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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/22/2023 was considered by the examiner.
Claim Objections
Claim 1, 5,7, 9-11, 13 and 16 is/are objected to because of the following informalities:
Claim 1: “obtaining for each of one or more of the vehicles” should read “obtaining for each of one or more of the plurality of vehicles”
claims 2-16: “a method according to claim” should read “the method according to claim”.
Claim 5/7/9/10/11: “one or more of the vehicles” should read “one or more of the plurality of vehicles”.
Claim 13/16: “the vehicles” should read “the plurality of vehicles”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a control unit or a group of control units in claim 19
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1/17/18/19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “obtaining for each vehicle of the plurality of vehicles, a first value of a balance parameter, indicative of a balance between a cost for operating the respective vehicle along at least a part of the route, and a progress of the respective vehicle along at least a part of the route, characterized by establishing, in dependence on the first balance parameter values, a desired number of completed missions as a function of time, after an initial of the missions has started, and before all missions are completed, determining a mission completion deviation comprising a deviation of an actual number of completed missions from the desired number of completed missions, obtaining for each of one or more of the vehicles a second balance parameter value, different from the respective first balance parameter value, the respective second balance parameter value being dependent on the mission completion deviation”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers optimization/calculation/manipulation of values to determine updated parameter values and guide vehicle performance which is a method that falls under certain mathematical concepts and mental processes. That is, the method allows for concepts that have mathematical relationships, formulas and calculations and concepts can be done in the human mind (with pen and paper).
This judicial exception is not integrated into a practical application. In particular, claim 1 does not recite any additional elements, claim 17 recites a computer program, program code, a computer or a group of computers, claim 18 recites a non-transitory computer readable medium carrying a computer program comprising program code, a computer or group of computers and claim 19 recites a control unit or a group of control units. The claim further recites “controlling the one or more of the vehicles in dependence on the respective second balance parameter value. “Although the claims nominally involve a plurality of vehicles and routes, the claims do not recite how the vehicles are controlled in a specific technical manner. The claims only state that they are controlled “in dependence on” an updated parameter value (i.e., control as a result of the abstract calculation). As drafted, the claim language is a result oriented functional language that merely links the abstract idea to a technological environment (vehicle missions) rather than reciting a concrete technical implementation that meaningfully limits the exception.
These additional elements are also recited at a high level of generality which amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, alone or in combination, are nothing more than mere instructions to apply the exception on a general computer.
Dependent claims 2-9 and 13-15 are also directed to an abstract idea without significantly more because they further narrow the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claims 10-12 are also directed to an abstract idea without significantly more because they further narrow the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“determining a velocity profile” and “controlling the respective of the one or more of the vehicles” are stated at a high level of generality, and the “vehicle model” and “route data” are generic inputs to the abstract optimization. The claims do not recite a particular model-based control law, constraint formulation, actuator command structure, sensor feedback scheme, or other concrete control mechanism that would reflect an improvement to vehicle control technology) or providing significantly more limitations.
Dependent claim 16 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“determined by a control unit located remotely from the vehicles” is recited at a high level of generality and amounts to apply it instructions) or providing significantly more limitations.
Claim 17 is directed to “a computer program comprising program code means.” This
computer program comprising program code means, consistent with the instant specification,
appears to solely comprise software elements; none of the elements of claim 17 appear to be
physical components. MPEP 2106.03(I) states that “Non-limiting examples of claims that are not directed to any of the statutory categories include: Products that do not have a physical or tangible form, such as… a computer program per se (often referred to as ‘software per se’) when claimed as a product without any structural recitations.” Functional descriptive material such as a computer program must be structurally and functionally interrelated with a non-transitory medium to allow its intended uses to be realized. Accordingly, claims directed to software per se are not statutory subject matter. In re Warmerdam, 33 F.3d 1354, 1361, 31 USPQ2d 1754, 1760 (Fed. Cir. 1994). Accordingly, the computer program comprising program code means is considered software per se, which is not a “process, machine, manufacture, or composition of matter” as defined in 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1, 3-7, 10-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Levis (US 2006/0235739) in view of Kumar (US 2008/0201019) in further view of NASA, “Project Planning and control handbook”, published by NASA on September 25, 2017, hereinafter “Nasa”.
As per claim 1/17-19, Levis discloses a method for controlling a plurality of vehicles performing missions along a respective route (paragraph 35, 46, “The dispatch plan is typically determined based on the packages to be delivered ("deliveries"), packages to be picked up ("pick-ups"), or both. The dispatch plan is typically provided to the driver, either in a paper format or electronically communicated to the portable computing device (e.g., the DIAD), which can be accessed as needed by the driver.”, “ 0046] Employing such shipping systems in conjunction with wireless communication to portable computers carried by service personal (e.g., the DIAD) or integrated into the delivery vehicles allows remote updating of the dispatch plan for package pick-up, even after the delivery vehicles have departed for the day's deliveries.” Levis discloses a computer impl, the method comprising,
In regards to “obtaining for each vehicle of the plurality of vehicles, a first value of a balance parameter, indicative of a balance between a cost for operating the respective vehicle along at least a part of the route, and a progress of the respective vehicle along at least a part of the route”
Kumar discloses determining for a vehicle an objective function that balances operating cost and progress, where the objective function includes cost related to fuel consumption, emission, wear/tear, travel time and arrival time (paragraph 56, 75, 77-79,” The coefficients of the linear combination depend on the importance (weight) given to each of the terms. Note that in equation (OP), u(t) is the optimizing variable that is the continuous notch position. If discrete notch is required, e.g. for older locomotives, the solution to equation (OP) is discretized, which may result in lower fuel savings. Finding a minimum time solution (.alpha..sub.1 set to zero and .alpha..sub.2 set to zero or a relatively small value) is used to find a lower bound for the achievable travel time (T.sub.f=T.sub.fmin). In this case, both u(t) and T.sub.f are optimizing variables. “, “ [0075] Throughout the document exemplary equations and objective functions are presented for minimizing locomotive fuel consumption. These equations and functions are for illustration only as other equations and objective functions can be employed to optimize fuel consumption or to optimize other locomotive/train operating parameters.”
In regards to “characterized by - establishing, in dependence on the first balance parameter values, a desired number of completed missions as a function of time”
Kumar discloses in fig. 5, “[0109] As discussed herein, exemplary embodiments of the present invention may employ a setup as illustrated in the exemplary flow chart depicted in FIG. 5, and as an exemplary 3 segment example depicted in detail in FIG. 6. As illustrated, the trip may be broken into two or more segments, T1, T2, and T3. Though as discussed herein, it is possible to consider the trip as a single segment. As discussed herein, the segment boundaries may not result in equal segments. Instead the segments use natural or mission specific boundaries. Optimal trip plans are pre-computed for each segment. If fuel use versus trip time is the trip object to be met, fuel versus trip time curves are built for each segment. As discussed herein, the curves may be based on other factors, wherein the factors are objectives to be met with a trip plan. When trip time is the parameter being determined, trip time for each segment is computed while satisfying the overall trip time constraints”.
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Kumar in the teaching of Levis, in order to optimize fuel efficiency, emission output, vehicle performance, infrastructure and environment mission performance of the diesel powered system (please see Kumar paragraph 4).
Alternatively, Levis discloses managing operation of a plurality of delivery vehicles that execute discrete service stops comprising deliveries and pickups along dispatch planned routes, where each service stop constitutes a mission performed by a vehicle ([0003] The number of service stops on a given route is typically based on monitoring the driver's average workload during past work days. Using a basic route plan, a dispatch plan or delivery schedule is derived using the planned deliveries or service stops required to be completed for that day.”, paragraph 35, “Each service stop (also simply references as a "stop") is typically planned as a one of a sequence of stops along a predetermined route. The sequence of stops along the route is called herein a dispatch plan. The sequence can be presented to the user in tabular or graphical form, as will be seen”).
However, Levis gives a dispatch plan, it does not explicitly disclose a desired number as a function of time but Nasa discloses establishing a time phased completion baseline for discrete tasks and determining, for a given point in time, now many tasks are planned to have been completed by that time. Nasa explicitly teaches defining a desired cumulative number of completed tasks per function of time (page 130, “This metric provides a graphic which calculates the Baseline Execution Index (BEI). The BEI indicates how well the project is following the baseline plan and completing baseline tasks as they are scheduled to be completed. The BEI is calculated using the following formula: BEI = cumulative number of baseline tasks completed divided by cumulative number of baseline tasks scheduled for completion. A good performance rating is indicated when the BEI value is 0.95 or greater, and a moderate performance rating exists when the BEI value is between 0.85 and 0.94, and poor rating results if the index is less than or equal to 0.84. As the BEI value gets lower, it is less likely that the project will be completed on time per the baseline plan. If the BEI value is greater than 1.0, this is an indicator that the project is doing well and performing ahead of the baseline plan.”)
When applied to the delivery missions/dispatch plan of levis, where service stops constitute missions, the time phased task baseline of Nasa corresponds to “establishing, in dependence on the first balance parameter values, a desired number of completed missions as a function of time”
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to apply the time phased task completion framework as taught by Nasa to the delivery mission management system of Levis, in order to express how well the project is following the baseline plan (Nasa, page 130)
In regards to “after an initial of the missions has started, and before all missions are completed, determining a mission completion deviation comprising a deviation of an actual number of completed missions from the desired number of completed missions,”
Levis discloses monitoring execution of the displatch plan after execution has begun and before all service stops have been completed, including tracking how many service stops have been completed at a given time (“[0088] For example, the process 29 may access a file containing Historical Data 28. Historical Data is reference data that can be used as an aid in determining whether and how to update the Original Dispatch Plan. It may be a subset of historical data used in a separate process (not shown) used to determine the Route Plan 22. The Historical Data 28 stored in the portable computer is only required to be limited to the serving area of the single service vehicle. The contents of the Historical Data can vary based on the business application, storage requirements, and type of input is to be analyzed. For example, the Historical Data could indicate completed service stops (e.g., completed deliveries or pickups) of the day's manifest. Because deliveries that have been already completed by the driver are not be impacted by subsequent developments, such as weather or traffic, it is only the remaining deliveries in the Dispatch Plan that must be analyzed in order to produce an Updated Dispatch Plan. The fact that FIG. 3 illustrates Historical Data as separate from the Manifest Data is for conceptual purposes only and is not intended to limit how the Historical Data is stored. In some embodiments, the indication of which deliveries/pickups have been completed are stored in conjunction with the Manifest Data or Dispatch Plan. Thus, conceptually, this portion of the Historical Data could be viewed as an augmentation of the Dispatch Plan. Typically a service stop completion flag in the Dispatch Plan is recorded indicating the service stop has been completed. Regardless of how the indication is recorded, data indicating past deliveries can be modeled as Historical Data…. [0090] This aspect of the Historical Data captures, in part, the "experience" aspect of a driver by way of storing past delivery information that is used to provide a benchmark to determine whether the execution of a Dispatch plan is on schedule or behind schedule. If behind schedule, there may be a need to modify (e.g., re-optimize) the remaining deliveries in the Original Dispatch Plan. For example, experienced drivers on a route benchmark their performance throughout the day by comparing their location at a known landmark with the current time, and mentally comparing these to past experience of when the landmark was encountered. Or they may compare the current time with a degree of completion of the required tasks. By comparing a delivery vehicle's current time and location relative to past average time and location measurements on that give route, a level of "experience" can be built into the system, so that a determination of the schedule status ("ahead", "behind", or "on-schedule") can be determined, as well as the time required for completion of the remaining service stops.”)
However, while Levis discloses marking and storing completed missions as the dispatch plan progress and determining a deviation from the dispatch plan by comparing location information to the current time, it does not disclose but Nasa discloses determining deviation between the actual number of completed tasks and the desired number of tasks at a given status date (page 130, “This metric provides a graphic which calculates the Baseline Execution Index (BEI). The BEI indicates how well the project is following the baseline plan and completing baseline tasks as they are scheduled to be completed. The BEI is calculated using the following formula: BEI = cumulative number of baseline tasks completed divided by cumulative number of baseline tasks scheduled for completion. A good performance rating is indicated when the BEI value is 0.95 or greater, and a moderate performance rating exists when the BEI value is between 0.85 and 0.94, and poor rating results if the index is less than or equal to 0.84. As the BEI value gets lower, it is less likely that the project will be completed on time per the baseline plan. If the BEI value is greater than 1.0, this is an indicator that the project is doing well and performing ahead of the baseline plan.”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to apply the time phased task completion framework as taught by Nasa to the delivery mission management system of Levis, in order to express how well the project is following the baseline plan (Nasa, page 130)
In regards to “obtaining for each of one or more of the vehicles a second balance parameter value, different from the respective first balance parameter value, the respective second balance parameter value being dependent on the mission completion deviation”
Kumar discloses dynamically adjusting the balance between operating cost and progress during execution in response to detected deviations from the optimized schedule (“[0017] In another exemplary embodiment a method discloses providing an optimized mission plan to be manually applied. The optimized mission plan is re-planned in response to a manual mission plan being implemented. The manual plan is adjusted when the manual plan deviates from the optimized plan by more than a predetermined amount.”, “[0147]… This information (actual estimated arrival time or information needed to derive off-board) can also be communicated to the dispatch center to allow the dispatcher or dispatch system to adjust the target arrival times. This allows the system to quickly adjust and optimize for the appropriate target function (for example trading off speed and fuel usage). “, “[0177] FIG. 20 depicts another closed loop system where an operator is in the loop. The optimizer 650 generates the power/operating characteristic required for the optimum performance. The information is communicated to the operator 647, such as but not limited to, through human machine interface (HMI) and/or display 649. This could be in various forms including audio, text or plots or video displays. The operator 647 in this case can operate the master controller or pedals or any other actuator 651 to follow the optimum power level. [0178] If the operator follows the plan, the optimizer continuously displays the next operation required. If the operator does not follow the plan, the optimizer may recalculate/re-optimize the plan, depending on the deviation and the duration of the deviation of power, speed, position, emission etc. from the plan. If the operator fails to meet an optimize plan to an extent where re-optimizing the plan is not possible or where safety criteria has been or may be exceeded, in an exemplary embodiment the optimizer may take control of the vehicle to insure optimize operation, annunciate a need to consider the optimized mission plan, or simply record it for future analysis and/or use. In such an embodiment, the operator could retake control by manually disengaging the optimizer.”).
In regards to “controlling the one or more of the vehicles in dependence on the respective second balance parameter value.”
Kumar discloses controlling the vehicle according to the updated balance parameter by generating control commands based on the reoptimized plan “[0106] Once a trip plan is created as discussed above, a trajectory of speed and power versus distance is used to reach a destination with minimum fuel and/or emissions at the required trip time. There are several ways in which to execute the trip plan. As provided below in more detail, in an exemplary embodiment, when in a coaching mode information is displayed to the operator for the operator to follow to achieve the required power and speed determined according to the optimal trip plan. In this mode, the operating information is suggested operating conditions that the operator should use. In another exemplary embodiment, acceleration and maintaining a constant speed are performed. However, when the train 31 must be slowed, the operator is responsible for applying a braking system 52. In another exemplary embodiment of the present invention commands for powering and braking are provided as required to follow the desired speed-distance path. [0107] Feedback control strategies are used to provide corrections to the power control sequence in the profile to correct for such events as, but not limited to, train load variations caused by fluctuating head winds and/or tail winds. Another such error may be caused by an error in train parameters, such as, but not limited to, train mass and/or drag, when compared to assumptions in the optimized trip plan. A third type of error may occur with information contained in the track database 36. Another possible error may involve un-modeled performance differences due to the locomotive engine, traction motor thermal deration and/or other factors. Feedback control strategies compare the actual speed as a function of position to the speed in the desired optimal profile. Based on this difference, a correction to the optimal power profile is added to drive the actual velocity toward the optimal profile. To assure stable regulation, a compensation algorithm may be provided which filters the feedback speeds into power corrections to assure closed-performance stability is assured. Compensation may include standard dynamic compensation as used by those skilled in the art of control system design to meet performance objectives.” (please see combination rationale above).
As per claim 3, Levis does not disclose but Kumar discloses the respective first value of the balance parameter is determined in dependence on the cost for operating the respective vehicle along at least a part of the respective route, wherein said cost is dependent on one or more of fuel consumption, electrical energy consumption, battery degradation, and another degradation of the respective vehicle (.”[0072] Based on the specification data input into the exemplary embodiment of the present invention, an optimal plan which minimizes fuel use and/or emissions produced subject to speed limit constraints along the route with desired start and end times is computed to produce a trip profile 12. The profile contains the optimal speed and power (notch) settings the train is to follow, expressed as a function of distance and/or time, and such train operating limits, including but not limited to, the maximum notch power and brake settings, and speed limits as a function of location, and the expected fuel used and emissions generated.”, “[0073] The procedure used to compute the optimal profile can be any number of methods for computing a power sequence that drives the train 31 to minimize fuel and/or emissions subject to locomotive operating and schedule constraints, as summarized below. In some cases the required optimal profile may be close enough to one previously determined, owing to the similarity of the train configuration, route and environmental conditions. In these cases it may be sufficient to look up the driving trajectory within a database 63 and attempt to follow it. When no previously computed plan is suitable, methods to compute a new one include, but are not limited to, direct calculation of the optimal profile using differential equation models which approximate the train physics of motion. The setup involves selection of a quantitative objective function, commonly a weighted sum (integral) of model variables that correspond to rate of fuel consumption and emissions generation plus a term to penalize excessive throttle variation.”)(please see claim 1 rejection for combination rationale).
As per claim 4, Levis does not disclose establishing, in dependence on the respective first balance parameter value, for each of the vehicles a correlation set comprising a plurality of desired position and time correlations for the travel of the respective vehicle along at least a part of the respective route([0058] … The Dispatch Plan information typically includes the consignee (destination address) and associated package service levels and/or delivery commitment times ("delivery commitments"). Each group of information associated with a service stop, delivery, or other service action, can be considered a record in a database. Thus, the Dispatch Plan can be viewed as comprising a sequence of records. Further, each record could include additional information regarding customer specific requirements--e.g., certain delivery time windows )(Kumar also disclose the limitation at [0103] A requirement of the exemplary embodiment of the present invention is the ability to initially create and quickly modify on the fly any plan that is being executed. This includes creating the initial plan when a long distance is involved, owing to the complexity of the plan optimization algorithm. When a total length of a trip profile exceeds a given distance, an algorithm 46 may be used to segment the mission wherein the mission may be divided by waypoints. Though only a single algorithm 46 is discussed, those skilled in the art will readily recognize that more than one algorithm may be used where the algorithms may be connected together. The waypoint may include natural locations where the train 31 stops, such as, but not limited to, sidings where a meet with opposing traffic, or pass with a train behind the current train is scheduled to occur on single-track rail, or at yard sidings or industry where cars are to be picked up and set out, and locations of planned work. At such waypoints, the train 31 may be required to be at the location at a scheduled time and be stopped or moving with speed in a specified range. The time duration from arrival to departure at waypoints is called dwell time.).
As per claim 5, Levis discloses determining for each of the one or more of the vehicles a progress deviation indicative of a deviation of an actual progress of the respective vehicle along the respective route from a desired progress of the respective vehicle ([0090] This aspect of the Historical Data captures, in part, the "experience" aspect of a driver by way of storing past delivery information that is used to provide a benchmark to determine whether the execution of a Dispatch plan is on schedule or behind schedule. If behind schedule, there may be a need to modify (e.g., re-optimize) the remaining deliveries in the Original Dispatch Plan. For example, experienced drivers on a route benchmark their performance throughout the day by comparing their location at a known landmark with the current time, and mentally comparing these to past experience of when the landmark was encountered. Or they may compare the current time with a degree of completion of the required tasks. By comparing a delivery vehicle's current time and location relative to past average time and location measurements on that give route, a level of "experience" can be built into the system, so that a determination of the schedule status ("ahead", "behind", or "on-schedule") can be determined, as well as the time required for completion of the remaining service stops.. [0120] Another common trigger is a manual update that is entered by the user (typically the driver of the vehicle). With the manual update, the user may simply request a "check" of the status, or the user may manually add further Manifest related information. A typical embodiment is the operator requesting a status check based on the current delivery status. For example, the driver may suspect that deliveries are behind schedule and request the system to ascertain whether an updating of the Dispatch Plan is appropriate. The system then compares the current time and/or location against either the Manifest and/or historical data to obtain a benchmark as to the current delivery status.”)(Kumar also disclose the limitation at “[0178] If the operator follows the plan, the optimizer continuously displays the next operation required. If the operator does not follow the plan, the optimizer may recalculate/re-optimize the plan, depending on the deviation and the duration of the deviation of power, speed, position, emission etc. from the plan. If the operator fails to meet an optimize plan to an extent where re-optimizing the plan is not possible or where safety criteria has been or may be exceeded, in an exemplary embodiment the optimizer may take control of the vehicle to insure optimize operation, annunciate a need to consider the optimized mission plan, or simply record it for future analysis and/or use. In such an embodiment, the operator could retake control by manually disengaging the optimizer.”).
As per claim 6, Levis discloses wherein the respective progress deviation comprises a deviation, for said point in time, of an actual position of the vehicle from a desired position according to the respective correlation set ([0101] The DM can use the current location and time to compare the location of the vehicle along a route with an expected location and time. This involves using historical data (e.g., including past delivery related times and location data) to allow the DM to determine the likelihood whether the current days' execution of the dispatch plan is on schedule, behind schedule, or ahead of schedule. In order to perform this comparison, the DM accesses a database containing historical data, including historical dispatch location and time data 36. The historical location and time data can be stored in various forms and may include a moving average of typical times associated with a given location.”). However, Levis does not disclose but Kumar discloses establishing, in dependence on the respective first balance parameter value, for each of the vehicles, a correlation set comprising a plurality of desired position and time correlations for the travel of the respective vehicle along at least a part of the respective route ([0103] A requirement of the exemplary embodiment of the present invention is the ability to initially create and quickly modify on the fly any plan that is being executed. This includes creating the initial plan when a long distance is involved, owing to the complexity of the plan optimization algorithm. When a total length of a trip profile exceeds a given distance, an algorithm 46 may be used to segment the mission wherein the mission may be divided by waypoints. Though only a single algorithm 46 is discussed, those skilled in the art will readily recognize that more than one algorithm may be used where the algorithms may be connected together. The waypoint may include natural locations where the train 31 stops, such as, but not limited to, sidings where a meet with opposing traffic, or pass with a train behind the current train is scheduled to occur on single-track rail, or at yard sidings or industry where cars are to be picked up and set out, and locations of planned work. At such waypoints, the train 31 may be required to be at the location at a scheduled time and be stopped or moving with speed in a specified range. The time duration from arrival to departure at waypoints is called dwell time.”)(please see clam 1 rejection for combination rationale).
As per claim 7, Levis does not disclose but Kumar discloses the step of obtaining for each of the one or more of the vehicles a second balance parameter value comprises determining the respective second balance parameter value in dependence on the respective progress deviation (“[0017] In another exemplary embodiment a method discloses providing an optimized mission plan to be manually applied. The optimized mission plan is re-planned in response to a manual mission plan being implemented. The manual plan is adjusted when the manual plan deviates from the optimized plan by more than a predetermined amount.”, “[0147]… This information (actual estimated arrival time or information needed to derive off-board) can also be communicated to the dispatch center to allow the dispatcher or dispatch system to adjust the target arrival times. This allows the system to quickly adjust and optimize for the appropriate target function (for example trading off speed and fuel usage). “, “[0177] FIG. 20 depicts another closed loop system where an operator is in the loop. The optimizer 650 generates the power/operating characteristic required for the optimum performance. The information is communicated to the operator 647, such as but not limited to, through human machine interface (HMI) and/or display 649. This could be in various forms including audio, text or plots or video displays. The operator 647 in this case can operate the master controller or pedals or any other actuator 651 to follow the optimum power level. [0178] If the operator follows the plan, the optimizer continuously displays the next operation required. If the operator does not follow the plan, the optimizer may recalculate/re-optimize the plan, depending on the deviation and the duration of the deviation of power, speed, position, emission etc. from the plan. If the operator fails to meet an optimize plan to an extent where re-optimizing the plan is not possible or where safety criteria has been or may be exceeded, in an exemplary embodiment the optimizer may take control of the vehicle to insure optimize operation, annunciate a need to consider the optimized mission plan, or simply record it for future analysis and/or use. In such an embodiment, the operator could retake control by manually disengaging the optimizer.”).
As per claim 10, Levis does not disclose but Kumar discloses determining, in dependence on the respective second balance parameter value, a respective velocity profile for a respective of the one or more of the vehicles for at least a portion of the respective remainder of the respective route, and controlling the respective of the one or more of the vehicles according to the respective determined velocity profile ([0072] Based on the specification data input into the exemplary embodiment of the present invention, an optimal plan which minimizes fuel use and/or emissions produced subject to speed limit constraints along the route with desired start and end times is computed to produce a trip profile 12. The profile contains the optimal speed and power (notch) settings the train is to follow, expressed as a function of distance and/or time, and such train operating limits, including but not limited to, the maximum notch power and brake settings, and speed limits as a function of location, and the expected fuel used and emissions generated. In an exemplary embodiment, the value for the notch setting is selected to obtain throttle change decisions about once every 10 to 30 seconds. Those skilled in the art will readily recognize that the throttle change decisions may occur at a longer or shorter duration, if needed and/or desired to follow an optimal speed profile.. [0074] An optimal control formulation is set up to minimize the quantitative objective function subject to constraints including but not limited to, speed limits and minimum and maximum power (throttle) settings and maximum cumulative and instantaneous emissions. Depending on planning objectives at any time, the problem may be setup flexibly to minimize fuel subject to constraints on emissions and speed limits, or to minimize emissions, subject to constraints on fuel use and arrival time. [0105]… Once travel times for each segment are allocated, a power/speed plan is determined for each segment from the previously computed solutions. If there are any waypoint constraints on speed between the segments, such as, but not limited to, a change in a speed limit, they are matched up during creation of the optimal trip profile. If speed restrictions change in only a single segment, the fuel use/travel-time curve 50 has to be re-computed for only the segment changed. This reduces time for having to re-calculate more parts, or segments, of the trip. [0106] Once a trip plan is created as discussed above, a trajectory of speed and power versus distance is used to reach a destination with minimum fuel and/or emissions at the required trip time. There are several ways in which to execute the trip plan. As provided below in more detail, in an exemplary embodiment, when in a coaching mode information is displayed to the operator for the operator to follow to achieve the required power and speed determined according to the optimal trip plan.”)
As per claim 11, Levis does not disclose but Kumar discloses obtaining a respective vehicle model in the form of a mathematical model for the respective of the one or more of the vehicles, wherein the respective velocity profile is determined by means of the respective vehicle model (“[0067]… Such input information includes, but is not limited to, train position, consist description (such as locomotive models), locomotive power description, performance of locomotive traction transmission, consumption of engine fuel as a function of output power, cooling characteristics, the intended trip route (effective track grade and curvature as function of milepost or an "effective grade" component to reflect curvature following standard railroad practices), the train represented by car makeup and loading together with effective drag coefficients, trip desired parameters including, but not limited to, start time and location, end location, desired travel time, crew (user and/or operator) identification, crew shift expiration time, and route. …[0073]…. direct calculation of the optimal profile using differential equation models which approximate the train physics of motion. The setup involves selection of a quantitative objective function, commonly a weighted sum (integral) of model variables that correspond to rate of fuel consumption and emissions generation plus a term to penalize excessive throttle variation.”)(please see claim 1 rejection for combination rationale).
As per claim 12, Levis does not disclose but Kumar discloses btaining data for the respective route, wherein the respective velocity profile is determined in dependence on the route data ([0098] A track characterization element 33 to provide information about a track, principally grade and elevation and curvature information, is also provided. The track characterization element 33 may include an on-board track integrity database 36…[0123]… As illustrated, such information is provided to an executive control element 62. Also supplied to the executive control element 62 is locomotive modeling information database 63, information from a track database 36 such as, but not limited to, track grade information and speed limit information, estimated train parameters such as, but not limited to, train weight and drag coefficients, and fuel rate tables from a fuel rate estimator 64. The executive control element 62 supplies information to the planner 12, which is disclosed in more detail in FIG. 1. Once a trip plan has been calculated, the plan is supplied to a driving advisor, driver or controller element 51. The trip plan is also supplied to the executive control element 62 so that it can compare the trip when other new data is provided.”).
As per claim 13, Levis discloses the missions that the vehicles perform form a present set of missions, the method comprising performing a previous set of missions ([0035] A typical package delivery service involves stopping at various locations on a route within a certain serving area and providing services at each stop. Each service stop typically involves delivery of one or more packages, as well as picking up one or more packages. Each service stop (also simply references as a "stop") is typically planned as a one of a sequence of stops along a predetermined route. The sequence of stops along the route is called herein a dispatch plan. The sequence can be presented to the user in tabular or graphical form, as will be seen. In many instances, the geographical serving area is typically static to a degree; i.e., it generally involves the same roads within a geographical area, although not all roads are necessarily traveled on a given day, since not every location on each road is typically associated with a service stop. In other instances, the serving area may alter in area (increase or decrease in size) based on the overall required deliveries in the dispatch plan. Thus, the actual route (e.g., the series of roads) traversed by a service vehicle can vary based on a particular day's work load or based on seasonal changes. Using a generally static route allows drivers to become familiar with the route and gain experience with typical driving times and other conditions and provides a baseline from which deviations can be referenced. While the route traversed or serving area may be static, the particular service stops scheduled along the route on a given day usually varies. The dispatch plan is typically determined based on the packages to be delivered ("deliveries"), packages to be picked up ("pick-ups"), or both.”). However, Levis does not disclose but Kumar discloses wherein the respective second balance parameter value is determined in dependence on the previous set of missions ([0088]… For example, the travel-time fuel use tradeoff curve as illustrated in FIG. 4 reflects a capability of a train on a particular route at a current time, updated from ensemble averages collected for many similar trains on the same route.”)(please see claim 1 rejection for combination rationale).
As per claim 14, Levis does not disclose but Kumar discloses determining, for the previous set of missions, a value of a reward parameter, in dependence of a deviation of an actual time to complete all missions in the previous set of missions, from a desired time to complete all missions in the previous set of missions, wherein the respective second balance parameter value is determined in dependence on the reward parameter value ([0091]… Using the actual speed, power and location of the locomotive, a comparison is made between a planned arrival time and the currently estimated (predicted) arrival time 25. Based on a difference in the times, as well as the difference in parameters (detected or changed by dispatch or the operator), the plan is adjusted 26… [0092] A re-plan may also be made when it is desired to change the original objectives. Such re-planning can be done at either fixed preplanned times, manually at the discretion of the operator or dispatcher, or autonomously when predefined limits, such a train operating limits, are exceeded. For example, if the current plan execution is running late by more than a specified threshold, such as thirty minutes, the exemplary embodiment of the present invention can re-plan the trip to accommodate the delay at expense of increased fuel as described above or to alert the operator and dispatcher how much of the time can be made up at all (i.e. what minimum time to go or the maximum fuel that can be saved within a time constraint). [0074]… Depending on planning objectives at any time, the problem may be setup flexibly to minimize fuel subject to constraints on emissions and speed limits, or to minimize emissions, subject to constraints on fuel use and arrival time. It is also possible to setup, for example, a goal to minimize the total travel time without constraints on total emissions or fuel use where such relaxation of constraints would be permitted or required for the mission... [0078] A commonly used and representative objective function is thus:… The coefficients of the linear combination depend on the importance (weight) given to each of the terms. Note that in equation (OP), u(t) is the optimizing variable that is the continuous notch position… [0088] In operation, the locomotive 42 will continuously monitor system efficiency and continuously update the trip plan based on the actual efficiency measured, whenever such an update would improve trip performance. Re-planning computations may be carried out entirely within the locomotive(s) or fully or partially moved to a remote location, such as dispatch or wayside processing facilities where wireless technology is used to communicate the plans to the locomotive 42. The exemplary embodiment of the present invention may also generate efficiency trends that can be used to develop locomotive fleet data regarding efficiency transfer functions. The fleet-wide data may be used when determining the initial trip plan, and may be used for network-wide optimization tradeoff when considering locations of a plurality of trains. For example, the travel-time fuel use tradeoff curve as illustrated in FIG. 4 reflects a capability of a train on a particular route at a current time, updated from ensemble averages collected for many similar trains on the same route.” These passages are equivalent to Kumar disclosing that the previous performance of the trains are measured by comparing the actual completion times/fuel performance to the scheduled completion times/fuel performance and using this different as a key signal for decision in later missions.)(please see claim 1 rejection for combination rationale).
As per claim 15, Levis does not disclose but Kumar discloses the respective second balance parameter value is determined in dependence on a cost for operating the vehicles at the previous set of missions (0004] This invention relates to a powered system, such as a train, an off-highway vehicle, a marine, a transport vehicle, an agriculture vehicle, and/or a stationary powered system and, more particularly to a method and computer software code for optimized fuel efficiency, emission output, vehicle performance, infrastructure and environment mission performance of the diesel powered system.. [0075] Throughout the document exemplary equations and objective functions are presented for minimizing locomotive fuel consumption. These equations and functions are for illustration only as other equations and objective functions can be employed to optimize fuel consumption or to optimize other locomotive/train operating parameters… 0083] To solve the resulting optimization problem, in an exemplary embodiment the present invention transcribes a dynamic optimal control problem in the time domain to an equivalent static mathematical programming problem with N decision variables, where the number `N` depends on the frequency at which throttle and braking adjustments are made and the duration of the trip. For typical problems, this N can be in the thousands. For example in an exemplary embodiment, suppose a train is traveling a 172-mile (276.8 kilometers) stretch of track in the southwest United States. Utilizing the exemplary embodiment of the present invention, an exemplary 7.6% saving in fuel used may be realized when comparing a trip determined and followed using the exemplary embodiment of the present invention versus an actual driver throttle/speed history where the trip was determined by an operator. The improved savings is realized because the optimization realized by using the exemplary embodiment of the present invention produces a driving strategy with both less drag loss and little or no braking loss compared to the trip plan of the operator.)
As per claim 16, Levis does not disclose but Kumar discloses the respective second balance parameter value is determined by a control unit located remotely from the vehicles (0067] FIG. 1 depicts an exemplary illustration of a flow chart for trip optimization. As illustrated, instructions are input specific to planning a trip either on board or from a remote location, such as a dispatch center 10. Such input information includes, but is not limited to, train position, consist description (such as locomotive models), locomotive power description, performance of locomotive traction transmission, consumption of engine fuel as a function of output power, cooling characteristics, the intended trip route (effective track grade and curvature as function of milepost or an "effective grade" component to reflect curvature following standard railroad practices), the train represented by car makeup and loading together with effective drag coefficients, trip desired parameters including, but not limited to, start time and location, end location, desired travel time, crew (user and/or operator) identification, crew shift expiration time, and route.”).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Levis (US 2006/0235739) in view of Kumar (US 2008/0201019) and NASA, as disclosed in the rejection of claim 1, in further view of Tulabandhula (US 2017/0316697), hereinafter “Tula”.
As per claim 2, Levis discloses a given route/dispatch plan for a driver (paragraph 8-9). However, Levis does not disclose but Tula discloses the respective routes are identical to each other (paragraph 23-24, “[0023] “One or more time instants” may refer to fixed time instants at which one or more vehicles are scheduled to be dispatched on a route. In an embodiment, the one or more time instants may be determined by a service provider of a transport agency based on a demand at one or more stations in the route. For example, a transport agency dispatches three buses from a total of ten buses at “9:00 AM,” “10:00 AM,” and “11:00 AM,” respectively. In such a case, “9:00 AM,” “10:00 AM,” and “11:00 AM” may correspond to the one or more time instants. [0024] A “route” may refer to a path that may be traversed by a vehicle to pick up or drop one or more individuals. In an embodiment, the route may include one or more stations corresponding to one or more locations “s.” The one or more stations may come in a predetermined order in the route. In an embodiment, the route may comprise at least one pair of stations having at least one source station and one destination station. For example, a city bus travels from Harlem to East Village in New York. Thus, the path from Harlem to East Village may correspond to a route with Harlem being a source station and East Village being a destination station. Hereinafter, the terms such as “stations” or “nodes” are used interchangeably.” Multiple vehicles are dispatched on the same route.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Tula in the teaching of Levis, in order to generate a dispatch schedule for one or more vehicles at one or more time instants based on a first demand for the one or more vehicles along a route (please see Tula, abstract).
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Levis (US 2006/0235739) in view of Kumar (US 2008/0201019) and NASA, as disclosed in the rejection of claim 5, in further view of Neiss (US 2004/0068359).
As per claim 8, in regards to “pre-determining a plurality of balance parameter values, each for a respective pair of an assumed progress deviation and an assumed mission completion deviation,”
Levis in view of Nasa discloses mission completion deviation as shown in claim 1.
Kumar discloses the progress deviation ([0091]… Using the actual speed, power and location of the locomotive, a comparison is made between a planned arrival time and the currently estimated (predicted) arrival time 25. Based on a difference in the times, as well as the difference in parameters (detected or changed by dispatch or the operator), the plan is adjusted 26. This adjustment may be made automatically following a railroad company's desire for how such departures from plan should be handled or manually propose alternatives for the on-board operator and dispatcher to jointly decide the best way to get back on plan. Whenever a plan is updated but where the original objectives, such as but not limited to arrival time remain the same, additional changes may be factored in concurrently, e.g. new future speed limit changes, which could affect the feasibility of ever recovering the original plan. In such instances if the original trip plan cannot be maintained, or in other words the train is unable to meet the original trip plan objectives, as discussed herein other trip plan(s) may be presented to the operator and/or remote facility, or dispatch.”).
Kumar further discloses pre-determining a plurality of balance parameter values, each for a respective pair of an assumed progress deviation and an assumed mission completion deviation (Kumar explicitly formulates the control as an optimal control function with an objective that is a weighted combination of fuel consumption, travel time and emission (paragraph 77-79). These weights are the equivalent of “balance parameter” that trades off “cost” versus progress. Kumar further discloses that the planner solves this optimization for various values of the arrival time constraint to generate a curve of fuel vs. trip time (fig. 4-6) and that these alternatives (different arrival times with corresponding fuel costs) are made available to operator to choose from and use later when re-planning if the train is running late or early. Kumar pre-computes multiple “mission balance” parameters, each associated with an assumed deviation of mission completion time and uses those to adjust the mission when deviations are detected (paragraph 68, 91-92, 94, 105-112, 178-179).
However, the combination of Levis in view of Kumar and Nasa does not expressly disclose that these “balance parameters” are stored and keyed to an assumed progress deviation and assumed mission completion deviation in a lookup table. However, Neiss fills this gap as to pre-determining a plurality of balance parameters in a lookup table indexed by operating state and then using that during mission execution ([0047] Look-up Table 26: The look-up table is a memory where desired velocity, throttle pedal, and controller gain values are stored for an area (the Prediction Horizon) around the current vehicle position. The outputs of the PCC block are determined by reading out the values belonging to the vehicle position supplied by the Position Estimator. [0048] Optimization Algorithm Module 27: This is the main module, in which optimal velocity trajectory, the optimal throttle pedal positions, and the controller gains are calculated for the approaching road within the Prediction Horizon, based on optimization of a cost function, as described hereinafter. These series are then stored in the look-up table. The start of a new calculation is triggered every time the vehicle has covered a specified distance. It may also be triggered by other events, such as the vehicle driver's changing the set vehicle speed.”)
in regards to “wherein the step of obtaining for each of the one or more of the vehicles a second balance parameter value comprises selecting the respective second balance parameter value from the pre-determined balance parameter values in dependence on the progress deviation of the respective vehicle, and the mission completion deviation, determined after the initial of the missions had started.”
Levis in view of Nasa discloses determining schedule status based on the ratio of completed vs. total stops and time/location comparisons to expected values as shown in claim 1. When schedule status indicates that a schedule is behind schedule, the system invokes dispatch plan updating algorithms that reorder the remaining stops to satisfy delivery commitments.
Kumar discloses continuously monitoring vehicle progress deviation by measuring the difference between actual vs. planned trajectory, speed, and position as shown in claim 1. It further continuously computes projected arrival times and comparing them to the planned arrival time. When deviation exceed threshold, adjusts the plan by adjusting the fuel/emission cost and producing new power/velocity commands (mission level deviation as shown above).
Neiss further discloses how to implement such conditional selection of control/balance parameters. Once the table of parameters is pre-computed, the controller reads out from the table the values corresponding to the current estimate state at run time (paragraph 47-48).
Therefore, a person of ordinary skill in the art, striving to implement the mission optimization of Kumar in a multi-vehicle fleet context of Levis, and aware of the performance/complexity tradeoffs of real time optimization, would have found it obvious to precompute a grid of “balance parameter” values expressing different trade-offs between cost and schedule for assumed combination so of vehicle progress deviation and mission completion deviation; and after missions have begun, measure each vehicle’s current deviations and select the appropriate pre-computed balance parameter from the lookup table design as taught by Neiss in order to reduce real time computation and improve responsiveness.
As per claim 9, in regards to “pre-determining a plurality of balance parameter values, each for a respective pair of an assumed progress deviation and an assumed mission completion deviation,”
Levis in view of Nasa discloses mission completion deviation as shown in claim 1.
Kumar also discloses the progress deviation which can be interpreted as Mission completion deviation ([0091]… Using the actual speed, power and location of the locomotive, a comparison is made between a planned arrival time and the currently estimated (predicted) arrival time 25. Based on a difference in the times, as well as the difference in parameters (detected or changed by dispatch or the operator), the plan is adjusted 26. This adjustment may be made automatically following a railroad company's desire for how such departures from plan should be handled or manually propose alternatives for the on-board operator and dispatcher to jointly decide the best way to get back on plan. Whenever a plan is updated but where the original objectives, such as but not limited to arrival time remain the same, additional changes may be factored in concurrently, e.g. new future speed limit changes, which could affect the feasibility of ever recovering the original plan. In such instances if the original trip plan cannot be maintained, or in other words the train is unable to meet the original trip plan objectives, as discussed herein other trip plan(s) may be presented to the operator and/or remote facility, or dispatch.” [0103] A requirement of the exemplary embodiment of the present invention is the ability to initially create and quickly modify on the fly any plan that is being executed. This includes creating the initial plan when a long distance is involved, owing to the complexity of the plan optimization algorithm. When a total length of a trip profile exceeds a given distance, an algorithm 46 may be used to segment the mission wherein the mission may be divided by waypoints. Though only a single algorithm 46 is discussed, those skilled in the art will readily recognize that more than one algorithm may be used where the algorithms may be connected together. The waypoint may include natural locations where the train 31 stops, such as, but not limited to, sidings where a meet with opposing traffic, or pass with a train behind the current train is scheduled to occur on single-track rail, or at yard sidings or industry where cars are to be picked up and set out, and locations of planned work. At such waypoints, the train 31 may be required to be at the location at a scheduled time and be stopped or moving with speed in a specified range. The time duration from arrival to departure at waypoints is called dwell time.” Examiner interprets not being at the waypoints at the certain time is being behind schedule which is a mission deviation).
Kumar further discloses pre-determining a plurality of balance parameter values, each for a respective assumed mission completion deviation (Kumar explicitly formulates the control as an optimal control function with an objective that is a weighted combination of fuel consumption, travel time and emission (paragraph 77-79). These weights are the equivalent of “balance parameter” that trades off “cost” versus progress. Kumar further discloses that the planner solves this optimization for various values of the arrival time constraint to generate a curve of fuel vs. trip time (fig. 4-6) and that these alternatives (different arrival times with corresponding fuel costs) are made available to operator to choose from and use later when re-planning if the train is running late or early. Kumar pre-computes multiple “mission balance” parameters, each associated with an assumed deviation of mission completion time and uses those to adjust the mission when deviations are detected (paragraph 68, 91-92, 94, 105-112, 178-179).
However, the combination of Levis in view of Kumar and Nasa does not expressly disclose that these “balance parameters” are stored and keyed to an assumed progress deviation and assumed mission completion deviation in a lookup table. However, Neiss fills this gap as to pre-determining a plurality of balance parameters in a lookup table indexed by operating state and then using that during mission execution ([0047] Look-up Table 26: The look-up table is a memory where desired velocity, throttle pedal, and controller gain values are stored for an area (the Prediction Horizon) around the current vehicle position. The outputs of the PCC block are determined by reading out the values belonging to the vehicle position supplied by the Position Estimator. [0048] Optimization Algorithm Module 27: This is the main module, in which optimal velocity trajectory, the optimal throttle pedal positions, and the controller gains are calculated for the approaching road within the Prediction Horizon, based on optimization of a cost function, as described hereinafter. These series are then stored in the look-up table. The start of a new calculation is triggered every time the vehicle has covered a specified distance. It may also be triggered by other events, such as the vehicle driver's changing the set vehicle speed.”)
in regards to “wherein the step of obtaining for each of the one or more of the vehicles a second balance parameter value comprises selecting the respective second balance parameter value from the pre-determined balance parameter values in dependence on the mission completion deviation, determined after the initial of the missions had started.”
Levis in view of Nasa discloses determining schedule status based on the ratio of completed vs. total stops and time/location comparisons to expected values as shown in claim 1. When schedule status indicates that a schedule is behind schedule, the system invokes dispatch plan updating algorithms that reorder the remaining stops to satisfy delivery commitments.
Kumar discloses continuously monitoring vehicle progress deviation by measuring the difference between actual vs. planned trajectory, speed, and position as shown in claim 1. It further continuously computes projected arrival times and comparing them to the planned arrival time. When deviation exceed threshold, adjusts the plan by adjusting the fuel/emission cost and producing new power/velocity commands (mission level deviation as shown above).
Neiss further discloses how to implement such conditional selection of control/balance parameters. Once the table of parameters is pre-computed, the controller reads out from the table the values corresponding to the current estimate state at run time (paragraph 47-48).
Therefore, a person of ordinary skill in the art, striving to implement the mission optimization of Kumar in a multi-vehicle fleet context of Levis, and aware of the performance/complexity tradeoffs of real time optimization, would have found it obvious to precompute a grid of “balance parameter” values expressing different trade-offs between cost and schedule for assumed combination so of vehicle progress deviation and mission completion deviation; and after missions have begun, measure each vehicle’s current deviations and select the appropriate pre-computed balance parameter from the lookup table design as taught by Neiss in order to reduce real time computation and improve responsiveness.
Conclusion
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3628