DETAILED ACTION
This final Office action is responsive to amendments filed October 28th, 2025. Claims 1-9 have been amended. Claims 10-13 have been added. Claims 1-13 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see page 8, filed 10/28/25, with respect to the specification have been fully considered and are persuasive. The objection of 7/28/25 has been withdrawn.
Applicant’s arguments, see page 8, filed 10/28/25, with respect to claim 2 have been fully considered and are persuasive. The objection of 7/28/25 has been withdrawn.
Applicant’s arguments, see page 8, filed 10/28/25, with respect to claims 1-9 have been fully considered and are persuasive. The claim interpretation of 7/28/25 has been withdrawn.
Applicant’s arguments, see page 8, filed 10/28/25, with respect to claims 1-9 have been fully considered and are persuasive. The 35 USC 112(b) rejection of 7/28/25 has been withdrawn.
Applicant's arguments regarding claim rejections under 35 USC 101 filed 10/28/25 have been fully considered but they are not persuasive.
On pages 9-12 of the provided remarks, Applicant argues that the amended claims present statutory subject matter. Beginning on page 9 of the provided remarks, Applicant argues under Step 2A Prong 2 analysis, that claim 1 is patent eligible. Specifically, on page 10 of the provided remarks, Applicant argues “controlling carriage machinery or facilities related to operation of the carriage machinery based on an operation plan cannot be considered fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people.” Examiner respectfully disagrees and asserts that the amended control of carriage machinery based on the operation plan are carrying carriage targets and facilities relating to an operation of the carriage machinery which is managing personal behavior & interactions as the control of the carriage machinery facilitates the movement and interaction of carriage targets (i.e., persons). Therefore, Applicant’s arguments are not persuasive.
Continuing on page 10 of the provided remarks, Applicant argues “these features not reasonably be performed by a human, even with pen and paper, and as a result cannot be considered mental processes.” Examiner respectfully disagrees and asserts that the high-level recitation of “control the carriage machinery or the facilities relating to the operation of the carriage machinery based on the operation plan” under broadest reasonable interpretation could include a mental evaluation of the operation to operate the carriage machinery or the facilities. Therefore, the claims recite the abstract idea of mental process. Applicant’s arguments are not persuasive.
On pages 10-11 of the provided remarks, Applicant argues that “by controlling carriage machinery or facilities relating to operation of the carriage machinery, these features integrate any alleged abstract idea into a practical application”. Citing the as-filed Specification (though specific paragraphs are not referenced), Applicant argues “amended claim 1 applies, relies on, or uses any allegedly judicial exception in a manner that imposes a meaningful limit on the allegedly judicial exception.” Examiner respectfully disagrees and asserts, as stated above, the high-level recitation of “control the carriage machinery or the facilities relating to the operation of the carriage machinery based on the operation plan” under broadest reasonable interpretation could include a mental evaluation of the operation to operate the carriage machinery or the facilities. Therefore, the claims recite the abstract idea of mental process. MPEP 2106.04(a) and 2106.05(a) provides analysis regarding determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology. Citing the 2019 PEG, the following is stated, “first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” Therefore, Applicant’s statement of support for the amended claim amendments is insufficient to determine whether the claim as a whole integrates a judicial exception into a practical application. Applicant’s arguments are not persuasive.
Regarding Step 2B analysis, Applicant argues that the amended limitations recite significantly more than any allegedly abstract idea. Specifically, on page 12 of the provided remarks, Applicant argues “the prior art does not teach or suggest the features of independent claim 1. Therefore, claim 1 provides an “inventive concept”, and does not simply append well-understood, routine or conventional activities.” Examiner respectfully disagrees and asserts, per 2019 PEG and MPEP 2106.05, evaluation under Step 2B includes the following “the examiner should explain why the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the exception”. Therefore, Step 2B analysis is not based on whether or not the prior art teaches or suggests the features of the claim. As stated above, the amended claim limitations argued as not suggested by the prior art are directed to the abstract idea. The additional elements of the claims merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. The 35 USC 101 rejection is maintained. Applicant’s arguments are not persuasive.
Applicant’s arguments, see pages 12-14, filed 10/28/25, with respect to the rejection(s) of claim(s) 1-13 under 35 USC 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Minakawa (U.S 2020/0357091 A1) in view of Miyazato (WO 2016/175134 A1).
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Step 1: Independent claims 1 (device), 8 (method), and 9 (non-transitory computer-readable storage medium) and dependent claims 2-7 and 10-13, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a device (i.e. machine), claim 8 is directed to a method (i.e. process), and claim 9 is directed to a non-transitory computer-readable storage medium (i.e. manufacture).
Step 2A Prong 1: The independent claims recite carrying carriage targets and facilities relating to an operation of the carriage machinery, the method comprising: producing an operation plan of the carriage machinery to improve a quantitative evaluation status of the carriage targets; controlling the carriage machinery or the facilities relating to the operation of the carriage machinery based on the operation plan, wherein the operation plan includes an operation plan having a failure in part of the system (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are carrying carriage targets and facilities relating to an operation of the carriage machinery, comprising producing an operation plan of the carriage machinery to improve a quantitative evaluation status of the carriage targets; controlling the carriage machinery or the facilities relating to the operation of the carriage machinery based on the operation plan, wherein the operation plan includes an operation plan having a failure in part of the system, which is managing personal behavior & interactions. The Applicant’s claimed limitations are producing an operation plan of the carriage machinery to improve a quantitative evaluation status of the carriage targets and controlling the carriage machinery or the facilities relating to the operation of the carriage machinery based on the operation plan, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are producing an operation plan of the carriage machinery to improve a quantitative evaluation status of the carriage targets; controlling the carriage machinery or the facilities relating to the operation of the carriage machinery based on the operation plan, which are functions of the human mind in the form of observation, judgment, and evaluation. The Applicant’s claimed limitations are producing an operation plan of the carriage machinery to improve a quantitative evaluation status of the carriage targets and controlling the carriage machinery or the facilities relating to the operation of the carriage machinery based on the operation plan, which recite the abstract idea of Mental Process.
In addition, dependent claims 2-7 and 10-13 further narrow the abstract idea and recite further defining the production of the operation plan; setting parameter values of carrier operations; the system; rewards evaluation; the setting of a signal and point; the failure of the operation plan; and simulating the operation of the carriage machinery. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include managing personal behavior as well as mental processes. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Step 2A Prong 2: In this application, the claimed “An operation determination device adapted to a system, the operation determination device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions; An operation determination method adapted to a system including a carriage machinery; A non-transitory computer-readable storage medium configured to store a program causing a computer adapted to a system including a carriage machinery” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 2-7 and 10-13 further narrow the abstract idea. Dependent claims 2-3 recite the following limitation, “via reinforcement learning using rewards relating to the stationary status of the carriage targets” and “and learn setting values via the reinforcement learning”. The “reinforcement learning” is recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that it represents no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “An operation determination device adapted to a system, the operation determination device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions; An operation determination method adapted to a system including a carriage machinery; A non-transitory computer-readable storage medium configured to store a program causing a computer adapted to a system including a carriage machinery” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claim 8; device claims 1-7 and 10-13; and non-transitory computer-readable storage medium claim 9 recites “An operation determination device adapted to a system, the operation determination device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions; An operation determination method adapted to a system including a carriage machinery; A non-transitory computer-readable storage medium configured to store a program causing a computer adapted to a system including a carriage machinery”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0081-84 and Figures 1-2, 8, and 10. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-7 and 10-13 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Next, when the “reinforcement learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Young et al., US 2003/0074338 A1, noting in paragraph [0009] that “While there have been other earlier attempts at applying conventional notions of reinforcement learning to particular control problems...” See also, Hamagami et al. US 2009/0234783 A1, noting in paragraph [0007] that “It is known that with Q-learning and other conventional reinforcement learning methods, this cannot in principle be handled.” Accordingly, the use of reinforcement learning to generate an operation plan of carriage machinery does not add significantly more to the claim.
The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 8-9, and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Minakawa (U.S 2020/0357091 A1) in view of Miyazato (WO 2016/175134 A1).
Claims 1, 8, and 9
Regarding Claim 1, Minakawa discloses the following:
An operation determination device adapted to a system including a carriage machinery for carrying carriage targets and facilities relating to an operation of the carriage machinery, the operation determination device comprising: [see at least Paragraph 0008 for reference to the timetable modification device that changes a timetable indicating a train control target in correspondence with a movement demand prediction result indicating a destination of passengers and the number of passengers in each station at which a train stops for every time period; Paragraph 0038 for reference to the target timetable modification device updating the target timetable so as to supply transportation capacity suitable for movement demand for every section between stations and for every time period on the basis of information stored in the device or received from other system elements; Figure 1 and related text regarding item 100 ‘Target Timetable Modification Device’ and item 300 ‘train’]
at least one memory configured to store instructions [see at least Paragraph 0040 for reference to the target timetable modification device including a memory; Figure 1 and related text regarding item 102 ‘memory’]
at least one processor configured to execute instructions to [see at least Paragraph 0040 for reference to the target timetable modification device including a CPU; Paragraph 0041 for reference to the central processing device being a processor that executes various software programs stored in the storage unit; Figure 1 and related text regarding item 101 ‘CPU’]
produce an operation plan of the carriage machinery to improve a quantitative evaluation status of the carriage targets [see at least Paragraph 0001 for reference to the timetable modification device correcting the timetable that is an operation plan of each of the trains in correspondence with an increase or a decrease in the movement demand; Paragraph 0050 for reference to the operation prediction processing being carried out using a train operation simulation by a macro model; Paragraph 0054 for reference to the “timetable” representing a train operation plan; Paragraph 0056 for reference to the passenger behavior model simulating the behavior of passengers who use the railway; Paragraph 0160 for reference to the evaluation index relating to the degree of congestion of the candidate timetable being calculated; Figure 1 and related text regarding item P01 ‘Timetable Updating Program’; Figure 12 and related text regarding item S309 ‘Is Evaluation Index Further Improved in Comparison to Provisionally Optimal Timetable?’; Figure 18 and related text regarding item S1203 ‘Obtain Evaluation Index on basis of number of people waiting for train, number of train passengers, and predicted timetable’; Examiner notes the ‘timetable updating program’ as analogous to the determination means]
control the carriage machinery or the facilities relating to the operation of the carriage machinery based on the operation plan [see at least Paragraph 0071 for reference to course control device controls a switch, a traffic signal, and the like which are disposed in a station and a track of a railway in accordance with operation of the train according to the target timetable, and controls the route of the train; Figure 2 and related text regarding item 202 ‘course control device’]
While Minakawa discloses the limitations above, it does not disclose wherein the operation plan includes an operation plan having a failure in part of the system.
However, Miyazato discloses the following:
wherein the operation plan includes an operation plan having a failure in part of the system [see at least Paragraph 0026 for reference to when a fault avoidance operation range that is a range is specified, a train operation plan is planned so that the train operates in the operation mode of the obstacle avoidance operation type in the failure avoidance operation time zone and the obstacle avoidance operation range, and the train operation; Paragraph 0027 for reference to the processing unit formulates the train operation plan in the failure avoidance operation time zone and the failure avoidance operation range so as to be consistent with the train operation in the time zone preceding the failure avoidance operation time zone; Paragraph 0028 for reference to a train operation plan for traveling a two-way train on one route can be planned by the operation management system, so that when a failure occurs, the commander can set a one-line bidirectional operation by an easy operation; Figure 3 and related text regarding a train operation plan]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the operation determination method to include the failure determination and plan adaptation of Miyazato. By quickly changing the train operation plan, the operation stoppage time when a transportation failure occurs is shortened, and each train can continue to operate according to the changed plan, as stated by Miyazato (Paragraph 0057).
Regarding claims 8 and 9, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 8, Minakawa teaches an operation determination method [Figures 10-18 and related text]. Regarding claim 9, Minakawa teaches a non-transitory computer-readable storage medium configured to store a program causing a computer adapted by the system [Paragraph 0042 and Figure 1]. Therefore, claims 8 and 9 are rejected as being unpatentable in view of Minakawa and Miyazato.
Claim 11
While the combination of Minakawa and Miyazato disclose the limitations above, regarding Claim 11, Minakawa discloses the following:
wherein the system is a railway system [see at least Paragraph 0035 for reference to the target timetable modification device creating a target timetable of train control on a railway line on which a train automatically runs; Paragraph 0071 for reference to the course control device controlling a switch, traffic signal, and the like which are disposed in a station and a track of a railway; Figure 2 and related text regarding the operation of the vehicle automatic control system and example railway wiring]
wherein the facilities relating to the operation of the carriage machinery include crossing or signal [see at least Paragraph 0071 for reference to course control device controls a switch, a traffic signal, and the like which are disposed in a station and a track of a railway in accordance with operation of the train according to the target timetable, and controls the route of the train; Figure 2 and related text regarding item 202 ‘course control device’]
wherein the operation plan includes a position or a speed of the carriage machinery [see at least Paragraph 0070 for reference to the train tracking device acquiring current state information and current position information as a travel record given in notification from the train is operating; Paragraph 0071 for reference to course control device controls a switch, a traffic signal, and the like which are disposed in a station and a track of a railway in accordance with operation of the train according to the target timetable, and controls the route of the train; Paragraph 0108 for reference to the timetable change pattern information including state variation position information; Figure 2 and related text regarding item 202 ‘course control device’]
Claim 12
While the combination of Minakawa and Miyazato disclose the limitations above, regarding Claim 12, Minakawa discloses the following:
wherein the system is a railway system [see at least Paragraph 0035 for reference to the target timetable modification device creating a target timetable of train control on a railway line on which a train automatically runs; Paragraph 0071 for reference to the course control device controlling a switch, traffic signal, and the like which are disposed in a station and a track of a railway; Figure 2 and related text regarding the operation of the vehicle automatic control system and example railway wiring]
While Minakawa discloses the limitations above, it does not disclose wherein the failure includes an interruption occurring between two adjacent stations, or unavailability of station.
However, Miyazato discloses the following:
wherein the failure includes an interruption occurring between two adjacent stations, or unavailability of station [see at least Paragraph 0011 for reference to the system containing a display unit which shows the next station ahead of the failure occurrence position and the station from which the failure can be traced back to the route where the train heading to the failure occurrence position; Paragraph 0028 for reference to a train operation plan for traveling a two-way train on one route can be planned by the operation management system, so that when a failure occurs, the commander can set a one-line bidirectional operation by an easy operation]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the operation determination method to include the failure interrupting between two adjacent stations of Miyazato. By quickly changing the train operation plan, the operation stoppage time when a transportation failure occurs is shortened, and each train can continue to operate according to the changed plan, as stated by Miyazato (Paragraph 0057).
Claim 13
While the combination of Minakawa and Miyazato disclose the limitations above, regarding Claim 13, Minakawa discloses the following:
wherein the at least one processor is configured to execute the instructions to simulate the operation of the carriage machinery [see at least Paragraph 0050 for reference to the operation prediction processing being carried out using a train operation simulation by a macro model; Paragraph 0056 for reference to the passenger behavior model simulating the behavior of passengers who use the railway]
Claim(s) 2-7 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Minakawa (U.S 2020/0357091 A1) in view of Miyazato (WO 2016/175134 A1), as applied in claims 1, 8, and 9, in view of Zhang (U.S 2019/0347933 A1).
Claim 2
While the combination of Minakawa and Miyazato disclose the limitations above, regarding Claim 2, Minakawa discloses the following:
The operation determination device according to claim 1, wherein the at least one processor is configured to execute the instructions to produce the operation plan of the carriage machinery in order to improve a stationary status of the carriage targets on a platform of the carriage targets by the carriage machinery via reinforcement learning using rewards relating to the stationary status of the carriage targets [see at least Paragraph 0001 for reference to the timetable modification device correcting the timetable that is an operation plan of each of the trains in correspondence with an increase or a decrease in the movement demand; Paragraph 0050 for reference to the operation prediction processing being carried out using a train operation simulation by a macro model; Paragraph 0054 for reference to the “timetable” representing a train operation plan; Paragraph 0056 for reference to the passenger behavior model simulating the behavior of passengers who use the railway; Paragraph 0160 for reference to the evaluation index relating to the degree of congestion of the candidate timetable being calculated; Figure 1 and related text regarding item P01 ‘Timetable Updating Program’; Figure 12 and related text regarding item S309 ‘Is Evaluation Index Further Improved in Comparison to Provisionally Optimal Timetable?’; Figure 18 and related text regarding item S1203 ‘Obtain Evaluation Index on basis of number of people waiting for train, number of train passengers, and predicted timetable’; Examiner notes the ‘timetable updating program’ as analogous to the determination means]
While Minakawa discloses the limitations above, it does not disclose produce the operation plan of the carriage machinery via reinforcement learning using rewards relating to a stationary status of the carriage targets.
However, Zhang discloses the following:
produce the operation plan of the carriage machinery via reinforcement learning using rewards relating to a stationary status of the carriage targets [see at least Paragraph 0046 for reference to the system trying to optimize (maximize/minimize) the cumulative reward for its action policy utilizing reinforcement learning; Paragraph 0056-57 for reference to the traffic optimization problem’s goal being to decrease the average traffic delay of commuters in the network; Paragraph 0104 for reference to the optimization results showing an improvement in terms of waiting time and queue length experienced at intersections; Figures 9-12 and related text regarding the average waiting time under different penetration rates of the reinforcement learning based control system]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the operation plan generation of Minakawa to include the reinforcement learning method of Zhang. Doing so would improve the efficiency of transport in a number of situations, i.e. road transport, traffic management, mobility, etc., as stated by Zhang (Paragraph 0005).
Claim 3
While the combination of Minakawa, Miyazato, and Zhang disclose the limitations above, regarding Claim 3, Minakawa discloses the following:
The operation determination device according to claim 2, wherein the at least one processor is configured to execute the instructions to: set parameter values representing operations to be implemented by part of the system based on a plurality of input values whose number is smaller than a number of the parameter values [see at least Paragraph 0075 for reference to the predicted timetable being created by the operation prediction program; Paragraph 0180 for reference to the timetable program setting a element value in the violation state vector; Paragraph 0181 for reference to the demand prediction processing limit being greater than an upper threshold value then the setting of the element value is “1”; Paragraph 0182 for reference to the demand prediction processing limit being less than the upper limit of the allowed range then the element value is set to “-1”; Figure 1 and related text regarding item P02 ‘operation prediction program’ and item P03 ‘demand prediction program’; Figure 14 and related text regarding the violation position extraction process by the timetable modification device]
While Minakawa discloses the limitation above, it does not disclose learn setting values via the reinforcement learning.
However, Zhang discloses the following:
learn setting values via the reinforcement learning [see at least Paragraph 0046 for reference to the system trying to optimize (maximize/minimize) the cumulative reward for its action policy utilizing reinforcement learning; Paragraph 0054 for reference to the system making an observation and taking an action in the form of keeping the current light phase or switching the light phase thus achieving smart or intelligent control of traffic]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the parameter setting of Minakawa to include the reinforcement learning method of Zhang. Doing so would improve the efficiency of transport in a number of situations, i.e. road transport, traffic management, mobility, etc., as stated by Zhang (Paragraph 0005).
Claim 4
While the combination of Minakawa, Miyazato, and Zhang disclose the limitations above, regarding Claim 4, Minakawa discloses the following:
The operation determination device according to claim 3, wherein the at least one processor is configured to execute the instructions to set the parameter values responsive to the input values thereof according to a restrictive condition used for an operation of the system [see at least Paragraph 0048 for reference to the timetable correction program correcting the target timetable when the degree of congestion have deviated from the allowed range; Paragraph 0180 for reference to the timetable program setting a element value in the violation state vector; Paragraph 0181 for reference to the demand prediction processing limit being greater than an upper threshold value then the setting of the element value is “1”; Paragraph 0182 for reference to the demand prediction processing limit being less than the upper limit of the allowed range then the element value is set to “-1”; Figure 1 and related text regarding item P01a ‘violation position extraction program’, P01b ‘timetable correction program’; Figure 14 and related text regarding the violation position extraction process by the timetable modification device]
Claim 5
While the combination of Minakawa, Miyazato, and Zhang disclose the limitations above, regarding Claim 5, Minakawa discloses the following:
The operation determination device according to claim 3, wherein the system is a railway system [see at least Paragraph 0035 for reference to the target timetable modification device creating a target timetable of train control on a railway line on which a train automatically runs; Paragraph 0071 for reference to the course control device controlling a switch, traffic signal, and the like which are disposed in a station and a track of a railway; Figure 2 and related text regarding the operation of the vehicle automatic control system and example railway wiring]
wherein the at least one processor is configured to execute the instructions to use according to a number of the carriage targets staying on a platform of a station [see at least Figure 1 and related text regarding item P07 ‘Number-of-people waiting for train information’; Figure 18 and related text regarding item S1202 ‘PREDICT MOVEMENT DEMAND, NUMBER OF PEOPLE WAITING FOR TRAIN, AND NUMBER OF TRAIN PASSENGERS ON BASIS OF PREDICTED TIMETABLE’]
While Minakawa discloses the limitations above, it does not disclose wherein the at least one processor is configured to execute the instructions to use the rewards according to a number of the carriage targets staying on a platform of a station.
However, Zhang discloses the following:
wherein the at least one processor is configured to execute the instructions to use the rewards according to a number of the carriage targets staying on a platform of a station [see at least Paragraph 0056-57 for reference to the traffic optimization problem’s goal being to decrease the average traffic delay of commuters in the network; Paragraph 0057 for reference to the goal being to find the best strategy such that ts -tmin is minimum; Paragraph 0104 for reference to the optimization results showing an improvement in terms of waiting time and queue length experienced at intersections]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the operation plan generation of Minakawa to include the reinforcement learning reward method of Zhang. Doing so would improve the efficiency of transport in a number of situations, i.e. road transport, traffic management, mobility, etc., as stated by Zhang (Paragraph 0005).
Claim 6
While the combination of Minakawa, Miyazato, and Zhang disclose the limitations above, regarding Claim 6, Minakawa discloses the following:
The operation determination device according to claim 5, wherein the at least one processor is configured to execute the instructions to set the parameter values to the station based on an input instructing whether or not to loop back a railway vehicle at the station and a status of the railway vehicle entering into the station [see at least Paragraph 0048 for reference to the timetable correction program correcting the target timetable when the degree of congestion have deviated from the allowed range; Paragraph 0070 for reference to the train tracking device acquiring current position information as a travel record given in notification from the train and notifies the timetable modification device of the train state information; Paragraph 0071 for reference to the course control device controlling the route of the train according to the target timetable; Paragraph 0072 for reference to the timetable management device notifying the train of the target timetable and setting the timetable as an automatic running input of the train; Figure 1 and related text regarding item P01a ‘violation position extraction program’, P01b ‘timetable correction program’, item 201 ‘Train tracking device’, item 202 ‘course control device’]
Claim 7
While the combination of Minakawa, Miyazato, and Zhang disclose the limitations above, regarding Claim 7, Minakawa discloses the following:
The operation determination device according to claim 6, wherein the at least one processor is configured to execute the instructions to set a signal and a point based on the input instructing whether or not to loop back the railway vehicle at the station and the status of the railway vehicle entering into the station as well as a restrictive condition relating to a setting of the signal at the station and a restrictive condition relating to a setting of the point at the station [see at least Paragraph 0050 for reference to the operation prediction program predicting a future operation situation of a train group in the train operation network based on the basis data, the travel record information, and the target timetable; Paragraph 0050 for reference to the operation prediction processing being carried out using a train operation simulation by a macro model; Paragraph 0070 for reference to the train tracking device acquiring current position information as a travel record given in notification from the train and notifies the timetable modification device of the train state information; Paragraph 0071 for reference to the course control device controlling the route of the train according to the target timetable; Paragraph 0072 for reference to the timetable management device notifying the train of the target timetable and setting the timetable as an automatic running input of the train; Paragraph 0148 for reference to the operation prediction program calculating a predicted time point at which an event of arrival or departure is expected to occur with respect to the target timetable, travel record information, and basis data]
Claim 10
While the combination of Minakawa, Miyazato, and Zhang disclose the limitations above, regarding Claim 10, Minakawa discloses the following:
wherein the system is a railway system [see at least Paragraph 0035 for reference to the target timetable modification device creating a target timetable of train control on a railway line on which a train automatically runs; Paragraph 0071 for reference to the course control device controlling a switch, traffic signal, and the like which are disposed in a station and a track of a railway; Figure 2 and related text regarding the operation of the vehicle automatic control system and example railway wiring]
wherein the at least one processor is configured to execute the instructions to use according to a number of the carriage targets staying on a platform of a station [see at least Figure 1 and related text regarding item P07 ‘Number-of-people waiting for train information’; Figure 18 and related text regarding item S1202 ‘PREDICT MOVEMENT DEMAND, NUMBER OF PEOPLE WAITING FOR TRAIN, AND NUMBER OF TRAIN PASSENGERS ON BASIS OF PREDICTED TIMETABLE’]
While Minakawa discloses the limitations above, it does not disclose wherein the at least one processor is configured to execute the instructions to use the rewards according to stationary times of the carriage targets and a number of the carriage targets staying on a platform of a station.
However, Zhang discloses the following:
wherein the at least one processor is configured to execute the instructions to uses the rewards according to stationary times of the carriage targets and a number of the carriage targets staying on a platform of a station [see at least Paragraph 0056-57 for reference to the traffic optimization problem’s goal being to decrease the average traffic delay of commuters in the network; Paragraph 0057 for reference to the goal being to find the best strategy such that ts -tmin is minimum; Paragraph 0104 for reference to the optimization results showing an improvement in terms of waiting time and queue length experienced at intersections]
Before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the operation plan generation of Minakawa to include the reinforcement learning reward method of Zhang. Doing so would improve the efficiency of transport in a number of situations, i.e. road transport, traffic management, mobility, etc., as stated by Zhang (Paragraph 0005).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ghaemi, Nadjla, Oded Cats, and Rob MP Goverde. "Railway disruption management challenges and possible solution directions." Public Transport 9.1 (2017): 343-364.
DOCUMENT ID
INVENTOR(S)
TITLE
EP 2426027 A2
Wakamiya, Takashi
Vehicle operation management method and server
US 2011/0098908 A1
Chun, Joong H.
Synchronized Express And Local Trains For Urban Commuter Rail Systems
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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3625