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 .
Acknowledgments
Applicant elected without traverse claims 1-8 and 13-20 based on election/restriction requirement filed 2/23/2026.
Applicant provided information disclosure statement.
Claim 1-8 and 13-20 are pending.
Allowable Subject Matter
Claims 5 is allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Ames (US8010220B1) in further view of Nusser (US9733646B1) in further view of Stallman (US10360531B1) in further view Miyazaki (US20030023334A1) who teaches waiting for additional data with respect to a manufacturing process. However, with respect to exemplary claim 5, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 5.
Claims 16 is allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Ames (US8010220B1) in further view of Nusser (US9733646B1) in further view Miyazaki (US20030023334A1) who teaches waiting for additional data with respect to a manufacturing process. However, with respect to exemplary claim 16, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 16.
Claims 8 is allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Ames (US8010220B1) in further view of Nusser (US9733646B1) in further view of Stallman (US10360531B1) in further view of Carrier (20190244172) who teaches approving vehicle routes with respect to orders. However, with respect to exemplary claim 8, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 8.
Claims 19 is allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Ames (US8010220B1) in further view of Nusser (US9733646B1) in further view of Jordan Peters (20160086285) who teaches routes with respect to zones and traffic conditions. However, with respect to exemplary claim 19, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 19.
Claims 20 is allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Ames (US8010220B1) in further view of Nusser (US9733646B1) in further view of Carrier (20190244172) who teaches approving vehicle routes with respect to orders. However, with respect to exemplary claim 20, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 20.
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.
Claims 1-8 and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-8 and 13-20 are directed to a method and system.
Regarding step 2A-1, Claims 1-8 and 13-20 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 13 recite the limitations of
receiving a part-supply schedule comprising a part identifier identifying a part to be supplied to the assembly-line process; receiving one or more reference point locations, the one or more reference point locations identifying a location; in response to receiving the one or more reference point locations…generating at least one mission comprising picking up the part from a pick-up location and delivering the part to a drop-off location, wherein the drop-off location is associated with the assembly-line process and at least one of the pick-up location or drop-off location is associated with the one or more reference point locations; selecting…for executing the at least one mission; receiving the at least one mission… and executing the at least one mission…to supply the part to the assembly- line process, comprising…scanning an area around the pick-up location…to automatically locate the part identified in the part-supply schedule; and in response to failing to locate the part: detecting…a visual indicia located on an object that is placed in proximity…identifying…a part identifier associated with the visual indicia; determining that the part identifier matches the part identifier in the part-supply schedule; and receiving the object… to drop-off the part.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of receiving, generating, selecting, executing, scanning, detecting, and determining data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system, the claim language encompasses simply receiving data regarding parts, generating a mission to retrieve the parts, making selections with respect to the mission, receiving the mission, and executing the mission. Additional limitations include scanning an area and detecting visual indices to identify parts as well as receiving and delivering the part. These are mere data manipulation steps that do not require a computer. For example, a shop manager can receive data regarding parts and generate a mission to retrieve and deliver the parts. A manager can select an employee to execute the mission. The employee is able to scan an area and detect visual indices to locate a part and carryout the mission. The claimed invention is merely an automating a manual process or locating and delivering parts. Locating and delivering parts is not novel and has been done before the technological age.
The claims also recite assembly line processes with respect to retrieving and delivering parts. The Applicant’s specification also recite optimizing inventory storage and optimizing retrieving and delivering parts in para 70-71 and 80. The specification also recites resource planning in para 0052. These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (fundamental economic principles or practices; business relations). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements of self-driving material-transport vehicle, controlling, by the vehicle processor, the self-driving vehicle to drive, sensors, vehicle processor, system, fleet management system, fleet management processor.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe what the pickup location is such as truck dock. In addition, the dependent claims describe where information is coming from such as from a user.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element.
Claim 1 further recites self-driving material-transport vehicle, controlling, by the vehicle processor, the self-driving vehicle to drive, sensors
Claim 13 recites system, fleet management system, fleet management processor, self-driving material-transport vehicle, vehicle processor
Claim 18 recites sensors.
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states general purpose computer configurations in para 40 and 53.
When looking at the additional elements in combination, the computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-8 and 13-20 are rejected under 35 U.S.C. 101.
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.
Claim(s) 1, 2, 3, 4, 6, and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ames (US8010220B1) in further view of Nusser (US9733646B1) in further view of Stallman (US10360531B1).
Regarding claim 1, Ames teaches
A method for autonomous lineside parts delivery to an assembly-line process (See abstract-A parts delivery management system and method that synchronizes production line side parts deliveries with a production schedule to facilitate delivering the right part to the right production line location at the right time.) This teaches a method with delivering parts to an assembly line process.
receiving a part-supply schedule comprising a part identifier identifying a part to be supplied to the assembly-line process (See col 3-4 A delivery management system application executing on a computer receives manufacturer production schedule data as well as part and container data to determine which parts are needed throughout the day or production time period at the various production line side locations and when they are needed.) (See col 9- part number 5555-SNE and 12345-SDA) This shows the DMS system receives party supply schedule such as the manufacturer production schedule data. This includes what parts needed. The parts have an identifier such as 5555-SNE and 12345-SDA.
receiving one or more reference point locations, the one or more reference point locations identifying a location (See col 3-4- A delivery management system application executing on a computer receives manufacturer production schedule data as well as part and container data to determine which parts are needed throughout the day or production time period at the various production line side locations and when they are needed.) (See fig. 5 and col. 7-8- At each line side location 504, 506, 508, 510, 512, 514, a different line side footprint may be used to accommodate the containers that hold the parts that are delivered line side). The DMS application also receives reference point locations. Reference point locations correspond to the drop-off/delivery locations. For example, items 504 to 514 are the drop/off delivery locations.
response to receiving the one or more reference point locations, automatically generating at least one mission comprising picking up the part from a pick-up location and delivering the part to a drop-off location (See col.5-6- The DMS application also provides information on where to deliver the part containers on the production line. The application generates a “cart master label” or CML 102 ) (See col. 5-6- Each cart on a train 116, 118 has a CML that identifies the parts to be loaded on the cart and the delivery locations and times for the parts loaded on the cart. Carts of the same or different types are linked according to a sequence determined by the DMS application and then loaded with the parts identified on the CML. As trains are configured for parts delivery, the cart license plates 106 and CML 104 are scanned to link and register them with the DMS application. The scanned data are compared to the CML to confirm that the correct cart types have been linked for the planned parts delivery. The CML is then placed in a plastic sleeve on the cart to facilitate the selection or “picking” of parts and delivery of them to the production line.) After receiving information from the manufacturer by the DMS application, such as a part-supply schedule, the DMS application generates a mission by the way of generating a CML for the cart to pick the parts from the picking locations and deliver the parts to drop-off location. The CML is scanned so the driver knows the mission as seen here (See col 7-8- A driver uses a “terminal monitor” 404 bar code reader to scan the CML. Other devices for communicating with drivers such as overhead display boards 402 may be used as well. The scanner is loaded with instructions for the driver and shows the dispatch time as well as the routes to the production line (delivery aisle) and to the line side location (delivery location) of the first cart to be delivered (which is the last cart attached to the train). )
wherein the drop-off location is associated with the assembly-line process and at least one of the pick-up location or drop-off location is associated with the one or more reference point locations; Examiner interprets the drop-off and the reference point location to be the same. They are both associated with the assembly line process since they correspond to the area in the assembly where they need to be dropped. (See fig. 5 and col. 7-8- At each line side location 504, 506, 508, 510, 512, 514, a different line side footprint may be used to accommodate the containers that hold the parts that are delivered line side). This shows the drop off/reference point locations which correspond to items 504-514.
However Ames doesn’t teach self-driving vehicle, however Nusser teaches
selecting a self-driving material-transport vehicle for executing the at least one mission (See col 3-4- A central control system may then instruct the fixed robotic manipulator to pick cases off the pallet and give them to individual AGVs based on target delivery locations for the cases. Each of the AGVs may then be assigned to transport the cases to robotic truck loaders for loading onto several adjacent delivery trucks.) This shows the central control system selects/assigns self-driving vehicles to execute the mission of delivering items. Self-driving vehicle can be seen in fig. 1A. The AGVs also correspond to the self-driving vehicles.
receiving the at least one mission at a vehicle processor on the vehicle; and (See col 9-10- FIG. 2A illustrates a robotic truck unloader, according to an example embodiment. In some examples, a robotic truck unloader may include one or more sensors, one or more computers, and one or more robotic arms) (See figure 4) (See col 15-16- As shown by block 402 of FIG. 4, method 400 may involve causing one or more mobile robots to deliver one or more objects to at least one location within an area of reach of a fixed robotic manipulator. In some examples, the mobile robots may include AGV's or other relatively small robots that transport individual objects or small numbers of objects to the fixed robotic manipulator. ) (See col 15-16- Additionally, a control system may cause the mobile robots to move to locations within reach of a fixed robotic manipulator in a number of different ways. ) This shows the control system assigns tasks to the robots such as AGVs. Examiner interprets these tasks are processed by the AGV computer to carry out. Computers include processors.
executing the at least one mission with the vehicle to supply the part…(See col 9-10- FIG. 2A illustrates a robotic truck unloader, according to an example embodiment. In some examples, a robotic truck unloader may include one or more sensors, one or more computers, and one or more robotic arms) (See figure 4) (See col 15-16- As shown by block 402 of FIG. 4, method 400 may involve causing one or more mobile robots to deliver one or more objects to at least one location within an area of reach of a fixed robotic manipulator. In some examples, the mobile robots may include AGV's or other relatively small robots that transport individual objects or small numbers of objects to the fixed robotic manipulator. ) (See col 15-16- Additionally, a control system may cause the mobile robots to move to locations within reach of a fixed robotic manipulator in a number of different ways) (See fig. 1a) This shows the robots such as AGVs carryout the tasks/mission to supply the part.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement the deliveries with respect to autonomous guided vehicles. This will allow for cost cutting since no drivers would be required to be hired and it would also provide more efficiency since the vehicles can be programmed to destinations instead of the drivers having to manually scan each CML to see where the carts go.
In addition Ames already teaches to the assembly- line process (See fig. 5) Fig. 5 shows assembly line locations 504-514.
In addition, Ames already teaches pickup locations such as when the system picks parts from the stock, however it is not clear that the vehicle drives to pickup locations to pick up items, however Nusser teaches
comprising: controlling, by the vehicle processor, the self-driving vehicle to drive to the pick- up location to pick-up the part (See col 11-13- In reference to FIG. 3A, a robotic fleet may include multiple AGV's 302 for quickly transporting small totes, such as individual boxes or objects. The AGV's 302 may be assigned by a centralized control system to move to particular areas of warehouse 300 to pick up boxes for transport to another location, such as to store boxes or to move boxes to a location to await delivery from the warehouse 300. ) (See fig. 3c and 3d) This shows the AGV is controlled to pickup items from a pickup location to move to another location such as a truck loading area as seen in item 118 in fig. 1A.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement the analysis with respect to picking up items from pick up locations. Ames already teaches pickup locations as taught above, but now the system of Ames would be more sophisticated by also calculating if the vehicle also had to drive pick up items before delivering them. The vehicle would have to drive to locations as seen in fig. 8 of Ames such as the yard.
In addition, even though Ames teaches scanning such as scanning labels on carts, however it is not clear is scanning is done to locate a part, however Nusser teaches
Nusser further teaches comprising: scanning an area around the pick-up location, via one or more sensors coupled to the vehicle processor, to automatically locate the part….(See col 9-10-The sensors may scan an environment containing one or more objects in order to capture visual data and/or three-dimensional (3D) depth information. Data from the scans may then be integrated into a representation of larger areas in order to provide digital environment reconstruction. In additional examples, the reconstructed environment may then be used for identifying objects to pick up ) This shows scans are done by the AGV to locate a part.
detecting, via the one or more sensors, a visual indicia located on an object that is placed in proximity of the one or more sensors; identifying, by the vehicle processor, a part identifier associated with the visual indicia; determining that the part identifier matches the part identifier (See col 13-14- In additional examples, some or all of the robots may scan for labels on objects at different points within the process. The scans may be used to look for visual tags that may be applied to individual components or specific items to facilitate finding or keeping track of components and items.) (See col 13-14 In additional examples, the visual tags may also contain identifying information, such as numbers that correspond to particular objects or particular types or products.) This shows the robots can scan visual tags to identify/find parts. Examiner interprets the robot to scan a visual tag and determine a match between the part number and visual tag to determine what part has been found.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement individual tags on the parts themselves. This would allow the driver to determine parts if they need to double check the load they are carrying instead of just scanning the cart full of parts. This would make the art of Ames more sophisticated.
Ames further teaches in the part supply-schedule (See col 3-4 A delivery management system application executing on a computer receives manufacturer production schedule data as well as part and container data to determine which parts are needed throughout the day or production time period at the various production line side locations and when they are needed.) (See col 9- part number 5555-SNE and 12345-SDA)
Ames further teaches dropping parts to assembly line locations as seen in figure 5 and also teaches receiving objects on the vehicle as also seen in fig. 5. However Ames doesn’t teach controlling the self-driving vehicle
However, Nusser further teaches and receiving the object on the vehicle; and controlling, by the vehicle processor, the self-driving vehicle to drive to the…to drop-off the part. (See fig. 3c and 3d) This shows a vehicle such as item 316 receiving an object and being controlled to drop it off.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement the deliveries with respect to autonomous guided vehicles. This will allow for cost cutting since no drivers would be required to be hired and it would also provide more efficiency since the vehicles can be programmed to destinations instead of the drivers having to manually scan each CML to see where the carts go.
Even though Ames and Nusser teach items to be dropped off and Nusser discusses lost items, it doesn’t teach failing to locate a part,
However Stallman teaches and in response to failing to locate the part (See col 6-7- The system may be intended to function fully automatically without human intervention, but the system may enable a robot to request assistance when a failure condition occurs, such as when the controller is unable to determine how to manipulate a target inventory item, unable to determine a location of the inventory item, unable to determine a destination for the inventory item, etc.) This teaches failing to determine location of an inventory item.
Ames, Nusser, and Stallman are analogous art because they are from the same problem-solving area of order management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's and Nusser’s invention by incorporating the method of Stallman because Ames and Nusser could both implement a dual assistance approach for order filling. If robots or vehicles are unable to carryout a task such as locating a part, the system of Ames and Nusser would be able to put in a request for human assistance. (See abstract Stallman). This would make the system of Ames and Stallman more sophisticated.
Regarding claims 2, Ames, Nusser, and Stallman teach the limitations of claim 1, however Ames further teaches
wherein at least one reference point location of the one or more reference point locations is received from a user. (See figure 1) A user can input the reference/dropoff location from using item 100. (See col 5-6- A delivery management system (DMS) application executing on a computer 100 provides features and functionality for determining how parts delivery trains should be configured throughout the production time period as well as their delivery schedules.)
Regarding claims 3, Ames, Nusser, and Stallman teach the limitations of claim 1, however Ames further teaches
wherein the pick-up location is associated with a reference point location of the one or more reference point locations, and the part-supply schedule further comprises an assembly-line location in the assembly-line process to be supplied with the part, (See fig. 5) The pickup location is associated with reference location such as the drop off location since both of these locations correspond to the part that needs to be supplied to the assembly line. Figure 5 shows parts being supplied to the reference location with respect to the parts being picked up from their respective pickup locations first. (See col 3-4 A delivery management system application executing on a computer receives manufacturer production schedule data as well as part and container data to determine which parts are needed throughout the day or production time period at the various production line side locations and when they are needed.) This shows assembly line location as well where the part should be supplied.
Even though Ames teaches selecting such as selecting parts, it does not teach selecting pickup locations, however Nusser teaches the pick-up location to be a location in proximity of the reference point location and accessible to the vehicle; (See fig. 1A) This shows a pickup location such as item 140 to be in proximity to the drop-off/reference point location such as item 118 and the vehicle can access item 114 to move the packages.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement selecting pickup locations. This would ensure Ames has the capability of getting parts by adding another layer of logistics for the vehicle to first pickup the part. This would make the system of Ames more sophisticated since adding pickup location would let the vehicle in Ames be capable of doing more tasks.
In addition, Ames teaches and selecting the drop-off location to correspond to the assembly-line location; (See col. 5-6-The DMS application also provides information on where to deliver the part containers on the production line. The application generates a “cart master label” or CML 102 for each cart that facilitates the selection of parts to deliver to the production line) The DMS here selects the drop off location an incorporates that into the CML.
Regarding claim 4, Ames, Nusser, and Stallman teach the limitations of claim 3, however Nusser further teaches
wherein the reference point location associated with the pick-up location is one of a delivery-truck dock assigned to receiving the part or an inventory shelf location assigned for storing the part. (See fig. 1A item 118) This shows delivery truck dock.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement a truck dock. This would expend upon the drop-off location that Ames is able to drop parts at. It would not limit Ames to just assembly line location.
Regarding claim 6, Ames, Nusser, and Stallman teach the limitations of claim 1, however Ames further teaches
wherein a fleet-management system generates the at least one mission, (See col.5-6- The DMS application also provides information on where to deliver the part containers on the production line. The application generates a “cart master label” or CML 102 ) (See col. 5-6- Each cart on a train 116, 118 has a CML that identifies the parts to be loaded on the cart and the delivery locations and times for the parts loaded on the cart. Carts of the same or different types are linked according to a sequence determined by the DMS application and then loaded with the parts identified on the CML. As trains are configured for parts delivery, the cart license plates 106 and CML 104 are scanned to link and register them with the DMS application. The scanned data are compared to the CML to confirm that the correct cart types have been linked for the planned parts delivery. The CML is then placed in a plastic sleeve on the cart to facilitate the selection or “picking” of parts and delivery of them to the production line.) After receiving information from the manufacturer by the DMS application, such as a part-supply schedule, the DMS application generates a mission by the way of generating a CML for the cart to pick the parts from the picking locations and deliver the parts to drop-off location. The CML is scanned so the driver knows the mission as seen here (See col 7-8- A driver uses a “terminal monitor” 404 bar code reader to scan the CML. Other devices for communicating with drivers such as overhead display boards 402 may be used as well. The scanner is loaded with instructions for the driver and shows the dispatch time as well as the routes to the production line (delivery aisle) and to the line side location (delivery location) of the first cart to be delivered (which is the last cart attached to the train). )
In addition, Nusser further teaches selects the vehicle for executing the at least one mission, and transmits the at least one mission to the vehicle. (See col 3-4- A central control system may then instruct the fixed robotic manipulator to pick cases off the pallet and give them to individual AGVs based on target delivery locations for the cases. Each of the AGVs may then be assigned to transport the cases to robotic truck loaders for loading onto several adjacent delivery trucks.) This shows the central control system selects/assigns self-driving vehicles to execute the mission of delivering items. Self-driving vehicle can be seen in fig. 1A. The AGVs also correspond to the self-driving vehicles.
and transmits the at least one mission to the vehicle. (See col 9-10- FIG. 2A illustrates a robotic truck unloader, according to an example embodiment. In some examples, a robotic truck unloader may include one or more sensors, one or more computers, and one or more robotic arms) (See figure 4) (See col 15-16- As shown by block 402 of FIG. 4, method 400 may involve causing one or more mobile robots to deliver one or more objects to at least one location within an area of reach of a fixed robotic manipulator. In some examples, the mobile robots may include AGV's or other relatively small robots that transport individual objects or small numbers of objects to the fixed robotic manipulator. ) (See col 15-16- Additionally, a control system may cause the mobile robots to move to locations within reach of a fixed robotic manipulator in a number of different ways. ) This shows the control system assigns tasks to the robots such as AGVs. Examiner interprets these tasks are processed by the AGV computer to carry out. Computers include processors.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement the deliveries with respect to autonomous guided vehicles. This will allow for cost cutting since no drivers would be required to be hired and it would also provide more efficiency since the vehicles can be programmed to destinations instead of the drivers having to manually scan each CML to see where the carts go.
Regarding claim 7, Ames, Nusser, and Stallman teach the limitations of claim 1, however Ames further teaches
wherein the part-supply schedule further comprises a delivery time for supplying the part to the drop-off location, and wherein generating the at least one mission comprises generating the mission to deliver the part to the drop-off location by the delivery time, and wherein executing the at least one mission comprises supplying the part to the drop-off location in accordance with the delivery time in the part-supply schedule. (See fig. 2) (See col. 3-4 A delivery management system application executing on a computer receives manufacturer production schedule data as well as part and container data to determine which parts are needed throughout the day or production time period at the various production line side locations and when they are needed. ) The manufacture production schedule would include when parts are needed by. Figure 2 shows the system generates a plan by calculating times to determine how to accommodate the manufacturer production schedule. The plan is made and executed since trains have status updates such as those in table 2.
Claim(s) 13, 14, 15, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ames (US8010220B1) in further view of Nusser (US9733646B1).
Regarding claim 13, Ames teaches
A system for lineside parts delivery to an assembly-line process, comprising (See abstract-A parts delivery management system and method that synchronizes production line side parts deliveries with a production schedule to facilitate delivering the right part to the right production line location at the right time.) This teaches a system with delivering parts to an assembly line process.
a fleet-management system having a fleet-management processor configured to (See fig. 5) This shows a DMS system which is a computer and the computer has a processor.
receive a part-supply schedule comprising a part identifier identifying a part to be supplied to the assembly-line process, wherein the part-supply schedule comprises an assembly-line location in the assembly-line process to be supplied with the part; (See col 3-4 A delivery management system application executing on a computer receives manufacturer production schedule data as well as part and container data to determine which parts are needed throughout the day or production time period at the various production line side locations and when they are needed.) (See col 9- part number 5555-SNE and 12345-SDA) This shows the DMS system receives party supply schedule such as the manufacturer production schedule data. This includes what parts needed. The parts have an identifier such as 5555-SNE and 12345-SDA. The schedule also includes where the parts needs to be with respect to lineside locations.
receive one or more reference point location, the one or more reference point locations identifying a location (See col 3-4- A delivery management system application executing on a computer receives manufacturer production schedule data as well as part and container data to determine which parts are needed throughout the day or production time period at the various production line side locations and when they are needed.) (See fig. 5 and col. 7-8- At each line side location 504, 506, 508, 510, 512, 514, a different line side footprint may be used to accommodate the containers that hold the parts that are delivered line side). The DMS application also receives reference point locations. Reference point locations correspond to the drop-off/delivery locations. For example, items 504 to 514 are the drop/off delivery locations.
in response to receiving the one or more reference point locations, automatically generate at least one mission comprising: picking up the part from a pick-up location and delivering the part to a drop-off location, (See col.5-6- The DMS application also provides information on where to deliver the part containers on the production line. The application generates a “cart master label” or CML 102 ) (See col. 5-6- Each cart on a train 116, 118 has a CML that identifies the parts to be loaded on the cart and the delivery locations and times for the parts loaded on the cart. Carts of the same or different types are linked according to a sequence determined by the DMS application and then loaded with the parts identified on the CML. As trains are configured for parts delivery, the cart license plates 106 and CML 104 are scanned to link and register them with the DMS application. The scanned data are compared to the CML to confirm that the correct cart types have been linked for the planned parts delivery. The CML is then placed in a plastic sleeve on the cart to facilitate the selection or “picking” of parts and delivery of them to the production line.) After receiving information from the manufacturer by the DMS application, such as a part-supply schedule, the DMS application generates a mission by the way of generating a CML for the cart to pick the parts from the picking locations and deliver the parts to drop-off location. The CML is scanned so the driver knows the mission as seen here (See col 7-8- A driver uses a “terminal monitor” 404 bar code reader to scan the CML. Other devices for communicating with drivers such as overhead display boards 402 may be used as well. The scanner is loaded with instructions for the driver and shows the dispatch time as well as the routes to the production line (delivery aisle) and to the line side location (delivery location) of the first cart to be delivered (which is the last cart attached to the train). )
wherein the drop-off location is associated with the assembly- line process and at least one of the pick-up location or drop-off location is associated with the one or more reference point locations, Examiner interprets the drop-off and the reference point location to be the same. They are both associated with the assembly line process since they correspond to the area in the assembly where they need to be dropped. (See fig. 5 and col. 7-8- At each line side location 504, 506, 508, 510, 512, 514, a different line side footprint may be used to accommodate the containers that hold the parts that are delivered line side). This shows the drop off/reference point locations which correspond to items 504-514.
wherein the pick-up location is associated with a reference point location of the one or more reference point locations; (See fig. 5) The pickup location is associated with reference location such as the drop off location since both of these locations correspond to the part that needs to be supplied to the assembly line. Figure 5 shows parts being supplied to the reference location with respect to the parts being picked up from their respective pickup locations first.
Even though Ames teaches selecting such as selecting parts, it does not teach selecting pickup locations, however Nusser teaches the pick-up location to be a location in proximity of the reference point location and accessible to the vehicle; (See fig. 1A) This shows a pickup location such as item 140 to be in proximity to the drop-off/reference point location such as item 118 and the vehicle can access item 114 to move the packages.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement selecting pickup locations. This would ensure Ames has the capability of getting parts by adding another layer of logistics for the vehicle to first pickup the part. This would make the system of Ames more sophisticated since adding pickup location would let the vehicle in Ames be capable of doing more tasks.
In addition, Ames teaches and selecting the drop-off location to correspond to the assembly-line location; (See col. 5-6-The DMS application also provides information on where to deliver the part containers on the production line. The application generates a “cart master label” or CML 102 for each cart that facilitates the selection of parts to deliver to the production line) The DMS here selects the drop off location an incorporates that into the CML.
However Ames doesn’t teach self-driving vehicle, however Nusser teaches
select a self-driving material-transport vehicle for executing the at least one mission; (See col 3-4- A central control system may then instruct the fixed robotic manipulator to pick cases off the pallet and give them to individual AGVs based on target delivery locations for the cases. Each of the AGVs may then be assigned to transport the cases to robotic truck loaders for loading onto several adjacent delivery trucks.) This shows the central control system selects/assigns self-driving vehicles to execute the mission of delivering items. Self-driving vehicle can be seen in fig. 1A. The AGVs also correspond to the self-driving vehicles.
and transmit the at least one mission to the vehicle; (See col 9-10- FIG. 2A illustrates a robotic truck unloader, according to an example embodiment. In some examples, a robotic truck unloader may include one or more sensors, one or more computers, and one or more robotic arms) (See figure 4) (See col 15-16- As shown by block 402 of FIG. 4, method 400 may involve causing one or more mobile robots to deliver one or more objects to at least one location within an area of reach of a fixed robotic manipulator. In some examples, the mobile robots may include AGV's or other relatively small robots that transport individual objects or small numbers of objects to the fixed robotic manipulator. ) (See col 15-16- Additionally, a control system may cause the mobile robots to move to locations within reach of a fixed robotic manipulator in a number of different ways. ) This shows the control system assigns tasks to the robots such as AGVs. Examiner interprets these tasks are processed by the AGV computer to carry out. Computers include processors.
and the self-driving vehicle having a vehicle processor configured to: receive the mission; (See col 9-10- FIG. 2A illustrates a robotic truck unloader, according to an example embodiment. In some examples, a robotic truck unloader may include one or more sensors, one or more computers, and one or more robotic arms) (See figure 4) (See col 15-16- As shown by block 402 of FIG. 4, method 400 may involve causing one or more mobile robots to deliver one or more objects to at least one location within an area of reach of a fixed robotic manipulator. In some examples, the mobile robots may include AGV's or other relatively small robots that transport individual objects or small numbers of objects to the fixed robotic manipulator. ) (See col 15-16- Additionally, a control system may cause the mobile robots to move to locations within reach of a fixed robotic manipulator in a number of different ways. ) This shows the control system assigns tasks to the robots such as AGVs. Examiner interprets these tasks are processed by the AGV computer to carry out. Computers include processors.
and execute the at least one mission to supply the part to the assembly-line process. (See col 9-10- FIG. 2A illustrates a robotic truck unloader, according to an example embodiment. In some examples, a robotic truck unloader may include one or more sensors, one or more computers, and one or more robotic arms) (See figure 4) (See col 15-16- As shown by block 402 of FIG. 4, method 400 may involve causing one or more mobile robots to deliver one or more objects to at least one location within an area of reach of a fixed robotic manipulator. In some examples, the mobile robots may include AGV's or other relatively small robots that transport individual objects or small numbers of objects to the fixed robotic manipulator. ) (See col 15-16- Additionally, a control system may cause the mobile robots to move to locations within reach of a fixed robotic manipulator in a number of different ways) (See fig. 1a) This shows the robots such as AGVs carryout the tasks/mission to supply the part.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement the deliveries with respect to autonomous guided vehicles. This will allow for cost cutting since no drivers would be required to be hired and it would also provide more efficiency since the vehicles can be programmed to destinations instead of the drivers having to manually scan each CML to see where the carts go.
Regarding claims 14, Ames and Nusser teach the limitations of claim 13, however Ames further teaches
wherein at least one reference point location of the one or more reference point locations is received from a user. (See figure 1) A user can input the reference/dropoff location from using item 100. (See col 5-6- A delivery management system (DMS) application executing on a computer 100 provides features and functionality for determining how parts delivery trains should be configured throughout the production time period as well as their delivery schedules.)
Regarding claim 15, Ames and Nusser teach the limitations of claim 13, however Nusser further teaches
wherein the reference point location associated with the pick-up location is one of a delivery-truck dock assigned to receiving the part or an inventory shelf location assigned for storing the part. (See fig. 1A item 118) This shows delivery truck dock.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement a truck dock. This would expend upon the drop-off location that Ames is able to drop parts at. It would not limit Ames to just assembly line location.
Regarding claim 17, Ames and Nusser teach the limitations of claim 13, however Nusser further teaches
wherein executing the at least one mission comprises the vehicle processor being further configured to: control the self-driving vehicle to drive to the pick-up location to pick-up the part; and control the self-driving vehicle to drive to …to drop-off the part. (See fig. 3c and 3d) This shows the vehicles are controlled to pickup and drop off the part to the truck dock.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement the deliveries with respect to autonomous guided vehicles. This will allow for cost cutting since no drivers would be required to be hired and it would also provide more efficiency since the vehicles can be programmed to destinations instead of the drivers having to manually scan each CML to see where the carts go.
In addition, Ames already teaches assembly line location as seen in figures 2 and 5.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ames (US8010220B1) in further view of Nusser (US9733646B1) in further view of Stallman (US10360531B1).
Regarding claim 18, Ames and Nusser teach the limitations of claim 13, however Nusser further teaches
wherein the vehicle further comprises one or more sensors coupled to the vehicle processor, and at the pick-up location the vehicle processor is further configured to: scan an area around the pick-up location, via the one or more sensors, to automatically locate the part identified (See col 9-10-The sensors may scan an environment containing one or more objects in order to capture visual data and/or three-dimensional (3D) depth information. Data from the scans may then be integrated into a representation of larger areas in order to provide digital environment reconstruction. In additional examples, the reconstructed environment may then be used for identifying objects to pick up ) This shows scans are done by the AGV to locate a part.(See fig. 2A).
…detect, via the one or more sensors, a visual indicia located on an object that is placed in proximity of the one or more sensors; identify a part identifier associated with the visual indicia; and determine that the part identifier matches the part identifier in the part-supply schedule. (See col 13-14- In additional examples, some or all of the robots may scan for labels on objects at different points within the process. The scans may be used to look for visual tags that may be applied to individual components or specific items to facilitate finding or keeping track of components and items.) (See col 13-14 In additional examples, the visual tags may also contain identifying information, such as numbers that correspond to particular objects or particular types or products.) This shows the robots can scan visual tags to identify/find parts. Examiner interprets the robot to scan a visual tag and determine a match between the part number and visual tag to determine what part has been found.
Ames and Nusser are analogous art because they are from the same problem-solving area of delivering items by transport. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's invention by incorporating the method of Nusser because Ames can implement individual tags on the parts themselves. This would allow the driver to determine parts if they need to double check the load that is being carried instead of just scanning the cart full of parts. This would make the art of Ames more sophisticated.
Ames further teaches in the part supply-schedule (See col 3-4 A delivery management system application executing on a computer receives manufacturer production schedule data as well as part and container data to determine which parts are needed throughout the day or production time period at the various production line side locations and when they are needed.) (See col 9- part number 5555-SNE and 12345-SDA)
Even though Ames and Nusser teach items to be dropped off and Nusser discusses lost items, it doesn’t teach failing to locate a part,
However Stallman teaches and in response to failing to locate the part (See col 6-7- The system may be intended to function fully automatically without human intervention, but the system may enable a robot to request assistance when a failure condition occurs, such as when the controller is unable to determine how to manipulate a target inventory item, unable to determine a location of the inventory item, unable to determine a destination for the inventory item, etc.) This teaches failing to determine location of an inventory item.
Ames, Nusser, and Stallman are analogous art because they are from the same problem-solving area of order management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Ame's and Nusser’s invention by incorporating the method of Stallman because Ames and Nusser could both implement a dual assistance approach for order filling. If robots or vehicles are unable to carry out a task such as locating a part, the system of Ames and Nusser would be able to put in a request for human assistance. (See abstract Stallman). This would make the system of Ames and Stallman more sophisticated.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Miyazaki (US20030023334A1) who teaches waiting for additional data with respect to a manufacturing process.
Carrier (20190244172) who teaches approving vehicle routes with respect to orders.
Jordan Peters (20160086285) who teaches routes with respect to zones and traffic conditions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
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