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 .
Response to Arguments
Claim Rejections - 35 USC § 101:
Applicant’s arguments filed 4/08/2026 with respect to claims 15-20 have been fully considered and are persuasive. Amendment to independent claim 15 (i.e., the computer program product comprising a non-transitory computer- readable storage medium and program instructions stored on the non-transitory computer- readable storage medium) overcomes the rejection. The rejection of claims 15-20 has been withdrawn.
Claim Rejections - 35 USC § 103:
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 (i.e. The cited sections of the applied references, whether taken alone or in any reasonable combination, do not disclose or suggest at least "wherein execution of the LNS is iteratively performed until a stop condition is achieved, and wherein the stop condition comprises a user input or a threshold travel time," as recited in claim 1, as amended. Independent claims 8 and 15, as amended, recite similar features).
Examiner Note:
Cited references are bold italicized. Examiner interpretations are preceded with an asterisk *.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Richey et al. (US 12321872 B1; hereinafter Richey) in view of Thompson et al. (US 10753751 B2; hereinafter Thompson) in further view of Park et al. (US 20250362137 A1; hereinafter Park).
Regarding claim 1, Richey teaches a computer system (see at least, Fig. 1B -Server System-180) for optimizing scheduling (see at least, Col 34 line 42, optimizing routes for a fleet of transport units ) and dispatching of tasks (see at least, Col 28 lines 52-53, dispatch collection vehicles to the refuse bins), the computer system comprising: a memory; and at least one processor communicatively coupled to the memory (see at least, Col 86 lines 53-56, non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that can be executed by the processors), the at least one processor configured to: receive a geographic map that comprises tasks assigned to a plurality of vehicles, and routes for the plurality of vehicles to follow to perform the tasks (see at least, Col 7 lines 63-67, The static route determination process uses this information to generate a graph, where the nodes of the graph represent locations to be serviced and the edges of the graph represent paths (e.g., roads) between those locations that may be traveled by transport units servicing those locations); cluster the routes into a plurality of subsets of routes based on travel times from geographic locations of the routes in the geographic map (see at least, Col 30 lines 65-67, Col 31 lines 1-5, The nodal groups provide a partition of the nodes into nodal groups representing locations that each transport unit should expect to visit…The initial route planning guidelines include recommendations for each transport unit's route based on the nodal groups…suggested order of visits within the given nodal group and the transport unit's estimated travel time) wherein each subset of routes corresponds to a different geographic location (see at least, Col 30 lines 65-67, The nodal groups provide a partition of the nodes into nodal groups representing locations that each transport unit should expect to visit); execute a large neighborhood search (LNS) on the plurality of subsets of routes to generate a plurality of modified subsets of routes for the plurality of vehicles to follow to perform the tasks (see at least, Col 58 lines 15-33, An ALNS-based DRRA includes…generating an initial rerouted
transport network…this includes adjacent routes…the unserviced locations of those routes affected); update the geographic map based on the plurality of modified subsets of routes (see at least, Col 22 lines 50-59, route management server…is tasked with making determinations as to static and dynamic routing and rerouting of transport unit routes…updating mapping information).
Richey does not explicitly teach dispatch the tasks to the plurality of vehicles via a communication channel based on the updated geographic map. However, Thompson teaches this limitation.
Thompson teaches dispatch the tasks to the plurality of vehicles via a communication channel based on the updated geographic map (see at least, Col 4 lines 25-33, the routing engine will respond by
updating routes and sending notifications as appropriate to maximize efficiency in the updated model…will attempt to compensate for the change in circumstances while making the minimum amount of changes to maintain route stability wherever possible).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Richey to include dispatch the tasks to the plurality of vehicles via a communication channel based on the updated geographic map as taught by Thompson so that a routing engine is able to continually seek optimizations and respond to events that occur (Thompson, Col 4 lines 20-21).
Richey further does not explicitly teach wherein execution of the LNS is iteratively performed until a stop condition is achieved, and wherein the stop condition comprises a user input or a threshold travel time. However, Park teaches this limitation.
Park teaches wherein execution of the LNS is iteratively performed until a stop condition is achieved (see at least, Fig 2, [0060] In step (230), it is determined whether the optimal path calculated in step (210) and the optimal path calculated in step (215) meet the termination conditions, and if not, the process returns to step (210) and step (215) to execute the LNS optimization), and wherein the stop condition comprises a user input or a threshold travel time (see at least, [0008] selecting an optimal route that satisfies a predetermined condition among different optimal routes according to the first optimization target and the second optimization target; [0039] a cost (including time) matrix used when following the optimization conditions applied between the nodes).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Richey to include execution of the LNS is iteratively performed until a stop condition is achieved, and wherein the stop condition comprises a user input or a threshold travel time as taught by Park in order to select the optimal route with the lowest cost (such as expense and time) in a route with multiple nodes (Park, [0003]).
Regarding claim 2, the combination of Richey, Thompson and Park teaches the computer system of claim 1. Thompson further teaches wherein the at least one processor is configured to identify geographic centers of the routes based on geographic coordinates of the routes calculated from drive time (see at least, Col 8 lines 7-10, before a route can be assembled, a cost of transitioning between nodes must be determined. Transition weights are calculated using…estimated travel time; Col 8 lines 12-15, FIG. 4 depicts an example of a street map showing illustrative geographic contours and transition weights associated with job locations), and cluster the routes into the plurality of subsets of routes based on the geographic centers of the routes (see at least , Col 7 lines 64-66, In FIG. 3B….The starting locations differ from the twenty-nine job locations, and in this case, three of the five starting locations overlap geographically on the map 310).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Richey to include identify geographic centers of the routes based on geographic coordinates of the routes calculated from drive time and cluster the routes into the plurality of subsets of routes based on the geographic centers of the routes as taught by Thompson so that a routing engine is able to continually seek optimizations and respond to events that occur (Thompson, Col 4 lines 20-21).
Regarding claim 3, the combination of Richey, Thompson and Park teaches the computer system of claim 1. Richey further teaches wherein the at least one processor is configured to remove one or more tasks from a first position of a route (see at least, Col 55 lines 65-66, Col 56 lines 1-8, LNS algorithm operates by iteratively exploring comparatively large neighborhoods of candidate solutions…such as removing a random route, removing a subset of locations from a route, or swapping locations between routes) and anneal the one or more tasks to a second position of the route to generate a modified route based on execution of the LNS (see at least, Col 55 lines 65-66, Col 56 lines 1-21, LNS algorithm operates by iteratively exploring comparatively large neighborhoods of candidate solutions…such as simulated annealing).
Regarding claim 4, the combination of Richey, Thompson and Park teaches the computer system of claim 3. Richey further teaches wherein the at least one processor is configured to determine that the modified route is more efficient than the route based on at least one of a travel time difference and a number of overall tasks difference between the route and the modified route, and in response, generate the updated geographic map to include the modified route (see at least, Col 54 lines 14-19, a dynamic route rerouting algorithm according to the present disclosure gives effect to such rerouting in a fast, efficient manner…e.g., at least within a minimum time for a transport unit to travel from a depot to affected locations…within the time a given location would otherwise expect service).
Regarding claim 5, the combination of Richey, Thompson and Park teaches the computer system of claim 3. Richey further teaches wherein the at least one processor is configured to insert the one or more tasks at the second position of the route based on execution of at least one of a greedy algorithm and a regret algorithm (see at least, Col 32 lines 66-67, Col 33 lines 1-6, more manageable numbers of nodes and links in and between nodes within groups, as well as a manageable number of nodal groupings and links there between, a greedy algorithm can be used both inter-nodally and intra-nodally).
Regarding claim 6, the combination of Richey, Thompson and Park teaches the computer system of claim 1. Richey further teaches wherein the at least one processor is configured to simultaneously execute a plurality of instances of the LNS on the plurality of subsets of routes using a plurality of different processing cores, respectively, to generate the plurality of modified subsets of routes (see at least, Col 56 lines 45-57, With regard to a DRRA based on an adaptive large neighborhood search (ALNS) algorithm for the collection/distribution of items, materials, and the like across various locations within a constrained timeframe and with resource limitations...in a DRRA based on an ALNS
algorithm, the DRRA explores a diverse landscape of potential solutions through three core components: destroy and repair heuristics, large neighborhoods, and adaptive mechanisms).
Regarding claim 7, the combination of Richey, Thompson and Park teaches the computer system of claim 1. Richey further teaches wherein the at least one processor is further configured to retrieve attributes of the tasks from a database (see at least, Col 31 lines 30-32, grouping analyses are reliant on sufficient historical data for accurate feature extraction and clustering), and execute the LNS on the attributes to generate the plurality of modified subsets of routes (see at least, Col 40 lines 51-58, the static route rerouting algorithm (SRRA) revises the road network (graph) of the nodes affected by the situation, connecting points representing intersections, locations, and other decision points).
Regarding claim 8, Richey teaches a computer-implemented method (see at least, Fig. 1B -Server System-180) for optimizing scheduling (see at least, Col 34 line 42, optimizing routes for a fleet of transport units ) and dispatching of tasks (see at least, Col 28 lines 52-53, dispatch collection vehicles to the refuse bins), the computer-implemented method comprising: receiving a geographic map that comprises tasks assigned to a plurality of vehicles , and routes for the plurality of vehicles to follow to perform the tasks (see at least, Col 7 lines 63-67, The static route determination process uses this information to generate a graph, where the nodes of the graph represent locations to be serviced and the edges of the graph represent paths (e.g., roads) between those locations that may be traveled by transport units servicing those locations); clustering the routes into a plurality of subsets of routes based on travel times from geographic locations of the routes in the geographic map (see at least, Col 30 lines 65-67, Col 31 lines 1-5, The nodal groups provide a partition of the nodes into nodal groups representing locations that each transport unit should expect to visit…The initial route planning guidelines include recommendations for each transport unit's route based on the nodal groups…
suggested order of visits within the given nodal group and the transport unit's estimated travel time), wherein each subset of routes corresponds to a different geographic location (see at least, Col 30 lines 65-67, The nodal groups provide a partition of the nodes into nodal groups representing locations that each transport unit should expect to visit); executing a large neighborhood search (LNS) on the plurality of subsets of routes to generate a plurality of modified subsets of routes for the plurality of vehicles to follow to perform the tasks (see at least, Col 58 lines 15-33, An ALNS-based DRRA includes…generating an initial rerouted transport network…this includes adjacent routes…the unserviced locations of those routes affected); updating the geographic map based on the plurality of modified subsets of routes (see at least, Col 22 lines 50-59, route management server…is tasked with making determinations as to static and dynamic routing and rerouting of transport unit routes…updating mapping information).
Richey does not explicitly teach dispatching the tasks to the plurality of vehicles via a communication channel based on the updated geographic map. However, Thompson teaches this limitation.
Thompson teaches dispatching the tasks to the plurality of vehicles via a communication channel based on the updated geographic map (see at least, Col 4 lines 25-33, the routing engine will respond by
updating routes and sending notifications as appropriate to maximize efficiency in the updated model…will attempt to compensate for the change in circumstances while making the minimum amount of changes to maintain route stability wherever possible).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Richey to include dispatching the tasks to the plurality of vehicles via a communication channel based on the updated geographic map as taught by Thompson so that a routing engine is able to continually seek optimizations and respond to events that occur (Thompson, Col 4 lines 20-21).
Richey further does not explicitly teach wherein execution of the LNS is iteratively performed until a stop condition is achieved, and wherein the stop condition comprises a user input or a threshold travel time. However, Park teaches this limitation.
Park teaches wherein execution of the LNS is iteratively performed until a stop condition is achieved (see at least, Fig 2, [0060] In step (230), it is determined whether the optimal path calculated in step (210) and the optimal path calculated in step (215) meet the termination conditions, and if not, the process returns to step (210) and step (215) to execute the LNS optimization), and wherein the stop condition comprises a user input or a threshold travel time (see at least, [0008] selecting an optimal route that satisfies a predetermined condition among different optimal routes according to the first optimization target and the second optimization target; [0039] a cost (including time) matrix used when following the optimization conditions applied between the nodes).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Richey to include execution of the LNS is iteratively performed until a stop condition is achieved, and wherein the stop condition comprises a user input or a threshold travel time as taught by Park in order to select the optimal route with the lowest cost (such as expense and time) in a route with multiple nodes (Park, [0003]).
Regarding claim 9, the combination of Richey, Thompson and Park teaches the computer-implemented method of claim 8. Thompson further teaches wherein the clustering comprises identifying geographic centers of the routes based on geographic coordinates of the routes calculated from drive time (see at least, Col 8 lines 7-10, before a route can be assembled, a cost of transitioning between nodes must be determined. Transition weights are calculated using…estimated travel time; Col 8 lines 12-15, FIG. 4 depicts an example of a street map showing illustrative geographic contours and transition weights associated with job locations), and clustering the routes into the plurality of subsets of routes based on the geographic centers of the routes (see at least , Col 7 lines 64-66, In FIG. 3B….The starting locations
differ from the twenty-nine job locations, and in this case, three of the five starting locations overlap
geographically on the map 310).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Richey to include identifying geographic centers of the routes based on geographic coordinates of the routes calculated from drive time and clustering the routes into the plurality of subsets of routes based on the geographic centers of the routes as taught by Thompson so that a routing engine is able to continually seek optimizations and respond to events that occur (Thompson, Col 4 lines 20-21).
Regarding claim 10, the combination of Richey, Thompson and Park teaches the computer-implemented method of claim 8. Richey further teaches wherein the executing comprises removing one or more tasks from a first position of a route(see at least, Col 55 lines 65-66, Col 56 lines 1-8, LNS algorithm operates by iteratively exploring comparatively large neighborhoods of candidate solutions…such as removing a random route, removing a subset of locations from a route, or swapping locations between routes) and annealing the one or more tasks to a second position of the route to generate a modified route based on execution of the LNS (see at least, Col 55 lines 65-66, Col 56 lines 1-21, LNS algorithm operates by iteratively exploring comparatively large neighborhoods of candidate solutions…such as simulated annealing).
Regarding claim 11, the combination of Richey, Thompson and Park teaches the computer-implemented method of claim 10. Richey further teaches further comprising determining that the modified route is more efficient than the route based on at least one of a travel time difference and a number of overall tasks difference between the route and the modified route, and in response, generating the updated geographic map to include the modified route (see at least, Col 54 lines 14-19, a dynamic route rerouting algorithm according to the present disclosure gives effect to such rerouting in a fast, efficient manner…e.g., at least within a minimum time for a transport unit to travel from a depot to affected locations…within the time a given location would otherwise expect service).
Regarding claim 12, the combination of Richey, Thompson and Park teaches the computer-implemented method of claim 10. Richey further teaches wherein the annealing comprises inserting the one or more tasks at the second position of the route based on execution of at least one of a greedy algorithm and a regret algorithm (see at least, Col 32 lines 66-67, Col 33 lines 1-6, more manageable numbers of nodes and links in and between nodes within groups, as well as a manageable number of nodal groupings and links there between, a greedy algorithm can be used both inter-nodally and intra-nodally).
Regarding claim 13, the combination of Richey, Thompson and Park teaches the computer-implemented method of claim 8. Richey further teaches wherein the executing comprises simultaneously executing a plurality of instances of the LNS on the plurality of subsets of routes using a plurality of different processing cores, respectively, to generate the plurality of modified subsets of routes (see at least, Col 56 lines 45-57, With regard to a DRRA based on an adaptive large neighborhood search (ALNS) algorithm for the collection/distribution of items, materials, and the like across various locations within a constrained timeframe and with resource limitations...in a DRRA based on an ALNS
algorithm, the DRRA explores a diverse landscape of potential solutions through three core components: destroy and repair heuristics, large neighborhoods, and adaptive mechanisms).
Regarding claim 14, the combination of Richey, Thompson and Park teaches the computer-implemented method of claim 8. Richey further teaches further comprising retrieving attributes of the tasks from a database (see at least, Col 31 lines 30-32, grouping analyses are reliant on sufficient historical data for accurate feature extraction and clustering), wherein the executing further comprises executing the LNS on the attributes to generate the plurality of modified subsets of routes (see at least, Col 40 lines 51-58, the static route rerouting algorithm (SRRA) revises the road network (graph) of the nodes affected by the situation, connecting points representing intersections, locations, and other decision points).
Regarding claim 15, Richey teaches a computer program product (see at least, Fig. 1B -Server System-180) for optimizing scheduling (see at least, Col 34 line 42, optimizing routes for a fleet of transport units ) and dispatching of tasks (see at least, Col 28 lines 52-53, dispatch collection vehicles to the refuse bins), the computer program product comprising a non-transitory computer-readable storage medium and program instructions stored on the non-transitory computer-readable storage medium, wherein the program instructions are executable by a computer processor causing the computer processor to perform one or more functions (see at least, Col 86 lines 53-56, non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that can be executed by the processors), the program instructions comprising program instructions to: to receive a geographic map that comprises tasks assigned to a plurality of vehicles, and routes for the plurality of vehicles to follow to perform the tasks (see at least, Col 7 lines 63-67, The static route determination process uses this information to generate a graph, where the nodes of the graph represent locations to be serviced and the edges of the graph represent paths (e.g., roads) between those locations that may be traveled by transport units servicing those locations); program instructions to cluster the routes into a plurality of subsets of routes based on travel times from geographic locations of the routes in the geographic map (see at least, Col 30 lines 65-67, Col 31 lines 1-5, The nodal groups provide a partition of the nodes into nodal groups representing locations that each transport unit should expect to visit…The initial route planning guidelines include recommendations for each transport unit's route based on the nodal groups…suggested order of visits within the given nodal group and the transport unit's estimated travel time), wherein each subset of routes corresponds to a different geographic location (see at least, Col 30 lines 65-67, The nodal groups provide a partition of the nodes into nodal groups representing locations that each transport unit should expect to visit); execute a large neighborhood search (LNS) on the plurality of subsets of routes to generate a plurality of modified subsets of routes for the plurality of vehicles to follow to perform the tasks (see at least, Col 58 lines 15-33, An ALNS-based DRRA includes…generating an initial rerouted transport network…this includes adjacent routes…the unserviced locations of those routes affected); update the geographic map based on the plurality of modified subsets of routes (see at least, Col 22 lines 50-59, route management server…is tasked with making determinations as to static and dynamic routing and rerouting of transport unit routes…updating mapping information).
Richey does not explicitly teach dispatch the tasks to the plurality of vehicles via a communication channel based on the updated geographic map. However, Thompson teaches this limitation.
Thompson teaches dispatch the tasks to the plurality of vehicles via a communication channel based on the updated geographic map (see at least, Col 4 lines 25-33, the routing engine will respond by updating routes and sending notifications as appropriate to maximize efficiency in the updated model…will attempt to compensate for the change in circumstances while making the minimum amount of changes to maintain route stability wherever possible).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Richey to include dispatch the tasks to the plurality of vehicles via a communication channel based on the updated geographic map as taught by Thompson so that a routing engine is able to continually seek optimizations and respond to events that occur (Thompson, Col 4 lines 20-21).
Richey further does not explicitly teach wherein execution of the LNS is iteratively performed until a stop condition is achieved, and wherein the stop condition comprises a user input or a threshold travel time. However, Park teaches this limitation.
Park teaches wherein execution of the LNS is iteratively performed until a stop condition is achieved (see at least, Fig 2, [0060] In step (230), it is determined whether the optimal path calculated in step (210) and the optimal path calculated in step (215) meet the termination conditions, and if not, the process returns to step (210) and step (215) to execute the LNS optimization), and wherein the stop condition comprises a user input or a threshold travel time (see at least, [0008] selecting an optimal route that satisfies a predetermined condition among different optimal routes according to the first optimization target and the second optimization target; [0039] a cost (including time) matrix used when following the optimization conditions applied between the nodes).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Richey to include execution of the LNS is iteratively performed until a stop condition is achieved, and wherein the stop condition comprises a user input or a threshold travel time as taught by Park in order to select the optimal route with the lowest cost (such as expense and time) in a route with multiple nodes (Park, [0003]).
Regarding claim 16, the combination of Richey, Thompson and Park teaches the computer program product of claim 15. Thompson further teaches wherein the clustering comprises identifying geographic centers of the routes based on geographic coordinates of the routes calculated from drive time(see at least, Col 8 lines 7-10, before a route can be assembled, a cost of transitioning between nodes must be determined. Transition weights are calculated using…estimated travel time; Col 8 lines 12-15, FIG. 4 depicts an example of a street map showing illustrative geographic contours and transition weights associated with job locations), and clustering the routes into the plurality of subsets of routes based on the geographic centers of the routes (see at least , Col 7 lines 64-66, In FIG. 3B….The starting locations differ from the twenty-nine job locations, and in this case, three of the five starting locations overlap geographically on the map 310).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Richey to include identifying geographic centers of the routes based on geographic coordinates of the routes calculated from drive time and clustering the routes into the plurality of subsets of routes based on the geographic centers of the routes as taught by Thompson so that a routing engine is able to continually seek optimizations and respond to events that occur (Thompson, Col 4 lines 20-21).
Regarding claim 17, the combination of Richey, Thompson and Park teaches the computer program product of claim 15. Richey further teaches wherein the executing comprises removing one or more tasks from a first position of a route (see at least, Col 55 lines 65-66, Col 56 lines 1-8, LNS algorithm operates by iteratively exploring comparatively large neighborhoods of candidate solutions…such as removing a random route, removing a subset of locations from a route, or swapping locations between routes) and annealing the one or more tasks to a second position of the route to generate a modified route based on execution of the LNS (see at least, Col 55 lines 65-66, Col 56 lines 1-21, LNS algorithm operates by iteratively exploring comparatively large neighborhoods of candidate solutions…such as simulated annealing).
Regarding claim 18, the combination of Richey, Thompson and Park teaches the computer program product of claim 17. Richey further teaches wherein the program instructions further comprise program instructions to perform determining that the modified route is more efficient than the route based on at least one of a travel time difference and a number of overall tasks difference between the route and the modified route, and in response, generating the updated geographic map to include the modified route (see at least, Col 54 lines 14-19, a dynamic route rerouting algorithm according to the present disclosure gives effect to such rerouting in a fast, efficient manner…e.g., at least within a minimum time for a transport unit to travel from a depot to affected locations…within the time a given location would otherwise expect service).
Regarding claim 19, the combination of Richey, Thompson and Park teaches the computer program product of claim 17. Richey further teaches wherein the annealing comprises inserting the one or more tasks at the second position of the route based on execution of at least one of a greedy algorithm and a regret algorithm (see at least, Col 32 lines 66-67, Col 33 lines 1-6, more manageable numbers of nodes and links in and between nodes within groups, as well as a manageable number of nodal groupings and links there between, a greedy algorithm can be used both inter-nodally and intra-nodally).
Regarding claim 20, the combination of Richey, Thompson and Park teaches the computer program product of claim 15. Richey further teaches wherein the executing comprises simultaneously executing a plurality of instances of the LNS on the plurality of subsets of routes using a plurality of different processing cores, respectively, to generate the plurality of modified subsets of routes (see at least, Col 56 lines 45-57, With regard to a DRRA based on an adaptive large neighborhood search (ALNS) algorithm for the collection/distribution of items, materials, and the like across various locations within a constrained timeframe and with resource limitations...in a DRRA based on an ALNS
algorithm, the DRRA explores a diverse landscape of potential solutions through three core components: destroy and repair heuristics, large neighborhoods, and adaptive mechanisms).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Meirom et al. (US 20250053826 A1) discloses a computer system for optimizing scheduling and dispatching of tasks (e.g. [0058] The scheduler unit 420 is coupled to a work distribution unit 425 that is configured to dispatch tasks for execution on the GPCs 450. The work distribution unit 425 may track a number of scheduled tasks received from the scheduler unit 420).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 TOYA PETTIEGREW whose telephone number is (313)446-6636. The examiner can normally be reached 8:30pm - 5:00pm M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at 571-270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662
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