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 .
Status of Claims
This action is in reply to the patent application filed on October 23, 2025.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
The examiner would like to note that this application is being handled by examiner Christine Huynh.
Response to Arguments
Applicant's arguments filed October 23, 2025 have been fully considered but they are not persuasive. With respect to the 35 U.S.C. 103 rejection, applicants argue in pages 8-9 that because there is a difference between the facility and the person of Yoshitake and Surace, then the combination of Baijens’s automated vehicle management Yoshitake’s timing and Surace’s repair time is not feasible for teaching the limit waiting time and the failure status. The applicant indicates that the limit waiting time refers to the time required for the destination point facility to unload the object, and in the amendment, clarifies that the unloading is done by destination point facility. The applicant argues that because Yoshitake teaches an unloading time, but includes a worker, and a not destination point facility to unload the object, then a limit waiting time is not being taught by the combination of Baijens and Yoshitake. The examiner respectfully disagrees, as it is not clear if the unloading at the destination point facility is done autonomously. In addition, Baijens teaches (“In addition or alternatively, loading/unloading and/or transportation in the staging area 112 can be performed using an automated system that includes automated pallet movers, such as automated guided vehicles (AGVs) described herein. For example, automated pallet movers including forklift devices for engaging, raising, and lowering pallets may be used to transfer pallets between trucks 114 and decks 116. As another example, such automated pallet movers may transfer pallets between trucks 114 and the pallet storage area 103.” Baijens [0027]), where the unloading of the objects can be done autonomously, instead of by a worker. Yoshitake, while using workers, teaches the limit waiting time (“The conveyance determination timing that triggers the determination shown in FIG. 11 is the sorting completion timing in the work station WS. In FIG. 11, assuming that there are two automatic conveyance vehicles AC as conveyance candidates and two storage shelves as conveyance candidates, specifically, the storage shelves DS1 and DS2, the estimated conveyance completion times are set to 20 seconds and 50 seconds, respectively. Further, assuming that there are two shelves, specifically, sorting shelves SS1 and SS2 that store shipping boxes into which products taken out from the storage shelves DS1 and DS2 are loaded, the estimated conveyance times are set to 10 seconds and 90 seconds, respectively.” [0124]), and the limit waiting time can be applied to an automated system that is shown in Baijens where either workers or an automated vehicle completes a similar job.
The applicant also argues that the combination of Baijens, Yoshitake, and Surace does not teach a failure status of the destination point facility. However, the examiner respectfully disagrees, because Yoshitake teaches (“If the operation management device 403 issues an instruction to convey the storage shelf DS1, even if the storage shelf DS1 arrives around the work station WS, there is a remaining work for 40 seconds, and hence it is necessary for the storage shelf DS1 to wait for the remaining work to be completed around the work station. In this waiting time, if the storage shelf DS1 may obstruct the passage of other automatic conveyance vehicles AC, the obstructed automatic conveyance vehicle AC waits for the movement of the automatic conveyance vehicle that is conveying the storage shelf DS1 or the automatic conveyance vehicle AC travels on the changed route instructed by the re-route search by the operation management device 403. With this, the movement to the destination is delayed, and as a result, the efficiency in the work station WS waiting for the arrival of the storage shelf conveyed by the another automatic conveyance vehicle AC is lowered.” [0091]), which shows that automated vehicles transport objects through transporting the shelves, and a failure in the movement of the automated vehicles cause failure in the path to the destination, where there is obstruction to the destination of the object and the destination is unable to be used. While Surace teaches the estimated repair time for a vehicle, the estimated repair time can be applied to a vehicle failure on a route, which would affect the entire route.
Therefore, the added limitation “herein the reference automated guided vehicle transports an object, the destination point facility unloads the object at the destination point, and the limit waiting time is a time required for the destination point facility to unload the object” can be taught by Baijens’s automated vehicle management and Yoshitake’s timing, as Baijens teaches (“In addition or alternatively, loading/unloading and/or transportation in the staging area 112 can be performed using an automated system that includes automated pallet movers, such as automated guided vehicles (AGVs) described herein. For example, automated pallet movers including forklift devices for engaging, raising, and lowering pallets may be used to transfer pallets between trucks 114 and decks 116. As another example, such automated pallet movers may transfer pallets between trucks 114 and the pallet storage area 103.” Baijens [0027]), and Yoshitake teaches (“The conveyance determination timing that triggers the determination shown in FIG. 11 is the sorting completion timing in the work station WS. In FIG. 11, assuming that there are two automatic conveyance vehicles AC as conveyance candidates and two storage shelves as conveyance candidates, specifically, the storage shelves DS1 and DS2, the estimated conveyance completion times are set to 20 seconds and 50 seconds, respectively. Further, assuming that there are two shelves, specifically, sorting shelves SS1 and SS2 that store shipping boxes into which products taken out from the storage shelves DS1 and DS2 are loaded, the estimated conveyance times are set to 10 seconds and 90 seconds, respectively.” [0124]), and the limit waiting time can be applied to an automated system that is shown in Baijens where either workers or an automated vehicle completes a similar job. Accordingly, the 35 USC 103 rejection is maintained. See detailed rejection below.
Dependent claims are rejected for similar reasons as listed above. See detailed rejection below.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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, 3-4, 6-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baijens et al. (US 20200317449 A1) in view of Yoshitake et al. (US 20200273132 A1) and Surace (US 20220414568 A1).
Regarding claims 1, 3-4, 6-18, and 20:
With respect to claims 1 and 18, Baijens teaches:
determining whether a task is assigned to a reference automated guided vehicle; (“At 302, identification of the pallets to be moved, their current positioning, and their destination positioning can be determined.” [0037], “the automated pallet mover 130a may receive another job from the control system” [0050]), this shows that it can be determined what tasks are assigned to a vehicle.
extracting a starting point and a destination point of the task when the task is assigned to the reference automated guided vehicle; (“At 302, identification of the pallets to be moved, their current positioning, and their destination positioning can be determined. At 304, optimal routes are determined for moving pallets to their destination positions. For example, optimal routes can be determined by identifying routes that provide minimum crossovers there between when the pallets are transported to their destination positions along those routes (320), by identifying fastest routes for moving pallets to their destination positions (322), by identifying shortest routes for moving pallets to their destination positions (324), and/or by identifying routes that result in fastest completion of a project of moving entire pallets in desired manner (326).” [0037]), which shows that the starting point and a destination point of the task can be determined.
estimating a first moving time which is a moving time of the reference automated guided vehicle from a current point of the reference automated guided vehicle to the destination point, and a limit waiting time; (“Algorithms can be configured to optimize operation of automated guided vehicles in the warehouse, and can be used to determine optimal routes of each automated guided vehicle from a start location to an end location… In addition or alternatively, the algorithms can be configured to optimize or minimize the time required to complete a particular project of moving pallets in a warehouse.” [0005], “At 306, once optimal routes are determined, timing sequences for the routes can be determined. At 308, routes and timing sequences can be adjusted in order to avoid collisions and to maintain a minimum threshold distance between the automated pallet movers. At 310, such adjusted routes and timings can then be provided for use to control operation of the automated pallet movers to execute on the determinations.” [0037]), which shows that an optimal time for a moving vehicle to complete a task from a start location to an end location is determined.
However, Baijens does not teach a limit waiting time, but Yoshitake teaches, (“The conveyance determination timing that triggers the determination shown in FIG. 11 is the sorting completion timing in the work station WS. In FIG. 11, assuming that there are two automatic conveyance vehicles AC as conveyance candidates and two storage shelves as conveyance candidates, specifically, the storage shelves DS1 and DS2, the estimated conveyance completion times are set to 20 seconds and 50 seconds, respectively. Further, assuming that there are two shelves, specifically, sorting shelves SS1 and SS2 that store shipping boxes into which products taken out from the storage shelves DS1 and DS2 are loaded, the estimated conveyance times are set to 10 seconds and 90 seconds, respectively.” [0124]), where the sorting time is comparable to a limit waiting time as it is the time it takes for the objects on the vehicle to be loaded and unloaded.
Baijens further teaches:
determining a departure time of the reference automated guided vehicle from the current point to the destination point based on the first moving time and the limit waiting time; (“Algorithms can be configured to optimize operation of automated guided vehicles in the warehouse, and can be used to determine optimal routes of each automated guided vehicle from a start location to an end location… In addition or alternatively, the algorithms can be configured to optimize or minimize the time required to complete a particular project of moving pallets in a warehouse.” [0005], “At 306, once optimal routes are determined, timing sequences for the routes can be determined. At 308, routes and timing sequences can be adjusted in order to avoid collisions and to maintain a minimum threshold distance between the automated pallet movers. At 310, such adjusted routes and timings can then be provided for use to control operation of the automated pallet movers to execute on the determinations.” [0037]), which shows that the optimal time of the vehicle to complete a task from a start location to an end location is determined and can be used to determine a timing of the vehicle.
starting the reference automated guided vehicle from the current point and arriving at the destination point; (“FIG. 4B is flowchart 450 of an example technique for operating automated pallet movers in the example physical space 400 depicted in FIG. 4A, according to the plurality of lanes 410a-c. A pallet can be retrieved (452). For example, the automated pallet mover 130a can move to pallet handling location 402b and retrieve a pallet that is waiting at that location. After retrieving the pallet, for example, the automated pallet mover 130a can enter and move along the looping slow lane 410a (454). If the automated pallet mover 130a arrives at its destination while travelling in the looping slow lane 410a (456), it may deliver its pallet (458).” [0045]), where the vehicle moves from the current point to the destination point.
determining whether a destination point facility is out of order; (“AGVs can include lasers and/or sensors configured to detect obstacles in its path and trigger them to stop automatically.” [0031], “AGVs can be configured to detect, avoid, and dynamically move around obstacles (including other AGVs) to continue to their destinations, reducing downtime.” [0032]) where it is determined that the path the vehicle is on is out of order. Thus, it would have been obvious to a person of ordinary skill in the art where the destination point is determined to be out of order in an attempt to provide an improved system or method, as a person with ordinary skill has good reason to pursue the known options within his or her technical grasp. In turn, because the product as claimed has the properties predicted by the prior art, it would have been obvious to make the system or product where the destination point is determined to be out of order, as it can be determined that there is an issue along the path of the vehicle, and could be at the destination point.
Yoshitake teaches:
determining a failure response command for the reference automated guided vehicle based on an estimated repair time of the destination point facility when the destination point facility is out of order; (“a standby timing at which the automatic conveyance vehicle AC returns the storage shelf DS, which has been picked in the work station WSi, to the storage area, and the state of the automatic conveyance vehicle AC changes to a state where the next conveyance task can be instructed may be adopted. Alternatively, a recovery timing at which the state of the automatic conveyance vehicle AC returns to a state where the conveyance task of the storage shelf can be executed from the failure state or the low state of the driving battery may be adopted.” [0059]), which shows that when a vehicle has a failure response command there is a failure in the conveyance task to the destination point.
However, Baijens does not teach that an estimated repair time, but Surace teaches (“Alternatively, the determined capability of the vehicle may be authorization for dispatch with at least one maintenance alert (“GO with Alert”) if the determined maintenance state is a maintenance issue requiring less than or equal to a predetermined amount of time for resolution. In addition, the determined capability of the vehicle may be prohibition of dispatch (“NO-GO”) if the determined maintenance state is a maintenance issue requiring greater than the predetermined amount of time for resolution.” [0048]) which, while is regarding the repair of a vehicle, teaches that a repair time can be compared to a predetermined time to make a determination of a task of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management Yoshitake’s timing and Surace’s repair time because (“it is desirable that the timing at which the storage shelf conveyed by the automatic conveyance vehicle arrives at the work station is a timing at which a time for the worker in the work station as a conveyance destination to wait for the start of the picking work on the storage shelf is not caused and a time for the storage shelf to wait for completion of the picking work on another storage shelf in the work station as a conveyance destination is not caused.” See Yoshitake [0007]), to better estimate a time it takes a vehicle to complete a task, and (“analyzing the received vehicle data includes performing the trend analysis by comparing vehicle data from a most-recent flight of the vehicle to vehicle data from previous flights of the vehicle, before the most-recent flight. If the determined capability is prohibition of dispatch (“NO-GO”), the maintenance state, including a result of the trend analysis, is transmitted to the vehicle maintenance service 220.” See Surace [0050]), therefore improving movement of a vehicle.
Baijens further teaches:
herein the reference automated guided vehicle transports an object, the destination point facility unloads the object at the destination point, and the limit waiting time is a time required for the destination point facility to unload the object, (“In addition or alternatively, loading/unloading and/or transportation in the staging area 112 can be performed using an automated system that includes automated pallet movers, such as automated guided vehicles (AGVs) described herein. For example, automated pallet movers including forklift devices for engaging, raising, and lowering pallets may be used to transfer pallets between trucks 114 and decks 116. As another example, such automated pallet movers may transfer pallets between trucks 114 and the pallet storage area 103.” [0027]), where the loading of the objects transported by the vehicle are unloaded by vehicles at the destination point facility.
However, Baijens does not teach a limit waiting time, but Yoshitake teaches, (“The conveyance determination timing that triggers the determination shown in FIG. 11 is the sorting completion timing in the work station WS. In FIG. 11, assuming that there are two automatic conveyance vehicles AC as conveyance candidates and two storage shelves as conveyance candidates, specifically, the storage shelves DS1 and DS2, the estimated conveyance completion times are set to 20 seconds and 50 seconds, respectively. Further, assuming that there are two shelves, specifically, sorting shelves SS1 and SS2 that store shipping boxes into which products taken out from the storage shelves DS1 and DS2 are loaded, the estimated conveyance times are set to 10 seconds and 90 seconds, respectively.” [0124]), where the sorting time is comparable to a limit waiting time as it is the time it takes for the objects on the vehicle to be loaded and unloaded, and the limit waiting time can be applied to an automated system that is shown in Baijens where either workers or an automated vehicle completes a similar job.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Yoshitake’s timing because (“As described above, when there are a plurality of combinations of the storage shelf DS and the sorting shelf SS as candidates, it is desirable that the operation management device 403 selects a combination with the smallest difference between the longer estimated conveyance completion time and the estimated remaining work time and to issue an instruction to convey these shelves.” See Yoshitake [0133]), in order to improve the timing for a vehicle task.
Baijens further teaches;
automated guided vehicles; (“The pallet transportation system described herein uses automated guided vehicles (AGVs) that replace the complex installed conveyor belt system to move pallets in a warehouse. Automated guided vehicles can automatically navigate through the warehouse and can be capable of picking up, moving, and dropping off pallets at various locations in the warehouse.” [0005]
a controller for communicating with the automated guided vehicles, (“an automated warehouse system can include a plurality of automated pallet movers, a physical space in which the plurality of automated pallet movers are configured to operate, and a control system configured to provide commands to each of the plurality of automated pallet movers for operating in the physical space.” [0007]).
With respect to claim 3, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens does not teach, but Yoshitake teaches:
wherein the first moving time is estimated based on a previous automated guided vehicle located ahead on a route for each of automated guided vehicles; (“As an example of another method for calculating the estimated conveyance completion time can also be calculated by machine-learning the movement data of the automatic conveyance vehicle AC in the past operation and inputting the operation state, position, destination of the automatic conveyance vehicle AC at the route search, the operation state of another automatic conveyance vehicle AC, and the arrangement state of the storage shelves DS in the area.” [0079]), which shows that the moving time can be calculated based on a vehicle past operation.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Yoshitake’s previous vehicle because (“it is desirable that the timing at which the storage shelf conveyed by the automatic conveyance vehicle arrives at the work station is a timing at which a time for the worker in the work station as a conveyance destination to wait for the start of the picking work on the storage shelf is not caused and a time for the storage shelf to wait for completion of the picking work on another storage shelf in the work station as a conveyance destination is not caused.” See Yoshitake [0007]), to better estimate a time it takes a vehicle to complete a task.
With respect to claim 4, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens further teaches:
wherein the first moving time is estimated based on an intersecting automated guided vehicle intersecting with the reference automated guided vehicle at an intersection point; (“n some implementations, the various algorithms may be configured to calculate a plurality of possible routes 142 for each automated pallet mover from a start location to an end location, and to determine an optimal route among them. For example, the algorithms can be configured to choose a shortest route for at least one of the automated pallet movers. In addition or alternatively, the algorithms can be configured to minimize the number of cross-overs of the routes taken by multiple automated pallet movers, thereby reducing the likelihood of collision between automated pallet movers. In addition or alternatively, the algorithms can be configured to optimize the timing of operation of respective automated pallet movers, thereby reducing the likelihood of collision between automated pallet movers.” [0035]), which shows that the timing is planned to avoid vehicle collisions, so the time is based on not having an intersection point for the vehicles.
With respect to claim 6, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens further teaches:
wherein the first moving time includes a second moving time which is a moving time of the reference automated guided vehicle from the current point to the starting point, and a third moving time which is a moving time of the reference automated guided vehicle from the starting point to the destination point; (“” [0041]), where the vehicle movement includes a first part of the route, when the vehicle moves from a first position to a starting position, and a second route, where the vehicle moves from a starting position to a transport destination.
With respect to claim 7, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens further teaches:
wherein the current point is a waiting point where the reference automated guided vehicle waits before being assigned the task; (“Rather than transporting the pallet 110 according to one of the routes 142, for example, the automated pallet mover 130 can be configured to move to a pick up location of the pallet 110 and to transport the pallet 110 from its pick up location to its destination location according to a route resulting from performance of the specified control algorithm, while other automated pallet movers concurrently transport other pallets to other destination locations according to other routes (e.g., potentially different routes) also resulting from performance of the same control algorithm.” [0041], “After delivering its pallet, for example, the automated pallet mover 130a may receive another job from the control system, may proceed to a wait area (not shown), may proceed to a charging station (not shown), or may perform another suitable operation.” [0050]), where the vehicle can be at a waiting point before doing a task.
With respect to claim 8, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens further teaches:
loading an object at the starting point and waiting for a confirmation of the destination point, (“the automated pallet mover 130a can move to pallet handling location 402b and retrieve a pallet that is waiting at that location. After retrieving the pallet, for example, the automated pallet mover 130a can enter and move along the looping slow lane 410a (454).” [0045]), where the object is loaded.
wherein the current point is not a waiting point where the reference automated guided vehicle waits before being assigned the task but the starting point; (“Rather than transporting the pallet 110 according to one of the routes 142, for example, the automated pallet mover 130 can be configured to move to a pick up location of the pallet 110 and to transport the pallet 110 from its pick up location to its destination location according to a route resulting from performance of the specified control algorithm, while other automated pallet movers concurrently transport other pallets to other destination locations according to other routes (e.g., potentially different routes) also resulting from performance of the same control algorithm.” [0041]), where the point at which the object is loaded at the current position before it completes the task at the destination point is not at the waiting point.
With respect to claims 9 and 20, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claims 18. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claims 18. Baijens does not teach, but Yoshitake teaches:
wherein the limit waiting time is estimated based on a total number of lot panels and a tact time; (“Further, the storage device may hold identification information and the number of articles taken out from or stored in each of the shelves (for example, FIG. 6), and the processor may calculate the work time based on at least any one of a type of the articles to be taken out from or stored in each of the shelves, the number of the types, and the number of the articles (for example, S904).” [0143]), where it is comparable that the work time is calculated from multiplying the number of items to be loaded or unloaded with the time it takes to load or unload an item.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Yoshitake’s time calculation because (“it is desirable that the timing at which the storage shelf conveyed by the automatic conveyance vehicle arrives at the work station is a timing at which a time for the worker in the work station as a conveyance destination to wait for the start of the picking work on the storage shelf is not caused and a time for the storage shelf to wait for completion of the picking work on another storage shelf in the work station as a conveyance destination is not caused.” See Yoshitake [0007]), to better estimate a time it takes a vehicle to complete a task.
With respect to claim 10, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 9. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 9. Baijens does not teach, but Yoshitake teaches:
wherein the limit waiting time is determined by multiplying the total number of lot panels and the tact time; (“Further, the storage device may hold identification information and the number of articles taken out from or stored in each of the shelves (for example, FIG. 6), and the processor may calculate the work time based on at least any one of a type of the articles to be taken out from or stored in each of the shelves, the number of the types, and the number of the articles (for example, S904).” [0143]), where it is comparable that the work time is calculated from multiplying the number of items to be loaded or unloaded with the time it takes to load or unload an item.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Yoshitake’s time calculation because (“it is desirable that the timing at which the storage shelf conveyed by the automatic conveyance vehicle arrives at the work station is a timing at which a time for the worker in the work station as a conveyance destination to wait for the start of the picking work on the storage shelf is not caused and a time for the storage shelf to wait for completion of the picking work on another storage shelf in the work station as a conveyance destination is not caused.” See Yoshitake [0007]), to better estimate a time it takes a vehicle to complete a task.
With respect to claim 11, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens does not teach, but Yoshitake teaches:
determining a spare time based on the first moving time and the limit waiting time, wherein the spare time is determined by subtracting the first moving time from the limit waiting time; (“With reference to FIG. 5 again, creation of an additional conveyance instruction and determination of whether or not to output the instruction will be described. For example, when the conveyance of the storage shelf DS by the automatic conveyance vehicle AC to a certain work station WS is already planned, the operation management device 403 may calculate the estimated remaining work time of the picking work performed by a worker on the storage shelf DS and the estimated conveyance completion time to the work station WS of the storage shelf DS by the automatic conveyance vehicle AC, and may instruct the automatic conveyance vehicle AC to convey the storage shelf DS when the difference between the calculated estimated remaining work time and the conveyance completion time satisfies a predetermined condition.” [0068], “In this case, the estimated remaining work time (100 seconds) is longer than the estimated conveyance completion time (90 seconds) of the storage shelf DS1 and the sorting shelf SS2, and the difference between the two is 10 seconds. Therefore, when the storage shelf DS1 and the sorting shelf SS2 are conveyed, the arrival waiting time of a shelf is not caused in the work station WS, and the waiting time for the sorting shelf SS2 to wait for completion of the picking work for another shelf in the work station WS is as short as 10 seconds, and there is little concern that it will hinder the conveyance of another shelf and cause traffic jams” [0127]), which shows that a spare time can be calculated from a work time and the conveyance time, or the moving time of the vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Yoshitake’s time calculation because (“it is desirable that the timing at which the storage shelf conveyed by the automatic conveyance vehicle arrives at the work station is a timing at which a time for the worker in the work station as a conveyance destination to wait for the start of the picking work on the storage shelf is not caused and a time for the storage shelf to wait for completion of the picking work on another storage shelf in the work station as a conveyance destination is not caused.” See Yoshitake [0007]), to better estimate a time it takes a vehicle to complete a task.
With respect to claim 12, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens does not teach, but Yoshitake teaches:
determining a spare time based on the first moving time and the limit waiting time, wherein as the spare time is shorter, the departure time is earlier; (“In this case, the estimated remaining work time (100 seconds) is longer than the estimated conveyance completion time (90 seconds) of the storage shelf DS1 and the sorting shelf SS2, and the difference between the two is 10 seconds. Therefore, when the storage shelf DS1 and the sorting shelf SS2 are conveyed, the arrival waiting time of a shelf is not caused in the work station WS, and the waiting time for the sorting shelf SS2 to wait for completion of the picking work for another shelf in the work station WS is as short as 10 seconds, and there is little concern that it will hinder the conveyance of another shelf and cause traffic jams. Therefore, in the example of FIG. 11, when the estimated remaining work time is 100 seconds, it is desirable that the operation management device 403 issues an instruction to convey the sorting shelf SS2.” [0127], “Limiting the total estimated remaining work time of the storage shelves DS that are determined to be conveyed by, for example, the threshold time TH1 leads to prevention of work schedules from being concentrated on a specific work station WS, thereby contributing to improvement of the work efficiency of the entire system. In addition, limiting the difference between the estimated conveyance completion time and the estimated remaining work time of the automatic conveyance vehicle AC by, for example, the threshold time TH2 leads to prevention of too early arrival of the storage shelf DS at the work station WS and occurrence of traffic jams due to the early arrival, thereby contributing to the improvement of the work efficiency of the entire system.” [0099]), which shows that when the spare time is calculated as being short, then instructions regarding the issue are sent. The times are calculated so that the vehicle departure allow for the correct amount to time to complete the task. Therefore, it would be obvious to a person of ordinary skill in the art where the departure time is set earlier in an attempt to provide an improved system or method, as a person with ordinary skill has good reason to pursue the known options within his or her technical grasp. In turn, because the product as claimed has the properties predicted by the prior art, it would have been obvious to make the system or product where the departure time is set earlier so the vehicle has more time to complete a task.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Yoshitake’s time calculation because (“it is desirable that the timing at which the storage shelf conveyed by the automatic conveyance vehicle arrives at the work station is a timing at which a time for the worker in the work station as a conveyance destination to wait for the start of the picking work on the storage shelf is not caused and a time for the storage shelf to wait for completion of the picking work on another storage shelf in the work station as a conveyance destination is not caused.” See Yoshitake [0007]), to better estimate a time it takes a vehicle to complete a task.
With respect to claim 13, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens does not teach, but Surace teaches:
wherein, when the estimated repair time is equal to or less than a first reference value, the reference automated guided vehicle waits at the destination point until the estimated repair time passes; (“Alternatively, the determined capability of the vehicle may be authorization for dispatch with at least one maintenance alert (“GO with Alert”) if the determined maintenance state is a maintenance issue requiring less than or equal to a predetermined amount of time for resolution. In addition, the determined capability of the vehicle may be prohibition of dispatch (“NO-GO”) if the determined maintenance state is a maintenance issue requiring greater than the predetermined amount of time for resolution.” [0048]) which, while is regarding the repair of a vehicle, when combined with the automated vehicle of Baijens that is configured to detect issues on the path and stop (See Baijens [0031]), teaches that a repair time can be compared to a predetermined time to make a determination of a task of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Surace’s repair time because (“analyzing the received vehicle data includes performing the trend analysis by comparing vehicle data from a most-recent flight of the vehicle to vehicle data from previous flights of the vehicle, before the most-recent flight. If the determined capability is prohibition of dispatch (“NO-GO”), the maintenance state, including a result of the trend analysis, is transmitted to the vehicle maintenance service 220.” See Surace [0050]), therefore improving movement of a vehicle.
With respect to claim 14, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 13. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 13. Baijens does not teach, but Surace teaches:
wherein, when the estimated repair time is greater than the first reference value and is less than or equal to a second reference value, the reference automated guided vehicle wanders around the destination point; (“Alternatively, the determined capability of the vehicle may be authorization for dispatch with at least one maintenance alert (“GO with Alert”) if the determined maintenance state is a maintenance issue requiring less than or equal to a predetermined amount of time for resolution. In addition, the determined capability of the vehicle may be prohibition of dispatch (“NO-GO”) if the determined maintenance state is a maintenance issue requiring greater than the predetermined amount of time for resolution.” [0048]) which, while is regarding the repair of a vehicle, when combined with the automated vehicle of Baijens that is configured to detect issues on the path and stop (See Baijens [0031]), teaches that a repair time can be compared to a predetermined time to make a determination of a task of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Surace’s repair time because (“analyzing the received vehicle data includes performing the trend analysis by comparing vehicle data from a most-recent flight of the vehicle to vehicle data from previous flights of the vehicle, before the most-recent flight. If the determined capability is prohibition of dispatch (“NO-GO”), the maintenance state, including a result of the trend analysis, is transmitted to the vehicle maintenance service 220.” See Surace [0050]), therefore improving movement of a vehicle.
With respect to claim 15, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 14. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 14. Baijens does not teach, but Yoshitake teaches:
when the estimated repair time is greater than the second reference value, the reference automated guided vehicle moves to the starting point; (“a standby timing at which the automatic conveyance vehicle AC returns the storage shelf DS, which has been picked in the work station WSi, to the storage area, and the state of the automatic conveyance vehicle AC changes to a state where the next conveyance task can be instructed may be adopted. Alternatively, a recovery timing at which the state of the automatic conveyance vehicle AC returns to a state where the conveyance task of the storage shelf can be executed from the failure state or the low state of the driving battery may be adopted.” [0059]), which shows that when a vehicle can return to a starting point if a time has passed.
However, Yoshitake does not teach that an estimated repair time is greater than the second reference value, but Surace teaches (“Alternatively, the determined capability of the vehicle may be authorization for dispatch with at least one maintenance alert (“GO with Alert”) if the determined maintenance state is a maintenance issue requiring less than or equal to a predetermined amount of time for resolution. In addition, the determined capability of the vehicle may be prohibition of dispatch (“NO-GO”) if the determined maintenance state is a maintenance issue requiring greater than the predetermined amount of time for resolution.” [0048]) which, while is regarding the repair of a vehicle, teaches that a repair time can be compared to a predetermined time to make a determination of a task of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management with Yoshitake’s timing and Surace’s repair time because (“it is desirable that the timing at which the storage shelf conveyed by the automatic conveyance vehicle arrives at the work station is a timing at which a time for the worker in the work station as a conveyance destination to wait for the start of the picking work on the storage shelf is not caused and a time for the storage shelf to wait for completion of the picking work on another storage shelf in the work station as a conveyance destination is not caused.” See Yoshitake [0007]), to better estimate a time it takes a vehicle to complete a task, and (“analyzing the received vehicle data includes performing the trend analysis by comparing vehicle data from a most-recent flight of the vehicle to vehicle data from previous flights of the vehicle, before the most-recent flight. If the determined capability is prohibition of dispatch (“NO-GO”), the maintenance state, including a result of the trend analysis, is transmitted to the vehicle maintenance service 220.” See Surace [0050]), therefore improving movement of a vehicle.
With respect to claim 16, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens further teaches:
unloading an object at the destination point when the destination point facility is not out of order; (“The warehouse 102 can include a staging area 112 (e.g., a loading/unloading area) to move pallets in and out of trucks 114 through doors 115.” [0027], “The technique 300, for example, can be used to determine optimal routes to move pallets in a warehouse, such as routes between staging area 112 (e.g., pallet loading/unloading area) and pallet storage area 103 (e.g., pallet storage racks). At 302, identification of the pallets to be moved, their current positioning, and their destination positioning can be determined.” [0037]), which shows unloading the object at the destination point.
With respect to claim 17, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 1. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 1. Baijens further teaches:
designating a limited number of the automated guided vehicles for each of routes from the current point to the destination point; (“the algorithms can be configured to optimize or minimize the number of cross-overs of the routes taken by the automated guided vehicles. In addition or alternatively, the algorithms can be configured to optimize the timing of operation of respective automated guided vehicles, thereby reducing the likelihood of collision between vehicles.” [0005], “At 308, routes and timing sequences can be adjusted in order to avoid collisions and to maintain a minimum threshold distance between the automated pallet movers.” [0037], “By using a same control algorithm for operating each of the automated pallet movers 130 according to defined lanes, for example, interactions between the automated pallet movers may be simplified, processing resources may be conserved, and a number of automated pallet movers operating in the pallet transportation area 120 may be increased.” [0039), this shows that the routes and timing of the vehicles are optimized, and the more optimized they become, the more vehicles can be permitted to run, therefore shows that an optimized algorithm only allows a certain amount of vehicles to run at a time to avoid collisions.
Claim(s) 2, 5, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baijens et al. (US 20200317449 A1) in view of Yoshitake et al. (US 20200273132 A1), Surace (US 20220414568 A1), and Fuerstenberg et al. (US 20200050719 A1).
Regarding claims 2, 5, and 19:
With respect to claims 2 and 19, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claims 1 and 18. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claims 1 and 18. Baijens further teaches:
wherein the first moving time is estimated based on current locations and simulation times of automated guided vehicles; (“Algorithms can be configured to optimize operation of automated guided vehicles in the warehouse, and can be used to determine optimal routes of each automated guided vehicle from a start location to an end location… In addition or alternatively, the algorithms can be configured to optimize or minimize the time required to complete a particular project of moving pallets in a warehouse.” [0005], “At 306, once optimal routes are determined, timing sequences for the routes can be determined. At 308, routes and timing sequences can be adjusted in order to avoid collisions and to maintain a minimum threshold distance between the automated pallet movers. At 310, such adjusted routes and timings can then be provided for use to control operation of the automated pallet movers to execute on the determinations.” [0037]), which shows that an optimal time for a moving vehicle to complete a task from a start location to an end location is determined.
In addition, Fuerstenberg teaches (“The simulation can generally comprise the simulated use of the mapped environment. Typical uses can be simulated in that they are recalculated along the time axis, preferably in a time discrete manner by recursive calculation. The calculation can perform the assumption of a starting state, the continued calculation of same into an end state after a specific time step according to relevant parameters, the evaluation of the end state, the adopting of the end state as a new starting state, and then repeating the continued calculation.” [0128], “The simulation comprises the occupying of the virtual mapped environment by virtual traffic that can, as described above, be recursively calculated. The virtual traffic can comprise a plurality (a large number) of virtual trips of a number (a large number) of virtual vehicles (incl. pedestrians, a bicycle, . . . ), with a trip being able to be defined as a movement between a starting point and an end point, optionally via one or more waypoints, optionally including stop points and stop times in the virtual environment.” [0129]), where a simulation can be used to determine the moving time a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Fuerstenberg’s simulation because (“In step 12, parameters are provided on the basis of which the simulation to be carried out should be performed. The parameters can comprise vehicle parameters and traffic parameters. Both can be based on or reflect real parameters or values observed in reality. The provision can take place by resetting/inputting and/or invoking and optionally changing stored values.” See Fuerstenberg [0127]), therefore can be used to determine solutions for situations in reality.
With respect to claim 5, Baijens in combination with Yoshitake and Surace, as shown in the rejection above, discloses the limitations of claim 4. The combination of Baijens, Yoshitake, and Surace teaches departure management of an automated guided vehicle of claim 4. Baijens further teaches:
wherein an automated guided vehicle having a longer simulation time among the reference automated guided vehicle and the intersecting automated guided vehicle acquires a priority; (“Algorithms can be configured to optimize operation of automated guided vehicles in the warehouse, and can be used to determine optimal routes of each automated guided vehicle from a start location to an end location. For example, the algorithms can be configured to optimize or minimize the number of cross-overs of the routes taken by the automated guided vehicles… In addition or alternatively, the algorithms can be configured to optimize or minimize the time required to complete a particular project of moving pallets in a warehouse.” [0005], “At 306, once optimal routes are determined, timing sequences for the routes can be determined. At 308, routes and timing sequences can be adjusted in order to avoid collisions and to maintain a minimum threshold distance between the automated pallet movers. At 310, such adjusted routes and timings can then be provided for use to control operation of the automated pallet movers to execute on the determinations.” [0037]), which shows that an optimal time for a moving vehicle to complete a task from a start location to an end location is determined.
In addition, Fuerstenberg teaches a simulation length, (“If no statistically significant count values are produced after a sufficiently long simulation time of a part simulation with respect to events of interest, this can be taken as an abort criterion for this simulation part. A further simulation having changed parameters can then be initiated, for instance having higher permitted top speeds, smaller distances, smaller protected fields and/or warning fields, or similar. A check can then again be made whether count values with statistical significance are produced and if so, the count values can be used as an evaluation of the respective parameter set.” [0086]), which shows the simulated path with no issues.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Baijens’s automated vehicle management and Fuerstenberg’s simulation because (“In step 12, parameters are provided on the basis of which the simulation to be carried out should be performed. The parameters can comprise vehicle parameters and traffic parameters. Both can be based on or reflect real parameters or values observed in reality. The provision can take place by resetting/inputting and/or invoking and optionally changing stored values.” See Fuerstenberg [0127]), therefore can be used to determine solutions for situations in reality.
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.
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/CHRISTINE NGUYEN HUYNH/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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