaining at least one distance to use as an offset” corresponds to Kanai’s acquisition/use of the offset factor αi determined as a distance.,
by the processor device, for the at least one vehicle, adjusting the configuration by offsetting the at least one position of the configuration based on the obtained at least one distance [0053] ”a process procedure of the offset amount determination unit 202. The offset amount determination unit 202 first acquires node information (coordinates: (X.sub.i, Y.sub.i), i=1, . . . , N) on the travel path 60 from the map information 251 in the storage device 250 (S901). Next, the offset amount determination unit 202 determines an offset amount (ΔX.sub.i, ΔY.sub.i) of each node using the acquired node information on the travel path 60 and the latest (common) offset information distributed by the offset information distribution unit 203 of the own vehicle or the other vehicle (S902). Then, the offset amount determination unit 202 transmits, as the target track 62, a sequence of coordinate points (X.sub.i+ΔX.sub.i, Y.sub.i+ΔY.sub.i) obtained by adding the offset amount to the coordinates of the original nodes to the autonomous travel control unit 201 (S903).” “Adjusting by offsetting positions based on the distance” corresponds to Kanai’s computation of (ΔXi,ΔYi) and generation of the offset coordinate sequence.,
thereby establishing an adjusted configuration for the at least one vehicle, and by the processor device [0046] “The offset amount determination unit 202 is adapted to determine an offset amount with respect to each node of the travel path 60 based on the offset information distributed by the own vehicle or the other vehicle and received via the wireless communication device 240 and the map information 251 representing the travel path 60 of the unmanned vehicle 20, and add the determined offset amount to the coordinates of the node, so as to generate a sequence of coordinate points serving as a target track when the unmanned vehicle 20 travels, and then send a target track and a target speed to the autonomous travel control unit 201.” “Adjusted configuration” corresponds to Kanai’s target track 62.,
triggering the at least one vehicle to operate in the predefined path based on the adjusted configuration [0054] “FIG. 10 is a flowchart of a process procedure of the autonomous travel control unit 201. The autonomous travel control unit 201 first acquires a target track and a target speed from the offset amount determination unit 202 (S1001). Next, the autonomous travel control unit 201 acquires an own-vehicle position, an own-vehicle speed, and a steering angle respectively from the position sensor 220, the speed sensor 230, and the steering angle sensor 260 (S1002). Then, the autonomous travel control unit 201 compares the target track with the own-vehicle position to generate a steering angle instruction value so that the own-vehicle position approaches the target track, and also compares the target speed with the own-vehicle speed to generate an acceleration/deceleration instruction value so that the own-vehicle speed approaches the target speed (S1003). Finally, the autonomous travel control unit 201 transmits to the travel drive device 210 the generated steering angle instruction value and acceleration/deceleration instruction value (i.e., travel instruction) (S1004).” “Triggering operation based on the adjusted configuration” corresponds to Kanai’s controller using target track/target speed to output steering and acceleration/deceleration instructions.
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist and Kanai to incorporate Kanai’s offset-distance based target track generation into Quist’s stored path execution to make the system by the processor device, obtaining at least one distance to use as an offset for at least one position of the configuration, by the processor device, for the at least one vehicle, adjusting the configuration by offsetting the at least one position of the configuration based on the obtained at least one distance, thereby establishing an adjusted configuration for the at least one vehicle, and by the processor device, triggering the at least one vehicle to operate in the predefined path based on the adjusted configuration.
A person that is skilled in the art would have been motivated to combine Quist and Kanai to improve overall system safety [Kanai 0009] “According to the present invention, since a plurality of vehicles operating in a worksite displaces target tracks based on common offset information, it is possible to effectively suppress generation of ruts. In addition, since the target tracks are displaced based on the common offset information when a plurality of unmanned vehicles travels on a transportation path including opposite lanes, it is possible to disperse travel tracks and suppress generation of ruts while maintaining a safe distance between the vehicles when passing each other.”
Regarding Claim 3, The combination of Quist and Kanai teaches the method of claim 2 wherein each respective operation of the configuration comprises any one or more out of:
Quist discloses adjusting a speed and/or acceleration of the at least one vehicle [Col.7, ll. 57-67] “The speed control algorithm uses the brake and throttle calibration data to predict the brake and/or throttle commands, respectively, necessary to achieve a given desired longitudinal acceleration. These predictions are updated or adjusted based on real-time measurements of actual performance, and result in dramatically reduced uncorrected error compared to control algorithms that depend entirely on measured feedback.”,
halt operation performed by the at least one vehicle, braking and/or decelerating the at least one vehicle, and shifting gear of the at least one vehicle [Col.5, ll. 27-35] “The Control Station Graphical User Interface Software 200 receives status information including position, speed, and vehicle health input in real time from the Guidance Processor 120 and provides the information in real time to the Control Station 190 for possible analysis and/or display. (5) Once the Vehicle 210 has traversed the recorded predefined path, it autonomously comes to a stop (i.e., halt), shifts the drive train transmission into the parking gear mode (i.e., shifting gear), and awaits further commands.” The “operations” correspond to Quist’s longitudinal control operations (accelerate/decelerate/brake/stop) and related vehicle state operations (e.g., shift to park/halt/stop) that occur in connection with executing the path.
Regarding Claim 4, The combination of Quist and Kanai teaches the method of claim 2 wherein the configuration comprises at least one pair of start and end operations such that a start operation is configured to be performed at a start position and a corresponding end operation is configured to be performed at an end position [Col.6, ll. 44-547] “For the speed profile of each segment, given the initial speed at the start of each segment, the speed profile along the segment is defined by an acceleration parameter (a).” [Col.6-7, ll. 54-69] “The Enhanced Waypoints 230 are sent from the Control Station Computer 260 to the vehicle's Guidance Processor system 120 as a path, after which they must be reconstructed into spirals in order to be driven by the Vehicle 210. The relationships for extracting length (s), sharpness (k), and acceleration (a) for any pair of enhanced waypoints, where the symbols with a 0 subscript suffix (i.e., x.sub.0) denote those associated with the second waypoint, is: Length, s: s=2*(dir-dir.sub.0)/(c+c.sub.0) Sharpness, k: k=(c-c.sub.0)/s Acceleration, a: a=(v-v.sub.0)/(t-t.sub.0) These are used in the algorithms for following the path and for finding the unique x, y, dir, c, v and time values at any intermediate point between waypoints.” Quist path is executed as segments between waypoints having a defined longitudinal profile; (i.e., the start position corresponds to the first waypoint of a segment and the end position corresponds to the second waypoint of that segment, with longitudinal behavior defined over the segment using an acceleration parameter),
Quist does not appear to teach the full claim limitation regarding “wherein adjusting the configuration comprises offsetting the start position and/or the end position”
However, Kanai teaches equivalent teachings wherein adjusting the configuration comprises offsetting the start position and/or the end position [0050] “In the present embodiment, the offset information distributed by the offset information distribution unit 203 of the own vehicle or the other vehicle is assumed to be an angle θ illustrated in FIG. 4(a). Herein, θ is a counterclockwise rotating angle with the X axis as an origin. An open circle 60 indicates the coordinates (X.sub.i, Y.sub.i) of a given node (node ID: i) on the travel path in the map information 251 and a filled circle 62 indicates the coordinates (X.sub.i+ΔX.sub.i, Y.sub.i+ΔY.sub.i) of a target track obtained by adding an offset amount (ΔX.sub.i, ΔY.sub.i) to the coordinates (X.sub.i, Y.sub.i). Using the angle θ that is the offset information and an offset factor α.sub.i described (held) in the map information 251” [0051] “Through the above calculation, as illustrated in FIG. 4(b), each node of the travel path 60 of each unmanned vehicle 20 is offset in the direction of the common angle θ by a magnitude of the offset factor α.sub.i for each node, and then a target track 62 of the unmanned vehicle 20 can be obtained.” Kanai teaches “adjusting the configuration comprises offsetting the start and/or end position” which corresponds to Kanai’s generation of offset coordinates for the nodes that define the path boundaries.
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist and Kanai to make the system wherein adjusting the configuration comprises offsetting the start position and/or the end position.
A person that is skilled in the art would have been motivated to combine Quist and Kanai to improve overall system safety [Kanai 0009] “According to the present invention, since a plurality of vehicles operating in a worksite displaces target tracks based on common offset information, it is possible to effectively suppress generation of ruts. In addition, since the target tracks are displaced based on the common offset information when a plurality of unmanned vehicles travels on a transportation path including opposite lanes, it is possible to disperse travel tracks and suppress generation of ruts while maintaining a safe distance between the vehicles when passing each other.”
Regarding Claim 5, The combination of Quist and Kanai teaches the method of claim 4 wherein the at least one pair of start and end operations comprises any one out of:
Quist discloses a start operation to start braking the at least one vehicle at the start position and an end braking operation to end braking the at least one vehicle at the end position [Col.7, ll. 57-67] “The speed control algorithm uses the brake and throttle calibration data to predict the brake and/or throttle commands, respectively, necessary to achieve a given desired longitudinal acceleration. These predictions are updated or adjusted based on real-time measurements of actual performance, and result in dramatically reduced uncorrected error compared to control algorithms that depend entirely on measured feedback.” [Col.6, ll. 44-53] “For the speed profile of each segment, given the initial speed at the start of each segment, the speed profile along the segment is defined by an acceleration parameter (a). The spirals are stored as enhanced waypoints 230, which include the following fields relative to an absolute origin for the whole path: x=position (meters north of origin); y=position (meters east of origin); dir=direction (radians clockwise from north); c=curvature (radians/meter of path length); v=along-path speed (meters/second); and, t=time of arrival (seconds).” [Col.6, ll. 20-28] “The second stage, preferably implemented in the on board Guidance Processor system 120, of the path generation process is calculation of the time of arrival and path length at each point along the fitted spirals and performing a least-squares fit to find the piecewise-constant acceleration that will provide the best fit. This means that the commanded velocity of the resulting path will not abruptly change but will be a gradual acceleration of deceleration similar to the original maneuvering of the Vehicle 210 over the manually driven course.”;
and/or a start operation to start a speed and/or acceleration increase of the at least one vehicle at the start position and an end operation to end the speed and/or acceleration increase of the at least one vehicle at the end position [Col.7, ll. 57-67] “The speed control algorithm uses the brake and throttle calibration data to predict the brake and/or throttle commands, respectively, necessary to achieve a given desired longitudinal acceleration. These predictions are updated or adjusted based on real-time measurements of actual performance, and result in dramatically reduced uncorrected error compared to control algorithms that depend entirely on measured feedback.” [Col.6, ll. 44-53] “For the speed profile of each segment, given the initial speed at the start of each segment, the speed profile along the segment is defined by an acceleration parameter (a). The spirals are stored as enhanced waypoints 230, which include the following fields relative to an absolute origin for the whole path: x=position (meters north of origin); y=position (meters east of origin); dir=direction (radians clockwise from north); c=curvature (radians/meter of path length); v=along-path speed (meters/second); and, t=time of arrival (seconds).” [Col.6, ll. 20-28] “The second stage, preferably implemented in the on-board Guidance Processor system 120, of the path generation process is calculation of the time of arrival and path length at each point along the fitted spirals and performing a least-squares fit to find the piecewise-constant acceleration that will provide the best fit. This means that the commanded velocity of the resulting path will not abruptly change but will be a gradual acceleration of deceleration similar to the original maneuvering of the Vehicle 210 over the manually driven course.”
Regarding Claim 8, The combination of Quist and Kanai teaches the method of claim 2,
Quist does not appear to teach the full claim limitation regarding “wherein obtaining the at least one distance to use as an offset for the at least one position of the configuration further comprises obtaining, for each type of vehicle in the at least one vehicle, at least one type-dependent distance, and wherein adjusting the configuration comprises adjusting the configuration by offsetting the at least one position of the configuration based on the obtained at least one type-dependent distance.”
However, Kanai teaches equivalent teachings wherein obtaining the at least one distance to use as an offset for the at least one position of the configuration further comprises obtaining, for each type of vehicle in the at least one vehicle at least one type-dependent distance, and wherein adjusting the configuration comprises adjusting the configuration by offsetting the at least one position of the configuration based on the obtained at least one type-dependent distance [Kanai 0045] “the map information 251. The map information 251 is information on a series of nodes representing the travel path 60 of the own vehicle. In the map information 251, each node is given a node ID, coordinates indicating a position in a mine, a target speed of the unmanned vehicle 20, and an offset factor for determining an offset amount of each node (described later). The map information 251 is provided in advance with information corresponding to sections required for the unmanned vehicle 20 to travel, and for example, an external control station or the like may set, for each unmanned vehicle 20, nodes of exclusive travel sections that prevent the unmanned vehicle 20 from interfering with another unmanned vehicle 20” [0057] “The offset factor α.sub.i of each node set in advance in the map information 251 is determined as a distance by which the travel path 60 can be offset considering the distance to the road shoulder (corresponding to a road width) on each point.” [0059] “FIG. 8 illustrates an example of generating a target track based on an offset amount determined considering a fixed work point, such as a loading point of the excavator 10 or the like. A node 60-2 is defined by coordinates of the fixed work point on which the unmanned vehicle 20 should stop with its own-vehicle position matched therewith so that the excavator 10 performs loading work for the unmanned vehicle 20.” [0076] “FIG. 14 illustrates an example of data representing the map information 251 according to the second embodiment. In the present embodiment, each node of the map information 251 holds both of an empty-load offset factor and a load offset factor.” [0077] “the offset amount determination unit 202 acquires (selects) an offset factor according to the load condition of the own vehicle, that is, either a load offset factor when the own vehicle is in the loaded state or an empty-load offset factor when the own vehicle is in the empty load state” [0078] “the unmanned vehicle 20-1 is a vehicle (moving from the dumping place to the loading place) in the empty load state, and the unmanned vehicle 20-2 is a vehicle (moving from the loading place to the dumping place) in the loaded state. The load offset factor (i.e., the offset factor when the body weight is relatively large) is set larger than the empty-load offset factor (i.e., the offset factor when the body weight is relatively small)”
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist and Kanai to make the system wherein obtaining the at least one distance to use as an offset for the at least one position of the configuration further comprises obtaining, for each type of vehicle in the at least one vehicle at least one type-dependent distance, and wherein adjusting the configuration comprises adjusting the configuration by offsetting the at least one position of the configuration based on the obtained at least one type-dependent distance.
A person that is skilled in the art would have been motivated to combine Quist and Kanai to improve overall system safety [Kanai 0009] “According to the present invention, since a plurality of vehicles operating in a worksite displaces target tracks based on common offset information, it is possible to effectively suppress generation of ruts. In addition, since the target tracks are displaced based on the common offset information when a plurality of unmanned vehicles travels on a transportation path including opposite lanes, it is possible to disperse travel tracks and suppress generation of ruts while maintaining a safe distance between the vehicles when passing each other.”
Regarding Claim 11, The combination of Quist and Kanai teaches the method of claim 2, Kanai teaches wherein adjusting the configuration for the at least one vehicle by offsetting the at least one position of the configuration based on the obtained at least one distance comprises: adjusting the configuration for a first vehicle and for a second vehicle, thereby establishing a first adjusted configuration for the first vehicle and a second adjusted configuration for the second vehicle [0033] “The autonomous travel system 1 includes a plurality of unmanned vehicles 20 having the identical configuration.” [0046]: “determine an offset amount with respect to each node of the travel path and add the determined offset amount to the coordinates of the node, so as to generate a target track” [0053]: “transmits, as the target track 62, a sequence of coordinate points (Xi+ΔXi, Yi+ΔYi) obtained by adding the offset amount to the coordinates of the original nodes” Kanai’s “target track 62” made of (Xi+ΔXi, Yi+ΔYi) is the adjusted configuration for each vehicle; Kanai explicitly teaches multiple vehicles doing this (i.e., first adjusted configuration for the first vehicle and a second adjusted configuration for the second vehicle),
the first adjusted configuration and the second adjusted configuration differing in that at least one position of the one or more positions is offset by a different distance [0076] “each node holds both of an empty-load offset factor and a load offset factor.” [0077] “determines whether loaded or empty load and acquires (selects) an offset factor according to the load condition either a load offset factor or an empty-load offset factor” [0078] “The load offset factor is set larger than the empty-load offset factor” [0050] “Using an offset factor αi the offset amount (ΔXi, ΔYi) is calculated ΔXi=αi×cosθ… ΔYi=αi×sinθ” [0050] “target track obtained by adding an offset amount to the coordinates (Xi, Yi).” Kanai’s system shows that if Vehicle 1 uses a larger offset factor (loaded) and Vehicle 2 uses a smaller offset factor (empty), then at least one node/position (Xi, Yi) becomes offset by a different distance (different αi i.e., different ΔXi, ΔYi), creating two differing adjusted configurations.,
and wherein triggering the at least one vehicle to operate in the predefined path based on the adjusted configuration comprises triggering the first vehicle to operate in the predefined path based on the first adjusted configuration and triggering the second vehicle to operate in the predefined path based on the second adjusted configuration [0046] “generate a target track and then send a target track and a target speed to the autonomous travel control unit 201.” [0047] “generate a steering instruction value so that the own-vehicle position approaches the target track and generate an acceleration/deceleration instruction value and sends to the travel drive device thereby controlling tracking to the target track.” Kanai system sends the target track and controlling tracking founds “triggering the vehicle to operate based on the adjusted configuration.” For two vehicles, each tracks its own generated target track.
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist and Kanai to make the system wherein adjusting the configuration for the at least one vehicle by offsetting the at least one position of the configuration based on the obtained at least one distance comprises: adjusting the configuration for a first vehicle and for a second vehicle, thereby establishing a first adjusted configuration for the first vehicle and a second adjusted configuration for the second vehicle.
A person that is skilled in the art would have been motivated to combine Quist and Kanai to improve overall system safety [Kanai 0009] “According to the present invention, since a plurality of vehicles operating in a worksite displaces target tracks based on common offset information, it is possible to effectively suppress generation of ruts. In addition, since the target tracks are displaced based on the common offset information when a plurality of unmanned vehicles travels on a transportation path including opposite lanes, it is possible to disperse travel tracks and suppress generation of ruts while maintaining a safe distance between the vehicles when passing each other.”
Regarding Claim 12, The claim recites a vehicle of the parallel limitations in claim 1 and 2, respectively for the reasons discussed above. Therefore, claim 12 is rejected using the same rational reasoning.
Regarding Claim 13, The claim recites a computer program of the parallel limitations in claim 1 and 2, respectively for the reasons discussed above. Therefore, claim 13 is rejected using the same rational reasoning.
Regarding Claim 14, The claim recites a control system of the parallel limitations in claim 1 and 2, respectively for the reasons discussed above. Therefore, claim 14 is rejected using the same rational reasoning.
Regarding Claim 15, The claim recites a non-transitory computer-readable storage medium of the parallel limitations in claim 1 and 2, respectively for the reasons discussed above. Therefore, claim 15 is rejected using the same rational reasoning.
Claim(s) 6-7 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Quist (US 8930058 B1), in view of Kanai (US 20230121070 A1), and further in view Stewart (US 20070179690 A1).
Regarding Claim 6, The combination of Quist and Kanai teaches the method of claim 2, wherein obtaining the at least one distance to use as an offset for the at least one position of the configuration comprises any one or more out of: obtaining at least one predefined distance [Kanai 0045] “the map information 251. The map information 251 is information on a series of nodes representing the travel path 60 of the own vehicle. In the map information 251, each node is given a node ID, coordinates indicating a position in a mine, a target speed of the unmanned vehicle 20, and an offset factor for determining an offset amount of each node (described later). The map information 251 is provided in advance with information corresponding to sections required for the unmanned vehicle 20 to travel, and for example, an external control station or the like may set, for each unmanned vehicle 20, nodes of exclusive travel sections that prevent the unmanned vehicle 20 from interfering with another unmanned vehicle 20” [0057] “The offset factor α.sub.i of each node set in advance in the map information 251 is determined as a distance by which the travel path 60 can be offset considering the distance to the road shoulder (corresponding to a road width) on each point.”
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist and Kanai to make the system wherein obtaining the at least one distance to use as an offset for the at least one position of the configuration comprises any one or more out of obtaining at least one predefined distance.
A person that is skilled in the art would have been motivated to combine Quist and Kanai to improve overall system safety [Kanai 0009] “According to the present invention, since a plurality of vehicles operating in a worksite displaces target tracks based on common offset information, it is possible to effectively suppress generation of ruts. In addition, since the target tracks are displaced based on the common offset information when a plurality of unmanned vehicles travels on a transportation path including opposite lanes, it is possible to disperse travel tracks and suppress generation of ruts while maintaining a safe distance between the vehicles when passing each other.”
The combination of Quist and Kanai does not appear to teach the full claim limitation regarding “and/or obtaining at least one distance based on a number generation from a random number generator and/or a pseudo-random number generator.”
However, Stewart teaches equivalent teachings wherein obtaining at least one distance based on a number generation from a random number generator and/or a pseudo-random number generator distance to use as an offset [0026] “Typically, a limit will be set on the amount of variance from the predetermined route. For example, in areas with little interaction with people or having a high frequency of AGVs traveling along a particular route, the AGVs may use a greater variance from the predetermined route than along a predetermined route that is adjacent to a walkway for people. The variance from the predetermined route can easily be limited by limiting the applied deviation, by measuring the desired distance (i.e., predefined distance) from the predetermined route and correcting when the distance becomes too great for a particular area, or a combination thereof.” [0024] “The variance or deviation from the predetermined route may be random, specific, or a combination thereof. For example, the controller 18 may be programmed to randomly vary the travel path from the predetermined route so that each time the predetermined path is followed, the travel path of the AGV along the predetermined route is randomly selected (i.e., a random number generator). The controller 18 may also be programmed so that each controller 18 on each AGV will apply a slightly different variation or deviation.”
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist, Kanai, and Stewart to make the system to obtaining at least one predefined distance, and/or obtaining at least one distance based on a number generation from a random number generator and/or a pseudo-random number generator.
A person that is skilled in the art would have been motivated to combine Quist, Kanai, and Stewart to improve overall system efficiency [Stewart 0009] “In view of the above, a need exists for an AGV that can efficiently transport material on a predetermined route while minimizing wear on the facility, and more particularly to an AGV that automatically varies the actual travel path from the predetermined route to reduce wear in the floor of the material handling facility.”
Regarding Claim 7, The combination of Quist and Kanai teaches the method of claim 2, Kanai teaches wherein obtaining the at least one distance to use as an offset for the at least one position of the configuration further comprises obtaining at least one condition of the predefined path [Kanai 0045] “the map information 251. The map information 251 is information on a series of nodes representing the travel path 60 of the own vehicle. In the map information 251, each node is given a node ID, coordinates indicating a position in a mine, a target speed of the unmanned vehicle 20, and an offset factor for determining an offset amount of each node (described later). The map information 251 is provided in advance with information corresponding to sections required for the unmanned vehicle 20 to travel, and for example, an external control station or the like may set, for each unmanned vehicle 20, nodes of exclusive travel sections that prevent the unmanned vehicle 20 from interfering with another unmanned vehicle 20” [0057] “The offset factor α.sub.i of each node set in advance in the map information 251 is determined as a distance by which the travel path 60 can be offset considering the distance to the road shoulder (corresponding to a road width) on each point.”,
The combination of Quist and Kanai teaches does not appear to teach the full claim limitation regarding “determining the at least one distance based on the obtained at least one condition, wherein the at least one condition of the predefined path pertains to any one or more out of: a driving condition of at least one part of the predefined path, a safety condition of at least one part of the predefined path, activities and/or events occurring in at least one part of the predefined path, obstacles in at least one part of the predefined path, at least one other vehicle in at least one part of the predefined path, and human actors and/or animals in at least one part of the predefined path.”
However, Stewart teaches equivalent teachings determining the at least one distance based on the obtained at least one condition, wherein the at least one condition of the predefined path [0026] “Typically, a limit will be set on the amount of variance from the predetermined route. For example, in areas with little interaction with people or having a high frequency of AGVs traveling along a particular route, the AGVs may use a greater variance from the predetermined route than along a predetermined route that is adjacent to a walkway for people. The variance from the predetermined route can easily be limited by limiting the applied deviation, by measuring the desired distance (i.e., predefined distance) from the predetermined route and correcting when the distance becomes too great for a particular area, or a combination thereof.” (Offset distance depends on a safety/traffic condition.) pertains to any one or more out of:
a driving condition of at least one part of the predefined path [0007] “guidance systems typically capable of controlling AGV speed, direction, start/stop functions”,
a safety condition of at least one part of the predefined path [0026] “Typically, a limit will be set on the amount of variance from the predetermined route. For example, in areas with little interaction with people or having a high frequency of AGVs traveling along a particular route, the AGVs may use a greater variance from the predetermined route than along a predetermined route that is adjacent to a walkway for people.”, activities and/or events occurring in at least one part of the predefined path [0026] “For example, in areas with little interaction with people or having a high frequency of AGVs traveling along a particular route”, obstacles in at least one part of the predefined path [0008] “difficulty in the AGV deviating to avoid an obstacle.” [0007] “control an AGVs movement relative to other AGVs, obstacles or even people”, at least one other vehicle in at least one part of the predefined path [0007]“control an AGVs movement relative to other AGVs”, and human actors and/or animals in at least one part of the predefined path [0008] “difficulty in the AGV deviating to avoid an obstacle.” [0007] “control an AGVs movement relative to other AGVs, obstacles or even people”.
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist, Kanai, and Stewart to make the system to determining the at least one distance based on the obtained at least one condition, wherein the at least one condition of the predefined path pertains to any one or more out of: a driving condition of at least one part of the predefined path, a safety condition of at least one part of the predefined path, activities and/or events occurring in at least one part of the predefined path, obstacles in at least one part of the predefined path, at least one other vehicle in at least one part of the predefined path, and human actors and/or animals in at least one part of the predefined path.
A person that is skilled in the art would have been motivated to combine Quist, Kanai, and Stewart to improve overall system efficiency [Stewart 0009] “In view of the above, a need exists for an AGV that can efficiently transport material on a predetermined route while minimizing wear on the facility, and more particularly to an AGV that automatically varies the actual travel path from the predetermined route to reduce wear in the floor of the material handling facility.”
Regarding Claim 9, The combination of Quist and Kanai teaches the method of claim 8, Kanai teaches wherein obtaining, for each type of vehicle in the at least one vehicle, the at least one type-dependent distance [0076]: “each node holds both of an empty-load offset factor and a load offset factor.” [0077] “determines an offset amount using the acquired offset factor and transmits coordinate points obtained by adding the offset amount [] determines whether the own vehicle is in the loaded state or the empty load state” [0078] “the unmanned vehicle 20-1 is a vehicle in the empty load state, and the unmanned vehicle 20-2 is a vehicle in the loaded state.” Kanai’s determining “loaded” vs “empty load” state before selecting the offset-distance parameter for each type of vehicle. comprises:
Quist and Kanai do not appear to teach the full claim limitation regarding “obtaining an interval comprising a maximum distance and a minimum distance based on a number generated from a random number generator and/or a pseudo-random number generator”
However, Stewart teaches equivalent teachings obtaining an interval comprising a maximum distance and a minimum distance for the respective type of vehicle [0026] “Typically, a limit will be set on the amount of variance from the predetermined route.” (i.e., maximum distance) [0024] “keep the AGV 10 on a travel path that is as identical as possible to the predetermined route” (i.e., minimum near zero deviation)
and obtaining some distance based on a number generated from a random number generator and/or a pseudo-random number generator [0024] “randomly vary the travel path from the predetermined route” [0026] “Typically, a limit will be set on the amount of variance” (i.e., random number generator and/or a pseudo-random number generator.)
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist, Kanai, and Stewart to make the system to obtaining an interval comprising a maximum distance and a minimum distance based on a number generated from a random number generator and/or a pseudo-random number generator.
A person that is skilled in the art would have been motivated to combine Quist, Kanai, and Stewart to improve overall system efficiency [Stewart 0009] “In view of the above, a need exists for an AGV that can efficiently transport material on a predetermined route while minimizing wear on the facility, and more particularly to an AGV that automatically varies the actual travel path from the predetermined route to reduce wear in the floor of the material handling facility.”
Regarding Claim 10, The combination of Quist and Kanai teaches the method of claim 2, The combination of Quist and Kanai does not appear to teach the full claim limitation regarding “further comprising obtaining an iteration number, the iteration number being indicative of any one of: a number of times the at least one vehicle has travelled the predefined path, and/or a number of times the at least one vehicle has travelled any of the one or more positions, and/or a number of times the at least one vehicle has performed a respective operation of one of the one or more operations; and wherein obtaining the at least one distance to use as an offset for at least one position of the configuration is obtained by using the iteration number.”
However, Stewart teaches equivalent teachings further comprising obtaining an iteration number [0026] “the controller 18 is programmed to calculate frequency rates of other AGVs having traversed a path.” Stewart calculating frequency rates having traversed a path necessarily relies on tracking how often traversal occurs (i.e., obtaining an iteration-like number/measure.),
the iteration number being indicative of any one of: a number of times the at least one vehicle has travelled the predefined path, and/or a number of times the at least one vehicle has travelled any of the one or more positions [0026] “the controller 18 is programmed to calculate frequency rates of other AGVs having traversed a path.” Stewart calculating frequency rates having traversed a path necessarily relies on tracking how often traversal occurs (i.e., obtaining an iteration-like number/measure.) [0024] “The controller 18 may also vary only portions along the predetermined route 12 to create various travel paths” Stewart shows that if only “portions” are varied, then the system tracks how often those portions/locations are traversed; that traversal frequency corresponds to an iteration number for positions/segments.
and/or a number of times the at least one vehicle has performed a respective operation of one of the one or more operations [0026] “The controller 18 may also log each variance and use a different variance for each subsequent travel path along a predetermined route.” Stewart logs each variance which corresponds to tracking how many times the deviation operation has been applied; each subsequent travel path corresponds to iteration index (1st run, 2nd run, etc.).; and
wherein obtaining the at least one distance to use as an offset for at least one position of the configuration is obtained by using the iteration number [0026] “log each variance and use a different variance for each subsequent travel path along a predetermined route.” Stewart links the selection of variance/deviation (i.e., the offset distance) to the sequence of repeated traversals (“subsequent” runs).
It would have been obvious to a person that is skilled in the art before the effective filling date to combine Quist, Kanai, and Stewart to make the system to further comprising obtaining an iteration number, the iteration number being indicative of any one of: a number of times the at least one vehicle has travelled the predefined path, and/or a number of times the at least one vehicle has travelled any of the one or more positions, and/or a number of times the at least one vehicle has performed a respective operation of one of the one or more operations; and wherein obtaining the at least one distance to use as an offset for at least one position of the configuration is obtained by using the iteration number.
A person that is skilled in the art would have been motivated to combine Quist, Kanai, and Stewart to improve overall system efficiency [Stewart 0009] “In view of the above, a need exists for an AGV that can efficiently transport material on a predetermined route while minimizing wear on the facility, and more particularly to an AGV that automatically varies the actual travel path from the predetermined route to reduce wear in the floor of the material handling facility.”
Conclusion
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/HUSSAM ALDEEN ALZATEEMEH/Examiner, Art Unit 3662
/Madison R. Inserra/Primary Examiner, Art Unit 3662