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 .
Notice to Applicant
The Office Action mailed on October 05, 2026 was intended to be a Final Office Action as stated in the Notice to Applicant and Conclusion. This is a replacement that includes a corrected PTO-326 Office Action Summary form indicating the status of the replay is FINAL.
The following is a Final Office Action for Application Serial Number: 18/058,123, filed on November 22, 2022. In response to Examiner’s Non-Final Rejection dated March 25, 2026, Applicant on July 27, 2026, amended claims 1-8, 10-12, 15, 17 and 19 and added new claim 20. Claims 1-8, 10-12, 15, 17, 19 and 20 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections of claims 1-8, 10-12, 15, 17, 19 are hereby amended in light of Applicants amendments to claims 1, 15 and 17. A new 35 U.S.C. § 103 rejection has been applied to new claim 20.
Response to Arguments
Applicant's Arguments/Remarks filed July 27, 2026 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks will be addressed herein below in the order in which they appear in the response filed July 27, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states the Examiner indicated that the proposed claim amendments presented during the interview appear to overcome the § 101 rejection, though "an autonomous vehicle or a driverless vehicle" seems to lack sufficient support in the original specification. Applicant amends the independent claims to recite, instead of the above limitation, "a vehicle that operates to transport people along a passageway within a facility according to a predetermined schedule." This is clearly supported at least in paragraphs [0090]-[0092] in the original specification. Similarly to "an autonomous vehicle or a driverless vehicle," the claimed "vehicle" operates to transport the people along the passageway within the facility according to the predetermined schedule.
In response Examiner respectfully disagrees. Examiner finds “a vehicle that operates to transport people along a passageway within a facility according to a predetermined schedule” is description of what, where and when the vehicle operates, however the limitation does not disclose how the vehicle performs the transporting of people in a facility based on a predetermined schedule. Furthermore, Examiner notes the control limitation is disclosed at a high level of generality without reciting any specific additional element assigned to perform the function. The specification is void of support suggesting the vehicle is autonomous/driverless, thus, the controlling of the vehicle can reasonably be interpreted broadly as a human activity (e.g. driver operated). Therefore, Examiner finds the amended claim is not similar to an autonomous or driverless vehicle.
Regarding the 35 U.S.C. 101 rejection, Applicant states amended independent claim 1 recites a specific technological process in which (see p. 13-14, Applicant Remarks). Amended independent claims 15 and 17 recite substantially similar limitations.
These features are technical operations involving the specific way to calculate an operation condition of the vehicle transporting the people in the airport, set the calculated operation condition as the actual operation condition for the vehicle traveling in the airport, and control the vehicle traveling in the airport based on the actual operating conditions for the vehicle. They cannot practically be performed in the human mind or by pen and paper, and do not fall under mental processes. Executing a simulation on a stored facility model, calculating a predictive waypoint-pass- through value that accounts for a deviation between an actual and a reference arrival time, and calculating, with an AI model having a machine-learned correspondence, people flow information for use in a constrained optimization are the kind of operations that courts has found incapable of practical performance in the human mind. See MPEP § 2106.04(a)(2)(III)(A).
Nor do the amended independent claims recite a certain method of organizing human activity, which is limited to fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior or relationships or interactions between people. See MPEP § 2106.04(a)(2)(II). The claims form no agreement, conduct no transaction, and include no step that directs or manages how one person behaves toward another. The arithmetic device instead treats people as moving objects within a facility model, executing a simulation of their movement to derive a predictive waypoint-pass-through value and, in turn, an operation condition for the vehicle. Simulating the physical movement of people through a facility to obtain an input for a vehicle-scheduling calculation is not managing relationships or interactions between people, and the Examiner's characterization of the claims as "managing relationships between people" does not identify any claim step that organizes, facilitates, or governs an interaction between two or more people. See MPEP § 2106.04(a)(2)(II)(C).
Accordingly, the amended independent claims are not directed to any of the three enumerated abstract-idea groupings (mathematical concepts, certain methods of organizing human activity, or mental processes) as provided in MPEP § 2106.04(a).
In response, Examiner respectfully disagrees. Examiner finds the pending claims are not technological in nature and merely limits the abstract idea to a particular environment and thus fails to add an inventive concept to the claims; see MPEP 2106.05(h). As stated in the previous Office Action, the claim discloses limitations that mimics human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind. Examiner respectfully reminds Applicant claims can recite a mental process even if they are claimed as being performed on a computer; see MPEP 2106.04(a)(2)(III)(C). Examiner finds the pending claims recite similar limitations to claims the courts have indicated may not be sufficient in showing an improvement in computer-functionality, such as accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; see MPEP 2106.05(a)(I) and MPEP 2106.05(a)(II). Additionally, Examiner notes the abovementioned AI model is not considered abstract and therefore will not be analyzed under Step 2A – Prong One. However, Examiner notes the AI model is being used as a tool to perform the instructions of an abstract idea without reciting limitations that are indicative of integrating the abstract idea into a practical application.
Additionally, Examiner notes when multiple abstract ideas, which may fall in the same or different groupings, or multiple laws of nature may be recited. In these cases, examiners should not parse the claim. For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A Prong One to make the analysis clear on the record; see MPEP 2106.04(II)(B). Examiner finds, as components of calculating and controlling vehicle operations the claims recite simulating the movement of vehicles and people, calculations for a predicted number of people passing through a waypoint and people flow information that minimizes movement times with a maximum wait time of people as a constraint. In addition, the specification details an object of the disclosure is to provide, in part, a people flow prediction method, and a program that can predict people flow with high accuracy (see par. 0005). All this evidence shows the claim recites a way of predicting the flow of people which constitutes managing relationships or interactions between people including social activities as well as commercial interactions regarding an activity that involves multiple people; see MPEP 2106.04(a)(2). Examiner maintains the claim recites an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states even if the amended independent claims were to be characterized as a judicial exception, each of the claims as a whole integrates that concept into a concrete technological implementation that improves accuracy and efficiency in calculating the predictive waypoint-pass- through value by reflecting the movement pattern of the people, and setting the vehicle operation condition. This provides a technological solution to a technological problem that "to efficiently set a vehicle operation schedule, it is necessary to increase the predictive accuracy of a people flow related to the number of passengers." Original specification, para. [0004].
This is not a case of merely confining an abstract idea to a particular technological environment. That the claims do not require an autonomous vehicle does not change this conclusion. Every step that produces the operation condition, namely executing the simulation, calculating the predictive waypoint-pass-through value, calculating the people flow information with the AI model, and performing the optimization calculation, is performed by the arithmetic device, not a person; how the vehicle subsequently executes that operation condition does not bear on the technical character of how the condition was derived. Here, the claimed advance is the process for calculating, from simulated movement and arrival time deviation processed with an AI model, the vehicle's operation condition.
In response, Examiner respectfully disagrees. In response, Examiner respectfully disagrees. Examiner maintains Applicants abovementioned arguments are not technological in nature and merely limits the abstract idea to a particular environment. Specifically, Examiner finds Applicants abovementioned arguments to be directed to an existing business process (e.g., managing operating conditions) and not the functioning of a computer, another technology or computer-related technology.
Applicant is describing the use of the additional element (AI model) as a tool to a calculating the people flow information without any improvement to how the AI model fundamentally functions, reflecting and/or submitting that the technology used is being improved or there was a technical problem with the technology that the claimed invention solves. Examiner finds the pending claims recite similar limitations to claims the courts have indicated may not be sufficient in showing an improvement in computer-functionality, such as accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; see MPEP 2106.05(a)(I) and MPEP 2106.05(a)(II). Examiner maintains the claim is directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states even if the amended independent claims were considered to recite a judicial exception that is not integrated into a practical application under Step 2A, the claims recite additional elements that, considered individually and as an ordered combination, amount to significantly more than that exception. For example, the limitation that the arithmetic device calculates the predictive waypoint- pass-through value, i.e., "a predictive value of the number of the people passing through the waypoint at a predetermined time in a future" based on the simulation under a condition that an arrival time to the waypoint deviates from a reference arrival time to the waypoint is not well-understood, routine, or conventional.
The specification confirms that it produces a concrete technical benefit. Specifically, the specification demonstrates that "even in a case where there is a time deviation from the scheduled time when the waypoint B is passed through, such as a case where people pass through the waypoint B at different times with respect to the departure time of an airplane or a case where the travel speed of each of the people is slow during congestion, the predictive waypoint-pass-through value can be calculated with such a time deviation absorbed" and thereby "the people flow can be predicted with high accuracy, in consideration of the time deviation." Id. at para. [0061].
The additional elements here execute a specific simulation on a facility model, apply an AI model having a machine-learned correspondence, and perform a constrained optimization that minimizes movement time subject to a per-node maximum-waiting-time constraint. Such an ordered combination is not well-understood, routine, or conventional. Therefore, the combination of the above limitations as a whole amounts to "unconventional steps that confine the claim to a particular useful application," namely, "significantly more" than the abstract idea. MPEP § 2106.05.
Eligibility Conclusion
"Examiners are reminded that if it is a "close call" as to whether a claim is eligible, they should only make a rejection when it is more likely than not (i.e., more than 50%) that the claim is ineligible under 35 U.S.C. 101." 2025 USPTO Memorandum, https://www.uspto.gov/sites/default /files/documents/memo-101-20250804.pdf (emphasis added). Here, when considered as a whole, each of the amended independent claims recites a specific computer-implemented process that improves accuracy and efficiency in setting the vehicle operation condition. Therefore, under Step 2A (Prongs 1 and 2) and Step 2B of the Alice/Mayo test, it is more likely than not that the claims are directed to patent-eligible subject matter.
At least for the above reasons, the amended claims are patent eligible under Step 2A/2B of the Alice/Mayo test. Accordingly, withdrawal of the rejection is respectfully requested.
In response, Examiner respectfully disagrees. Again, Examiner finds Applicant is attempting to say the Step 2A-Prong One limitations, the abstract idea, is what makes the claim eligible. Examiner notes the analysis in Step 2B addresses the question on whether an additional element (or combination of additional elements) represents well-understood, routine and/or conventional activities. Examiner maintains Applicant has not identified any disclosure in the claimed invention showing and/or submitting that the computer technology used is being improved, there was a technical problem in the computer technology that the claimed invention solves, or the ordered combinations of the known elements is significantly more than identified abstract idea. Examiner maintains the additional elements (e.g., an operation condition setting device comprising a storage, AI model, and an arithmetic device that comprises a central processing unit (CPU) and devices comprising units and non-transitory computer readable storage mediums) recited in the claim perform generic functions (i.e., such as receiving or transmitting data over a network, repetitive calculations, and storing and retrieving information in memory) without reciting any limitations that amount to “significantly more” than the judicial exception. Applicant has not presented an argument to alter this analysis. For at least these reasons, the claim remains rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Applicant’s arguments, see pg. 18, filed July 27, 2026, with respect to the rejection(s) of claims 1-3, 6-8, 10, 12, 15, 17 and 19 under 35 U.S.C. 103 have been fully considered. However, upon further consideration, a new ground(s) of rejection is made. Applicant’s arguments are considered moot because they are directed to newly amended subject matter and do not apply to the combination of references being used in the current rejection. Please refer to the 35 U.S.C. 103 rejection for further explanation and rationale.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 10-12, 15, 17, 19 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-8, 10-12, 19 and 20 are directed towards a device, claim 15 is directed towards a method and claim 17 is directed towards a non-transitory computer readable storage medium, which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-8 and 10-12, 15, 17, 19 and 20 recite using facility model simulations to predict movement routes for a flow of people passing through waypoints and operation conditions of a vehicle transporting people within a facility.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, sets, in the facility model, the passageway of the vehicle between two points among the points, sets, in the facility model, a movement route of people from, among the points, a starting point to a destination point beside the passageway through a waypoint, executes a simulation on the facility model where the people are moving along the movement route to the destination point and then are transported by the vehicle moving along the passageway, based on the simulation under a condition that an arrival time to the waypoint deviates from a reference arrival time to the waypoint, the reference arrival time being set based on a distance from the waypoint to the destination point and a walking speed of the people, calculates a predictive waypoint-pass-through value that is a predictive value of the number of the people passing through the waypoint at a predetermined time in a future, based on the predictive waypoint-pass-through value, calculates people flow information indicating a flow of people in the airport, a learning correspondence between the predictive waypoint-pass-through value and the number of people traveling between two points among the points by applying the people flow information to the facility model and performing an optimization calculation to minimize a total value of movement times of the people with a maximum waiting time of the people at each of the nodes as a constraint condition, calculates an operation condition of the vehicle transporting the people in the airport, outputs the calculated operation condition and sets the calculated operation condition as an actual operation condition for the vehicle traveling in the airport, and based on the actual operating conditions for the vehicle, controls the vehicle traveling in the airport constitutes methods based on commercial or legal interactions, i.e., business relations and managing relationships between people (including social activities), as well as, observations, evaluations, judgements and/or opinion that can be performed by a combination of the human mind and a human using pen and paper. The recitation of the operation condition setting device comprising storage and an arithmetic device that comprises a central processing unit (CPU) and AI model does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus, the claim recites an abstract idea. Claims 15 and 17 recite certain method of organizing human activity and mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites a storage that stores a facility model including nodes respectively corresponding to points in an airport as the facility and a link connecting the nodes, which is considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites an operation condition setting device comprising a storage; and an arithmetic device that comprises a central processing unit (CPU) at a high-level of generality such that it amounts to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Furthermore, claim 1 recites wherein the Al model has a machine-learned correspondence. The general use of artificial intelligence does not provide a meaningful limitation to transform the abstract idea into a practical application. Here the AI model is disclosed at a high level of generality and is solely used as a tool to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The method recited in claim 15 and non-transitory computer readable storage medium storing a program executable by a computer in claim 17 also amount to no more than mere instructions to apply the exception using generic computer components; see MPEP 2106.05(f). Thus, the additional elements recited in claims 15 and 17 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including an operation condition setting device comprising a storage and an arithmetic device that comprises a central processing unit (CPU) and devices comprising units and non-transitory computer readable storage mediums amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The Artificial Intelligence (AI) model recited in the claim are disclosed at a high-level of generality (see at least Specification [0051]) and does not amount to significantly more than the abstract idea Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claim 12 recites limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-8, 10, 11 and 19 recite steps that further narrow the abstract idea constituting certain methods of organizing human activity and mental processes. Claim 20 recite steps directed to an abstract idea based on certain methods of organizing human activity, mental processes and mathematical concepts constituting commercial or legal interactions, i.e., business relations and managing relationships between people (including social activities), observations, evaluations, judgements and/or opinion that can be performed by a combination of the human mind and a human using pen and paper and mathematical relationships, mathematical formulas or equations and mathematical calculations. No additional elements are disclosed in the dependent claims that were not considered in independent claim 1. Therefore claims 2-8, 10-12, 19 and 20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6-8, 10, 12, 15, 17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wei, China Publication No. CN 111369181 A (EPO English Translation) [hereinafter Wei], in view of Sefair Cristancho et al., U.S. Publication No. 2022/0092442 [hereinafter Cristancho], and further in view of Chastain et al., U.S. Publication No. 2023/0077800 [hereinafter Chastain].
Referring to Claim 1, Wei teaches:
An operation condition setting device for a vehicle that operates to transport people along a passageway within a facility according to a predetermined schedule, comprising:
a storage that stores a facility model including nodes respectively corresponding to points in a facility and a link connecting the nodes (Wei, [0159]), “The system's integrated database stores historical data, real-time data, equipment data, model data, geographic information data, and a 3D network model”; (Wei, [0103]), “Network transportation process simulation is a comprehensive simulation of train operation lines, sections, and network passenger flow”; (Wei, [0105]), “The transportation network contains the main attribute information of nodes and arcs, and can clearly represent the relationships between nodes, between arcs, and between nodes and arcs in the transportation network”; (Wei, [0090]); and
an arithmetic device that comprises a central processing unit (CPU) and that (Wei, [0:
sets, in the facility model, the passageway of the vehicle between two points among the points (Wei, [0106]), “Train flow simulation: simulation of train entry and exit from stations, simulation of train tracking and operation in sections”; (Wei, [0108]), “Train section tracking simulation uses train operation control technology as its core to achieve safe and efficient train operation within a section. This function can simulate train tracking operations under different block systems and obtain the minimum train tracking interval time”; (Wei, [0088]; [0111]),
sets, in the facility model, a movement route of people from, among the points, a starting point to a destination point beside the passageway through a waypoint (Wei, [0109]), “Passenger Flow Simulation: Simulation of Macro-level Online Passenger Flow and Micro-level Online Passenger Travel Chain”; (Wei, [0110]), “This function takes real-time passenger flow as input, realizes the spatiotemporal matching of real-time passenger flow with transportation network and vehicle flow, and predicts the distribution of passenger flow in the network in the current and future period. The implementation of this function is based on the construction of the transportation network environment and the simulation of the transportation network train flow”,
executes a simulation on the facility model where the people are moving along the movement route to the destination point and then are transported by the vehicle moving along the passageway (Wei, [0173]), “The 3D display unit is directly connected to the simulation engine and the rail transit simulation kernel. Through 3D models, it displays the status and behavior of facilities and equipment, passenger flow/passenger behavior inside stations/hubs, and 3D simulation of train operation in real time, making it convenient for researchers to intuitively observe and analyze the simulation process”; (Wei, [0092]), “The simulation of rail transit systems includes two parts: hub/station simulation and network transportation process simulation. Simulation of actual rail transit systems allows for consideration of the interrelationships between trains, between trains and transportation plans, and between trains and passenger flow within the autonomous train scheduling model…”; (Wei, [0094]),
based on the simulation under a condition that an arrival time to the waypoint deviates from a reference arrival time to the waypoint, calculates a predictive waypoint-pass-through value that is a predictive value of the number of the people passing through the waypoint at a predetermined time in a future (Wei, [0135]), “The on-time cost of a train is 0 when the train's arrival time at the station falls between the shortest time…when the train arrives at the station later than, the on-time cost also increases linearly with the delay time”; (Wei, [0109]-[0111]), “Passenger Flow Simulation: Simulation of Macro-level Online Passenger Flow and Micro-level Online Passenger Travel Chain… This function takes real-time passenger flow as input, realizes the spatiotemporal matching of real-time passenger flow with transportation network and vehicle flow, and predicts the distribution of passenger flow in the network in the current and future period… Based on passenger travel attributes, transportation network characteristics, and external information, the complete travel process of passengers in the network is obtained through simulation. The travel process includes complete travel information and the correspondence between each travel link and the transportation network and traffic flow. The travel chain is evaluated based on the simulation results”; (Wei, [0172]), “The train dispatching strategy reward evaluation unit extracts relevant data from the data acquisition and monitoring unit based on the implementation structure of the train dispatching scheme in the simulation module, which is injected by the train dispatching scheme simulation implementation interface. It then calculates the on-time reward…passenger waiting time reward of the train dispatching scheme. The rewards calculated here are then entered into the reward function calculation unit in the deep reinforcement learning module for further calculation, which yields the cost of exceeding the on-time limit… and the passenger waiting time cost”; (Wei, [0089]),
by applying the people flow information to the facility model and performing an optimization calculation to minimize a total value of movement times of the people with a maximum waiting time of the people at each of the nodes as a constraint condition, calculates an operation condition of the vehicle transporting the people in the facility, outputs the calculated operation condition and sets the calculated operation condition as an actual operation condition for the vehicle traveling in the facility, and based on the actual operating conditions for the vehicle, controls the vehicle traveling in the facility (Wei, [0162]), “In the main workflow of the train autonomous dispatching system, firstly, real-time train operation data is collected from the actual rail transit system by the real-time data acquisition interface, which serves as the data basis for the simulation module to ensure a high degree of consistency between the simulation module and the actual rail transit system. Secondly, the decision-making ability of the train autonomous scheduling model is continuously improved by using the simulation module and the deep reinforcement learning module for continuous interactive training. Simulation modules can also be used to evaluate the trained autonomous train scheduling model. Finally, the model trained by the deep reinforcement learning module is output to the scheduling scheme module. The scheduling scheme generated by the scheduling scheme module based on the train autonomous scheduling scheme decision model is transmitted to the actual rail transit system for implementation of the scheduling scheme”; (Wei, [0189]), “The reward function-related data of the train autonomous scheduling model is used to describe the training objective of the train autonomous scheduling model. It is calculated, stored and managed by the reward function unit in the deep reinforcement learning module. The data related to the train autonomous scheduling reward function includes several types: passenger waiting time cost, energy consumption cost of train actions, cost of exceeding safety interval limits, and cost of exceeding punctuality limits. These data are collected and preliminarily calculated by the scheduling scheme reward evaluation function module in the simulation module, and finally calculated by the reward function unit in the deep reinforcement learning module”; (Wei, [0063]), “The deep reinforcement learning method and module for autonomous train scheduling of the present invention uses a quadruple of rail transit data to train a value function neural network, thereby obtaining a trained value function neural network. The trained value function neural network can be used for train scheduling. By inputting the current train running status into the value function neural network, the optimal action of the train can be obtained, thereby improving the real-time performance and flexibility of scheduling”; (Wei, [0092]; [0114]; [0178]; [0190]).
Wei teaches an invention belonging to the field of rail transit (see par. 0002), dynamic simulation evaluation of passenger flow within the hub based on actual passenger flow demand (see par. 0100) and when training deep reinforcement learning modules based on deep reinforcement learning, the goal is to reduce the energy consumption of the trains themselves and reduce the waiting time for passengers while ensuring the safety and punctuality of each train (see par. 0112), but Wei does not explicitly teach:
an airport as the facility;
the reference arrival time being set based on a distance from the waypoint to the destination point and a walking speed of the people; and
based on the predictive waypoint-pass-through value, calculates, with an Al model, people flow information indicating a flow of people in the facility, wherein the Al model has a machine-learned correspondence between the predictive waypoint-pass-through value and the number of people traveling between two points among the points.
However Cristancho teaches:
an airport as the facility (Cristancho, [0040]), “predict the number of passengers arriving at airport security screening checkpoints (SSCPs) for screening prior to boarding a commercial flight and then to allocate available Transportation Security Officers (TSOs) to minimize passenger wait times while complying with security protocols”; (Cristancho, [0041]; [0044]);
based on the predictive waypoint-pass-through value, calculates, with an Al model, people flow information indicating a flow of people in the facility, wherein the Al model has a machine-learned correspondence between the predictive waypoint-pass-through value and the number of people traveling between two points among the points (Cristancho, [0040]), “The system models Travel Document Checking (TDC) and Baggage Screening for both Precheck and Standard lanes. The procedure fuses data from multiple public sources and uses those in a mechanistic and machine learning model to estimate arrivals. Those estimates are then used in an optimization algorithm to allocate the available TSOs to SSCPs over time to minimize queue lengths and wait times”; (Cristancho, [0122]), “resulting learning approach thus forms a hybrid forecasting model that uses mechanistic model estimates at a prior date and compares them with observed passenger arrivals at each SSCP. As passenger arrivals at the beginning of the SSCP's queue are not generally recorded, the number of screened passengers is used as a proxy…”; (Cristancho, [0041]-[0043]; [0169]; [0242]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the evaluation of passenger flow and deep reinforcement learning modules in Wei to include the airport and people flow limitations as taught by Cristancho. The motivation for doing this would have been to improve the method of a deep reinforcement learning method for autonomous train scheduling in Wei (see par. 0007) to efficiently include the results of obtaining a more accurate prediction of the time-dependent arrivals of passengers (see Cristancho par. 0042).
Wei teaches predicting the distribution of passenger flow in the network in the current and future period (see par. 0110), but Wei does not explicitly teach:
the reference arrival time being set based on a distance from the waypoint to the destination point and a walking speed of the people.
However Chastain teaches:
the reference arrival time being set based on a distance from the waypoint to the destination point and a walking speed of the people (Chastain, [0016]), “the transportation carrier server may receive an estimated wait time at a security checkpoint from an airport computing system or from a computing system of a security service provider. The transportation carrier server may then calculate an estimated time of arrival (ETA) at the gate for the traveler based on the traveler's current location, the wait time at the security checkpoint, an expected time for the traveler to walk to the gate, which may be calculated based on the traveler's recent walking speed(s) and the distance to the gate from the traveler's current (or most recently available) location”; (Chastain, [0037]), “As such, server(s) 116 may possess information on lengths and/or processing times of queues 181-183, for example. Server(s) 116 may further possess data regarding distances within an airport, such as a distance from check-in counter 131 to Gate A. Thus, server(s) 116 may calculate an estimated arrival time at the gate for traveler 140 using any or all of such information, e.g., estimating walking speed based upon recent walking speed(s), determining a time to cover the walking distance from a current location to the gate, determining times to transit one or more queues, and combining these calculations to determine an overall time to reach Gate A. In various examples, server(s) 116 may further obtain or possess information regarding average times for travelers to move between various points, where walking speed is not necessarily indicative of how long the movement will take”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the passenger flow in Wei to include the arrival time limitation as taught by Chastain. The motivation for doing this would have been to improve the method of improving the real-time performance and flexibility of scheduling in Wei (see par. 0063) to efficiently include the results of enabling more efficient and precise scheduling of transportation systems where multiple passengers are using a common vehicle (see Chastain par. 0013).
Referring to Claim 2, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 1. Wei teaches predicting the distribution of passenger flow in the network in the current and future period (see par. 0110), but Wei does not explicitly teach:
wherein the arithmetic device calculates the predictive waypoint-pass-through value based on a predictive value of the number of people located at a first point upstream from the waypoint in a movement direction of the people before the predetermined time.
However Cristancho teaches:
wherein the arithmetic device calculates the predictive waypoint-pass-through value based on a predictive value of the number of people located at a first point upstream from the waypoint in a movement direction of the people before the predetermined time (Cristancho, [0051]), “Using passenger arrival data and TSO availability, the VADSS platform dashboard displays the recommended checkpoint configuration as well as the predictive queue estimates and wait times”; (Cristancho, [0169]), “TSA reports hourly throughput counts at security checkpoints throughout the country. As these counts are obtained in the screening area (once a passenger goes through the AIT or WTMD), this data is lagged compared with the passenger arrivals given the queuing process”; (Cristancho, [0100]), “Each SSCP has one or more queues leading up to Travel Document Checkers (TDCs), who verify every passenger's boarding pass against their identity. Upon verification, TSOs at the TDC stations direct passengers to a set of primary screening lanes. Typically, each lane has its own conveyor belt feeding a scanner, a walk-through metal detector (WTMD), and an Advanced Imaging Technology (AIT) body scanner. When a person or an item fails to pass the primary inspection, that person or item will be directed to a secondary inspection (open bag or pat-down)”; (Cristancho, [0150]), “equation (7) calculate the effective processing rate at each area as a function of the chosen configuration”; (Cristancho, [0063]; [0152]; [0138]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the passenger flow in Wei to include the calculating limitation as taught by Cristancho. The motivation for doing this would have been to improve the method of improving the real-time performance and flexibility of scheduling in Wei (see par. 0063) to efficiently include the results of obtaining a more accurate prediction of the time-dependent arrivals of passengers (see Cristancho par. 0042).
Referring to Claim 3, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 2. Wei teaches predicting the distribution of passenger flow in the network in the current and future period (see par. 0110), but Wei does not explicitly teach:
wherein the arithmetic device calculates the predictive waypoint-pass-through value based on respective predictive values of the numbers of people located at the first point at different times before the predetermined time and an influence degree, on the predictive waypoint-pass-through value, set for each of the predictive values at the different times
However Cristancho teaches:
wherein the arithmetic device calculates the predictive waypoint-pass-through value based on respective predictive values of the numbers of people located at the first point at different times before the predetermined time and an influence degree, on the predictive waypoint-pass-through value, set for each of the predictive values at the different times (Cristancho, [0137]), “an autoregressive prediction is described by a weighted linear sum of prior information. This could take the form {circumflex over (τ)}.sub.gjh=δ.sub.g+Σ.sub.(j′,h′) ϵτihϕ.sub.j′h′τ.sub.gj′h′, where {circumflex over (τ)}.sub.gjh is the prediction of the number of passengers arriving at checkpoint g at time interval t within hour h, T.sub.jh is the set of pairs of indices (j′, h′) corresponding to day j′ and hour h′ that is used to predict the number of passengers expected in day jϵD at hour hϵH, with j′≤j, h′≤h, and (j′,h′)≠(j,h), and δ.sub.g and ϕ.sub.j′h′ are constants”; (Cristancho, [0107]), “the first component is a mechanistic model to predict passenger arrivals based on business fundamentals such as flight departure schedules, airplane capacities, and expected number of passengers, among other factors. The second component is a learning model that improves the mechanistic prediction by using adjusting factors obtained from a training set of historical information. And the third component is a time series (auto-regressive) model that predicts passenger volumes based on historical data”; (Cristancho, [0109]; [0123]; [0125]-[0126]; [0138]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the passenger flow in Wei to include the calculating limitation as taught by Cristancho. The motivation for doing this would have been to improve the method of improving the real-time performance and flexibility of scheduling in Wei (see par. 0063) to efficiently include the results of obtaining a more accurate prediction of the time-dependent arrivals of passengers (see Cristancho par. 0042).
Referring to Claim 6, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 3. Wei teaches importance sampling weights are needed to compensate for this estimation bias (see par. 0145), but does not explicitly teach:
wherein the arithmetic device sets the influence degree based on a measured waypoint-pass-through value being a measured value of the number of people passing through the waypoint at a past time, and the predictive waypoint-pass-through value at the past time.
Cristancho further teaches:
wherein the arithmetic device sets the influence degree based on a measured waypoint-pass-through value being a measured value of the number of people passing through the waypoint at a past time, and the predictive waypoint-pass-through value at the past time (Cristancho, [0107]), “the first component is a mechanistic model to predict passenger arrivals based on business fundamentals such as flight departure schedules, airplane capacities, and expected number of passengers, among other factors. The second component is a learning model that improves the mechanistic prediction by using adjusting factors obtained from a training set of historical information. And the third component is a time series (auto-regressive) model that predicts passenger volumes based on historical data”; (Cristancho, [0127]), “The objective function in equation (2) minimizes the sum of squared errors between the observed throughput and the corrected mechanistic prediction for each of the training days in S Note that there is a single adjusting parameter for each checkpoint-day-hour combination that needs to be estimated to minimize the error across training days subject to the bound constraints in equation (3). Furthermore, model [T.sub.gjh] is a convex univariate optimization problem with linear constraints that can be solved using first-order optimality conditions. Solving [T.sub.gjh] for each checkpoint-day-hour combination provides a full set of β-parameters that can be used to adjust mechanistic predictions”; (Cristancho, [0035]; [0042]; [0108]; [0133]; [0216]; [0235]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the sampling weights in Wei to include the influence degree limitation as taught by Cristancho. The motivation for doing this would have been to improve the method of a deep reinforcement learning method for autonomous train scheduling in Wei (see par. 0007) to efficiently include the results of obtaining a more accurate prediction of the time-dependent arrivals of passengers (see Cristancho par. 0042).
Referring to Claim 7, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 3. Wei teaches importance sampling weights are needed to compensate for this estimation bias (see par. 0145), but does not explicitly teach:
wherein the arithmetic device makes the influence degree different in respective time slots of the predetermined time to be subjected to calculation of the predictive waypoint-pass-through value.
Cristancho further teaches:
wherein the arithmetic device makes the influence degree different in respective time slots of the predetermined time to be subjected to calculation of the predictive waypoint-pass-through value (Cristancho, [0107]), “the first component is a mechanistic model to predict passenger arrivals based on business fundamentals such as flight departure schedules, airplane capacities, and expected number of passengers, among other factors. The second component is a learning model that improves the mechanistic prediction by using adjusting factors obtained from a training set of historical information. And the third component is a time series (auto-regressive) model that predicts passenger volumes based on historical data”; (Cristancho, [0127]), “The objective function in equation (2) minimizes the sum of squared errors between the observed throughput and the corrected mechanistic prediction for each of the training days in S Note that there is a single adjusting parameter for each checkpoint-day-hour combination that needs to be estimated to minimize the error across training days subject to the bound constraints in equation (3). Furthermore, model [T.sub.gjh] is a convex univariate optimization problem with linear constraints that can be solved using first-order optimality conditions. Solving [T.sub.gjh] for each checkpoint-day-hour combination provides a full set of β-parameters that can be used to adjust mechanistic predictions”; (Cristancho, [0042]; [0108]; [0133]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the sampling weights in Wei to include the influence degree limitation as taught by Cristancho. The motivation for doing this would have been to improve the method of a deep reinforcement learning method for autonomous train scheduling in Wei (see par. 0007) to efficiently include the results of obtaining a more accurate prediction of the time-dependent arrivals of passengers (see Cristancho par. 0042).
Referring to Claim 8, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 1. Wei further teaches:
wherein the arithmetic device sets the people flow information based on past movement information of people in the facility (Wei, [0107]), “In the rail transit network, the path selection of passengers follows the shortest path principle. In this embodiment, based on the shortest path principle, the predicted values of the network OD passenger flow (Original Destination) matrix are distributed on the network to obtain the inbound passenger flow and transfer passenger flow of each station on each line, which serves as the basis for decision-making based on the autonomous train scheduling scheme”.
Wei teaches an invention belonging to the field of rail transit (see par. 0002), but Wei does not explicitly teach:
airport
However Cristancho teaches:
airport (Cristancho, [0040]), “predict the number of passengers arriving at airport security screening checkpoints (SSCPs) for screening prior to boarding a commercial flight and then to allocate available Transportation Security Officers (TSOs) to minimize passenger wait times while complying with security protocols”; (Cristancho, [0041]; [0044]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the rail transit in Wei to include the facility limitation as taught by Cristancho. The motivation for doing this would have been to improve the method of a deep reinforcement learning method for autonomous train scheduling in Wei (see par. 0007) to efficiently include the results of insights into security screening processing times, predictive system throughput, and actionable operational policies, data, and recommendations (see Cristancho par. 0009).
Referring to Claim 10, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 1. Wei further teaches:
wherein the arithmetic device sets, as the operation condition of the vehicle, an operation schedule of the vehicle (Wei, [0186]), “real-time communication is required between the various functional units in the entire deep reinforcement learning module. Therefore, the data transmission unit can meet the real-time communication needs between the learning agent and the simulation module, and between the learning agent and units such as the cache playback memory, deep reinforcement learning and reward function unit. With the support of the data transmission unit, the learning agent can interact efficiently with the simulation module, and simultaneously perform real-time training and data parameter storage, thereby enabling the continuous training and evolution of the train autonomous scheduling model”; (Wei, [0089]), “Deep reinforcement learning algorithms modify their own action strategies using the generated data, then interact with the simulation module to generate new data, and use the new data to further improve their behavior. After several iterations of learning, the deep reinforcement learning module can eventually learn the optimal action to complete the corresponding task (i.e., the optimal strategy to generate the optimal action)”; (Wei, [0171]-[0172]), “The technical indicator statistics and evaluation unit then uses the simulated "rail transit system" operation status data provided by the data acquisition and monitoring unit to statistically evaluate technical indicators such as train punctuality rate, operational safety, train operation energy consumption, and passenger waiting time… The train dispatching strategy reward evaluation unit extracts relevant data from the data acquisition and monitoring unit based on the implementation structure of the train dispatching scheme in the simulation module, which is injected by the train dispatching scheme simulation implementation interface. It then calculates the on-time reward, safety reward, energy consumption reward, and passenger waiting time reward of the train dispatching scheme. The rewards calculated here are then entered into the reward function calculation unit in the deep reinforcement learning module for further calculation, which yields the cost of exceeding the on-time limit, the cost of exceeding the safety interval limit, the energy consumption cost, and the passenger waiting time cost”; (Wei, [0085]; [0156]).
Referring to Claim 12, Wei in view of Cristancho in view of Chastain teaches the operation conditioning setting device according to claim 1. Wei teaches an invention belonging to the field of rail transit (see par. 0002), but Wei does not explicitly teach:
wherein the waypoint is a security check point in the airport.
However Cristancho further teaches:
wherein the waypoint is a security check point in the airport (Cristancho, [0039]-[0040]), “various embodiments is a workforce allocation and configuration decisions at airport security checkpoints (e.g., number of lanes open) are usually based on passenger volume forecasts…”; (Cristancho, [0146]; [0209]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the rail transit in Wei to include the facility limitation as taught by Cristancho. The motivation for doing this would have been to improve the method of a deep reinforcement learning method for autonomous train scheduling in Wei (see par. 0007) to efficiently include the results of improving airport security operations (see Cristancho par. 0097).
Referring to Claim 15, Uematsu teaches:
An operation condition setting method for a vehicle the operates to transport people along a passageway within a facility according to a predetermined schedule, comprising:
Claim 15 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Referring to Claim 17, Wei teaches:
Claim 17 disclose substantially the same subject matter as claim 15, and is rejected using the same rationale as previously set forth.
Wei teaches a data regularization processing unit (see par. 0060), but Wei does not explicitly teach:
A non-transitory computer readable storage medium storing a program for causing a computer to execute an operation condition setting method for a vehicle that operates to transport people along a passageway within a facility according to a predetermined schedule, the program causing the computer to execute.
However Cristancho teaches:
A non-transitory computer readable storage medium storing a program for causing a computer to execute an operation condition setting method for a vehicle (Cristancho, [0215]; [0212]), the program causing the computer to execute.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified Wei to include the non-transitory computer readable storage medium limitation as taught by Cristancho providing the benefit of configuring and customizing how the specialized analytical model's algorithm will process input data (Cristancho, [0212]).
Referring to Claim 19, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 1. Wei further teaches:
wherein the arithmetic device performs the optimization calculation such that the operation condition is optimized within the facility model in which the people moves along the movement route, with a maximum number of vehicles capable of operating simultaneously as the constraint condition (Wei, [0092]), “Research on train autonomous scheduling models and scheduling schemes using deep reinforcement learning requires not only modeling the train autonomous scheduling agent of rail transit, but also simulating the entire actual rail transit system. The simulation of rail transit systems includes two parts: hub/station simulation and network transportation process simulation. Simulation of actual rail transit systems allows for consideration of the interrelationships between trains, between trains and transportation plans, and between trains and passenger flow within the autonomous train scheduling model. This enables the optimization of autonomous train scheduling schemes to ensure the safety and punctuality of train operations, while reducing train energy consumption and passenger waiting time”; (Wei, [0172]), “The train dispatching strategy reward evaluation unit extracts relevant data from the data acquisition and monitoring unit based on the implementation structure of the train dispatching scheme in the simulation module, which is injected by the train dispatching scheme simulation implementation interface. It then calculates the on-time reward, safety reward, energy consumption reward, and passenger waiting time reward of the train dispatching scheme. The rewards calculated here are then entered into the reward function calculation unit in the deep reinforcement learning module for further calculation, which yields the cost of exceeding the on-time limit, the cost of exceeding the safety interval limit, the energy consumption cost, and the passenger waiting time cost”; (Wei, [0063]; [0112]; [0132]; [0184]).
Referring to Claim 20, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 1. Wei teaches predicting the distribution of passenger flow in the network in the current and future period (see par. 0110), but Wei does not explicitly teach:
wherein the arithmetic device:
calculates a first total value, which is a sum of the values obtained by multiplying respective predictive values of the numbers of people located at the first point at different times before the predetermined time by a first influence degree on the predictive values set for each time, and calculates a second total value, which is a sum of the values obtained by multiplying respective predictive values of the numbers of people located at the second point at different times after the predetermined time by a second influence degree on the predictive values set for each time, and calculates the predictive waypoint-pass-through value by summing the first total value and the second total value using Equation (1) below, and outputs the predictive waypoint-pass-through value:
PNG
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152
542
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where Nt indicates the predictive waypoint-pass-through value at time t, G indicates the number of first and second points, Dt+l,i indicates a predictive value of the number of people located at the second point at future time t + 1, and At-1,i indicates a predictive value of the number of people located at the first point at past time t - 1.
However Cristancho teaches:
wherein the arithmetic device:
calculates a first total value, which is a sum of the values obtained by multiplying respective predictive values of the numbers of people located at the first point at different times before the predetermined time by a first influence degree on the predictive values set for each time, and calculates a second total value, which is a sum of the values obtained by multiplying respective predictive values of the numbers of people located at the second point at different times after the predetermined time by a second influence degree on the predictive values set for each time, and calculates the predictive waypoint-pass-through value by summing the first total value and the second total value using Equation (1) below, and outputs the predictive waypoint-pass-through value:
PNG
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146
519
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where Nt indicates the predictive waypoint-pass-through value at time t, G indicates the number of first and second points, Dt+l,i indicates a predictive value of the number of people located at the second point at future time t + 1, and At-1,i indicates a predictive value of the number of people located at the first point at past time t – 1 (Cristancho, [0153]), “the objective function in equation (29) penalizes any delay in processing passengers after the time they are in the queue, discouraging long wait times. Due to the fast increase in the penalization factor (t′−t+1).sup.2, the objective function in equation (29) also induces a queue priority, where passengers are cleared in a first-in-first-out fashion. The model that focuses on minimizing wait times consists of minimizing the objective function in equation (29), subject to Constraints at equations (6)-(20) and (25)-(28), or alternatively, subject to Constraints at equations (6)-(16) and (18)-(28), with equation 29 presented as follows”
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90
424
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(Cristancho, [0160]), “minimize a function that penalizes any delay in processing passengers, maintaining the same performance from Stage 1. As an output of Stage 2, the model obtains the maximum number of periods required to clear a passenger (i.e., maximum wait time) in the queue at the end of period t in area i, K*.sub.it, which is not allowed to deteriorate in future stages. The objective in Stage 3 is to minimize the number of flexible TSOs used, maintaining the same queue performance from Stages 1 and 2. The goal of this stage is to release (if possible) some TSOs from duties at the SSCPs in order to perform other activities, such as training and screening operations at baggage processing. The problem solved in Stage 4 seeks to minimize the summation of queue lengths across all areas and time periods while enforcing that the number of flexible TSOs is no more than the optimal level from Stage 3 and preserving the quality of service from Stages 1 and 2. This stage also aims to bring queue lengths to their minimum throughout the entire day, even at times where queues are not at their worst-case length. That is, Stage 4 discourages the model from keeping unnecessary queues and will utilize processing capacity whenever passengers are present in the TDC or screening queues; (Cristancho, [0157]-[0158]; [0131]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the passenger flow in Wei to include the calculating limitations as taught by Cristancho. The motivation for doing this would have been to improve the method of improving the real-time performance and flexibility of scheduling in Wei (see par. 0063) to efficiently include the results of obtaining a more accurate prediction of the time-dependent arrivals of passengers (see Cristancho par. 0042).
Claims 4, 5 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wei, China Publication No. CN 111369181 A (EPO English Translation) [hereinafter Wei], in view of Sefair Cristancho et al., U.S. Publication No. 2022/0092442 [hereinafter Cristancho], in view of Chastain et al., U.S. Publication No. 2023/0077800 [hereinafter Chastain], and further in view of Uematsu et al., U.S. Publication No. 2023/0311965 [hereinafter Uematsu].
Referring to Claim 4, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 1. Wei teaches predicting the distribution of passenger flow in the network in the current and future period (see par. 0110), but Wei does not explicitly teach:
wherein the arithmetic device calculates the predictive waypoint-pass-through value based on a predictive value of the number of people located at a second point downstream from the waypoint in a movement direction of the people after the predetermined time.
However Uematsu teaches:
wherein the arithmetic device calculates the predictive waypoint-pass-through value based on a predictive value of the number of people located at a second point downstream from the waypoint in a movement direction of the people after the predetermined time (Uematsu, [0155]), “executes passenger flow prediction which outputs predicted values of passenger riding information (in the form of the passenger riding data D40) and passenger staying information (in the form of the passenger staying data D50)… a passenger flow prediction unit (e.g., passenger flow prediction program P04) further calculates passenger riding information (e.g., passenger riding data D40) including information indicating the number of riding passengers at the time of departure of respective trains from respective stations. In addition, as described below, a timetable rescheduling unit (e.g., timetable rescheduling program P01) creates a rescheduled timetable for a planned timetable with use of passenger staying information (e.g., passenger staying data D50) output from the passenger flow prediction unit and the passenger riding information described above. Accordingly, a rescheduled timetable in which passenger flow prediction and passenger staying prediction are taken into consideration can be output”; (Uematsu, [0062]), “actual passenger flow data D03 is data indicating actual performance concerning from which station to which station and from what time each passenger desires to travel, how many passengers rode on each train and where each train departed, and how many passengers stay at each station and from what time to what time these passengers stay”; (Uematsu, [0107]), “An example of the passenger staying data D50 will be described with reference to FIG. 6. For example, the first row in FIG. 6 indicates that “60” passengers as “number of passengers” intending to travel to “arrival station” of “St. Y” are staying at “station” of “St. X” at “time” of “10:00.” The second row in FIG. 6 indicates that “40” passengers as “number of passengers” intending to travel to “arrival station” of “St. Z” are staying at “station” of “St. X” at “time” of “10:15.” FIG. 6 includes omitted parts indicated by ellipsis points. In practice, however, there are at least the same number of rows as the number of combinations each including the number of stations, the time, and the arrival station”; (Uematsu, [0063]), “travel route information (e.g., station arrangement for each railway line and each inbound/outbound direction, platforms to be used, railway lines to be used, information indicating stop or passage at each station)”; (Uematsu, [0068]; [0197]; [0241]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the passenger flow in Wei to include the calculating limitation as taught by Uematsu. The motivation for doing this would have been to improve the method of improving the real-time performance and flexibility of scheduling in Wei (see par. 0063) to efficiently include the results of an evaluation value of train congestion, an evaluation value of train delay, an evaluation value of operational cost, and an overall evaluation value of these values (see Uematsu par. 0068).
Referring to Claim 5, Wei in view of Cristancho in view of Chastain in view of Uematsu teaches the operation condition setting device according to claim 4. Wei teaches predicting the distribution of passenger flow in the network in the current and future period (see par. 0110), but Wei does not explicitly teach:
wherein the arithmetic device calculates the predictive waypoint-pass-through value based on respective predictive values of the numbers of people located at the second point at different times after the predetermined time and an influence degree, on the predictive waypoint-pass-through value, set for each of the predictive values at the different times.
However Cristancho teaches:
wherein the arithmetic device calculates the predictive waypoint-pass-through value based on respective predictive values of the numbers of people located at the second point at different times after the predetermined time and an influence degree, on the predictive waypoint-pass-through value, set for each of the predictive values at the different times (Cristancho, [0035]), “evaluating wait times and queue lengths at multi-station and multi-stage screening zones via a deterministic decision support algorithm… a parameter input interface to receive observed wait times and queue lengths at multi-station and multi-stage screening zones… an analytical model to apply a specialized algorithm to yield future predicted wait times and queue lengths at the multi-station and multi-stage screening zones based at least in part on the observed wait times and queue lengths and the user specified configuration selections; in which the processor executes the instructions stored in the memory to cause the analytical model to accept the observed wait times and queue lengths as initial starting conditions and to incrementally update queue lengths at each stage to the start of the next period by adding any arrivals during a previous period and subtracting throughput for the respective stage based on the number of customers served; and in which the processor executes the instructions stored in the memory to cause the analytical model to further sequentially process each of the stages of the multi-station and multi-stage screening zones to compute a number served at each stage during the time interval as the minimum of the service capacity based on (i) the number of service stations open and based further on (ii) a service rate per station provided by the user specified configuration selections, (iii) a number of initial customers in queue plus those arriving, and (iv) the service rate of the subsequent workstation when the subsequent station buffer space is full; and in which the processor executes the instructions stored in the memory to cause the analytical model to further compute and output the predicted wait time for any passenger by progressing that passenger on a first-come first-served manner through the network of service queues affiliated with each of the multi-station and multi-stage screening zones”; (Cristancho, [0216]; [0235]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the passenger flow in Wei to include the people limitation as taught by Cristancho. The motivation for doing this would have been to improve the method of improving the real-time performance and flexibility of scheduling in Wei (see par. 0063) to efficiently include the results of obtaining a more accurate prediction of the time-dependent arrivals of passengers (see Cristancho par. 0042).
Referring to Claim 11, Wei in view of Cristancho in view of Chastain teaches the operation condition setting device according to claim 10. Wei each train adjusts its operation strategy based on its own status, established transportation plan, relationship with other trains, etc. (see par. 0085), but Cristancho does not explicitly teach:
wherein the arithmetic device sets the operation condition of the vehicle including a plurality of coupled vehicle bodies and sets, under a constraint condition that a plurality of the vehicles include identical numbers of vehicle bodies, the operation schedule of the vehicle.
However Uematsu teaches:
wherein the arithmetic device sets the operation condition of the vehicle including a plurality of coupled vehicle bodies and sets, under a constraint condition that a plurality of the vehicles include identical numbers of vehicle bodies, the operation schedule of the vehicle (Uematsu, [0081]-[0083]), “A train number of a previous operation train of a target train is specified in “previous operation train.” The previous operation train here refers to a train which runs immediately before a target train among trains each using the same cars (or car sets) as those of the target train. For example, described in the second row in FIG. 3 is that the train of “HR101” which uses the same cars runs immediately before the train of “HR102,” such as a case of running until a destinated arrival station and turning back from this destinated arrival station… A train number of a next operation train of a target train is specified in “next operation train.” The next operation train here refers to a train which runs immediately after a target train among trains each using the same cars (or car sets) as those of the target train… An example of the operation data D20 will be described with reference to FIG. 3. For example, the first row in FIG. 3 indicates that there is no “previous operation train” for the train having “train no.” of “HR101” and that “next operation train” of “HR102” is a train which uses the same cars and runs immediately after the train “HR101.” The second row in FIG. 3 indicates that “previous operation train” of “101” is a train which uses the same cars and runs immediately before the train “HR102” and that “next operation train” of “HR103” is a train which uses the same cars and runs immediately after the train “HR102.” …”; (Uematsu, [0038]; [0193]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the operational strategy in Wei to include the operation conditions of a vehicle limitation as taught by Uematsu. The motivation for doing this would have been to improve the method of inputting the current train running status into the value function neural network, the optimal action of the train can be obtained, thereby improving the real-time performance and flexibility of scheduling in Wei (see par. 0063) to efficiently include the results of an evaluation value of train congestion, an evaluation value of train delay, an evaluation value of operational cost, and an overall evaluation value of these values. The criterion data is data indicating a calculation result of a criterion concerning a staying amount at each station, congestion of each train, a delay time of each train, each operational cost, and the like (see Uematsu par. 0068).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Krome et al. (US 11035676 B2) – A navigation system, method and computer program product are provided to provide navigational assistance at a venue. The navigation system is configured to obtain information regarding the venue to be navigated by a user including a plurality of waypoints at the venue. The navigation system is also configured to determine a time to be expended by the user between respective pairs of the waypoints at the venue. Based on the time and a rate of travel of the user, the navigation system is configured to determine one or more points of interest that are accessible by the user between at least one pair of waypoints. The navigation system is further configured to cause presentation of a representation of the venue including the plurality of waypoints and the one or more points of interest that are accessible by the user between the at least one pair of waypoints.
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.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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