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 .
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella (US20230252047) in view of Lehoux (US 20190156218 A1).
Claim 1.
Cella teaches the following limitations:
A computerized method comprising:
generating a routing graph comprising nodes and edges populated from routes of vehicles, (Cella – [0330] a robotic/autonomous vehicle system, a packaging system, a packing system, a picking system, an inventory tracking system, an inspection system, a routing system for mobile robots; [0392] the management platform includes a set of value chain networks 3102 from which network data 3110 is collected from a set of information routing activities, the information including outcomes, parameters, routing activity information and the like. Within the set of value chain networks 3102 is selected a select value chain network 3104 for which at least one information routing recommendation 3130 is provided; [2404] In some embodiments, a set of two or more digital twins may be represented by a graph database that includes nodes and edges that connect the nodes.) the nodes identifying locations through which the vehicles travel along the routes and the edges identifying routing data for route segments for the vehicles traveling between connected nodes; (Cella – [2473] In embodiments, the digital twin module 13420 may analyze data received from the digital twin I/O system 13308 to refine, remove, or add conditions. For example, the digital twin module 13420 may optimize data collection times for movable elements that are updated more frequently than needed (e.g., multiple consecutive received positions being identical or within a predetermined margin of error).
removing nodes and edges from the routing graph for which arrival times exceed departure times for a node connecting at least two route segments, thereby forming a refined routing graph; (Cella – [2653] The report may be used to reconfigure/retrain the intelligent agent. In embodiments, the reconfiguring/retraining an intelligent agent may include removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system)
receiving an origin–destination input identifying a destination node and an origin node; (Cella – [0401] In embodiments, recognizing problem states may be based on variance analysis, such as variances that occur in value chain measures (e.g., loading, latency, delivery time, cost, and the like), particularly in a specific measure over time; [2325] Examples of freight storage and/or transportation service order parameters may include, but are not limited to: origin location and destination location information and/or other physical locations along a route;)
filtering, from the refined routing graph, nodes and edges according to a first set of routing constraints, thereby forming a reduced route search space; and (Cella – [0959] In embodiments, the digital twin access controller 8112 informs the generation system 8108 of specific constraints around the roles of users able to view the digital twin as well as providing for dynamically adjustable digital twins that can adapt to constrain or release views of the data or other features specific to each user role)
invoking a quantum annealer to generate an optimal route from the reduced route search space according to an objective and a second set of routing constraints. (Cella – [2741] In embodiments, quantum computers that may run analog machines include, but are not limited to, quantum annealers, adiabatic quantum computers, and direct quantum simulators).
However, Cella does not explicitly disclose:
“…removing from the refined routing graph nodes and edges that do not meet one or more routing constraints of the first set of routing constraints and by removing from the refined routing graph nodes and edges that do not connect between the origin node and the destination node, thereby forming a reduced route search space having routes through remaining nodes.”
Lehoux teaches:
Filtering a routing graph to remove nodes and edges that do not connect between a specified origin and destination, thereby forming a reduced route search space comprising only routes through remaining nodes.
(Lehoux – [0107]: “The method may include filtering the graph to remove nodes and edges that do not meet the constraint…”; claim 6: “removing, from the graph, nodes and edges that are not part of any path between the origin and destination nodes.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cella and Lehoux, as both references are directed to optimizing routing in graphs for vehicles or logistics. The motivation to combine arises from the desire to further improve computational efficiency and search performance by reducing the route search space to only those nodes and edges that are relevant to the specific origin-destination request, as taught by Lehoux ([0107], claim 6). This would result in a more efficient route computation
Claim 2.
Cella teaches the following limitations:
The method of claim 1, wherein the reduced route search space is formed by further filtering routes that exclude at least one of the destination node and the origin node. (Cella – [0332] a logistics application 912 (such as for managing logistics for pickups, deliveries, transfer of goods onto hauling facilities, loading, unloading, packing, picking, shipping, driving, and other activities involving in the scheduling and management of the movement of products 650 and other items between points of origin and points of destination through various intermediate locations; [1956] A workflow definition system may determine which, if any, workflow in the library 12314 (base workflow) is suitable for use in the current job workflow definition instance; determine adjustments to the retrieved workflow; and produce an instance-specific job workflow that may include additional tasks not found in the base workflow and/or exclude unnecessary tasks found in the base workflow, and the like.)
Claim 3.
Cella teaches the following limitations:
The method of claim 1, wherein the origin–destination input corresponds to an item delivery, the item delivery having an associated item delivery time by which to deliver the item, and wherein the first set of routing constraints includes the associated item delivery time such that the reduced route search space is formed by filtering routes that have arrival times at the destination node that are beyond the item delivery time. (Cella – [0401] In embodiments, recognizing problem states may be based on variance analysis, such as variances that occur in value chain measures (e.g., loading, latency, delivery time, cost, and the like), particularly in a specific measure over time. Variances that exceed a variance threshold (e.g., an optionally dynamic range of results of a value chain operation, such as production, shipping, clearing customs, and the like) may be indicative of a pain point; [0440] In embodiments, analytics 838 may be used to identify which environments or activities would most benefit from automation for purposes of improved delivery times, mitigation of congestion, and other performance improvements; [0469] In embodiments, the demand factors 1540 as mentioned throughout this disclosure may include, for example and without limitation, ones involving product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall…)
Claim 4.
Cella teaches the following limitations:
The method of claim 1, wherein the first set of routing constraints includes a weight of an item such that the reduced route search space is formed by filtering routes by vehicles for which the weight of the item exceeds a vehicle weight capacity. (Cella – [2060] The MPR 12100 may be configured with one or more of the end effectors, such that the one or more end effectors may be selected based on multiple factors including the task(s) to be performed; the size, shape, surface and weight of the object to be manipulated; environment of the object including the material clearance available around the object; [2325] Examples of freight storage and/or transportation service order parameters may include, but are not limited to: origin location and destination location information and/or other physical locations along a route; weight requirements; volume requirements; cargo descriptions)
Claim 5.
Cella teaches the following limitations:
The method of claim 1, wherein the first set of routing constraints includes a volume of an item such that the reduced route search space is formed by filtering routes by vehicles for which the volume of the item exceeds a vehicle volume capacity. (Cella – [2155] container usage requirements (e.g., full container (FCL) vs. shared container (LCL)), container size requirements for FCL shipments (e.g., 20-ft container, 40-ft container, 40-ft high-cube, tank, or the like), cargo descriptions for LCL shipments (e.g., number of packages, total volume, total weight, or the like [2325] Examples of freight storage and/or transportation service order parameters may include, but are not limited to: origin location and destination location information and/or other physical locations along a route; weight requirements; volume requirements; cargo descriptions)
Claim 6.
Cella teaches the following limitations:
The method of claim 1, wherein the objective minimizes duration, and the second set of routing constraints constrains the objective to the destination node and the origin node and constrains the objective to connected route segments. (Cella - [1987] job-specific demand data 12476 may be allocated throughout a duration of time within which a requested job is constrained to be performed. The fleet configuration scheduler 12468 (e.g., with support from other platform resources such as fleet intelligence layer 12004, fleet provisioning system 12014 and the like) may allocate, based on conditions in the job request; [2369] In some embodiments, a fleet configuration scheduler may respond to a freight storage and/or transportation service order by allocating fleet resources to meet the freight storage and/or transportation service order needs. These needs may be preprocessed, as described herein by a job configuration system 13018 and specifically by the task definition system to facilitate fleet configuration, allocation, and scheduling. The fleet configuration scheduler processes inputs that describe fleet inventories, such as smart container operating unit inventories and traditional container operating unit inventories to identify candidate inventory elements for satisfying a freight storage and/or transportation service order)
Claim 7.
Cella teaches the following limitations:
The method of claim 1, wherein the reduced route search space comprises a route segment having a plurality of offset departure and arrival times for vehicles traveling between a first node and a second node of the route segment, and wherein the quantum annealer selects a vehicle having a departure and arrival time from the plurality of offset departure and arrival times for inclusion in the optimal route. (Cella – [0329] For example, an IoT system deployed in a fulfillment center 628 may coordinate with an intelligent product 1510 that takes customer feedback about the product 1510, and an application 630 for the fulfillment center 628 may, upon receiving customer feedback via a connection path to the intelligent product 1510 about a problem with the product 1510, initiate a workflow to perform corrective actions on similar products 650 before the products 650 are sent out from the fulfillment center 628.; [2158] For instance, a smart container or team of smart containers may encounter an unidentified object obstructing a route and may need to generate a decision related to re-routing. In some embodiments, the smart container fleet management system may obtain relevant data (e.g., LIDAR data, video feeds, environment maps, and the like) which may be depicted in an environment digital twin. The user may view the current scenario in the environment digital twin and may provide instructions to the smart container fleet on how to proceed given the scenario presented in the environment digital twin. The foregoing are non-limiting examples of digital twins that may be used in connection with a smart container fleet management system and other examples are discussed below.; [2741] In embodiments, quantum computers that may run analog machines include, but are not limited to, quantum annealers, adiabatic quantum computers, and direct quantum simulators)
Claim 8.
Cella teaches the following limitations:
A system comprising: a quantum annealer; at least one processor; and one or more computer storage media storing computer-readable instructions thereon that when executed by the at least one processor cause the at least one processor to perform operations comprising: (Cella – [2741] In embodiments, quantum computers that may run analog machines include, but are not limited to, quantum annealers, adiabatic quantum computers, and direct quantum simulators)
generating a routing graph comprising nodes and edges populated from routes of vehicles, (Cella – [0330] a robotic/autonomous vehicle system, a packaging system, a packing system, a picking system, an inventory tracking system, an inspection system, a routing system for mobile robots; [0392] the management platform includes a set of value chain networks 3102 from which network data 3110 is collected from a set of information routing activities, the information including outcomes, parameters, routing activity information and the like. Within the set of value chain networks 3102 is selected a select value chain network 3104 for which at least one information routing recommendation 3130 is provided; [2404] In some embodiments, a set of two or more digital twins may be represented by a graph database that includes nodes and edges that connect the nodes.) the nodes identifying locations through which the vehicles travel along the routes and the edges identifying routing data for route segments for the vehicles traveling between connected nodes; (Cella – [2473] In embodiments, the digital twin module 13420 may analyze data received from the digital twin I/O system 13308 to refine, remove, or add conditions. For example, the digital twin module 13420 may optimize data collection times for movable elements that are updated more frequently than needed (e.g., multiple consecutive received positions being identical or within a predetermined margin of error).
removing nodes and edges from the routing graph for which arrival times exceed departure times for a node connecting at least two route segments, thereby forming a refined routing graph; receiving an origin–destination input identifying a destination node and an origin node; (Cella – [2653] The report may be used to reconfigure/retrain the intelligent agent. In embodiments, the reconfiguring/retraining an intelligent agent may include removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system)
filtering, from the refined routing graph, nodes and edges according to a first set of routing constraints, thereby forming a reduced route search space; and (Cella – [0959] In embodiments, the digital twin access controller 8112 informs the generation system 8108 of specific constraints around the roles of users able to view the digital twin as well as providing for dynamically adjustable digital twins that can adapt to constrain or release views of the data or other features specific to each user role)
invoking the quantum annealer to generate an optimal route from the reduced route search space according to an objective and a second set of routing constraints. (Cella – [2741] In embodiments, quantum computers that may run analog machines include, but are not limited to, quantum annealers, adiabatic quantum computers, and direct quantum simulators)
However, Cella does not explicitly disclose:
“…removing from the refined routing graph nodes and edges that do not meet one or more routing constraints of the first set of routing constraints and by removing from the refined routing graph nodes and edges that do not connect between the origin node and the destination node, thereby forming a reduced route search space having routes through remaining nodes.”
Lehoux teaches:
Filtering a routing graph to remove nodes and edges that do not connect between a specified origin and destination, thereby forming a reduced route search space comprising only routes through remaining nodes.
(Lehoux – [0107]: “The method may include filtering the graph to remove nodes and edges that do not meet the constraint…”; claim 6: “removing, from the graph, nodes and edges that are not part of any path between the origin and destination nodes.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cella and Lehoux, as both references are directed to optimizing routing in graphs for vehicles or logistics. The motivation to combine arises from the desire to further improve computational efficiency and search performance by reducing the route search space to only those nodes and edges that are relevant to the specific origin-destination request, as taught by Lehoux ([0107], claim 6). This would result in a more efficient route computation
Claim 9.
Rejected under the same rationale as claim 2.
Claim 10.
Rejected under the same rationale as claim 3.
Claim 11.
Rejected under the same rationale as claim 4.
Claim 12.
Rejected under the same rationale as claim 5.
Claim 13.
Rejected under the same rationale as claim 6.
Claim 14.
Rejected under the same rationale as claim 7.
Claim 15.
Rejected under the same rationale as claim 1.
Claim 16.
Rejected under the same rationale as claim 2.
Claim 17.
Rejected under the same rationale as claim 3.
Claim 18.
Rejected under the same rationale as claim 4.
Claim 19.
Rejected under the same rationale as claim 5.
Claim 20.
Rejected under the same rationale as claim 6.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 HELAL A ALGAHAIM whose telephone number is (571)270-5227. The examiner can normally be reached 9-5.
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/HELAL A ALGAHAIM/SPE , Art Unit 3645
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