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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 06, 2026 has been entered.
Notice to Applicant
The following is a Non-Final Office Action for Application Serial Number: 18/058,123, filed on November 22, 2022. In response to Examiner's Final Office Action dated November 06, 2025, Applicant on February 06, 2026, amended claims 1, 15 and 17 and added new claim 19. Claims 1-8, 10-12, 15, 17 and 19 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
The English translation of Japanese Patent Application No. 2021-191343 is 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 and 17 are hereby amended in light of Applicants amendments to claims 1, 15 and 17. A new 35 U.S.C. § 103 rejection is applied to new claim 19.
Response to Arguments
Applicant's Arguments/Remarks filed February 06, 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 February 06, 2026.
Regarding the 35 U.S.C. 101 rejection, Applicant states the amended claims do not merely recite observations, evaluations, judgments, or opinions. Rather, as shown in the above limitations, they require execution of a simulation on a stored facility model, application of an AI model that has "a machine-learned correspondence," constrained optimization across nodes of the facility model, and control of a vehicle based on the calculated operation condition. Furthermore, the amended claims do not merely recite results that could practically be determined mentally. Accordingly, the claim does not fall within the "mental processes" grouping.
Nor are the amended claims directed to "certain methods of organizing human activity" within the meaning of MPEP § 2106.04(a)(2).III.B (see p. 13-14, Applicants Remarks).
The amended claims are not directed to any of the above. Instead, they recite technical operations performed on a "facility model including nodes respectively corresponding to points in a facility and a link connecting the nodes," followed by simulation, optimization, and control of a vehicle. Although the amended claims model movement of people, this modeling is recited as part of a technical simulation and optimization process used to determine and control vehicle operation. Modeling people as objects in a simulation does not transform the claim into a method of organizing human activity. The amended claims are plainly not directed to any rules for organizing or managing people themselves. Accordingly, the claim does not fall within this abstract-idea grouping.
Lastly, the amended claims do not recite any mathematical concept because they do not set forth any mathematical relationship, formula, or calculation as a claimed result, nor do they define the invention in terms of a series of mathematical calculations divorced from a technological context. Rather, they merely involve calculations as part of the recited technological operations, and thus are not directed to a mathematical concept.
Accordingly, when considered as a whole, the amended claim is not directed to an abstract idea under Step 2A, Prong 1.
In response Examiner respectfully disagrees. First, Examiner finds Applicants arguments towards the AI limitations are not considered abstract and therefore were not analyzed under Step 2A-Prong One. Examiner maintains the AI limitations do not take the claim out of the certain methods of organizing human activity and mental processes groupings.
In view of the most recent amendments, Examiner maintains the claimed invention recite limitations based on certain methods of organizing human activity and mental processes. 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).
The claim also discloses limitations the mimic human thought processes of calculating passenger related predictions and a correspondence between the predictive waypoint-pass-through value and the number of people traveling between two points among the points, 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, calculating an operation condition of the vehicle transporting the people in the facility, outputting the calculated operation condition and sets the calculated operation condition as an actual operation condition for the vehicle traveling in the facility, which constitutes evaluations and judgement steps that can be reasonable performed by a combination of the human mind and a human using pen and paper. 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). Examiner maintains the claims recite an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states the amended claims are nonetheless patent eligible under Step 2A, Prong 2 because they integrate that judicial exception into a practical application by providing specific improvements over conventional vehicle operation condition setting devices/systems. See MPEP § 2104(d)(1) ("One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field.").
Consistent with this guidance, Applicant directs the Examiner to the specific details of the amended claims requiring the above limitations. The above limitations of the amended claims apply results of simulation, prediction, and optimization to control the operation of a vehicle in the physical world, which improves vehicle operation within a facility. (see p. 15-17, Applicant Remarks).
As already explained above with reference to the specification, these specific sequence of operations as required by the amended claims enable the operation condition setting device to execute the simulation on the facility model having the configuration corresponding to the actual facility, obtain the predictive way-point-pass-through value considering the deviation of the arrival time without the complicated computational processing, and control the vehicle traveling in the facility based on the actual operating conditions based on the predictive waypoint-pass-through value. This improves system performance by ensuring that vehicle operation is determined with explicit consideration of congestion constraints distributed across the facility, rather than local or instantaneous conditions.
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). The vehicle control limitations are disclosed at a high level of generality without reciting any specific additional element assigned to perform the function. The specification appears to be 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). Examiner notes the advancements disclosed in RCT v. Microsoft, Diamond v. Diehr, and SiRF Technology v. ITC recite improvements to the functioning of a computer, or an improvement to another technology or technical field. Specifically, in RCT v. Microsoft, the claims are directed to a process of halftoning an image comprising the steps of generating a mask, comparing pixels, and using the results of the comparison to convert a binary image to a halftoned image. The process uses less memory, had faster computation times, and processed improved image quality compared to other masks, Diamond v. Diehr utilized the Arrhenius equation to improve the process of controlling the operations of a mold in curing rubber parts, and SiRF Technology v. ITC disclosed a GPS receiver utilizing software that applies a mathematical formula to improve the ability to determine its position in weak environments. In contrast, Examiner finds there are no similar improvements here. Examiner finds Applicant’s arguments are directed to improvements to an existing business process (e.g. optimizing vehicle operation conditions/scheduling) and not the recited device components or AI model. Merely confining the abstract idea to a particular technological environment does not establish a practical application. See Guidance, 84 Fed. Reg. at 54. “A claim does not cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit.” In re Mohapatra, 842 F. App’x 635, 638 (Fed. Cir. 2021).
Regarding the 35 U.S.C. 101 rejection, Applicant argues Example 42 (see p. 17-18, Applicant Remarks) and states similarly, the amended claims requiring the combination of the above limitations do not simply calculate the predictive waypoint-pass-through value using an AI model, but rather specify how people flow is simulated on the facility model having the configuration corresponding to the actual facility, without the complicated computational processing, and controls the vehicle traveling in the facility under the optimized operation condition of the vehicle transporting the people in the facility. This is plainly a practical application in the vehicle operation industry.
Accordingly, when properly evaluated under Step 2A, Prong 2, the additional elements of the amended claims integrate any judicial exception into a practical application.
In response, Examiner respectfully disagrees. Claim 1 of Example 42 as a whole, integrates the method of organizing human activity into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. Examiner finds there are no comparable improvements here and Applicants pending claims recite additional elements (i.e., an operation condition setting device comprising a storage and an arithmetic device that comprises a central processing unit (CPU), devices comprising units and non-transitory computer readable storage mediums and an AI model) at a high-level of generality such that they are no more than generic computer components used as tools to apply the instructions of the abstract idea. Examiner finds specifying how people flow is simulated on the facility model having the configuration corresponding to the actual facility, without the complicated computational processing, and controls the vehicle traveling in the facility under the optimized operation condition of the vehicle transporting the people in the facility is considered an abstract idea based on certain methods of organizing human activity. The claim does not reflect any improvement to the vehicle itself, but instead the improvement is directed towards the operating conditions (e.g., scheduling) of the vehicle based on vehicle passageways and the data analysis related to people flow. Examiner finds Applicant has not identified any limitations in the claimed invention that show or submit that the technology used is being improved or there was a problem in or with the technology that the claimed invention solves.
Additionally, Examiner maintains the present invention performs a calculation for people flow information in a facility with an AI model and has a machine-learned correspondence between the predictive waypoint-pass-through value and the number of people traveling between two points among the points, which strictly limits the abstract idea to a particular environment without reciting any improvement to the technology, computer-related technology or technological field. Both the calculation and correspondence limitations can be performed by a human without the use of merely applying the AI model as a tool. Examiner maintains the claims are directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states the amended claims do not recite isolated, conventional computer functions performed independently. Instead, they require a particular sequence of interdependent operations at a level of specificity that demonstrates that the recited elements are not merely tools for performing an abstract idea. As noted in MPEP § 2106.05(d)(II), claims that recite more than "generic computer functions" and instead define a particular way of achieving a technical result may amount to significantly more.
Even if generic computer components may be used to implement the claimed invention, the ordered combination of elements in the amended claims are not well-understood, routine, or conventional. Accordingly, when considered as a whole, the amended claim is also patent eligible under Step 2B.
In response, Examiner respectfully disagrees. 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 finds Applicant is attempting to say the Step 2A-Prong One elements (i.e., sequence of interdependent operations), the abstract idea, is what makes the claim eligible. Applicant has not identified any disclosure in the claimed invention showing and/or submitting that the technology used is being improved, there was a technical problem in the technology that the claimed invention solves, or the ordered combinations of the known elements is significantly more than instructions (i.e., sequence of interdependent operations) used to facilitate 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. Examiner maintains the additional elements recited in the claims do not perform any unconventional functions that can be considered “significantly more” than the judicial exception. Therefore, Examiner maintains the claims recite additional elements used as tools to perform the instructions of the abstract idea without disclosing limitations that integrate the abstract idea into a practical application, nor do these elements provide meaningful limitations that transforms the judicial exception into significantly more than the abstract idea itself. For at least these reasons, the pending claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Regarding the 35 U.S.C. 101 rejection, Applicant states the outstanding rejection reflects an improper "shotgun-style" analysis in which the Examiner asserts, in conclusory fashion, that the claims are directed to virtually every recognized category of abstract idea, while never developing any single coherent theory of ineligibility. As a result, the rejection consists largely of boilerplate arguments based on generalized characterizations of isolated claim limitations, rather than the reasoned "claim as a whole" analysis required by law. Furthermore, quoting legal authority without applying it to the facts at hand also does not establish ineligibility (see p. 20-23, Applicants Remarks).
In response, Examiner acknowledges Applicants remarks. Examiner notes when performing the § 101 analysis, Examiner did consider each claim and every limitation, both individually and in combination according to the current PTO's guidelines for § 101 eligibility. Examiner notes there is no requirement that Examiner provide additional, extrinsic or supporting evidence in the 101 analysis to demonstrate an abstract idea. The enumerated groupings are rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea to generally applying the wide body of case law spanning all technologies and claim types. Thus, Examiner asserts the rejection provided in the Office Action is proper and provides a sufficient level of detail according to the PTO's guidelines for § 101 eligibility; MPEP 2106.07(a)(I). Examiner finds the present claims recite limitations more similar to concepts identified by the courts that are not indicative of improvements to computer-functionality; see MPEP 2106.05(a)(I). Examiner maintains computing and controlling vehicle operations based on vehicle passageways and people flow to be an existing business process and the claimed subject matter does not recite an improvements to a technology, technological field or computer related technology. Applicant has not presented an argument that alters this analysis.
Regarding the 35 U.S.C. 101 rejection, Applicant states "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 (see Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101 (August 4, 2025) Memo) (emphasis added).
Here, when considered as a whole, the amended claims recite a concrete, non-generic system that applies specific techniques to achieve desired result. Therefore, under Alice/Mayo framework, Applicant respectfully submits that it is more likely than not that the amended claims are directed to patent-eligible subject matter.
In view of the above, withdrawal of this rejection is respectfully requested.
In response, Examiner respectfully disagrees. Applicants arguments are not persuasive for reasons set forth above and notes MPEP 706(I), as it is the corresponding footnote to the cited language from the August Memo. Specifically, § 706(I) states,
The standards of patentability applied in the examination of claims must be the same throughout the Office. In every art, whether it be considered "complex," "newly developed," "crowded," or "competitive," all of the requirements for patentability (e.g., patent eligible, useful, novel, nonobvious, enabled, and clearly described as provided in 35 U.S.C. 101, 102, 103 and 112 ) must be met before a claim is allowed. The mere fact that a claim recites in detail all of the features of an invention (i.e., is a "picture" claim) is never, in itself, justification for the allowance of such a claim.
An application should not be allowed, unless and until issues pertinent to patentability have been raised and resolved in the course of examination and prosecution, since otherwise the resultant patent would not justify the statutory presumption of validity (35 U.S.C. 282 ), nor would it "strictly adhere" to the requirements laid down by Congress in the 1952 Act as interpreted by the Supreme Court. The standard to be applied in all cases is the "preponderance of the evidence" test. In other words, an examiner should reject a claim if, in view of the prior art and evidence of record, it is more likely than not that the claim is unpatentable.
Examiner respectfully disagrees with Applicants remarks regarding the system. Examiner maintains the system and its associated components are disclosed at a high level of generality (e.g., 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) such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea. The mere fact that a claim applies specific techniques to achieve desired result is never, in itself, justification for the allowance of such a claim”. For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Applicant’s arguments, see pg. 24-25, filed February 06, 2026, with respect to the rejection(s) of claims 1-8. 10-12, 15 and 17 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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-8, 10-12 and 19 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, 10-12, 15, 17 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-8 and 10-12, 15, 17 and 19 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, a 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, 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 facility, 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 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 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 a 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. Additionally, claims 2-8, 10, 11 and 19 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claim 1. Therefore claims 2-8, 10-12 and 19 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 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wei, China Publication No. CN 111369181 A (EPO English Translation) [hereinafter Wei], and further in view of Sefair Cristancho et al., U.S. Publication No. 2022/0092442 [hereinafter Cristancho].
Referring to Claim 1, Wei teaches:
An operation condition setting device for a vehicle, 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, a 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 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:
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:
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 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).
Referring to Claim 2, Wei in view of Cristancho 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 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 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 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 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 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 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”.
Referring to Claim 10, Wei in view of Cristancho 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 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 facility is an airport, and the waypoint is a security check point in the airport.
However Cristancho further teaches:
wherein the facility is an airport, and 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, 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, 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 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]).
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], 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 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 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 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 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 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.
Ootsuka et al. (US 20190228358 A1) – A schedule proposal system includes a data server for storing information collected at a plurality of stations and related to passengers and a computing server storing a program for predicting the numbers of passengers who wait for trains and a program for determining an increase or reduction in the number of trains to be operated. The computing server executes, at predetermined time intervals, the program for predicting the numbers of passengers who wait for trains and the program for determining an increase or reduction in the number of trains to be operated. The program for predicting the numbers of passengers who wait for trains predicts, based on the information related to passengers, the numbers of passengers who wait for trains at the plurality of stations in a predetermined time zone. The program for determining an increase or reduction in the number of trains to be operated determines, based on the numbers of passengers who wait for trains at the plurality of stations, whether or not the number of trains to be operated is to be increased or reduced.
Aoki et al. (US 20150262101 A1) – A technique which comprehensively optimize passengers' convenience and a power consumption amount. An operation schedule evaluation apparatus has a passenger flow calculator and a power consumption amount calculator. The passenger flow calculator creates passenger flow information related to a passenger flow generated by transportation of a train, based on operation schedule information of each train and passenger information related to an entry and an exit of a passenger at a station. The power consumption amount calculator calculates the number of passengers or a car occupancy of each train based on the passenger flow information created by the passenger flow calculator, the operation schedule information and car information of each train, and calculates a power consumption amount of each train per unit time which reflects car weight corresponding to the number of passengers or the car occupancy.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624