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 .
Application Status
This Office action has been issued in response to application filed on 04/26/2024.
Claims 1-17 are pending. Claims 1- 17 are rejected.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/07/2025 and 04/26/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
Receipt is acknowledged of a certified copy of application PCT/CN2022/085811 filled 04/08/2022.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, 9, 13, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Singh et al. (A Matheuristic for AGV Scheduling with Battery Constraints).
Regarding claim 1, Singh discloses, an automated guided vehicle (AGV) scheduling method, comprising: acquiring an optimal value of a threshold of an AGV scheduling-related parameter (6.5.3, in this experiment, we vary critical and non-critical battery thresholds of AGVs (i.e., bVk and bVk ). While the former threshold defines a must condition of visiting a charging station of an AGV, the latter only presents a possibility of the visit. We select three parameter sets, representing a small, medium, and large setting, where bVk is tested with five values from 10 to 50 (percent of the full charge)), comprising: through a pre-established simulation model for a real AGV system, conducting a simulation on different values of the threshold of the AGV scheduling-related parameter to determine an obtained value of a related performance index at the corresponding value of the threshold (6.5.3, bVk is tested with five values from 10 to 50 (percent of the full charge) and bVk is varied with three levels 60, 80, and 100. The results are shown in Table 6 and Fig. 5.); and taking the value of the threshold of the AGV scheduling-related parameter corresponding to an obtained value of the related performance index meeting a preset condition as the optimal value of the threshold of the AGV scheduling-related parameter (6.5.3, the medium and large settings, the lowest cost is obtained when bVk is around 30%, whereas, in a small setting, the best performing critical thresholds are higher, which implies that having a small critical threshold is not necessarily profitable); and using the optimal value of the threshold of the AGV scheduling-related parameter to conduct AGV scheduling in the real AGV system (6.5.3, we vary critical and non-critical battery thresholds of AGVs (i.e., bVk and bVk ). While the former threshold defines a must condition of visiting a charging station of an AGV).
Regarding claim 6, Singh discloses, the method according to claim 1, wherein the greater a quantity of the work orders is, the higher a ratio of a quantity of the AGVs to the number of the AGV charging piles is (Nomenclature, E set of charging nodes in a layout) … (Algorithm 1 Matheuristic, Require: set of AGVs V, set of transport requests R, set of charging tasks B) … (3. Problem description, in addition, let E ⊂ N denote the set of charging stations and B = {1, 2, . . ., nB} denote the set of charging requests, where nB is a safe upper bound, computed as nB = |V| · |E|, which allows AGVs to charge as many times as needed), the higher a ratio of a quantity of the AGVs to the number of the AGV charging piles is, the higher the charging rate of the AGVs is (3. Problem description, the battery is recharged at a charging rate of cˆ V k . The charge level of an AGV (measured as a percentage) must take values between the minimum and maximum battery charge level denoted by bl percent and bu percent), the higher the movement speed of the AGVs is, the smaller the optimal value of the second threshold is (1. Introduction, first, the fleet of AGVs considered in this study is heterogeneous in terms of not only travel speeds and costs, charging rates).
Regarding claim 9, Singh discloses, the method according to claim 1, wherein the lower limit of the second range is calculated based on the current positions of the AGVs (Nomenclature, N set of all nodes in a layout), the movement speed of the AGVs (Nomenclature, time to travel between node i and node j by vehicle k), and the positions of the charging piles (Nomenclature, distance between node i and node j).
Regarding claim 13, Singh discloses, an AGV system, comprising: AGVs (Abstract, this paper considers the problem of scheduling automated guided vehicles (AGVs) with battery constraints. Each transport request involves a soft time window, and the AGV fleet), an AGV simulation system configured to acquire the optimal value of the threshold of the AGV scheduling-related parameter (6.5.3, in this experiment, we vary critical and non-critical battery thresholds of AGVs (i.e., bVk and bVk ). While the former threshold defines a must condition of visiting a charging station of an AGV, the latter only presents a possibility of the visit. We select three parameter sets, representing a small, medium, and large setting, where bVk is tested with five values from 10 to 50 (percent of the full charge)), comprising: through a pre-established simulation model for a real AGV system, conducting a simulation on different values of the threshold of the AGV scheduling-related parameter to determine an obtained value of a related performance index at the corresponding value of the threshold (6.5.3, bVk is tested with five values from 10 to 50 (percent of the full charge) and bVk is varied with three levels 60, 80, and 100. The results are shown in Table 6 and Fig. 5.); and taking the value of the threshold of the AGV scheduling-related parameter corresponding to an obtained value of the related performance index meeting a preset condition as the optimal value of the threshold of the AGV scheduling-related parameter (6.5.3, the medium and large settings, the lowest cost is obtained when bVk is around 30%, whereas, in a small setting, the best performing critical thresholds are higher, which implies that having a small critical threshold is not necessarily profitable); and an AGV scheduling controller configured to use the optimal value of the threshold of the AGV scheduling-related parameter to schedule the AGVs (6.5.3, the medium and large settings, the lowest cost is obtained when bVk is around 30%, whereas, in a small setting, the best performing critical thresholds are higher, which implies that having a small critical threshold is not necessarily profitable).
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 2-4, 7-8, 11, 14-15, and 17 are all rejected under 35 U.S.C. 103 as being unpatentable over Damon et al (US10794711B2) in view of Nakano et al (AN INTEGRATED ANALYTICAL/SIMULATION APPROACH FOR ECONOMIC DESIGN OF AN AGV SYSTEM).
Regarding claim 2, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Singh discloses a second threshold corresponding to a remaining electric quantity of AGVs, (Abstract, the AGV batteries can be recharged partially under consideration of a critical battery threshold). However Singh does not explicitly disclose, the threshold of the AGV scheduling-related parameter comprises one or both of a first threshold corresponding to the remaining material handling time of production equipment and wherein the related performance index comprises one or both of an AGV utilization rate and a production line yield.
Nevertheless, Shimono who is in the same field of endeavor of simulations for the design of an AGV system discloses, the threshold of the AGV scheduling-related parameter comprises one or both of a first threshold corresponding to the remaining material handling time of production equipment (2. Statement of Problem, in the JIT environment, when the level of component inventory at an input buffer falls below a threshold level, a delivery order is placed at the dispatching station), and wherein the related performance index comprises one or both of an AGV utilization rate (2. Statement of Problem, let U; be the utilization of machine i and Si the planned utilization of machine i that is determined from a forecast demand of the completed components of machine i), and a production line yield (2. Statement of Problem, the throughput of completed components at each workstation is required to achieve a forecast demand) … (2. Statement of Problem, the throughput of completed components of part i equals to ui (M, 6, x}pi (i = 1, , N)),
It would have been prima facie obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have combined Singh with aspects of Nakano. This would serve to integrate the two complementary thresholds into a single AGV scheduling framework to improve the overall system effectiveness and performance using a standard AGV system.
Regarding claim 3, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Singh discloses in the case that the remaining electric quantity of the AGVs is lower than the second threshold, an AGV charging task is generated to schedule the AGVs to charging piles for charging (3. Problem description, the recharging duration should be long enough to allow the charge level to reach at least this threshold. In other words, before starting a new request, the charge level of the AGV must be at or above bVk percent) … (5.5.2. Station repair operators, Critical charge insertion (CCI): This operator estimates the battery charge level after servicing each request in a partial sequence of AGV k and inserts a charging task whenever its charge level falls below the critical battery threshold bVk).
Additionally, Nakano discloses, when the remaining material handling time of the production equipment is lower than the first threshold, an AGV transport task is generated to schedule the AGVs to supply material to the production equipment (2. Statement of Problem, in the JIT environment, when the level of component inventory at an input buffer falls below a threshold level, a delivery order is placed at the dispatching station).
Regarding claim 4, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Singh discloses the simulation is conducted according to at least one of the following factors to determine the obtained value of the related performance index: the number of work orders (5. Proposed matheuristic, we propose a matheuristic based on the integration of ALNS and LP to solve the problem in Section 3. In our matheuristic, the ALNS is responsible for solving the assignments of AGVs and sequencing of requests, while the LP model is used to determine the schedule of requests, i.e., starting times and recharging duration) … (Algorithm 1 Matheuristic, Require: set of AGVs V, set of transport requests), the number of the AGVs, (Nomenclature, V set of automated guided vehicles (AGVs)) … (3. Problem description, the problem in this paper concerns a set of transport requests R that is serviced by a heterogeneous AGV fleet V with battery constraints).
Additionally, Nakano discloses, the number of the production equipment (2. Statement of Problem, Workstation i consists of machine i and input buffer i (i = 1,2, , N) . Part i is processed only at machine i), the material handling rate of the production equipment (2. Statement of Problem, the distribution of the processing times of part i at machine i is generally d istributed with mean l/Ui), the transport capacity of the AGVs (I. Introduction, the design of AGVSs is a complex task since a design engineer has to take into account the following design factors which many researches have addressed (for example … (1) Number of AGVs (2) Guided path layout (3) Unit load size).
Regarding claim 7, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Singh discloses the simulation is conducted on a value in a first range corresponding to the first threshold (2. Statement of Problem, Let U; be the utilization of machine i and Si the planned utilization of machine i that is determined from a forecast demand of the completed components of machine i. The utilization U, is a nonlinear function of decision factors described below. The value of u, is obtained by simulation in this paper).
Additionally, Nakano discloses, a value in a second range corresponding to the second threshold to determine the corresponding production line yield (6.5.3, in this experiment, we vary critical and non-critical battery thresholds of AGVs (i.e., bVk and bVk ). While the former threshold defines a must condition of visiting a charging station of an AGV, the latter only presents a possibility of the visit. We select three parameter sets, representing a small, medium, and large setting, where bVk is tested with five values from 10 to 50 (percent of the full charge)).
Regarding claim 8, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Nakano discloses, the lower limit of the first range is calculated based on the transport capacity of the AGVs (2. Statement of Problem, AGVs deliver part i to workstation i and are assumed to carry one unit at a time. The number of AGVs is denoted by M) … (3.2 Optimal Location, We consider optimal locations of workstations when M and b are given by the lower bounds), and the material handling rate of the production equipment (5. Examples and Computational Results, The distribution of the processing times at machine i is assumed to be the Erlang distribution of phase ki with mean l/yi) … (Since the components consumed by machine i during the AGV delivery time to machine i are given by Gqigi, bi must satisfy the following inequality: bi 2 %pigi, i = l,--. 7 N. (5) Then the lower bound, biLB, of bi under constraint (2) is given by bm = \uipigil, i = 1;- -,N), and the upper limit of the first range is calculated based on the transport capacity of the AGVs (2. Statement of Problem, the capacity of the input buffer in front of machine i is denoted by bi) … (Since the unit load size of an AGV is one, the threshold level can be set as bi without loss of generality), the material handling rate of the production equipment (1. Introduction, For example as the number of facilities increases, both the total cost and production rate of the system increase. As the buffer size between machines increases, the production rate of the system increases), and a material storage capacity of the production equipment (2. Statement of Problem, the capacity of the input buffer in front of machine i is denoted by bi).
Regarding claim 11, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Singh discloses the determined values of the first threshold corresponding to the maximum AGV utilization rate and/or the maximum production line yield are different for at least one part of the production equipment (2. Statement of Problem, Let U; be the utilization of machine i and Si the planned utilization of machine i that is determined from a forecast demand of the completed components of machine i. The utilization U, is a nonlinear function of decision factors described below. The value of u, is obtained by simulation in this paper).
Additionally, Nakano discloses, the determined values of the second threshold corresponding to the maximum AGV utilization rate (2. Statement of Problem, Since the unit load size of an AGV is one, the threshold level can be set as bi without loss of generality), and/or the maximum production line yield are different for at least one part of the AGVs (6.5.3, in this experiment, we vary critical and non-critical battery thresholds of AGVs (i.e., bVk and bVk ). While the former threshold defines a must condition of visiting a charging station of an AGV, the latter only presents a possibility of the visit. We select three parameter sets, representing a small, medium, and large setting, where bVk is tested with five values from 10 to 50 (percent of the full charge)).
Regarding claim 14, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Singh discloses the second threshold corresponding to the remaining electric quantity of the AGVs (Abstract, the AGV batteries can be recharged partially under consideration of a critical battery threshold).
Additionally, Nakano discloses, the threshold of the AGV scheduling-related parameter comprises one or both of the first threshold corresponding to the remaining material handling time of the production equipment (2. Statement of Problem, in the JIT environment, when the level of component inventory at an input buffer falls below a threshold level, a delivery order is placed at the dispatching station), and wherein the related performance index comprises one or both of the AGV utilization rate and the production line yield (2. Statement of Problem, let U; be the utilization of machine i and Si the planned utilization of machine i that is determined from a forecast demand of the completed components of machine i).
Regarding claim 15, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Singh discloses in the case that the remaining electric quantity of the AGVs is lower than the second threshold, the AGV charging task is generated to schedule the AGVs to the charging piles for charging (3. Problem description, the recharging duration should be long enough to allow the charge level to reach at least this threshold. In other words, before starting a new request, the charge level of the AGV must be at or above bVk percent) … (5.5.2. Station repair operators, Critical charge insertion (CCI): This operator estimates the battery charge level after servicing each request in a partial sequence of AGV k and inserts a charging task whenever its charge level falls below the critical battery threshold bVk).
Additionally, Nakano discloses, when the remaining material handling time of the production equipment is lower than the first threshold, the AGV transport task is generated to schedule the AGVs to supply the material to the production equipment (2. Statement of Problem, in the JIT environment, when the level of component inventory at an input buffer falls below a threshold level, a delivery order is placed at the dispatching station).
Regarding claim 17, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Singh discloses the determined values of the first threshold corresponding to the maximum AGV utilization rate and/or the maximum production line yield are different for at least one part of the production equipment (2. Statement of Problem, Let U; be the utilization of machine i and Si the planned utilization of machine i that is determined from a forecast demand of the completed components of machine i. The utilization U, is a nonlinear function of decision factors described below. The value of u, is obtained by simulation in this paper).
Additionally, Nakano discloses, the determined values of the second threshold corresponding to the maximum AGV utilization rate (2. Statement of Problem, Since the unit load size of an AGV is one, the threshold level can be set as bi without loss of generality), and/or the maximum production line yield are different for at least one part of the AGVs (6.5.3, in this experiment, we vary critical and non-critical battery thresholds of AGVs (i.e., bVk and bVk ). While the former threshold defines a must condition of visiting a charging station of an AGV, the latter only presents a possibility of the visit. We select three parameter sets, representing a small, medium, and large setting, where bVk is tested with five values from 10 to 50 (percent of the full charge)).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (A Matheuristic for AGV Scheduling with Battery Constraints) in view of Tuan Le Anh (Intelligent Control of Vehicle-Based Internal Transport Systems).
Regarding claim 5, Singh discloses, the method according to claim 1, as discussed supra. Additionally, Tuan Le Anh who is in the same field of endeavor of intelligent control of vehicle-based internal transport systems discloses, the greater a quantity of the work orders is, the smaller a quantity of the AGVs is (2.2.2, The use of multi-load capacity vehicles can reduce the number of vehicles needed or increase the throughput of a system. A multi-load vehicle can pick-up additional loads while transporting a previously assigned load), the lower the production rate of the production equipment is, the higher the transport capacity of the AGVs is (I. Introduction, 385, Machine i is referred to as a withdrawing machine if its order is not yet filled. Since the unit load size of an AGV is one, the threshold level can be set as bi without loss of generality), the higher the movement speed of the AGVs is, the smaller the optimal value of the first threshold is (Algorithm 5, Input: maximum distance in layout dmax, minimum speed of AGV vmin) … (6: lRr ← eRr +(dmax/vmin)(0.25 + 0.25 y[0,1))).
It would have been prima facie obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have combined Singh with aspects of Tuan Le Anh. Applying Tuan Le Anh’s threshold tuning principle to Singh’s scheduling yields monotonic results that result in the first threshold for triggering material handling support can be set to maintain higher yield performance.
Claims 10, 12, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (A Matheuristic for AGV Scheduling with Battery Constraints) in view of Gao et al. (Effect of Human-Robot Interaction on the Fleet Size of AIV Transporters in FMS).
Regarding claim 10, Singh discloses the method according to claim 1, as discussed supra. Additionally, Gao who is in the same field of endeavor of AGV utilization and production discloses, the obtained value of the related performance index meeting the preset condition is a maximum value of the AGV utilization rate (II. LITERATURE REVIEW, By applying a global metamodel-based optimisation such as response surface methodology (RSM), the obtained responses on the AGV utilisation and throughput are optimised) and/or a maximum value of the production line yield (II. LITERATURE REVIEW, In this analysis, the number of required vehicles is based on the most statistical influencing parameters for a given production facility such as the number of production machines, total vehicle routing distance, job shop’s layout number of intersection and number of nodes, maximum machine utilisation).
It would have been prima facie obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to have combined Singh with aspects of Gao. Gao explains that using simulation or optimization yields the obtained responses on the AGV utilization and throughout are optimized. This supports using the maximum AGV utilization rate and production line yields as the related performance index for selecting the best scheduling related parameter values in Singh.
Regarding claim 12, Singh discloses the method according to claim 1, as discussed supra. Additionally, Gao discloses, the simulation is performed through Plant Simulation or Flexsim software (II. LITERATURE REVIEW, researchers proposed a two-stage simulation optimisation mechanism where simulation models with different charging system are constructed using FlexSim simulation software),
Regarding claim 16, Singh discloses the method according to claim 1, as discussed supra. Additionally, Gao discloses, the obtained value of the related performance index meeting the preset condition is a maximum value of the AGV utilization rate (II. LITERATURE REVIEW, By applying a global metamodel-based optimisation such as response surface methodology (RSM), the obtained responses on the AGV utilisation and throughput are optimised), and/or a maximum value of the production line yield (II. LITERATURE REVIEW, In this analysis, the number of required vehicles is based on the most statistical influencing parameters for a given production facility such as the number of production machines, total vehicle routing distance, job shop’s layout number of intersection and number of nodes, maximum machine utilisation).
Conclusion
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/S.E.D./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665