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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2024-001747, filed on 01/10/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/20/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
The following is a Non-Final Office Action.
Claims 1-10 are considered in this Office Action. Claims 1-10 are currently pending.
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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) 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), because the claim limitations use 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 limitations are: an acquisition unit in claim 1, a calculation unit in claims 1 and 8, and plan instructing unit in claim 8. The specification has support for sufficient structure in figure 2 and the text corresponding to the figure.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they 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 these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “Patent Subject Matter Eligibility” (MPEP 2106).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-8), the method (claim 9), and the computer-readable non-transitory storage medium (claim 10) are directed to an eligible category of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied. Claims 14-20 are not in one of the four statutory categories of invention.
With respect to Step 2, and in particular Step 2A Prong One of MPEP 2106, it is next noted that the claims recite an abstract idea by reciting concepts of formulating of “operation plan” for vehicles transporting passengers between boarding and drop-off location which can be categorized as “Mental process”, “Certain methods of organizing human activity”, and “Mathematical concept”. The abstract idea is categorized as “Certain methods of organizing human activity” because it is directed to commercial and logistic management. The abstract idea can be categorized as “mental process” because it is directed concept performed in the human mind (including an observation, evaluation, judgment, opinion) or by the aid of a pen and/or paper and within the enumerated groupings of abstract ideas set forth in the 2106.04(a). The abstract idea can further be categorized as “Mathematical concept”. The limitations reciting the abstract idea are highlighted in italics and the limitation directed to additional elements highlighted in bold, as set forth in exemplary claim 1, are: An operation plan formulating device formulating an operation plan of a plurality of vehicles in a service for a vehicle to transport a passenger from a boarding location to a drop-off location, the operation plan formulating device comprising: an acquisition unit configured to acquire an energy function equation defined as a sum of objective functions relating to the operation plan of the vehicles; and a calculation unit configured to calculate an operation plan minimizing a value of the energy function equation as an optimal solution, wherein the objective functions include one or more first objective functions based on whether or not restriction conditions are satisfied and one or more second objective functions for evaluating a degree of achievement of a target event, wherein the restriction conditions include that a destination of the vehicle is one place, wherein the energy function equation obtains a weighted sum of the one or more first objective functions and the one or more second objective functions, and wherein a weighting assigned to the one or more first objective functions is larger than a weighting assigned to the one or more second objective functions with such a degree that an operation plan for which the restriction conditions are not satisfied is not selected. Claims 9, and 10 recite substantially the same limitation as claim 1 and therefore subject to the same rationale.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to an operation plan formulating device, acquisition unit, calculation unit, and computer-readable non-transitory storage medium storing a program to implement the abstract idea. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s specification describes high level general purpose computer, “Constituent elements other than the storage unit 150, for example, are realized by a hardware processor such as a central processing unit (CPU) executing a program (software)”) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to: to a system for generating a rehabilitation plan for a commodity pipe network, at least one computing device, and a computer-readable medium with computer-executable instructions stored thereon that when executed by the at least one computing device cause the system to implement the abstract idea. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s specification describes high level general purpose computer, “Constituent elements other than the storage unit 150, for example, are realized by a hardware processor such as a central processing unit (CPU) executing a program (software)”) describe generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) (“The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.”). See MPEP 2106.05(d)(II).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however (claim 8 recites a plan instructing unit configured to transmit the operation plan calculated by the calculation unit to at least a vehicle. However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Furthermore, these elements have been fully considered, however they are directed to the use of generic computing elements (Applicant’s specification describes high level general purpose computer, “Constituent elements other than the storage unit 150, for example, are realized by a hardware processor such as a central processing unit (CPU) executing a program (software)”) to perform the abstract idea, which is not sufficient to amount to a practical application and is tantamount to simply saying “apply it” using a general purpose computer, which merely serves to tie the abstract idea to a particular technological environment (computer based operating environment) by using the computer as a tool to perform the abstract idea, which is not sufficient to amount to particular application. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim. In addition, Applicant’s Specification (Applicant’s specification describes high level general purpose computer, “Constituent elements other than the storage unit 150, for example, are realized by a hardware processor such as a central processing unit (CPU) executing a program (software)”) describe generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added))), similar to the finding for claims above, these claims are similarly directed to the abstract idea of mental processes, certain methods of organizing human activity, and mathematical concept, without integrating it into a practical application and with, at most, a general-purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 4, 5, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Alonso-Mora (US 20180231984 A1, hereinafter “Alonso-Mora”) in view of Yuta (WO 2023203769 A1, hereinafter “Yuta”) in view of Gkiotsalitis (US 20180268705 A1, hereinafter “Gkiotsalitis”).
Claim 1/9/10
Alonso-Mora teaches:
An operation plan formulating device formulating an operation plan of a plurality of vehicles in a service for a vehicle ([0159] a MoD fleet controller 702 in communication with one or more vehicles 704a -704k. [0067] assigning one or more travel requests R (e.g. online travel requests) to one or more vehicles in a fleet of vehicles)to transport a passenger from a boarding location to a drop-off location([0071] Each travel request includes at least a time of request, a pickup location and a drop-off location), the operation plan formulating device comprising:
and a calculation unit configured to calculate an operation plan ([0088]solving an ILP to compute the assignment of vehicles to trips (and ideally to compute optimal assignment of vehicles to trips));
and one or more second objective functions for evaluating a degree of achievement of a target event([0086] The cost C of an assignment is defined as the sum of the total travel delays over all assigned requests and passengers. [0099] Once feasibility is determined, the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. This optimization is formalized as an ILP, initialized with a greedy assignment (or any other technique well-known to those of ordinary skill in the art that quickly provides a feasible solution) obtained directly from an RTV-graph. To compute the greedy assignment (Σ.sub.greedy,) trips are assigned to vehicles iteratively in decreasing size of the trip and increasing cost (e.g. sum of travel delays). The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost),
wherein the restriction conditions include that a destination of the vehicle is one place([0117] Two types of constraints are included. Line 3 in Table 3 below imposes that each vehicle is assigned to one trip at most).
While Alonso-Mora teaches [0159] a MoD fleet controller 702 in communication with one or more vehicles 704a -704k. [0067] assigning one or more travel requests R (e.g. online travel requests) to one or more vehicles in a fleet of vehicles, where ([0071] Each travel request includes at least a time of request, a pickup location and a drop-off location. Paragraph [0088] discloses solving an ILP to compute the assignment of vehicles to trips (and ideally to compute optimal assignment of vehicles to trips). [0086] The cost C of an assignment is defined as the sum of the total travel delays over all assigned requests and passengers. [0099] Once feasibility is determined, the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. This optimization is formalized as an ILP, initialized with a greedy assignment (or any other technique well-known to those of ordinary skill in the art that quickly provides a feasible solution) obtained directly from an RTV-graph. To compute the greedy assignment (Σ.sub.greedy,) trips are assigned to vehicles iteratively in decreasing size of the trip and increasing cost (e.g. sum of travel delays). The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost. [0117] Two types of constraints are included. Line 3 in Table 3 below imposes that each vehicle is assigned to one trip at most. Alonso-Mora does not explicitly teach the following. However, analogues reference, in the field pf optimization of operational plans for vehicle fleets, Yuta teaches:
an acquisition unit configured to acquire an energy function equation ([037] The input unit 1 receives each objective function and each constraint term in a formula representing the energy of a combinatorial optimization problem ) defined as a sum of objective functions relating to the operation plan of the vehicles([0012] This equation expresses the weighted sum of one or more objective functions and one or more constraint terms as energy);
minimizing a value of the energy function equation as an optimal solution([010]The Ising model and the QUBO energy function are input to a solver that performs simulated annealing. The solver uses simulated annealing to find the state of each spin that corresponds to the solution to the combinatorial optimization problem),
wherein the energy function equation obtains a weighted sum of the one or more first objective functions and the one or more second objective functions( [0031]A computer-readable recording medium according to the present invention provides a computer with an input means for inputting each constraint term included in an expression expressing energy in a combinatorial optimization problem. [0012] The energy function of QUBO is obtained by transforming the expression expressing energy in a combinatorial optimization problem. This equation expresses the weighted sum of one or more objective functions and one or more constraint terms as energy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Alonso-Mora to incorporate the teachings of Yuta to include an acquisition unit configured to acquire an energy function equation, minimizing a value of the energy function equation as an optimal solution, and wherein the energy function equation obtains a weighted sum of the one or more first objective functions and the one or more second objective functions as part of the optimization problem taught by Alonso-Mora. Doing so would provide optimization efficiency by including a unified weighted energy function capable of being minimized to obtain an optimal solution.
While Alonso-Mora teaches [0159] a MoD fleet controller 702 in communication with one or more vehicles 704a -704k. [0067] assigning one or more travel requests R (e.g. online travel requests) to one or more vehicles in a fleet of vehicles, where [0071] Each travel request includes at least a time of request, a pickup location and a drop-off location. Paragraph [0088] discloses solving an ILP to compute the assignment of vehicles to trips (and ideally to compute optimal assignment of vehicles to trips). [0086] The cost C of an assignment is defined as the sum of the total travel delays over all assigned requests and passengers. [0099] Once feasibility is determined, the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. This optimization is formalized as an ILP, initialized with a greedy assignment (or any other technique well-known to those of ordinary skill in the art that quickly provides a feasible solution) obtained directly from an RTV-graph. To compute the greedy assignment (Σ.sub.greedy,) trips are assigned to vehicles iteratively in decreasing size of the trip and increasing cost (e.g. sum of travel delays). The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost. [0117] Two types of constraints are included. Line 3 in Table 3 below imposes that each vehicle is assigned to one trip at most. Alonso-Mora does not explicitly teach the following. However, analogues reference, in the field pf optimization of operational plans for vehicle fleets, Gkiotsalitis teaches:
wherein the objective functions include one or more first objective functions based on whether or not restriction conditions are satisfied ([0030] constraints are included in the objective function with the use of positive penalty terms)
and wherein a weighting assigned to the one or more first objective functions is larger than a weighting assigned to the one or more second objective functions with such a degree that an operation plan for which the restriction conditions are not satisfied is not selected([0030] The weights of the penalty terms have high values to ensure that priority is given to the satisfaction of constraints over the objective function minimization. Their values can be determined randomly with the only requirement being that they are high enough for ensuring that the violation of a constraint penalizes the objective function more than any potential increase of the objective function).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Alonso-Mora and Yuta to incorporate the teachings of Gkiotsalitis to include wherein the objective functions include one or more first objective functions based on whether or not restriction conditions are satisfied and wherein a weighting assigned to the one or more first objective functions is larger than a weighting assigned to the one or more second objective functions with such a degree that an operation plan for which the restriction conditions are not satisfied is not selected as part of the optimization problem taught by Alonso-Mora. Doing so would improve reliability of the vehicle-dispatch optimization process by ensuring that higher operational restriction is prioritized [0030].
Claim 4
Alonso-Mora teaches:
The operation plan formulating device according to claim 1, wherein the target event includes the passenger whose waiting time has been long being prioritized and going to pick up many passengers([0124]If a request is matched to a vehicle at any given iteration, its latest pickup time is reduced to the expected pickup time by that vehicle and the cost X.sub.ko of ignoring it is increased for subsequent iterations.[0089] the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost).
Claim 5
Alonso-Mora teaches:
The operation plan formulating device according to claim 1, wherein the target event includes shortening of a total required time until arrival at the boarding location at the time of going to pick up a passenger([0081] (i) for each request r, the waiting time ω.sub.r, given by the difference between a pickup time t.sub.r.sup.p and a request time t.sub.r.sup.r, must be below a maximum waiting time Ω, for example, 2 minutes; [0082] (ii) for each passenger or request r the total travel delay δ.sub.r=t.sub.r.sup.d−t.sub.r* must be lower than a maximum travel delay Δ, for example, 4 min. [0089] This route reduces (and ideally minimizes) a sum of delays (e.g. the sum of delays Σ.sub.r∈PvURvδ.sub.r) subject to one or more constraints Z (e.g. one, some (e.g. a subset) or all of: waiting time, delay, and capacity) ).
Claims 2, 3, and 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Alonso-Mora in view of Yuta in view of Gkiotsalitis, as applied in claim 1, and further in view of Colijn (US 20190179336 A1, hereinafter “Colijn”).
Claim 2
Alonso-Mora teaches:
The operation plan formulating device according to claim 1, wherein the operation plan defines which vehicle is heading toward a certain boarding location([0032] a method for controlling and continuously rerouting a fleet of vehicles based up on real-time ride requests, includes assigning specific vehicles from the fleet of vehicle to specific trips. [0071] Each travel request includes at least a time of request, a pickup location and a drop-off location.),
While Alonso-Mora teaches [0159] a MoD fleet controller 702 in communication with one or more vehicles 704a -704k. [0067] assigning one or more travel requests R (e.g. online travel requests) to one or more vehicles in a fleet of vehicles, where ([0071] Each travel request includes at least a time of request, a pickup location and a drop-off location. Paragraph [0088] discloses solving an ILP to compute the assignment of vehicles to trips (and ideally to compute optimal assignment of vehicles to trips). [0086] The cost C of an assignment is defined as the sum of the total travel delays over all assigned requests and passengers. [0099] Once feasibility is determined, the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. This optimization is formalized as an ILP, initialized with a greedy assignment (or any other technique well-known to those of ordinary skill in the art that quickly provides a feasible solution) obtained directly from an RTV-graph. To compute the greedy assignment (Σ.sub.greedy,) trips are assigned to vehicles iteratively in decreasing size of the trip and increasing cost (e.g. sum of travel delays). The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost. [0117] Two types of constraints are included. Line 3 in Table 3 below imposes that each vehicle is assigned to one trip at most. Alonso-Mora does not explicitly teach the following. However, analogues reference, in the field pf optimization of operational plans for vehicle fleets, Colijn teaches:
and which vehicle is heading toward a certain vehicle depot for a given boarding request ([0063] In addition, the dispatching system may use this information to assign the vehicle to the closest parking location available which meets the needs of that vehicle. [0075] the dispatching system 410 may assign vehicles based on proximity to the passenger's pick up location in time or distance, availability of vehicles, location of future expected trips for a vehicle relative to the passenger's destination, location of other users requesting trips to the same or nearby destinations (for ridesharing), etc. [0077] As such, the dispatching system 410 may assign vehicle 100 to parking location 280, and send instructions to vehicle 100, for instance using network 460, to proceed directly to parking location 280).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Alonso-Mora, Yuta, and Gkiotsalitis to incorporate the teachings of Colijn to include which vehicle is heading toward a certain vehicle depot for a given boarding request as part of the optimization problem taught by Alonso-Mora. Doing so would improve reduction of passenger waiting time and balancing of vehicle distribution across service areas.
Claim 3
While Alonso-Mora teaches [0159] a MoD fleet controller 702 in communication with one or more vehicles 704a -704k. [0067] assigning one or more travel requests R (e.g. online travel requests) to one or more vehicles in a fleet of vehicles, where ([0071] Each travel request includes at least a time of request, a pickup location and a drop-off location. Paragraph [0088] discloses solving an ILP to compute the assignment of vehicles to trips (and ideally to compute optimal assignment of vehicles to trips). [0086] The cost C of an assignment is defined as the sum of the total travel delays over all assigned requests and passengers. [0099] Once feasibility is determined, the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. This optimization is formalized as an ILP, initialized with a greedy assignment (or any other technique well-known to those of ordinary skill in the art that quickly provides a feasible solution) obtained directly from an RTV-graph. To compute the greedy assignment (Σ.sub.greedy,) trips are assigned to vehicles iteratively in decreasing size of the trip and increasing cost (e.g. sum of travel delays). The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost. [0117] Two types of constraints are included. Line 3 in Table 3 below imposes that each vehicle is assigned to one trip at most. Alonso-Mora does not explicitly teach the following. However, analogues reference Colijn teaches:
The operation plan formulating device according to claim 2, wherein the operation plan further defines an arrangement of one or more vehicle depots([0081] the dispatching system 410 may iterate through different arrangements of vehicles, for instance assigning one or more different vehicles to different parking locations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Alonso-Mora, Yuta, and Gkiotsalitis to incorporate the teachings of Colijn to include the operation plan further defines an arrangement of one or more vehicle depots as part of the optimization problem taught by Alonso-Mora. Doing so would improve reduction of passenger waiting time and balancing of vehicle distribution across service areas.
Claim 6
While Alonso-Mora teaches [0159] a MoD fleet controller 702 in communication with one or more vehicles 704a -704k. [0067] assigning one or more travel requests R (e.g. online travel requests) to one or more vehicles in a fleet of vehicles, where ([0071] Each travel request includes at least a time of request, a pickup location and a drop-off location. Paragraph [0088] discloses solving an ILP to compute the assignment of vehicles to trips (and ideally to compute optimal assignment of vehicles to trips). [0086] The cost C of an assignment is defined as the sum of the total travel delays over all assigned requests and passengers. [0099] Once feasibility is determined, the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. This optimization is formalized as an ILP, initialized with a greedy assignment (or any other technique well-known to those of ordinary skill in the art that quickly provides a feasible solution) obtained directly from an RTV-graph. To compute the greedy assignment (Σ.sub.greedy,) trips are assigned to vehicles iteratively in decreasing size of the trip and increasing cost (e.g. sum of travel delays). The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost. [0117] Two types of constraints are included. Line 3 in Table 3 below imposes that each vehicle is assigned to one trip at most. Alonso-Mora does not explicitly teach the following. However, analogues reference Colijn teaches:
The operation plan formulating device according to claim 3, wherein the target event includes placing many vehicles at a vehicle depot near a place at which an appearance frequency of the passenger is high([0031]the dispatching system may also control the number of vehicles in service. [0095] the dispatching system may send certain vehicles to the “staging pad” locations when demand is or is expected to be high in the area, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Alonso-Mora, Yuta, and Gkiotsalitis to incorporate the teachings of Colijn to include wherein the target event includes placing many vehicles at a vehicle depot near a place at which an appearance frequency of the passenger is high as part of the optimization problem taught by Alonso-Mora. Doing so would improve reduction of passenger waiting time and balancing of vehicle distribution across service areas.
Claim 7
While Alonso-Mora teaches [0159] a MoD fleet controller 702 in communication with one or more vehicles 704a -704k. [0067] assigning one or more travel requests R (e.g. online travel requests) to one or more vehicles in a fleet of vehicles, where ([0071] Each travel request includes at least a time of request, a pickup location and a drop-off location. Paragraph [0088] discloses solving an ILP to compute the assignment of vehicles to trips (and ideally to compute optimal assignment of vehicles to trips). [0086] The cost C of an assignment is defined as the sum of the total travel delays over all assigned requests and passengers. [0099] Once feasibility is determined, the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. This optimization is formalized as an ILP, initialized with a greedy assignment (or any other technique well-known to those of ordinary skill in the art that quickly provides a feasible solution) obtained directly from an RTV-graph. To compute the greedy assignment (Σ.sub.greedy,) trips are assigned to vehicles iteratively in decreasing size of the trip and increasing cost (e.g. sum of travel delays). The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost. [0117] Two types of constraints are included. Line 3 in Table 3 below imposes that each vehicle is assigned to one trip at most. Alonso-Mora does not explicitly teach the following. However, analogues reference Colijn teaches:
The operation plan formulating device according to claim 2, wherein the target event includes shortening of a total required time until arrival of a vehicle at a vehicle depot([0083] [0083] Each of these factors may be assigned a cost. For example, a cost may be assigned for each of a total time for all vehicles of the fleet to reach the assigned parking locations. [0087] The iterations may be differentiated by adjusting the assignments for one or more vehicles which contributed the greatest costs to the total costs of the previous iteration. The iterations may continue until the costs can no longer be reduced (i.e. the next iteration increases the overall cost) or until the total cost meets, for instance, is less than or equal to a threshold value. [0088] The iteration associated with the lowest total cost or that meets the threshold value may be selected and used to assign the vehicles to the parking locations [0096] a total cost for the at least one assignment is determined by determining a cost value for a plurality of factors including how quickly vehicles of the subset are able to reach respective assigned parking location locations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Alonso-Mora, Yuta, and Gkiotsalitis to incorporate the teachings of Colijn to include the target event includes shortening of a total required time until arrival of a vehicle at a vehicle depot as part of the optimization problem taught by Alonso-Mora. Doing so would improve reduction of passenger waiting time and balancing of vehicle distribution across service areas.
Claim 8
While Alonso-Mora teaches [0159] a MoD fleet controller 702 in communication with one or more vehicles 704a -704k. [0067] assigning one or more travel requests R (e.g. online travel requests) to one or more vehicles in a fleet of vehicles, where [0071] Each travel request includes at least a time of request, a pickup location and a drop-off location. Paragraph [0088] discloses solving an ILP to compute the assignment of vehicles to trips (and ideally to compute optimal assignment of vehicles to trips). [0086] The cost C of an assignment is defined as the sum of the total travel delays over all assigned requests and passengers. [0099] Once feasibility is determined, the assignment of vehicles to trips (and ideally the optimal assignment Σ.sub.optimum of vehicles to trips) is computed. This optimization is formalized as an ILP, initialized with a greedy assignment (or any other technique well-known to those of ordinary skill in the art that quickly provides a feasible solution) obtained directly from an RTV-graph. To compute the greedy assignment (Σ.sub.greedy,) trips are assigned to vehicles iteratively in decreasing size of the trip and increasing cost (e.g. sum of travel delays). The general concept is to increase (and ideally maximize) the number (i.e. amount) of requests served while reducing (and ideally, minimizing) the cost. [0117] Two types of constraints are included. Line 3 in Table 3 below imposes that each vehicle is assigned to one trip at most. Alonso-Mora does not explicitly teach the following. However, analogues reference Colijn teaches:
A vehicle dispatch management device comprising: the operation plan formulating device according to claim 1; and a plan instructing unit configured to transmit the operation plan calculated by the calculation unit to at least a vehicle ([0076] Once a vehicle, such as vehicle 100B, is assigned to a trip, the dispatching system 410 may send dispatch instructions to the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Alonso-Mora, Yuta, and Gkiotsalitis to incorporate the teachings of Colijn to include a plan instructing unit configured to transmit the operation plan calculated by the calculation unit to at least a vehicle as part of the optimization problem taught by Alonso-Mora. Doing so would improve reduction of passenger waiting time and balancing of vehicle distribution across service areas.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
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/REHAM K ABOUZAHRA/ Examiner, Art Unit 3625
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