The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 03/23/2026, the following is a Final Office Action for Application No. 18911863.
Status of Claims
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement(s) (IDS) filed 04/16/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 18911863 filed 10/10/2024 is a Continuation in Part of 18501608 , filed 11/03/2023 ,now U.S. Patent # 12217199 and having 1 RCE-type filing therein.
Response to Amendments
Applicant’s amendments have been fully considered. Applicant’s amendments to the claims overcome the 35 U.S.C 101 rejection and hence the 35 U.S.C. 101 rejection has been withdrawn. As per the DP rejection, Terminal Disclaimer was approved on 04/03/2026.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
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 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 of this title, 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 Sakakibara et al. (US 20210272065 A1) hereinafter referred to as Sakakibara in view of Oren et al. (US 20230028672 A1) hereinafter referred to as Oren in further view of Kliewer et al. (NPL: A time–space network based exact optimization model for multi-depot bus scheduling) hereinafter referred to as Oren.
Sakakibara teaches:
Claim 1. A method of allocating chassis resources of a hub based on dynamic classification of chassis pools associated with the hub, comprising:
configuring a plurality of chassis pool classification, wherein a configuration of each of the plurality of chassis pool classifications includes a ruleset for managing utilization of chassis in a chassis pool (¶0024 In the present embodiment, the delivery device is a truck and the delivery device data is track data. The track data is associated with information such as a truck type, a maximum volume, a maximum capacity, a regular truck fee, a charter truck fee, the number of regular trucks, and the number of charter trucks. In the track data, the truck type, the maximum volume, and the maximum capacity are related to the delivery restrictions, whereas the regular truck fee, the charter truck fee, the number of regular trucks, and the number of charter trucks are related to the delivery costs. The regular truck fee is the fee required to rent trucks on a regular basis, and the charter truck fee is the fee required to occasionally rent trucks when the number of packages increases suddenly and regular trucks cannot afford to deliver such packages.);
obtaining an optimized operating schedule including a consolidated time-space network representing a consolidation operational stream and a deconsolidated time-space network representing a deconsolidation operational stream over a planning horizon, wherein the optimized operating schedule is generated by layering the consolidated time-space network and the deconsolidated time-space network together to align nodes of the consolidated time-space network to nodes of the deconsolidated time-space network at each time increment of the planning horizon such that a status of a chassis of the hub affects both the consolidation operational stream and the deconsolidation operational stream, wherein the optimized operating schedule includes one or more chassis recommendations to allocate chassis resources to containers arriving at the hub based on the plurality of chassis pool classifications (¶0027 FIG. 5 is a diagram illustrating an example of a delivery plan created by the delivery plan creator 8. The delivery plan is associated with information such as track data, a representative point, an arrival time, and a departure time. The arrival time and the departure time can be calculated from the inter-point distance time data. As illustrated in FIG. 5, for example, in the case of a delivery plan 1, as a result of optimization in view of delivery restrictions and delivery costs, track data “1” is selected and a delivery is performed in the delivery order of representative points 1, 7, and 9. This optimization method will be described later. ¶0033 The delivery plan creator 8 creates an optimized delivery plan based on the delivery device data stored in the delivery device data storage 4, the inter-point distance time data stored in the inter-point distance time data storage 5, and the representative point extracted by the extractor 7 (STEP 5). The delivery plan creator 8 calculates a total delivery distance and a total delivery time in the delivery plan based on the departure time and arrival time calculated from the inter-point distance data. ¶0034 At this time, two objective functions, that is, an objective function f(x) that minimizes the delivery costs (total delivery distance, total delivery time, and delivery fee) and an objective function g(x) that maximizes the number of packages in each delivery device, the number of transfers, and all or some of the numbers of appearances of each package in the past data may be optimized. These two objective functions may be linearly combined using a real coefficient d(d<=1), so that h(x)=d×f(x)−(1−d)×g(x) is derived where h(x) is minimized (or −h(x) is maximized). Alternatively, in a method of performing sequential optimization such as neighborhood search, after f(x) is optimized, the objective function is changed and then g(x) may be optimized.);
receiving a container associated with a customer at the hub at a first time increment of the planning horizon (¶0023 As illustrated in FIG. 2, for example, in the case of package historical data 1, information that the point is “1”, the delivery date is “2019 Apr. 3”, the delivery time zone is “12:00-14:00”, the volume is “0.1”, the weight is “0.001”, the unloading time is “5”, the package form is “box”, the package type is “normal”, the shipper is “◯◯”, the weekday/holiday is “weekday”, the season is “spring”, and the peak season is “normal” is associated with the package historical data 1. In this way, the historical data of each package is associated with each point and the parameter information about the characteristics of each package and then is stored in the historical data storage 3. ¶0024 The delivery device data storage 4 stores delivery device data about the characteristics of a delivery device. The delivery device is, for example, a vehicle such as a truck, an airplane, an automated guided vehicle, or the like. The delivery device data storage 4 acquires delivery device data from a delivery company, a delivery device manufacturer, or the like in advance and stores the delivery device data.);
allocating a chassis to the container based on the optimized operating schedule (¶0024 As illustrated in FIG. 3, for example, in the case of track data 1, information that the truck type is “2 t truck”, the maximum volume is “12”, the maximum capacity is “2000”, the regular truck fee is “3000”, the charter truck fee is “4000”, the number of regular trucks is “10”, and the number of charter trucks is “1” is associated with the track data 1. In this way, the information such as the truck type, the maximum volume, the maximum capacity, the regular truck fee, the charter truck fee, the number of regular trucks, and the number of charter trucks is associated with each track data, and is then stored.); and
automatically sending, during execution of the optimized operating schedule, a control signal to a controller to cause the container to be placed onto the chassis based on the optimized operating schedule (¶0049 The package data determined not to match the representative point is stored as a list. In addition, if there is a delivery plan that includes a representative point that does not match the point included in the package data, the delivery plan is deleted. The determiner 22 transmits the revised delivery plan and a package data list again to a delivery plan creator 8″.).
Although not explicitly taught by Sakakibara, Oren teaches in the analogous art of lightweight chassis and container for transportation of goods:
a ruleset for managing utilization of chassis in a chassis pool (¶0016 A method for utilizing such a lightweight chassis and container for transporting goods/commodities may include actuating the lifting jacks and/or dual dollies. Such actuation may lift the front end of the chassis and container about 12 inches. When the chassis is flat or the dollies are not in an actuated or lowered position, the front end or forward section of the container may be about 24 inches from the ground, while the rear end or rearward section may be about 18 inches from the ground ¶0079 In embodiments, a method is provided for transporting, shipping and/or delivery of various sized goods/commodities including H3D goods or commodities utilizing the system 10 including embodiments of a lightweight chassis and container or box);
a deconsolidation operational stream over a planning horizon (¶0082 In addition, as indicated in FIGS. 6A and 6B, in some embodiments, the container or box can be provided with a loading guide or markings 76 can be used to assist a driver or operator in loading cargo into the interior chamber of the container or box, with varying size packages being placed or located according to an optimized loading plan. Such a loading plan can be determined prior to the goods making up the cargo load being received at the warehouse or other loading location, and can be used to help guide the driver or operator or other personnel loading the container or box as to the placement of packages based on size, weight, configuration, and/or delivery schedule.);
a controller to cause the container to be placed onto the chassis (¶0079 In embodiments, a method is provided for transporting, shipping and/or delivery of various sized goods/commodities including H3D goods or commodities utilizing the system 10 including embodiments of a lightweight chassis and container or box disclosed herein. In some application, the container or box can be received or placed onto a lightweight chassis 11, as indicated in FIG. 2, can be secured in place by corner castings attached along the frame 20 of the chassis. ¶0082 In addition, as indicated in FIGS. 6A and 6B, in some embodiments, the container or box can be provided with a loading guide or markings 76 can be used to assist a driver or operator in loading cargo into the interior chamber of the container or box, with varying size packages being placed or located according to an optimized loading plan. Such a loading plan can be determined prior to the goods making up the cargo load being received at the warehouse or other loading location, and can be used to help guide the driver or operator or other personnel loading the container or box as to the placement of packages based on size, weight, configuration, and/or delivery schedule ¶0083 As a result, rather than having to receive and assemble all of the packages making up a cargo load at a warehouse or other receiving facility prior to loading the cargo load into the truck or trailer, typically using a first in-last out, type of methodology, individual packages of the cargo load can be placed into the interior chamber of the container or box at selected or prescribed locations as received, according to the loading guide laid out on the floor of the container or box through each of the end and side door panels.).
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 lightweight chassis and container for transportation of goods of Oren with the system for a delivery plan creator configured to create a first delivery plan with respect to a representative point of Sakakibara for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sakakibara ¶0003 teaches that it is desirable to easily create a package delivery plan to deliver packages to the respective delivery destinations;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sakakibara Abstract teaches a delivery plan creator configured to create a first delivery plan with respect to a representative point, and Oren Abstract teaches system and methods for transportation/shipping and delivery of goods or commodities, including H3D goods/commodities; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Sakakibara at least the above cited paragraphs, and Oren at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the lightweight chassis and container for transportation of goods of Oren with the system for a delivery plan creator configured to create a first delivery plan with respect to a representative point of Sakakibara. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Sakakibara in view of Oren, Kliewer teaches in the analogous art of a time–space network based exact optimization model:
wherein the optimized operating schedule is generated by layering the consolidated time-space network and the deconsolidated time-space network together to align nodes of the consolidated time-space network to nodes of the deconsolidated time-space network at each time increment of the planning horizon such that a status of a chassis of the hub affects both the consolidation operational stream and the deconsolidation operational stream (Pg. 1619 Right Column: Fig. 2 illustrates an application of the traditional modeling approach to the example from Fig. 1. Each trip to be scheduled is represented by a departure node and an arrival node, together with an arc joining them. Other arcs join arrival nodes with later departure nodes, representing the possibility of a single vehicle undertaking the two corresponding trips in succession. Further arcs represent bus movements to and from depot nodes. Each arc has an associated cost factor (deadhead cost for arcs joining arrival nodes to later departure nodes, vehicle cost for pull-out and/or pull-in arcs), and capacity (one for service trips, depot capacities for circular arcs). The network involves two network layers: one layer for each depot. A flow solution is feasible only if the flow value is equal to one on exactly one of all service trip arcs representing a certain service trip in different layers (see for example Fig. 2: unbroken arcs a in d1 and d2 layers). The optimal flow (feasible flow with minimum total costs) for this network now determines the optimal vehicle schedule for the original timetable. A circulation flow down to the right gives an example for feasible and fleet-minimal solution.).
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 time–space network based exact optimization model of Kliewer with the system for a delivery plan creator configured to create a first delivery plan with respect to a representative point of Sakakibara in further view of Oren for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sakakibara ¶0003 teaches that it is desirable to easily create a package delivery plan to deliver packages to the respective delivery destinations;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sakakibara Abstract teaches a delivery plan creator configured to create a first delivery plan with respect to a representative point, and Oren Abstract teaches system and methods for transportation/shipping and delivery of goods or commodities, including H3D goods/commodities and Kliewer Abstract teaches addressing the task of assigning buses to cover a given set of timetabled trips with consideration of practical requirements, such as multiple depots and vehicle types as well as depot capacities; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Sakakibara in further view of Oren at least the above cited paragraphs, and Kliewer at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the time–space network based exact optimization model of Kliewer with the system for a delivery plan creator configured to create a first delivery plan with respect to a representative point of Sakakibara in further view of Oren. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Sakakibara teaches:
Claim 2. The method of claim 1, wherein a ruleset of a dynamic chassis pool classification includes one or more of: one or more operating rules to govern how chassis in a chassis pool classified with the dynamic chassis classification are utilized; one or more constraints for restricting operations utilizing the chassis in a chassis pool classified with the dynamic chassis classification; one or more operating guidelines to limit utilization of the chassis in a chassis pool classified with the dynamic chassis classification; and one or more performance metrics to collect associated with the one or more operating guidelines (¶0024 The delivery device data storage 4 stores delivery device data about the characteristics of a delivery device. The delivery device is, for example, a vehicle such as a truck, an airplane, an automated guided vehicle, or the like. The delivery device data storage 4 acquires delivery device data from a delivery company, a delivery device manufacturer, or the like in advance and stores the delivery device data. The delivery device data is data relating to delivery restrictions and delivery costs. Delivery restrictions are restrictions that take into account the characteristics of packages and the characteristics of delivery devices (for example, volume of package to be delivered is less than or equal to maximum capacity of delivery device or when package type is frozen package, truck type is refrigerated vehicle, and the like) and restrictions that take into account the burden on the driver of a delivery device.).
Sakakibara teaches:
Claim 3. The method of claim 2, wherein the one or more operating rules for an interim loaner chassis pool classification for implementing an interim loaner pool include: permitting borrowing a chassis from a first pool to receive a deramped container belonging to a customer associated with a second pool in response to a determination that a chassis from the second pool is not currently available to receive the deramped container; requiring the container to be flipped to a chassis from the second pool once the chassis from the second pool is available to receive the container; and returning the chassis from the first pool to the interim loaner pool once the container is flipped to the chassis from the second pool (¶0024 The delivery cost is a cost required to deliver a package. In the present embodiment, the delivery device is a truck and the delivery device data is track data. The track data is associated with information such as a truck type, a maximum volume, a maximum capacity, a regular truck fee, a charter truck fee, the number of regular trucks, and the number of charter trucks. In the track data, the truck type, the maximum volume, and the maximum capacity are related to the delivery restrictions, whereas the regular truck fee, the charter truck fee, the number of regular trucks, and the number of charter trucks are related to the delivery costs. The regular truck fee is the fee required to rent trucks on a regular basis, and the charter truck fee is the fee required to occasionally rent trucks when the number of packages increases suddenly and regular trucks cannot afford to deliver such packages ¶0027 The delivery plan creator 8 creates a delivery plan based on the track data stored in the delivery device data storage 4, the inter-point distance time data stored in the inter-point distance time data storage 5, and the representative point extracted by the extractor 7. This delivery plan does not include points other than the representative point, and is created for the representative point. As the delivery plan is created for the representative point, it is possible to create a regular delivery plan that corresponds only to a regular delivery. By performing daily deliveries based on this regular delivery plan, the burden on a driver can be reduced, for example, the route to a delivery destination can be fixed, the place where a truck stops at the delivery destination can be fixed, and unloading at the delivery destination becomes easy).
Sakakibara teaches:
Claim 4. The method of claim 3, wherein the one or more constraints include: preventing the chassis from the first pool from being removed from the hub while the container remains mounted on the chassis from the first pool (¶0033 the delivery plan creator 8 sets delivery devices that can be used in creating a delivery plan is created based on the delivery device data, and calculates restrictions on packages to be loaded on each delivery device, restrictions on volume and capacity, and delivery fees. This makes it possible to calculate delivery restrictions and delivery costs (total delivery distance, total delivery time, and delivery fee), and thus optimization is possible so as to meet the delivery restrictions and minimize the delivery costs (total delivery distance, total delivery time, and delivery fee).).
Sakakibara teaches:
Claim 5. The method of claim 3, wherein the one or more operating guidelines include one or more of: an imbalance threshold to limit customers whose imbalance measurement exceed the imbalance threshold; a fairness deficit threshold to limit customers whose fairness measurement does not exceed the fairness deficit threshold; a misuse threshold to limit customers whose number of misuse instances exceeds the misuse threshold; an overloading threshold to limit customers whose average weight mounted on each borrowed chassis exceeds the overloading threshold; and a number of borrowing instances per time increment threshold to limit customers whose number of instances per time increment that the customers have borrowed from another chassis pool in the interim loaner pool exceeds the number of borrowing instances per time increment threshold (¶0024 Delivery restrictions are restrictions that take into account the characteristics of packages and the characteristics of delivery devices (for example, volume of package to be delivered is less than or equal to maximum capacity of delivery device or when package type is frozen package, truck type is refrigerated vehicle, and the like) and restrictions that take into account the burden on the driver of a delivery device. The delivery cost is a cost required to deliver a package. In the present embodiment, the delivery device is a truck and the delivery device data is track data. The track data is associated with information such as a truck type, a maximum volume, a maximum capacity, a regular truck fee, a charter truck fee, the number of regular trucks, and the number of charter trucks).
Sakakibara teaches:
Claim 6. The method of claim 2, wherein the one or more operating rules for a reciprocating chassis pool classification for implementing a reciprocating pool includes: permitting borrowing a chassis from a first pool to receive a deramped container belonging to a customer associated with a second pool in response to a determination that a number of chassis from the second pool currently available to receive a container is below a supply threshold; requiring the container to be flipped to a chassis from the second pool once the number of chassis from the second pool currently available to receive a container is no longer below the supply threshold; and returning the chassis from the first pool to the reciprocating pool once the container is flipped to the chassis from the second pool (¶0024 The delivery cost is a cost required to deliver a package. In the present embodiment, the delivery device is a truck and the delivery device data is track data. The track data is associated with information such as a truck type, a maximum volume, a maximum capacity, a regular truck fee, a charter truck fee, the number of regular trucks, and the number of charter trucks. In the track data, the truck type, the maximum volume, and the maximum capacity are related to the delivery restrictions, whereas the regular truck fee, the charter truck fee, the number of regular trucks, and the number of charter trucks are related to the delivery costs. The regular truck fee is the fee required to rent trucks on a regular basis, and the charter truck fee is the fee required to occasionally rent trucks when the number of packages increases suddenly and regular trucks cannot afford to deliver such packages ¶0027 The delivery plan creator 8 creates a delivery plan based on the track data stored in the delivery device data storage 4, the inter-point distance time data stored in the inter-point distance time data storage 5, and the representative point extracted by the extractor 7. This delivery plan does not include points other than the representative point, and is created for the representative point. As the delivery plan is created for the representative point, it is possible to create a regular delivery plan that corresponds only to a regular delivery. By performing daily deliveries based on this regular delivery plan, the burden on a driver can be reduced, for example, the route to a delivery destination can be fixed, the place where a truck stops at the delivery destination can be fixed, and unloading at the delivery destination becomes easy.).
Sakakibara teaches:
Claim 7. The method of claim 6, wherein the one or more constraints of the reciprocating chassis pool classification include: preventing a participant of the reciprocating pool from borrowing further chassis in response to a determination that a number of chassis borrowed from the reciprocating pool by the participant exceeds a borrowing threshold; and allowing the chassis from the first pool to be removed from the hub while the container remains mounted on the chassis from the first pool (¶0024 The delivery cost is a cost required to deliver a package. In the present embodiment, the delivery device is a truck and the delivery device data is track data. The track data is associated with information such as a truck type, a maximum volume, a maximum capacity, a regular truck fee, a charter truck fee, the number of regular trucks, and the number of charter trucks. In the track data, the truck type, the maximum volume, and the maximum capacity are related to the delivery restrictions, whereas the regular truck fee, the charter truck fee, the number of regular trucks, and the number of charter trucks are related to the delivery costs. The regular truck fee is the fee required to rent trucks on a regular basis, and the charter truck fee is the fee required to occasionally rent trucks when the number of packages increases suddenly and regular trucks cannot afford to deliver such packages).
Sakakibara teaches:
Claim 8. The method of claim 6, wherein the one or more operating guidelines include one or more of: an imbalance threshold to limit customers whose imbalance measurement exceed the imbalance threshold; a fairness deficit threshold to limit customers whose fairness measurement does not exceed the fairness deficit threshold; a misuse threshold to limit customers whose number of misuse instances exceeds the misuse threshold; and an overloading threshold to limit customers whose average weight mounted on each borrowed chassis exceeds the overloading threshold (¶0024 The delivery cost is a cost required to deliver a package. In the present embodiment, the delivery device is a truck and the delivery device data is track data. The track data is associated with information such as a truck type, a maximum volume, a maximum capacity, a regular truck fee, a charter truck fee, the number of regular trucks, and the number of charter trucks. In the track data, the truck type, the maximum volume, and the maximum capacity are related to the delivery restrictions, whereas the regular truck fee, the charter truck fee, the number of regular trucks, and the number of charter trucks are related to the delivery costs. The regular truck fee is the fee required to rent trucks on a regular basis, and the charter truck fee is the fee required to occasionally rent trucks when the number of packages increases suddenly and regular trucks cannot afford to deliver such packages).
Sakakibara teaches:
Claim 9. The method of claim 2, wherein the one or more operating rules for a single chassis pool classification for implementing a single pool includes: determining whether a current utilization of a customer exceeds a pre-allocated share threshold; permitting the customer to draw a chassis from the single pool to receive a deramped container belonging to the customer in response to a determination the current utilization of the customer does not exceed the pre-allocated share threshold; and preventing the customer from drawing a chassis from the single pool to receive the deramped container belonging to the customer in response to a determination the current utilization of the customer exceeds the pre-allocated share threshold (¶0024 Delivery restrictions are restrictions that take into account the characteristics of packages and the characteristics of delivery devices (for example, volume of package to be delivered is less than or equal to maximum capacity of delivery device or when package type is frozen package, truck type is refrigerated vehicle, and the like) and restrictions that take into account the burden on the driver of a delivery device. The delivery cost is a cost required to deliver a package. In the present embodiment, the delivery device is a truck and the delivery device data is track data. The track data is associated with information such as a truck type, a maximum volume, a maximum capacity, a regular truck fee, a charter truck fee, the number of regular trucks, and the number of charter trucks.).
Sakakibara teaches:
Claim 10. The method of claim 9, wherein the one or more constraints of the single chassis pool classification include: establishing a borrowing limit for each participant of the single pool; and dynamically modifying the borrowing limits for each participant based on replenishment levels of the chassis in the single pool (¶0052 Further, as illustrated in FIG. 12, a distance limitation input unit 23 that limits a driver travel distance may be further provided in FIG. 10. For example, the upper limit value of the driver travel distance is input to the distance limitation input unit 23, and the upper limit value is acquired by a delivery plan creator 8′″. The delivery plan creator 8′″ creates a delivery plan in view of the load on the driver when creating a delivery plan. ¶0024 FIG. 3 is a diagram illustrating an example of the track data. As illustrated in FIG. 3, for example, in the case of track data 1, information that the truck type is “2 t truck”, the maximum volume is “12”, the maximum capacity is “2000”, the regular truck fee is “3000”, the charter truck fee is “4000”, the number of regular trucks is “10”, and the number of charter trucks is “1” is associated with the track data 1. In this way, the information such as the truck type, the maximum volume, the maximum capacity, the regular truck fee, the charter truck fee, the number of regular trucks, and the number of charter trucks is associated with each track data, and is then stored.).
Sakakibara teaches:
Claim 11. The method of claim 9, wherein the one or more operating guidelines include one or more of: a maximum withdrawal threshold to limit customers who have drawn a number of chassis from the single pool exceeding the maximum withdrawal threshold; a minimum own chassis threshold to limit customers with a number of chassis owned by the customers in the single pool that is less than the minimum own chassis threshold; and a misuse threshold to limit customers whose number of misuse instances exceeds the misuse threshold (¶0052 Further, as illustrated in FIG. 12, a distance limitation input unit 23 that limits a driver travel distance may be further provided in FIG. 10. For example, the upper limit value of the driver travel distance is input to the distance limitation input unit 23, and the upper limit value is acquired by a delivery plan creator 8′″. The delivery plan creator 8′″ creates a delivery plan in view of the load on the driver when creating a delivery plan. ¶0024 FIG. 3 is a diagram illustrating an example of the track data. As illustrated in FIG. 3, for example, in the case of track data 1, information that the truck type is “2 t truck”, the maximum volume is “12”, the maximum capacity is “2000”, the regular truck fee is “3000”, the charter truck fee is “4000”, the number of regular trucks is “10”, and the number of charter trucks is “1” is associated with the track data 1. In this way, the information such as the truck type, the maximum volume, the maximum capacity, the regular truck fee, the charter truck fee, the number of regular trucks, and the number of charter trucks is associated with each track data, and is then stored.).
As per claims 12-19 and 20, the system and computer-based tool tracks the method of claims 1-6,8-9 and 1, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-6,8-9 and 1 are applied to claims 12-19 and 20, respectively. Sakakibara discloses that the embodiment may be found as a system and computer-based tool (Fig. 1 and ¶0059).
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURTIS GILLS whose telephone number is (571)270-3315. The examiner can normally be reached on M-F 8-5 PM.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624
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