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 .
Drawings
The drawings were received on July 20th 2023. These drawings are accepted.
Status of the Claims
This Final action is in response to the applicant’s filing on November 12th 2025.
Claims 1-21 are pending and examined below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 16th 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware of, in the specification.
Response to Arguments
Applicant’s amendments with respect to the rejection of claims under 35 USC § 102(a)(1) have been fully considered but are moot. While the Examiner notes that the applicant is arguing the claim limitations recite " …generating one or more dispatch routes through the one or more route segments based on an optimal routing of resources that maximizes a density metric of the multiple inventory items included in the one or more dispatch routes, wherein the density metric indicates a number of inventory items located within a unit of area… “. Therefore, the rejection has been withdrawn; However, upon further consideration a new ground(s) of rejection is made for Claims 1 and 11 and 20 over Wang (Patent No. US10210212A1) in view of Douglas (Patent No. US20210245956A1).
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.
Claims 1-2, 7-10, 11-12, 17-19 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (Patent No. US10210212A1) in view of Douglas (Patent No. US20210245956A1).
Regarding claim 1 Wang teaches a computer-implemented system for segment based approach to routing picking, the system comprising a memory storing instructions and at least one processor configured to execute the instructions to perform operations comprising; (See Wang column 7, line 32-38; “FIG. 5 illustrates hardware of a special purpose computing machine configured to implement robotics warehouse layout according to an embodiment. In particular, computer system 501 comprises a processor 502 that is in electronic communication with a non-transitory computer-readable storage medium comprising a database 503.”); receiving a floorplan of a first set of location IDs, wherein the first set of location IDs correspond to locations of multiple inventory items arranged in a floor; (See Wang column 8-9, line 63-4; “the warehouse layout and mapping based on the minispace concept are now provided as follows. Typical locating technology for robot travel in the warehouse is based upon location tag recognition. The location tag may be a two-dimensional QRCode.
Behind the QRCode is its unique location ID. Normally that ID is used to identify the lane in which the robot is traveling, or the rackspace to which the robot is going to dock on a rack.”); generating one or more base segments that connect the first set of location IDs of the multiple inventory items; (See Wang column 9, line 8-11; “Each minispace can be identified by the tagging with a unit location ID and a plurality of attributes for instance coordinates. FIG. 9 shows a simplified generic view of a minispace including location tagging.”); generating one or more route segments by combining the one or more base segments with one or more demand points corresponding to a second set of location IDs, wherein the second set of location IDs correspond to inventory items included in customer orders; (See Wang column 8-9, line 63-4; “the warehouse layout and mapping based on the minispace concept are now provided as follows. Typical locating technology for robot travel in the warehouse is based upon location tag recognition. The location tag may be a two-dimensional QRCode.
Behind the QRCode is its unique location ID. Normally that ID is used to identify the lane in which the robot is traveling, or the rackspace to which the robot is going to dock on a rack.”; Also see Wang column 8, line 31-38; “The engine receives from a user 730, a task 732 implicating a product 734 of the warehouse space. In certain embodiments the task may request the workstation to store the product in a particular rack (i.e., for later retrieval). In some embodiments the task may request the workstation to pick the product from a particular rack (i.e., for shipping to a customer).”);
and assigning a first user to a combination of the one or more dispatch routes; (See Wang column 9, line 56-60; “Route planning under the minispace concept is shown at 1108. After the robot is assigned with warehouse task, the WMS starts planning the route from the start point of robot to the Rackspace and then from the rackspace to the workstation or next rackspace.”).
Wang does not explicitly teach but Douglas teaches, generating one or more dispatch routes through the one or more route segments based on an optimal routing of resources that maximizes a density metric of the multiple inventory items included in the one or more dispatch routes; (See Douglas Paragraph 0039, 0076 and 0118; “ A picking AGV 114a . . . 114n may include an automated guided vehicle or robot that may be configured to autonomously transport items from a high-density storage area 304 of the order fulfillment facility to a pick-cell station 316, replenishment area 318, and/or finalizing area 314. The picking AGV 114 may include a drive unit adapted to provide motive force to the picking AGV 114, a guidance system adapted to locate the picking AGV 114 in the order fulfillment facility, and a shelving unit, which may be adapted to hold modular storage units 601, containers, or other items. The picking AGV 114 may include a container handling mechanism (CHM) 616 (e.g., as shown in FIG. 6) that retrieves items or modular storage units 601 from storage shelves (e.g., in the high-density storage area), places items on an item holder (e.g., an AGV shelf) coupled with the picking AGV, and replaces items on storage shelves or at a pick-cell station. In some implementations, a picking AGV 114 may autonomously retrieve modular storage unit(s) 601 containing items to be picked in an order from the high-density storage area… the picking system 108 may identify static or dynamic zones. For instance, a facility may have statically defined zones based on areas of shelving, conveyors, density of items that may be picked, etc. In some implementations, the picking system 108 may dynamically identify and/or define a zone. For example, the picking system 108 may determine, using a clustering algorithm, one or more boundaries of a plurality of zones based on locations of items to be picked, tasks, and/or other attributes. Zones may include various locations of tasks to be performed, such as picks from locations of items in the fulfillment center. Accordingly, the zones may be defined by the quantity and/or locations of picks/tasks or a set of tasks may be defined based on the zone… FIG. 3A depicts a schematic of an example configuration of an order fulfillment center, which may be an operating environment of AGVs, pickers, or other equipment. In some instances, some or all of the operating environment may be divided into one or more zones, as described above. It should be understood that various distribution facilities may include different picking zones having different stocking infrastructure and picking configurations. For instance, high-volume and/or velocity items (e.g., items appearing above a defined threshold of frequency in orders) may be stored in a pick-to-cart area 302 and be available for immediate picking, and relatively moderate and/or low-volume and/or velocity items may be stored in high-density storage area 304 on modular storage units 601 which may be retrieved by picking AGVs 114 for an upcoming pick.”); wherein the density metric indicates a number of inventory items located within a unit of area; (See Douglas Paragraph 0076; “… the picking system 108 may identify static or dynamic zones. For instance, a facility may have statically defined zones based on areas of shelving, conveyors, density of items that may be picked, etc…”).
Both Wang and Douglas are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to alternative routing to pick the inventory item with Douglas density matric. No new functionality would arise from the combination and the combination would improve usability of Wang by including the density metrics that will allow better routing that will include the items density metrics. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang further teaches, wherein the operations further comprise: splitting the one or more route segments into subparts based on distance between a subset of the multiple inventory items connected by the one or more route segments; (See Wang column 11, line 29-37; “For the robot actions execution phase the WMS communicates to the robot, the actions group by group. After completion of execution of the first actions group the WMS performs the following steps before communicating the next group of actions: lock the next forwarding minispace. check that no other Robot is on the lane, and travel to the next minispace. Repeat where the distance is more than 2 minispace”).
Regarding claim 7 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang further teaches, wherein the optimal routing of resources comprises at least one of: minimizing changes in a direction of the resources; minimizing a linear length of travel by the resources; minimizing an interference between the resources; (See Wang column 5, line 1-3; “These basic rules establish a minimum requirement for continuous operation of a robotics warehouse according to this specific example.”; also see Wang column 7, line 50-60; “The warehouse space is divided into a plurality of equally sized minispaces, each corresponding to an area that can hold a rack of standard square dimensions. Each minispace is identified by the tagging with a unit location ID and attribute(s) such as coordinates. Minispaces may be referenced in a task planning process flow initially comprising determining stock and rack, followed by the determination of a relevant workstation. The task is then assigned to an appropriate robot, and a travel route planned (e.g., one involving a shorted path in the layout of minispaces).”).
Wang does not explicitly teach but Douglas teaches, or maximizing the density metric of the one or more dispatch routes; (See Douglas Paragraph 0039, 0076 and 0118; “ A picking AGV 114a . . . 114n may include an automated guided vehicle or robot that may be configured to autonomously transport items from a high-density storage area 304 of the order fulfillment facility to a pick-cell station 316, replenishment area 318, and/or finalizing area 314. The picking AGV 114 may include a drive unit adapted to provide motive force to the picking AGV 114, a guidance system adapted to locate the picking AGV 114 in the order fulfillment facility, and a shelving unit, which may be adapted to hold modular storage units 601, containers, or other items. The picking AGV 114 may include a container handling mechanism (CHM) 616 (e.g., as shown in FIG. 6) that retrieves items or modular storage units 601 from storage shelves (e.g., in the high-density storage area), places items on an item holder (e.g., an AGV shelf) coupled with the picking AGV, and replaces items on storage shelves or at a pick-cell station. In some implementations, a picking AGV 114 may autonomously retrieve modular storage unit(s) 601 containing items to be picked in an order from the high-density storage area… the picking system 108 may identify static or dynamic zones. For instance, a facility may have statically defined zones based on areas of shelving, conveyors, density of items that may be picked, etc. In some implementations, the picking system 108 may dynamically identify and/or define a zone. For example, the picking system 108 may determine, using a clustering algorithm, one or more boundaries of a plurality of zones based on locations of items to be picked, tasks, and/or other attributes. Zones may include various locations of tasks to be performed, such as picks from locations of items in the fulfillment center. Accordingly, the zones may be defined by the quantity and/or locations of picks/tasks or a set of tasks may be defined based on the zone… FIG. 3A depicts a schematic of an example configuration of an order fulfillment center, which may be an operating environment of AGVs, pickers, or other equipment. In some instances, some or all of the operating environment may be divided into one or more zones, as described above. It should be understood that various distribution facilities may include different picking zones having different stocking infrastructure and picking configurations. For instance, high-volume and/or velocity items (e.g., items appearing above a defined threshold of frequency in orders) may be stored in a pick-to-cart area 302 and be available for immediate picking, and relatively moderate and/or low-volume and/or velocity items may be stored in high-density storage area 304 on modular storage units 601 which may be retrieved by picking AGVs 114 for an upcoming pick.”).
Both Wang and Douglas are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to alternative routing to pick the inventory item with Douglas density matric. No new functionality would arise from the combination and the combination would improve usability of Wang by including the density metrics that will allow better routing that will include the items density metrics. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang further teaches, wherein each of the first set of locations IDs is individually addressable identifiers associated with a physical location in the floor; (See Wang column 9, line 1-4; “Behind the QRCode is its unique location ID. Normally that ID is used to identify the lane in which the robot is traveling, or the rackspace to which the robot is going to dock on a rack.”).
Regarding claim 9 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang further teaches, assigning the first user to the combination of the one or more dispatch routes comprises: determining a location of the first user based on a location of a first user device; and assigning the first user to a first combination of the one or more dispatch routes, wherein the first user is located closest to a starting location of the first combination of the one or more dispatch routes; (See Wang column 3-4, line 66-16; “the warehouse layout model serves as a bridge between the warehouse map/location model specific to a particular physical location and robotics system, and an overlying generalized warehouse management system. The layout model acts as a translator across different robotics systems, facilitating adaptation as a warehouse changes and evolves. The layout model allows the warehouse management system to customize, setup, and deploy for different customers and use cases. Embodiments allow the process of robotics warehouse design to focus upon the space of storage and productivity by number of workstations, with the warehouse management system focusing upon the planning and execution tracking with underlying robotics systems. Use of the warehouse model according to embodiments also offers ready scalability. Based upon the changes in demand for products, it becomes relatively simple to correspondingly adapt warehouse features such as available storage space and numbers of workstations.”).
Regarding claim 10 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang does not teach but Douglas teaches, wherein maximizing the density metric of the multiple inventory items included in the one or more dispatch routes comprises maximizing one or more of: a first density of the multiple inventory items in the one or more route segments or a second density between the one or more route segments; (See Douglas Paragraph 0076-0078; “In some implementations, the picking system 108 may identify static or dynamic zones. For instance, a facility may have statically defined zones based on areas of shelving, conveyors, density of items that may be picked, etc. In some implementations, the picking system 108 may dynamically identify and/or define a zone. For example, the picking system 108 may determine, using a clustering algorithm, one or more boundaries of a plurality of zones based on locations of items to be picked, tasks, and/or other attributes. Zones may include various locations of tasks to be performed, such as picks from locations of items in the fulfillment center. Accordingly, the zones may be defined by the quantity and/or locations of picks/tasks or a set of tasks may be defined based on the zone.
In some implementations, the picking system 108 may identify zones in a warehouse or distribution facility based on a quantity or attributes of tasks, available pickers, and/or carts. In some instances, identification or definition of zones may also be based on locations of picks, tasks, carts, and pickers or other aspects of an operating environment. For example, the picking system 108 may determine the quantity and locations of the items to be picked (e.g., during a given period) and may divide up the picks based on a quantity of available pickers and/or their locations. The zone may include a group of shelving bays from which a picker would pick items and place them into one or more cartons transported by one or more cart AGVs 116 in the zone. Accordingly, a picker may perform tasks for a number of carts together to increase pick density through that zone, for example, by reducing the distance that the picker would walk and familiarity with locations of items in the zone. For example, the timing of the tasks performed by the picker may be scheduled to coordinate with a timing at which the cart is located at the zone, at a particular location in the zone (e.g., nearest the picks), or near the picker.
In some implementations, the picking system 108 may identify or define zones dynamically, for example, using a clustering algorithm such as k-means, auction, mean-shift, centroid, density-based, other clustering algorithms, or a combination of algorithms (e.g., k-means in conjunction with an auction algorithm). For instance, the picking system 108 may adjust the size, shape (e.g., boundaries), or location of zones based on a quantity and location of picks, so that zones for each picker have roughly balanced workload. The picking system 108 may balance the workload of pickers, etc., based on distance traveled between picks, distance traveled from storage locations to cartons (e.g., on cart AGVs 116), quantity of items, etc.”).
Both Wang and Douglas are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to alternative routing to pick the inventory item with Douglas density matric. No new functionality would arise from the combination and the combination would improve usability of Wang by including the density metrics that will allow better routing that will include the items density metrics. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With respect to the independent claim 11, please see rejection above with respect to claim 1 which is commensurate in scope to claim 11, with claim 1 being drown to system, claim 11 being drawn to an invention method.
With respect to the dependent claims 12 and 17-19, please see rejection above with respect to claims 2 and 7-9 which are commensurate in scope to claims 12 and 17-19, with claims 2 and 7-9 being drown to system, claims 12 and 17-19 being drawn to an invention method.
Regarding claim 21 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang does not teach but Douglas teaches, wherein the one or more route segments comprise a first and second route segment, and wherein the operations further comprise: determining an intersegment route connecting the first and second route segments based on a cross density between the first and second route segments, a density of the second route segment, and density around a last demand point of the second route segment, and generating the one or more dispatch routes through the intersegment route; (See Douglas Paragraph 0039, 0076 and 0118; “ A picking AGV 114a . . . 114n may include an automated guided vehicle or robot that may be configured to autonomously transport items from a high-density storage area 304 of the order fulfillment facility to a pick-cell station 316, replenishment area 318, and/or finalizing area 314. The picking AGV 114 may include a drive unit adapted to provide motive force to the picking AGV 114, a guidance system adapted to locate the picking AGV 114 in the order fulfillment facility, and a shelving unit, which may be adapted to hold modular storage units 601, containers, or other items. The picking AGV 114 may include a container handling mechanism (CHM) 616 (e.g., as shown in FIG. 6) that retrieves items or modular storage units 601 from storage shelves (e.g., in the high-density storage area), places items on an item holder (e.g., an AGV shelf) coupled with the picking AGV, and replaces items on storage shelves or at a pick-cell station. In some implementations, a picking AGV 114 may autonomously retrieve modular storage unit(s) 601 containing items to be picked in an order from the high-density storage area… the picking system 108 may identify static or dynamic zones. For instance, a facility may have statically defined zones based on areas of shelving, conveyors, density of items that may be picked, etc. In some implementations, the picking system 108 may dynamically identify and/or define a zone. For example, the picking system 108 may determine, using a clustering algorithm, one or more boundaries of a plurality of zones based on locations of items to be picked, tasks, and/or other attributes. Zones may include various locations of tasks to be performed, such as picks from locations of items in the fulfillment center. Accordingly, the zones may be defined by the quantity and/or locations of picks/tasks or a set of tasks may be defined based on the zone… FIG. 3A depicts a schematic of an example configuration of an order fulfillment center, which may be an operating environment of AGVs, pickers, or other equipment. In some instances, some or all of the operating environment may be divided into one or more zones, as described above. It should be understood that various distribution facilities may include different picking zones having different stocking infrastructure and picking configurations. For instance, high-volume and/or velocity items (e.g., items appearing above a defined threshold of frequency in orders) may be stored in a pick-to-cart area 302 and be available for immediate picking, and relatively moderate and/or low-volume and/or velocity items may be stored in high-density storage area 304 on modular storage units 601 which may be retrieved by picking AGVs 114 for an upcoming pick.”).
Both Wang and Douglas are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to alternative routing to pick the inventory item with Douglas density matric. No new functionality would arise from the combination and the combination would improve usability of Wang by including the density metrics that will allow better routing that will include the items density metrics. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 3-6, 13-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (Patent No. US10210212A1) in view of Douglas (Patent No. US20210245956A1) and Nair (Patent No. US20180075521A1).
Regarding claim 3 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang does not teach but Nair teaches, wherein the operations further comprise: detecting an erroneous route segments based on the density metric calculated for the one or more route segments; and updating the floorplan to remove the erroneous route segments; (See Nair paragraph 0037; “…the algorithm is adjusted based on at least one of the following: changes in inventory stocking at the physical store, changes in weightings assigned, aggregated store arrangements, department specific assessments/rankings, product specific assessments/rankings, etc.”).
Both Wang and Nair are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to picking the routing with Nair the updating the floorplan. No new functionality would arise from the combination and the combination would improve usability of Wang by including the updating the floorplan that allows better routing when the inventory and floor planning changes. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang does not teach but Nair teaches, wherein the operations further comprise: updating the floorplan to reflect a physical reconfiguration of the floor; and regenerating the one or more route segments based on the updated floorplan, wherein the physical reconfiguration comprises at least one of: an addition or a removal of a first inventory item; or an installation or removal of a barrier in the floor; (See Nair paragraph 0021; “One embodiment of the invention provides an application for dynamically learning an optimized picking path within a physical store based on actions of merchandise pickers when a store layout of the physical store is unknown to the application. The application is configured to determine an optimized picking path based on historical data comprising previously executed picking paths. The invention allows a retail company to change a store layout and item locations as often as possible while still providing optimized picking paths without additional cost for additional resources (e.g., hardware).”).
Both Wang and Nair are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to picking the routing with Nair the updating the floorplan. No new functionality would arise from the combination and the combination would improve usability of Wang by including the updating the floorplan that allows better routing when the inventory and floor planning changes. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5 Wang in view of Douglas teaches the computer-implemented system of claim 1, Wang further teaches, mapping a second set of location IDs associated with the one or more ordered items to the one or more route segments; generating the one or more dispatch routes by streamlining the one or more route segments to connect the second set of location IDs; (See Wang column 8-9, line 63-4; “the warehouse layout and mapping based on the minispace concept are now provided as follows. Typical locating technology for robot travel in the warehouse is based upon location tag recognition. The location tag may be a two-dimensional QRCode. Behind the QRCode is its unique location ID. Normally that ID is used to identify the lane in which the robot is traveling, or the rackspace to which the robot is going to dock on a rack.”; Also see Wang column 8, line 31-38; “The engine receives from a user 730, a task 732 implicating a product 734 of the warehouse space. In certain embodiments the task may request the workstation to store the product in a particular rack (i.e., for later retrieval). In some embodiments the task may request the workstation to pick the product from a particular rack (i.e., for shipping to a customer).”).
Wang does not teach but Nair teaches, wherein generating the one or more dispatch routes comprises: receiving one or more ordered items among the multiple inventory items; (See Nair paragraph 0030; “…As shown in FIG. 2B, the optimized picking path is presented as a list of items arranged in an order/sequence that results in the shortest and most efficient walking path for a merchandise picker 30 when picking all items fulfilling a merchandise request…”).
Both Wang and Nair are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to picking the routing with Nair the updating the floorplan. No new functionality would arise from the combination and the combination would improve usability of Wang by including the updating the floorplan that allows better routing when the inventory and floor planning changes. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6 Wang in view of Douglas teaches the computer-implemented system of claim 5, Wang does not teach but Nair teaches, wherein the one or more ordered items are time-gated to comprise items associated with urgent orders; (See Nair paragraph 0034; “The picking path analysis unit 100 is configured to: (1) receive one or more merchandise requests, (2) forward the merchandise requests to one or more devices 50 carried by one or more merchandise pickers 30, (3) receive, from the devices 50, picking data identifying one or more previously executed picking paths performed by the merchandise pickers 30 in fulfilling the merchandise requests, (4) aggregate the picking data, (5) analyze the picking data and the merchandise requests to identify an algorithm suitable for determining an optimized picking path, (6) in response to receiving a new online merchandise request, apply the algorithm to identify an optimized picking path for the new online merchandise request, and (7) provide the optimized picking path to at least one of the merchandise pickers 30.”).
Both Wang and Nair are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to picking the routing. No new functionality would arise from the combination and the combination would improve usability of Wang by including the updating the floorplan that allows better routing when the inventory and floor planning changes. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With respect to the dependent claims 13 -16, please see rejection above with respect to claims 3-6 which are commensurate in scope to claims 13 -16, with claims 3-6 being drown to system, claims 13 -16 being drawn to an invention method.
With respect to the independent claim 20, please see rejection above with respect to claim 11, except for following limitations; wherein the one or more dispatch routes maximize a density metric, calculated between first adjacent pairs of the second set of location IDs or between second adjacent pairs of one or more route segments; (See Wang column 8-9, line 48-4; “At 804, a task is received specifying a product of the warehouse space. At 806, in response to the task a plurality of minispaces is referenced to determine a rack of the warehouse space relevant to the product. At 808, the plurality of minispaces is referenced to determine a static workstation of the warehouse space to receive the rack. At 810, the plurality of minispaces and a warehouse layout model are referenced to prepare a route plan between the rack and the static workstation. At 812, a location map and the route plan are referenced to prepare a robot action plan to move the rack to the static workstation. At 814, the robot action plan is communicated to cause a robot to execute a series of atomic actions to deliver the rack to the static workstation. Further details regarding the warehouse layout and mapping based on the minispace concept are now provided as follows. Typical locating technology for robot travel in the warehouse is based upon location tag recognition. The location tag may be a two-dimensional QRCode. Behind the QRCode is its unique location ID. Normally that ID is used to identify the lane in which the robot is traveling, or the rackspace to which the robot is going to dock on a rack.”); determining a first location of a first user device configured to communicate the first location of a user in possession of the first user device; and generating a signal to the first user device to traverse the one or more dispatch routes, wherein the first user device is located closest to a starting point of the one or more dispatch routes as determined by the first location; (See Wang column 3-4, line 66-16; “the warehouse layout model serves as a bridge between the warehouse map/location model specific to a particular physical location and robotics system, and an overlying generalized warehouse management system. The layout model acts as a translator across different robotics systems, facilitating adaptation as a warehouse changes and evolves. The layout model allows the warehouse management system to customize, setup, and deploy for different customers and use cases. Embodiments allow the process of robotics warehouse design to focus upon the space of storage and productivity by number of workstations, with the warehouse management system focusing upon the planning and execution tracking with underlying robotics systems. Use of the warehouse model according to embodiments also offers ready scalability. Based upon the changes in demand for products, it becomes relatively simple to correspondingly adapt warehouse features such as available storage space and numbers of workstations.”).
Wang does not teach but Nair teaches, receiving one or more urgent items among the multiple inventory items; a second set of location IDs of the one or more urgent items; (See Nair paragraph 0034; “The picking path analysis unit 100 is configured to: (1) receive one or more merchandise requests, (2) forward the merchandise requests to one or more devices 50 carried by one or more merchandise pickers 30, (3) receive, from the devices 50, picking data identifying one or more previously executed picking paths performed by the merchandise pickers 30 in fulfilling the merchandise requests, (4) aggregate the picking data, (5) analyze the picking data and the merchandise requests to identify an algorithm suitable for determining an optimized picking path, (6) in response to receiving a new online merchandise request, apply the algorithm to identify an optimized picking path for the new online merchandise request, and (7) provide the optimized picking path to at least one of the merchandise pickers 30.”).
Both Wang and Nair are in the same field of warehouse management and routing. It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to modify Wang computer-implemented approach to picking the routing. No new functionality would arise from the combination and the combination would improve usability of Wang by including the updating the floorplan that allows better routing when the inventory and floor planning changes. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIDIA KWIATKOWSKA whose telephone number is (571)272-5161. The examiner can normally be reached Monday-Friday 8:00-5:00.
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/L.K./Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666