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 02/07/2024 and 06/25/2026 were filed. The submission 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
This action is responsive to the amendment filed June 25, 2026. Claims 1–4, 6, 8, and 9 are amended, and claim 5 is canceled. Claims 1–4 and 6–9 are pending and are rejected.
Response to Amendment
The amendment filed June 25, 2026, in reply to the non-final Office action, has been entered and fully considered. Applicant amended independent claims 1 and 8 and dependent claims 2–4, 6, and 9, canceled claim 5, and left claim 7 unchanged. The changes and their effect on claim scope are set out below. Claim scope has been evaluated under the broadest reasonable interpretation consistent with the specification. MPEP 2111.
A. Independent Claim 1
Original claim 1 recited four steps: obtaining boarding/alighting information for each time slot for each of a plurality of boarding/alighting points; obtaining departure/arrival information for each passenger; determining at least one passenger movement flow vector for each time slot; and determining a movement path of a vehicle based on that vector. Amended claim 1 retains all four steps and changes the claim as follows.
(1) Source of the boarding/alighting information. The first obtaining step now requires that the boarding/alighting information be “counted by sensors of boarding/alighting point terminals respectively installed at the plurality of boarding/alighting points and received via a communication network from the boarding/alighting point terminals.” Original claim 1 placed no limit on how or where the information was acquired. The amendment narrows the claim to information counted at the boarding/alighting points themselves, by sensors of terminals installed at each point, and received over a communication network. No original claim recited this subject matter.
(2) Definition of the passenger movement flow vector. The vector is now defined as “having a direction representing a movement flow of passengers and a magnitude representing a number of passengers included in the movement flow.” Original claim 1 used the term without definition. Because the specification describes the vector in the same terms ([0028], [0063], [0071]), the definition makes express what the term already meant under the broadest reasonable interpretation, and it adds little substantive narrowing. Its practical effect is to supply antecedent basis for “the magnitude” relied on in the updating step.
(3) Magnitude-dependent updating, taken from canceled claim 5. Claim 1 now recites “updating the at least one passenger movement flow vector at a first time interval when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value, and at a second time interval different from the first time interval when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value.” This subject matter was previously recited in dependent claim 5, now canceled. It was not carried over verbatim. First, claim 5’s requirement of “updating the passenger movement flow vector for each time slot at predetermined time intervals” was deleted, so the amended claim no longer requires that the intervals be predetermined. Second, “based on a magnitude … being” became “when the magnitude … is.” Third, the plural “first time intervals” and “second time intervals” became “a first time interval” and “a second time interval.” Fourth, claim 5’s separate clause requiring that “the first time interval is different from the second time interval” was folded into the step as “a second time interval different from the first time interval.” As to this limitation alone, the amended language is somewhat broader than claim 5, because the intervals need not be predetermined. The claim requires only that the two intervals differ; it does not specify which is longer. The specification’s statement that the first time interval “may be set to be less than” the second ([0082]) describes a preferred embodiment and is not read into the claim. MPEP 2111.01(II).
(4) Re-determination of the movement path. Claim 1 now recites “re-determining the movement path of the vehicle based on the updated at least one passenger movement flow vector.” No original claim recited this step.
(5) Editorial changes. “[D]etermining a movement path of a vehicle” now reads “determining movement path of vehicle,” and the conjunction “and” now appears both before that step and before the final step. These changes do not affect claim scope. They are addressed in the claim objections below.
In sum, amended claim 1 is narrower than original claim 1, and it is of a scope not previously presented in any claim. It combines the subject matter of canceled claim 5, in modified form, with limitations (1), (2), and (4), none of which appeared in any original claim.
B. Independent Claim 8
Claim 8 is the apparatus counterpart of claim 1. Its memory and processor elements are unchanged. The operations the processor is “further configured to” perform were amended in the same five respects described for claim 1, recast in “obtain,” “determine,” “update,” and “re-determine” form. The analysis of claim 1 applies equally to claim 8.
C. Claim 9
Claim 9 now recites “[a] non-transitory computer-readable recording medium.” The amendment excludes transitory propagating signals from the claim, so claim 9 now falls within a statutory category of invention. See Subject Matter Eligibility of Computer Readable Media, 1351 Off. Gaz. Pat. Office 212 (Feb. 23, 2010). The amendment is supported by [0133], which describes only physical storage media. Claim 9 otherwise incorporates the amended method of claim 1. As explained in the rejection under 35 U.S.C. 101 below, the amendment resolves Step 1 only and does not affect the judicial-exception analysis.
D. Dependent Claims 2–4
Claims 2, 3, and 4 were amended only to change “the passenger movement flow vector for each time slot” to “the at least one passenger movement flow vector for each time slot,” conforming their antecedent basis to claim 1. These amendments do not change claim scope.
E. Dependent Claims 6 and 7
Claim 6 is identified as “Currently Amended,” but its text is identical to that of the previously presented claim 6. Applicant is reminded that each claim must carry the status identifier that accurately reflects whether it has been changed, and that any change must be shown by underlining and strike-through. 37 CFR 1.121(c). Claim 6 also still recites “the passenger movement flow vector for each time slot,” which was not conformed to amended claim 1. See the claim objections below. Claim 7 is identified as “Original” and is unchanged.
F. Written Description Support
Applicant identifies support for the amendments at [0028], [0032]–[0033], [0043], [0049]–[0051], [0054], [0063], [0071], and [0081]–[0084]. The examiner has reviewed the application as filed and finds the amendments supported, as follows.
(1) Terminals, sensors, and network. Boarding/alighting point terminals 500 are installed at the respective boarding/alighting points and transmit traffic information to the traffic information providing apparatus ([0049]–[0051]; FIG. 1), over communication network 300 ([0032]–[0033], [0043]). The terminal performs counting functions, including counting the number of people waiting at the point, the number of people who want to board, and the number of alighting passengers, and “may include a device such as a sensor necessary for counting the number of people” ([0054]). Under the broadest reasonable interpretation, “boarding/alighting information” encompasses these counts.
(2) Vector direction and magnitude: [0028], [0063], [0071].
(3) Magnitude-dependent update intervals: original claim 5, which is part of the original disclosure, and [0080]–[0083].
(4) Re-determination based on the updated vector: [0084] (determining a movement path of a vehicle “based on an updated passenger movement flow vector”); see also [0093].
Accordingly, the amendments do not introduce new matter, and no rejection under 35 U.S.C. 112(a) is made.
Claim Objections
Claims 1, 6, and 8 are objected to because of the following informalities:
(a) In claim 1, “determining movement path of vehicle” should read “determining the movement path of the vehicle,” consistent with the antecedent “a movement path of a vehicle” in the preamble. In claim 8, “determine movement path of vehicle” should likewise read “determine the movement path of the vehicle.”
(b) In claim 1, the word “and” following “included in the movement flow;” should be deleted, because the step that follows is not the final step of the claim. In claim 8, the word “and” following “included in the movement flow,” should likewise be deleted.
(c) In claim 6, “the passenger movement flow vector for each time slot” should read “the at least one passenger movement flow vector for each time slot,” consistent with claim 1 and amended claims 2–4.
Appropriate correction is required.
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 therefore, subject to the conditions and requirements of this title.
Claims 1–4 and 6–9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more. The claims recite collecting passenger boarding/alighting counts, deriving passenger trips from those counts, tallying directional passenger flows per time period, planning a vehicle route from the tallied flows, re-tallying the flows on a schedule that depends on the size of each flow, and re-planning the route. These steps are mental processes. This judicial exception is not integrated into a practical application because the additional elements amount to (i) mere data gathering from generic sensors and terminals over a generic network, (ii) generic computer components used as tools to perform the abstract idea, and (iii) a field-of-use limitation to vehicles. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, for the same reasons and because those additional elements are well-understood, routine, and conventional, as evidenced below. The detailed analysis follows.
I. Claim 1
Step 1. Claim 1 recites a method, i.e., a process, which is a statutory category of invention (MPEP 2106.03).
Step 2A, Prong One – the claim recites an abstract idea. Claim 1 recites the following limitations, which fall within the mental processes grouping of abstract ideas, i.e., concepts performed in the human mind or with pen and paper, including observation, evaluation, judgment, and opinion (MPEP 2106.04(a)(2)(III); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372–73 (Fed. Cir. 2011)). The limitations reciting how the boarding/alighting information is counted and received are not included in this grouping; they are additional elements addressed under Prong Two and Step 2B below.
(a) “obtaining departure/arrival information for each passenger based on the boarding/alighting information for each time slot.” Inferring, from records of where passengers boarded and alighted during a period, where each passenger’s trip began and ended is an evaluation a person can perform by inspecting the records.
(b) “determining at least one passenger movement flow vector for each time slot based on the departure/arrival information for each passenger, the at least one passenger movement flow vector having a direction representing a movement flow of passengers and a magnitude representing a number of passengers included in the movement flow.” Counting how many passengers traveled from one place to another during a period, and noting the direction of that travel, is observation and evaluation; a person can record such a tally on paper as an arrow whose direction indicates the flow and whose label or size indicates the count. To the extent the claimed “vector,” a quantity defined by magnitude and direction, is characterized as a mathematical concept (MPEP 2106.04(a)(2)(I)), the limitation also recites an abstract idea under that grouping.
(c) “determining movement path of vehicle based on the at least one passenger movement flow vector for each time slot.” Choosing a route that serves the observed passenger flows is a judgment of the type a transit planner makes by reviewing demand tallies and a map.
(d) “updating the at least one passenger movement flow vector at a first time interval when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value, and at a second time interval different from the first time interval when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value.” Comparing a count against two threshold values and deciding, based on the comparison, how often to re-tally that flow is evaluation and judgment; a person can review some tallies every half hour and others every hour according to their size.
(e) “re-determining the movement path of the vehicle based on the updated at least one passenger movement flow vector.” This repeats the judgment of limitation (c) on updated tallies.
Under the broadest reasonable interpretation, claim 1 is not limited to any number of boarding/alighting points, time slots, passengers, or routes, or to any speed of processing, that would place limitations (a)–(e) beyond practical performance in the human mind or with pen and paper. The claim encompasses, for example, a small number of points and time slots and a single route, for which each recited step can practically be performed mentally. Limitations (a)–(e) therefore are not limitations that cannot practically be performed in the human mind (MPEP 2106.04(a)(2)(III)(A)). That the steps may be performed on a computer (see claim 8) does not change this conclusion; a claim that requires a computer may still recite a mental process (MPEP 2106.04(a)(2)(III)(C)). Where limitations fall within more than one grouping, they are considered together as a single abstract idea for the remainder of the analysis (MPEP 2106.04, subsection II.B).
Step 2A, Prong Two – the abstract idea is not integrated into a practical application. The additional elements of claim 1 are: (1) “obtaining boarding/alighting information for each time slot for each of a plurality of boarding/alighting points”; (2) “the boarding/alighting information being counted by sensors of boarding/alighting point terminals respectively installed at the plurality of boarding/alighting points”; and (3) “received via a communication network from the boarding/alighting point terminals.”
Additional elements (1)–(3) together amount to gathering the input data on which the abstract idea operates. Obtaining data for use in a mental process is insignificant extra-solution activity (MPEP 2106.05(g); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353–55 (Fed. Cir. 2016) (collecting information, including from multiple sources, and analyzing it is within the realm of abstract ideas)).
Additional element (2) does not change this. It identifies only the source of the data, namely information “counted by sensors” of terminals at the boarding/alighting points. The claim recites no particular sensor, no particular configuration or placement of sensors relative to the terminal or the point, no particular counting technique, and no change to how any sensor or terminal operates. The sensors and terminals are recited only by function, at a high level of generality, and therefore are not a “particular machine” that integrates the abstract idea (MPEP 2106.05(b)); any sensor at any boarding/alighting point would satisfy the limitation. Compare Thales Visionix Inc. v. United States, 850 F.3d 1343, 1348–49 (Fed. Cir. 2017) (claims specifying an unconventional configuration of inertial sensors that improved measurement accuracy). Claim 1 specifies no sensor configuration at all.
Additional element (3), receiving the counts over “a communication network,” is generic data transmission and likewise is extra-solution activity (MPEP 2106.05(g)). See ChargePoint, Inc. v. SemaConnect, Inc., 920 F.3d 759, 768–70 (Fed. Cir. 2019) (networking generic physical stations so that they communicate data with a remote server did not render claims directed to that communication eligible). The networked boarding/alighting point terminals of claim 1 serve the same role: they convey data to where the abstract analysis is performed.
The recitation that the path is a “movement path of a vehicle” generally links the abstract idea to a particular field of use (vehicle routing) and does not integrate it into a practical application (MPEP 2106.05(h); Parker v. Flook, 437 U.S. 584, 590 (1978)). Claim 1 does not recite using the determined or re-determined movement path for anything: it does not recite dispatching a vehicle along the path, transmitting the path to a vehicle, or controlling any vehicle to travel the path. The claim ends with the abstract determination itself, and therefore does not apply or use the judicial exception in a manner that imposes a meaningful limit on it (MPEP 2106.04(d), subsection I; MPEP 2106.05(e)).
Nor does claim 1 reflect an improvement in the functioning of a computer or in any other technology or technical field (MPEP 2106.05(a)). The threshold-dependent update schedule of limitation (d) and the re-determination of limitation (e) concern when passenger flows are re-tallied and how a route is re-planned, which are features of the abstract planning process itself. The claim recites no change to how the sensors count, how the terminals operate, how data is transmitted over the network, or how any computer processes data. An improvement in the abstract idea itself, however beneficial, is not an improvement in technology. SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167–68 (Fed. Cir. 2018); MPEP 2106.05(a), subsection II. See also Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025) (claims to generating schedules and network maps, and dynamically updating them in response to changing real-time inputs, held ineligible where the claims did not recite how any technology was improved).
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not integrate the abstract idea into a practical application. Claim 1 is directed to the abstract idea.
Step 2B – the claim does not recite significantly more. As explained above, additional element (1) is mere data gathering and additional element (3) is the transmission and receipt of data over a network. Receiving and transmitting data over a network is well-understood, routine, and conventional. MPEP 2106.05(d), subsection II (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014)).
Additional element (2), boarding/alighting point terminals having sensors that count people and transmit data over a network, was found at Step 2A to be insignificant extra-solution activity. It is therefore re-evaluated at Step 2B (MPEP 2106.05(g)) and is also well-understood, routine, and conventional. In accordance with Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), and MPEP 2106.07(a), subsection III, this determination is supported by the following:
(i) Applicant’s specification (MPEP 2106.07(a), subsection III(A)), which describes these elements only generically, in a manner indicating that they are sufficiently well known that their particulars need not be described: “The boarding/alighting point terminal 500 may include a device such as a sensor necessary for counting the number of people” ([0054]); the terminal “may refer to a communication terminal capable of transmitting and receiving data to and from other devices in a wired/wireless communication environment” ([0050]); and the network may be “a wired network such as a local area network (LAN), a wide area network (WAN) … or a wireless network such as a wireless LAN (WLAN) … but the scope of the present disclosure is not limited thereto” ([0043]). No particular sensor technology, counting method, or terminal architecture is disclosed.
(ii) US 10,083,609 B2 (Dannat et al.), as corroborating evidence (MPEP 2106.07(a), subsection III(C)). Dannat claims obtaining passenger location information from a passenger system having “a station fixed data collection terminal” (claim 20), and describes such terminals as installed at stations with sensor devices (a camera and beacon devices) for detecting passengers, which communicate with a manager engine over a network (description of FIGS. 4A and 4D). Like applicant’s specification, Dannat recites the station terminal and its sensors only at a high level of generality, without describing any particular sensor technology or counting technique, which is consistent with such terminals having been well known in the art before the effective filing date.
The specification’s generic treatment of these elements (item (i)) is consistent with their being well-understood, routine, and conventional, and Dannat (item (ii)) corroborates that determination.
Considered as an ordered combination, the additional elements add nothing beyond what they provide individually: data is gathered from sensors at the boarding/alighting points, transmitted over a network, and analyzed according to the abstract idea. This is the conventional sequence of collecting, analyzing, and acting on information. Elec. Power Grp., 830 F.3d at 1355–56. Claim 1 is not patent eligible.
II. Claim 8
Step 1. Claim 8 recites an apparatus, i.e., a machine, which is a statutory category of invention.
Step 2A, Prong One. Claim 8 recites a processor “configured to” perform operations corresponding to limitations (a)–(e) of claim 1. Those operations recite the same abstract idea, for the reasons given for claim 1.
Step 2A, Prong Two. In addition to additional elements (1)–(3) of claim 1, which are analyzed above and apply equally here, claim 8 recites “a memory storing at least one program” and “a processor configured to execute the at least one program to perform an operation.” These are generic computer components recited at a high level of generality as tools for performing the abstract idea. Using a computer as a tool to perform an abstract idea does not integrate it into a practical application. MPEP 2106.05(f); Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 223–24 (2014); In re TLI Commc’ns LLC Patent Litig., 823 F.3d 607, 612–13 (Fed. Cir. 2016). As with claim 1, claim 8 does not configure the processor to use the re-determined path to dispatch or control any vehicle.
Step 2B. The memory and processor are generic, as confirmed by the specification, which describes the processor as implementable by “at least one of application-specific integrated circuits (ASICs), digital signal processors (DSPs) … microprocessors, and other electrical units for performing functions” ([0129]) and the memory as conventional storage such as “random-access memory (RAM) … read-only memory (ROM) … a hard disk drive (HDD), a solid-state drive (SSD), or flash memory” ([0126]). Mere instructions to implement an abstract idea on a generic computer cannot provide an inventive concept. Alice, 573 U.S. at 223–26. The remaining additional elements are well-understood, routine, and conventional for the reasons, and on the evidence, given for claim 1. Claim 8 is not patent eligible.
III. Claim 9
Step 1. Claim 9 recites a non-transitory computer-readable recording medium, i.e., a manufacture, which is a statutory category of invention.
Steps 2A and 2B. Claim 9 incorporates the method of claim 1 and adds only the non-transitory recording medium on which the program is recorded. A generic storage medium storing instructions to perform an abstract idea is a mere instruction to apply the exception using a generic computer component (MPEP 2106.05(f)). The specification confirms the medium is conventional ([0133], listing, e.g., a hard disk, floppy disk, CD-ROM, DVD, ROM, RAM, and flash memory). For these reasons and those given for claim 1, claim 9 is not patent eligible.
IV. Dependent Claims 2–4, 6, and 7
Each dependent claim has been considered individually and in combination with the claims from which it depends. None adds an element that integrates the abstract idea into a practical application or amounts to significantly more.
Claim 2 recites that the magnitude for a time slot is determined “based on the number of passengers who board at the first boarding/alighting point and alight at the second boarding/alighting point during the certain time slot,” and that the direction is determined “based on location information of the first boarding/alighting point and location information of the second boarding/alighting point.” Counting passengers between two points and noting the direction from one location to the other further recite the mental process of claim 1 (and, to the extent computed from location data, a mathematical concept). Claim 2 adds no further additional element.
Claim 3 recites generating a boarding cluster “by clustering a plurality of boarding/alighting points included in a first region where a large number of boarding passengers are concentrated during a certain time slot,” generating a corresponding alighting cluster, and determining the flow vector “based on the boarding cluster and the alighting cluster.” Grouping nearby stops according to where passengers are observed to concentrate, and tallying the flow between the groups, is observation, evaluation, and judgment that can be performed by inspecting a map and the tallies. Claim 3 recites no clustering algorithm or technical implementation, and adds no further additional element.
Claim 4 recites generating pairs of clusters, determining a flow vector for each pair, “obtaining information about the number of operable vehicles,” and “allocating vehicles to pairs of clusters of which magnitudes of passenger movement flow vectors are greater than or equal to a preset value, considering the information about the number of operable vehicles.” Pairing clusters, tallying their flows, and deciding which pairs are assigned vehicles by comparing the flows to a preset value in view of the size of the fleet are further mental steps of planning. “Obtaining information about the number of operable vehicles” is mere data gathering (MPEP 2106.05(g)) and, at Step 2B, well-understood, routine, and conventional receipt of information (MPEP 2106.05(d), subsection II). The claimed “allocating” is not tied to any physical act implementing it. Claim 4 does not recite dispatching any vehicle, transmitting any assignment to a vehicle or operator, or causing any vehicle to travel to or between the clusters. As claimed, the allocation is an assignment within a plan, which is a further step of the mental process of claim 1.
Claim 6 recites determining the movement path “further considering … at least one of a boarding period for each passenger and a waiting period for each passenger.” Taking additional passenger time factors into account when choosing a route is further evaluation and judgment within the abstract idea. Claim 6 adds no further additional element.
Claim 7 recites “identifying a starting point, intermediate points, and a destination” from the movement path and “determining a return path such that, after the vehicle arrives at the destination, the vehicle returns to the starting point without passing through the intermediate points.” Identifying points on a route and selecting a return route that omits the intermediate points is further route-planning judgment. Claim 7 adds no further additional element and recites determining the return path, not causing any vehicle to travel it.
Accordingly, claims 1–4 and 6–9 are rejected under 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; and (4) considering objective evidence present in the application indicating obviousness or nonobviousness. Graham v. John Deere Co., 383 U.S. 1 (1966). The level of ordinary skill in the art is reflected by the prior art of record. Okajima v. Bourdeau, 261 F.3d 1350, 1355 (Fed. Cir. 2001).
Grounds of Rejection
Claims 1–4 and 6–9 are rejected under 35 U.S.C. 103 as being unpatentable over Xiaolei (CN 108053062 A) in view of Gkiotsalitis et al. (US 2018/0268705 A1), Koga (JP 2014-010493 A), Zhao (CN 105868861 A), and Nakamura (US 10,096,241 B2).
The analysis of claims 1, 2, 8, and 9 is set forth first, followed by claims 3, 4, 6, and 7.
Regarding Claim 1,
Disclosure by Xiaolei (CN 108053062 A)
Xiaolei teaches:
A method of determining a movement path of a vehicle
See at least:
“Finally, design a kind of heuritic approach based on genetic manipulation and solve the Optimized model, according to the target optimized, Different types of customization public bus network set is generated, reference is provided for actual railroad embankment.” (Xiaolei, p. 6)
Rationale:
Xiaolei teaches a method that generates customized bus routes. The machine translation renders the term for a bus route as “public bus network” or “circuit.” Each generated route is the ordered path a customized bus travels between stations, i.e., a movement path of a vehicle (the bus). This teaching is express.
considering a movement flow of passengers,
See at least:
“4th step, on the basis of trip requirements extraction, consider gauze demand coverage rate and passenger's travel time into This structure customizes public bus network Optimized model.” (Xiaolei, p. 6)
Rationale:
Xiaolei constructs its route optimization model on the basis of extracted passenger trip requirements and the demand coverage of the line network, so the routes are generated considering where and how passengers travel, i.e., a movement flow of passengers. This teaching is express.
the method comprising:
See at least:
“The realization of the present invention includes following Step.” (Xiaolei, p. 5)
Rationale:
Xiaolei describes its invention as a method implemented as a sequence of steps (first through fifth steps), corresponding to the open-ended transitional phrase introducing the recited steps. This teaching is express.
obtaining boarding/alighting information for each time slot
See at least:
“In the present invention, monthly to get on or off the bus 5 times in same time period and identical platform and above for standard, system It counts in passenger's whole day period (using a hour as section) in the frequency of getting on or off the bus of all bus platforms, determines customization public transport demand OD.” (Xiaolei, pp. 6–7)
Rationale:
Xiaolei counts, for every bus platform (stop), the frequency of passengers getting on or off the bus “using a hour as section,” i.e., in one-hour time slots over the whole day. The per-stop, per-hour boarding and alighting counts are boarding/alighting information for each time slot. This teaching is express.
for each of a plurality of boarding/alighting points,
See at least:
“Based on Bus IC Card Data critical field includes card number, charge time, circuit volume Number, car number, website of getting on the bus (urban), get-off stop (urban)” (Xiaolei, p. 6)
Rationale:
Xiaolei’s IC-card records identify, for each trip, the stop at which the passenger got on (“website of getting on the bus”) and the stop at which the passenger got off (“get-off stop”). The machine translation renders the term for a bus stop as “website” or “platform.” Because the counts are taken at “all bus platforms” (pp. 6–7), the information is obtained for each of a plurality of boarding/alighting points. This teaching is express.
obtaining departure/arrival information for each passenger
See at least:
“In the case of IC card data recorded passenger getting on/off website, The origin and destination (OD) of passenger can directly be extracted; In the case of IC card data do not record passenger getting on/off website, GPS need to be combined Data extract passenger getting on/off website.” (Xiaolei, p. 6)
“By this step, the beginning-of-line that goes out of user, terminal and temporal information can be extracted.” (Xiaolei, p. 6)
Rationale:
Xiaolei extracts each passenger’s origin and destination (OD) and the associated trip time from the passenger’s recorded getting-on and getting-off stops. Origin-destination information for each passenger is departure/arrival information for each passenger. This teaching is express.
determining at least one passenger movement flow vector for each time slot
See at least:
“Make P={P1,P2,…,PN} It is the customization public transport candidate stations extracted in second step Point, any two website Pi To Pj Between passenger flow demand can be represented respectively with matrix TD and TC with hourage” (Xiaolei, p. 7)
Rationale:
Xiaolei represents the passenger flow demand between any two candidate stations Pi and Pj as entries of matrix TD, built from demand ODs determined per one-hour section (pp. 6–7). Each entry TDij is, expressly, a count of passengers travelling from a specific station to a specific station. Claim 1 itself defines the passenger movement flow vector by its direction (representing the movement flow) and its magnitude (representing the number of passengers), and TDij carries both. To the extent the claimed “vector” is read to require more than a directed passenger count, representing each directed OD count as a vector whose direction runs from station i to station j and whose magnitude is the passenger count would have been obvious to a PHOSITA: the direction and the count are both already fixed by TDij, and expressing a directed quantity as a vector is an elementary, predictable mathematical representation. Likewise, forming these quantities for each hourly section is the direct use of Xiaolei’s hour-segmented OD data, and customized bus serves passengers whose “travel time are similar” (p. 5).
based on the departure/arrival information for each passenger,
See at least:
“Passenger's trip terminus that the first step extracts is corresponded to the candidate stations in second step after Cluster-Fusion by the 3rd step Point has thus obtained the volume of the flow of passengers of each candidate's website.” (Xiaolei, p. 7)
Rationale:
Xiaolei maps the extracted per-passenger trip origins and destinations (termini) to the clustered candidate stations to obtain the passenger flows that populate TD. The flow quantities are thus determined based on the per-passenger departure/arrival information. This teaching is express.
the at least one passenger movement flow vector having a direction representing a movement flow of passengers
See at least:
“Wherein, TDij It is vacancies of the website i to website j, the denominator in formula (1) represents whole trips of survey region Demand.” (Xiaolei, p. 7)
Rationale:
TDij is defined “of the website i to website j,” i.e., from station i to station j. Read in context, “vacancies of the website i to website j” denotes the passenger demand from station i to station j: Xiaolei describes the entries of TD as the “passenger flow demand” between stations Pi and Pj (p. 7) and states that the sum of the TD entries “represents whole trips of survey region Demand” (p. 7). That the flow runs from i to j is express. For the reasons given for the preceding limitation, characterizing that directed flow as the direction of a vector representing the movement flow of passengers is, at minimum, obvious to a PHOSITA.
and a magnitude representing a number of passengers included in the movement flow; and
See at least:
“In this example, since website is excessive, 70 demand OD of passenger flow maximum are filtered out to carrying out example.” (Xiaolei, p. 9)
Rationale:
Xiaolei ranks OD pairs by the size of their passenger flow and selects the 70 with the largest flow, which presupposes that each OD flow has a size. TDij is that size, the number of persons in the flow from station i to station j (p. 7). That the flow quantity is a passenger count is express; treating it as the magnitude of the vector is obvious for the reasons given above.
determining movement path of vehicle
See at least:
“Define R={R1,R2,..,Rn} It is to meet one group of sets of lines of model constraints, R Middle kth line definitions are Rk=R1kR2k…Rik…Rnk, wherein Rik For website sequence number described in P.” (Xiaolei, p. 7)
Rationale:
Each generated line Rk is an ordered sequence of station numbers (R1k R2k … Rnk) that the customized bus serves, i.e., the path along which the vehicle moves. This teaching is express.
based on the at least one passenger movement flow vector for each time slot;
See at least:
“TD (Ri) represent circuit Ri Aggregate demand; The molecule of formula (1) represents whole demands of candidate line collection R.” (Xiaolei, p. 7)
Rationale:
Xiaolei’s first objective function selects the route set R by maximizing the share of total TD demand that R serves, so the movement path is determined based on the passenger flow quantities in TD. That the path is determined from TD is express; determining it from the hourly TD for each time slot is obvious for the reasons given above.
Claim Limitations Not Explicitly Disclosed by Xiaolei
Xiaolei does not explicitly teach the following claim limitations. As to the limitation “based on the boarding/alighting information for each time slot,” Xiaolei derives its departure/arrival information from its own IC-card and GPS records, not from boarding/alighting information counted by sensors of boarding/alighting point terminals as recited in the antecedent limitations; that limitation is therefore treated below.
the boarding/alighting information being counted by sensors of boarding/alighting point terminals
respectively installed at the plurality of boarding/alighting points
and received via a communication network from the boarding/alighting point terminals;
based on the boarding/alighting information for each time slot;
updating the at least one passenger movement flow vector at a first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
and at a second time interval different from the first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value; and
re-determining the movement path of the vehicle
based on the updated at least one passenger movement flow vector.
Disclosure by Gkiotsalitis (US 2018/0268705 A1)
Gkiotsalitis teaches:
updating the at least one passenger movement flow vector at a first time interval
See at least:
“In performing demand-responsive automated fleet dispatching, the input database 101 provides updated spatio-temporal demand data to the DRAFD 102 at (1). At (2), the VLG 103 provides a set of virtual lines to the DRAFD 102. At (3), the DRAFD introduces on-demand virtual lines as new service lines and allocates vehicles to those lines. At (4), the DRAFD modifies the allocation of vehicles to the original lines in order to reduce operations costs through superior demand matching.” (Gkiotsalitis, [0026])
“The allocation of available vehicles to the original lines and virtual lines can be updated every time a significant demand variation is observed. For example, in public transport systems and logistics operations, a one-day period can be split into three to six time periods within which observed demand is relatively homogeneous and the allocation of available vehicles can be updated as one such time period transitions to another.” (Gkiotsalitis, [0025])
Rationale:
Gkiotsalitis expressly stores point-to-point demand “and variation of such demand over time” ([0023]), expressly supplies “updated spatio-temporal demand data” to the dispatcher ([0026]), and expressly performs its demand-responsive updating on a recurring basis, as each of three to six daily demand periods transitions to the next ([0025]). Gkiotsalitis does not use the term “passenger movement flow vector,” and [0025] itself speaks of updating the vehicle allocation. Applying Gkiotsalitis’s recurring updating of point-to-point demand to Xiaolei’s point-to-point passenger flow quantities TDij, so that those quantities are refreshed at a recurring (first) time interval, is PHOSITA-obvious for the reasons stated in the motivation below.
re-determining the movement path of the vehicle
See at least:
“The aforementioned method can be reapplied in a demand-responsive manner (e.g., when spatio-temporal demand changes significantly).” (Gkiotsalitis, [0021])
Rationale:
Gkiotsalitis teaches that its route and line optimization, which ends by “dispatching vehicles into different routes and lines” ([0021]), is reapplied when spatio-temporal demand changes. Reapplying the route optimization is re-determining the movement path of the vehicle. This teaching is express.
based on the updated at least one passenger movement flow vector.
See at least:
“In performing demand-responsive automated fleet dispatching, the input database 101 provides updated spatio-temporal demand data to the DRAFD 102 at (1). At (2), the VLG 103 provides a set of virtual lines to the DRAFD 102. At (3), the DRAFD introduces on-demand virtual lines as new service lines and allocates vehicles to those lines. At (4), the DRAFD modifies the allocation of vehicles to the original lines in order to reduce operations costs through superior demand matching.” (Gkiotsalitis, [0026])
Rationale:
The reapplied optimization operates on “updated spatio-temporal demand data” supplied by the input database, and the dispatcher modifies the vehicle allocation to the lines accordingly. As applied to Xiaolei, the re-determination is based on the updated passenger movement flow vectors. This teaching is express as to re-optimization on updated demand.
Motivation to Combine Xiaolei and Gkiotsalitis
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei and Gkiotsalitis before them, to update Xiaolei’s time-slot passenger flow quantities on a recurring schedule and to re-determine Xiaolei’s bus routes based on the updated quantities, as taught by Gkiotsalitis. Both references are directed to demand-responsive generation of bus routes from origin-destination demand between stops, so the teachings are technically compatible and complementary. Xiaolei describes customized bus as a demand-responsive service (“Demand response formula Public Transport Service pattern,” p. 5) and states that planners adapt the generated routes “according to different situations” (p. 9), but it generates its routes from one month of data (p. 9). Gkiotsalitis identifies the shortcoming of static planning, that it “hinders the operation of a demand-responsive route optimization where … the original lines are re-adjusted for matching the demand changes over time across the service network” ([0016]), and teaches refreshing point-to-point demand ([0023], [0026]) and reapplying the optimization on updated demand to “satisfy a time-varying demand” ([0022]) and to “reduce operations costs through superior demand matching” ([0026]). A PHOSITA would have applied Gkiotsalitis’s recurring update and re-optimization to Xiaolei’s method to keep the routes matched to current passenger flows, which serves the demand coverage and efficiency objectives of Xiaolei’s own model (p. 6). This is the use of a known technique to improve a similar method in the same way, with predictable results. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417 (2007); MPEP 2143(I)(C).
Claim Limitations Not Explicitly Disclosed by the Combination of Xiaolei and Gkiotsalitis
After combining the teachings of Xiaolei and Gkiotsalitis, the following claim limitations are not explicitly disclosed:
the boarding/alighting information being counted by sensors of boarding/alighting point terminals
respectively installed at the plurality of boarding/alighting points
and received via a communication network from the boarding/alighting point terminals;
based on the boarding/alighting information for each time slot;
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
and at a second time interval different from the first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value; and
Disclosure by Koga (JP 2014-010493 A)
Koga teaches:
the boarding/alighting information being counted by sensors of boarding/alighting point terminals
See at least:
“Further, the bus stop device 20 analyzes the video taken by the camera 22 to determine the number of passengers (number of passengers) who get on the bus stopped at the bus stop and the number of passengers who get off the stopped bus (number of passengers getting off). Count each.” (Koga, p. 9)
Rationale:
Koga’s bus stop device 20 includes camera 22, which captures “how a person gets on and off the bus” (p. 8), and the device analyzes the camera video to count the number of passengers who get on and the number who get off the bus at the stop. The camera is a sensor, the bus stop device is a boarding/alighting point terminal, and the boarding and alighting counts are boarding/alighting information counted by that sensor. This teaching is express.
respectively installed at the plurality of boarding/alighting points
See at least:
“The bus stop information providing system 10 includes a bus stop device 20 installed at each bus stop 3 and a central device 40 installed at an information center for managing bus operations.” (Koga, p. 7)
Rationale:
Koga installs a bus stop device 20 “at each bus stop 3,” i.e., the terminals are respectively installed at the plurality of boarding/alighting points. This teaching is express.
and received via a communication network from the boarding/alighting point terminals;
See at least:
“When it is detected by video analysis that the bus has departed from the bus stop, the bus position information including the bus system number, the number of passengers, the number of passengers getting off, and the identification information of the bus stop is sent to the central device 40 in the mesh network. 12 through.” (Koga, p. 9)
Rationale:
After the bus departs, each bus stop device sends the boarding count, the alighting count, and the stop’s identification information to central device 40 over mesh network 12, which is formed by wirelessly connecting the access points of the bus stop devices (p. 8). The mesh network is a communication network, and central device 40 receives the counts from the bus stop devices through it. This teaching is express.
Motivation to Combine Xiaolei, Gkiotsalitis, and Koga
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, and Koga before them, to obtain the boarding/alighting information of the combination from camera sensors of bus stop devices installed at the respective stops, which count the boarding and alighting passengers and report the counts over a communication network to a central device, as taught by Koga. Xiaolei identifies its problem as the inadequacy of existing demand data collection: demand data is “relatively single, in most cases Based on survey data, trip requirements can not be accurately captured,” and new collecting methods are needed (p. 5). Xiaolei’s solution is to combine multiple data sources to “extract customization bus trip demand to a greater extent” (p. 6). Koga supplies a known, automated source of stop-level boarding and alighting counts, delivered to a central processing location without on-board equipment (pp. 8–9). Adding Koga’s stop-counted data as a further source to Xiaolei’s multi-source demand extraction is technically compatible, because both operate on boarding and alighting events at identified bus stops, and it improves the completeness and timeliness of the demand data, which is Xiaolei’s stated goal. Koga’s counts are stop-level counts rather than individual trip records; how the departure/arrival information is obtained from such counts is addressed by Zhao below. KSR, 550 U.S. at 417; MPEP 2143(I)(C).
Claim Limitations Not Explicitly Disclosed by the Combination of Xiaolei, Gkiotsalitis, and Koga
After combining the teachings of Xiaolei, Gkiotsalitis, and Koga, the following claim limitations are not explicitly disclosed:
based on the boarding/alighting information for each time slot;
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
and at a second time interval different from the first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value; and
Disclosure by Zhao (CN 105868861 A)
Zhao teaches:
based on the boarding/alighting information for each time slot;
See at least:
“select any bus line l, and select the card-swipe information and vehicle arrival/departure information transmitted back in real time within the time slot [T1+(m−1)τ, T1+mτ], forming a new card-swipe information dataset ΔQ1 and a new arrival/departure record set ΔQ2, and forming a new boarding information dataset ΔQ3” (Zhao, [0080])
“calculate the real-time trip volume: for the real-time boarding flows Q_D^im and Q_U^jm, the station trip OD from the current boarding station to all downstream stations can be estimated according to the trip alighting probability” (Zhao, [0082])
“q_D^ijm is the real-time trip volume in the down direction from station s_D^i to station s_D^j” (Zhao, [0085])
Rationale:
Zhao collects boarding information within each time slot [T1+(m−1)τ, T1+mτ] ([0080]), accumulates the real-time boarding flow at each station ([0081]), and estimates from that boarding flow, using the trip alighting probability, the station-to-station trip OD from the boarding station to every downstream station ([0082]–[0083]). The result, q_D^ijm, is the trip volume from station i to station j in time slot m ([0085]). Zhao thus expressly teaches deriving origin-destination (departure/arrival) information from stop-level boarding information for each time slot, without requiring that each passenger’s alighting stop be individually recorded. Under the broadest reasonable interpretation consistent with the specification, “departure/arrival information for each passenger” encompasses origin-destination information estimated from boarding and alighting counts at the respective points: the specification itself obtains it by estimating the numbers of passengers travelling between pairs of points from aggregate counts ([0060]–[0062]). Applied to the combination, Zhao teaches obtaining the departure/arrival information based on the boarding/alighting information that Koga’s sensors count for each time slot.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, and Zhao
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, and Zhao before them, to obtain the departure/arrival information of the combination from the stop-level boarding/alighting counts for each time slot by estimating station-to-station origin-destination flows from those counts using alighting probabilities, as taught by Zhao. Zhao identifies the problem that arises when boarding is recorded at a stop but the alighting stop is not, and explains that its alighting-probability method “solves the problem that the alighting stops of passenger flows are difficult to estimate” ([0066]). That is precisely the condition presented when Koga’s stop-level counts are used as the source of boarding/alighting information. Zhao further explains that the resulting real-time OD provides data support for real-time intelligent bus dispatching and network optimization ([0023]), which is the purpose of Xiaolei’s and Gkiotsalitis’s route determination. Xiaolei itself recognizes the same need, supplementing IC-card records with GPS data when alighting stops are not recorded (p. 6). Zhao, Xiaolei, and Koga all address passenger boarding and alighting at bus stations, so the teachings are compatible, and applying Zhao’s known estimation technique to Koga’s counts yields predictable origin-destination estimates for Xiaolei’s demand model. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A), (C).
Claim Limitations Not Explicitly Disclosed by the Combination of Xiaolei, Gkiotsalitis, Koga, and Zhao
After combining the teachings of Xiaolei, Gkiotsalitis, Koga, and Zhao, the following claim limitations are not explicitly disclosed:
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
and at a second time interval different from the first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value; and
Disclosure by Nakamura (US 10,096,241 B2)
Nakamura teaches:
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
See at least:
“The “transmission data number reduction correction” is to calculate a correction value to reduce the number of uploads of the probe data within a predetermined time by each vehicle 30, and is carried out when the total number of vehicles within the predetermined traveling area is equal to or greater than the predetermined value Th but is relatively small (when the total number of vehicles is equal to or greater than the first threshold value Th1 and less than a second threshold value Th2 (>Th1)). That is, in this “transmission data number reduction correction,” in which probe data are being transmitted from each vehicle 30 at 30-second intervals, a certain vehicle M is corrected to transmit at 60-second intervals (that is, the number of uploads is halved compared to before correction), and a different vehicle N is corrected to transmit at 90-second intervals (the number of uploads is set to 1/3 compared to before correction).” (Nakamura, col. 6, ll. 22–38)
Rationale:
Nakamura detects the total number of vehicles present in a traveling area, i.e., the count of entities moving through the area, and compares it with a first threshold value Th1 and a second threshold value Th2. When the count is equal to or greater than Th1 and less than Th2, Nakamura changes the recurring interval at which each source’s data is updated, from 30 seconds to 60 seconds (or 90 seconds); in the worked example, every vehicle’s upload interval is set to 60 seconds (col. 11, ll. 15–17). This is a first time interval applied when the count is greater than or equal to a first threshold value and less than a second threshold value. The threshold structure and the interval are express. Applying that count-band interval to the passenger count that forms the magnitude of the combination’s passenger movement flow vector is PHOSITA-obvious for the reasons stated in the motivation below.
and at a second time interval different from the first time interval
See at least:
“As a result, the upload times of vehicle B, vehicle D, vehicle G, and vehicle H are respectively corrected to “zero (none).” … As a result, the server 10 receives probe data at 00 seconds (vehicle A)→07 seconds (vehicle E)→15 seconds (vehicle F)→22 seconds (vehicle C)→30 seconds (vehicle A)→37 seconds (vehicle E)→45 seconds (vehicle F)→52 seconds (vehicle C)→00 seconds (vehicle A) . . . every minute.” (Nakamura, col. 12, ll. 31–43)
Rationale:
When the count is equal to or greater than Th2, Nakamura applies a different correction: it stops data transmission from some vehicles and leaves the remaining vehicles uncorrected. The remaining vehicles continue to report every 30 seconds; after the correction the server receives vehicle A’s data at 00 and 30 seconds of every minute. In the ≥ Th2 band the retained data is therefore updated at a 30-second interval, which is different from the 60-second interval of the [Th1, Th2) band. The aggregate reception interval also differs between the bands in the worked examples (five vehicles at 60 seconds versus four vehicles at 30 seconds). Either way, the interval in the second band is different from the interval in the first band. This teaching is express. The claim requires only that the second time interval be different from the first; it does not require that either be the longer one.
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value; and
See at least:
“Furthermore, the “data transmission vehicle reduction correction” is to calculate a correction value to reduce the number of vehicles that transmit probe data, and is carried out when the total number of vehicles within the predetermined traveling area is equal to or greater than the predetermined value Th, and is relatively large (when the total number of vehicles is equal to or greater than the second threshold value Th2).” (Nakamura, col. 6, ll. 42–49)
Rationale:
The second correction regime applies “when the total number of vehicles is equal to or greater than the second threshold value Th2,” i.e., when the count is greater than or equal to the second threshold value. Applied to the combination, a passenger movement flow vector whose magnitude is at or above the second threshold value is updated at the second, different time interval. This teaching is express as to the threshold condition.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura before them, to update each passenger movement flow vector of the combination at a time interval selected according to which of two count-defined bands the vector’s magnitude falls in, as taught by Nakamura, such that a vector whose magnitude is greater than or equal to a first threshold value and less than a second threshold value is updated at a first time interval and a vector whose magnitude is greater than or equal to the second threshold value is updated at a second, different time interval, and to re-determine the movement path based on the updated vectors. Nakamura teaches the reason for doing so: by changing data update frequency according to the counted number of moving entities, “[i]t is thereby possible to dynamically change the reception frequency of the probe data by the server 10 in accordance with the vehicle density for each traveling area, and to appropriately prevent an excessive communication load on the server 10 and secure the real-time nature of the probe data” (col. 9, ll. 23–28). The combination presents the same design pressure. Gkiotsalitis refreshes demand on a recurring cycle ([0025]) and emphasizes that its re-optimization is computationally intensive ([0029]: “finding a globally optimal solution for the allocation of vehicles to lines via a one-stage optimization is not possible with the use of state-of-the-art approaches”), and Koga’s distributed stop terminals report counts to a central device over a shared mesh network (p. 9). Nakamura is in the same field of endeavor (collection and processing of traffic flow data for traffic management) and is reasonably pertinent to the problem of how often count-based flow data should be refreshed. Applying Nakamura’s count-band update intervals to the passenger counts of the combination is the predictable use of a known technique according to its established function, balancing timeliness of the flow data against communication and processing load. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A), (D).
Regarding Claim 2,
The combination of Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura establishes the method of Claim 1, which is the basis for Claim 2.
Disclosure by Xiaolei (CN 108053062 A)
Xiaolei teaches:
wherein the plurality of boarding/alighting points comprise a first boarding/alighting point and a second boarding/alighting point, and
See at least:
“Make P={P1,P2,…,PN} It is the customization public transport candidate stations extracted in second step Point, any two website Pi To Pj Between passenger flow demand can be represented respectively with matrix TD and TC with hourage” (Xiaolei, p. 7)
Rationale:
Xiaolei defines passenger flow demand between “any two website Pi To Pj,” i.e., between a first stop (Pi) and a second stop (Pj) among the plurality of candidate stops. This teaching is express.
the determining of the at least one passenger movement flow vector for each time slot comprises
See at least:
“Passenger's trip terminus that the first step extracts is corresponded to the candidate stations in second step after Cluster-Fusion by the 3rd step Point has thus obtained the volume of the flow of passengers of each candidate's website.” (Xiaolei, p. 7)
Rationale:
Xiaolei obtains the flow quantities that populate TD by mapping the extracted trip termini to candidate stations; this is the step by which the passenger movement flow quantities for each time slot are determined, and the following sub-steps are part of it. This teaching is express.
determining a magnitude of a passenger movement flow vector for a certain time slot
See at least:
“In the present invention, monthly to get on or off the bus 5 times in same time period and identical platform and above for standard, system It counts in passenger's whole day period (using a hour as section) in the frequency of getting on or off the bus of all bus platforms, determines customization public transport demand OD.” (Xiaolei, pp. 6–7)
Rationale:
Xiaolei determines the demand ODs, and hence the size of each OD flow, within a one-hour section of the day, which is a certain time slot. That the flow size is determined per hourly section is express; treating it as the magnitude of a vector is obvious for the reasons given for Claim 1.
based on the number of passengers who board at the first boarding/alighting point and alight at the second boarding/alighting point during the certain time slot, and
See at least:
“In the case of IC card data recorded passenger getting on/off website, The origin and destination (OD) of passenger can directly be extracted; In the case of IC card data do not record passenger getting on/off website, GPS need to be combined Data extract passenger getting on/off website.” (Xiaolei, p. 6)
“Wherein, TDij It is vacancies of the website i to website j, the denominator in formula (1) represents whole trips of survey region Demand.” (Xiaolei, p. 7)
Rationale:
Xiaolei identifies, for each passenger, the stop at which the passenger got on and the stop at which the passenger got off, and TDij is the number of persons travelling from station i to station j. The flow size TDij is therefore based on the number of passengers who board at the first stop (i) and alight at the second stop (j), counted within the hourly section. This teaching is express.
determining a direction of the passenger movement flow vector for the certain time slot
See at least:
“Wherein, TDij It is vacancies of the website i to website j, the denominator in formula (1) represents whole trips of survey region Demand.” (Xiaolei, p. 7)
Rationale:
TDij is defined from station i to station j, which fixes the direction of the flow for the time slot in which it is counted. That the flow runs from i to j is express; characterizing it as the direction of a vector is obvious for the reasons given for Claim 1.
based on location information of the first boarding/alighting point and location information of the second boarding/alighting point.
See at least:
“(1) n website coordinates are inputted, from K initial website is wherein chosen as K initial cluster center.” (Xiaolei, p. 7)
“Mobile facility planning data critical field includes User ID, planning time, trip mode (such as walking is ridden, automobile), starting point longitude, starting point latitude, terminal longitude, terminal latitude” (Xiaolei, p. 6)
Rationale:
Xiaolei represents each stop by its coordinates and records trip origins and destinations by longitude and latitude. Xiaolei does not expressly compute a geometric direction from those coordinates. Doing so would have been obvious to a PHOSITA: the coordinates of both endpoints are already inputs to Xiaolei’s method, a flow defined from station i to station j runs from the location of station i toward the location of station j, and the direction between two located points follows from their coordinates as a matter of ordinary geometry, with entirely predictable results.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura before them, to determine the magnitude of each time-slot passenger movement flow vector of the combination from the number of passengers boarding at a first stop and alighting at a second stop, and its direction from the locations of those stops, as taught by and obvious from Xiaolei. These are the quantities on which Xiaolei’s own model is built (p. 7), so carrying them into the combined method requires no modification of any reference and serves the same purpose for which the references were combined for Claim 1: an accurate, current representation of passenger demand from which routes are determined and re-determined. The results are predictable. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A).
Regarding Claim 8,
Disclosure by Xiaolei (CN 108053062 A)
Xiaolei discloses:
An apparatus for determining a movement path of a vehicle
See at least:
“Finally, design a kind of heuritic approach based on genetic manipulation and solve the Optimized model, according to the target optimized, Different types of customization public bus network set is generated, reference is provided for actual railroad embankment.” (Xiaolei, p. 6)
“The parameter of genetic manipulation is arranged to population scale 60, each circuit Line concentration way is 10, crossover probability 0.6, mutation probability 0.1.In 500 generation of iterations, the results are shown in Table 1 for each objective optimization.” (Xiaolei, p. 9)
Rationale:
Xiaolei teaches a method that generates customized bus routes. The machine translation renders the term for a bus route as “public bus network” or “circuit.” Each generated route is the ordered path a customized bus travels between stations, i.e., a movement path of a vehicle (the bus). This teaching is express. Xiaolei’s route generation is performed by K-means clustering and a genetic algorithm run for 500 generations over a population of 60 route sets, a computation that is necessarily carried out by a computing apparatus. Xiaolei therefore implicitly discloses an apparatus for performing it.
considering a movement flow of passengers,
See at least:
“4th step, on the basis of trip requirements extraction, consider gauze demand coverage rate and passenger's travel time into This structure customizes public bus network Optimized model.” (Xiaolei, p. 6)
Rationale:
Xiaolei constructs its route optimization model on the basis of extracted passenger trip requirements and the demand coverage of the line network, so the routes are generated considering where and how passengers travel, i.e., a movement flow of passengers. This teaching is express.
the apparatus comprising:
See at least:
“The realization of the present invention includes following Step.” (Xiaolei, p. 5)
Rationale:
Xiaolei describes the functions its implementation performs as a sequence of steps; as explained above, those functions are carried out by a computing apparatus, which corresponds to the open-ended transitional phrase introducing the recited apparatus elements. This disclosure is implicit.
obtain boarding/alighting information for each time slot
See at least:
“In the present invention, monthly to get on or off the bus 5 times in same time period and identical platform and above for standard, system It counts in passenger's whole day period (using a hour as section) in the frequency of getting on or off the bus of all bus platforms, determines customization public transport demand OD.” (Xiaolei, pp. 6–7)
Rationale:
Xiaolei counts, for every bus platform (stop), the frequency of passengers getting on or off the bus “using a hour as section,” i.e., in one-hour time slots over the whole day. The per-stop, per-hour boarding and alighting counts are boarding/alighting information for each time slot. This teaching is express.
for each of a plurality of boarding/alighting points,
See at least:
“Based on Bus IC Card Data critical field includes card number, charge time, circuit volume Number, car number, website of getting on the bus (urban), get-off stop (urban)” (Xiaolei, p. 6)
Rationale:
Xiaolei’s IC-card records identify, for each trip, the stop at which the passenger got on (“website of getting on the bus”) and the stop at which the passenger got off (“get-off stop”). The machine translation renders the term for a bus stop as “website” or “platform.” Because the counts are taken at “all bus platforms” (pp. 6–7), the information is obtained for each of a plurality of boarding/alighting points. This teaching is express.
obtain departure/arrival information for each passenger
See at least:
“In the case of IC card data recorded passenger getting on/off website, The origin and destination (OD) of passenger can directly be extracted; In the case of IC card data do not record passenger getting on/off website, GPS need to be combined Data extract passenger getting on/off website.” (Xiaolei, p. 6)
“By this step, the beginning-of-line that goes out of user, terminal and temporal information can be extracted.” (Xiaolei, p. 6)
Rationale:
Xiaolei extracts each passenger’s origin and destination (OD) and the associated trip time from the passenger’s recorded getting-on and getting-off stops. Origin-destination information for each passenger is departure/arrival information for each passenger. This teaching is express.
determine at least one passenger movement flow vector for each time slot
See at least:
“Make P={P1,P2,…,PN} It is the customization public transport candidate stations extracted in second step Point, any two website Pi To Pj Between passenger flow demand can be represented respectively with matrix TD and TC with hourage” (Xiaolei, p. 7)
Rationale:
Xiaolei represents the passenger flow demand between any two candidate stations Pi and Pj as entries of matrix TD, built from demand ODs determined per one-hour section (pp. 6–7). Each entry TDij is, expressly, a count of passengers travelling from a specific station to a specific station. Claim 1 itself defines the passenger movement flow vector by its direction (representing the movement flow) and its magnitude (representing the number of passengers), and TDij carries both. To the extent the claimed “vector” is read to require more than a directed passenger count, representing each directed OD count as a vector whose direction runs from station i to station j and whose magnitude is the passenger count would have been obvious to a PHOSITA: the direction and the count are both already fixed by TDij, and expressing a directed quantity as a vector is an elementary, predictable mathematical representation. Likewise, forming these quantities for each hourly section is the direct use of Xiaolei’s hour-segmented OD data, and customized bus serves passengers whose “travel time are similar” (p. 5).
based on the departure/arrival information for each passenger,
See at least:
“Passenger's trip terminus that the first step extracts is corresponded to the candidate stations in second step after Cluster-Fusion by the 3rd step Point has thus obtained the volume of the flow of passengers of each candidate's website.” (Xiaolei, p. 7)
Rationale:
Xiaolei maps the extracted per-passenger trip origins and destinations (termini) to the clustered candidate stations to obtain the passenger flows that populate TD. The flow quantities are thus determined based on the per-passenger departure/arrival information. This teaching is express.
the at least one passenger movement flow vector having a direction representing a movement flow of passengers
See at least:
“Wherein, TDij It is vacancies of the website i to website j, the denominator in formula (1) represents whole trips of survey region Demand.” (Xiaolei, p. 7)
Rationale:
TDij is defined “of the website i to website j,” i.e., from station i to station j. Read in context, “vacancies of the website i to website j” denotes the passenger demand from station i to station j: Xiaolei describes the entries of TD as the “passenger flow demand” between stations Pi and Pj (p. 7) and states that the sum of the TD entries “represents whole trips of survey region Demand” (p. 7). That the flow runs from i to j is express. For the reasons given for the preceding limitation, characterizing that directed flow as the direction of a vector representing the movement flow of passengers is, at minimum, obvious to a PHOSITA.
and a magnitude representing a number of passengers included in the movement flow, and
See at least:
“In this example, since website is excessive, 70 demand OD of passenger flow maximum are filtered out to carrying out example.” (Xiaolei, p. 9)
Rationale:
Xiaolei ranks OD pairs by the size of their passenger flow and selects the 70 with the largest flow, which presupposes that each OD flow has a size. TDij is that size, the number of persons in the flow from station i to station j (p. 7). That the flow quantity is a passenger count is express; treating it as the magnitude of the vector is obvious for the reasons given above.
determine movement path of vehicle
See at least:
“Define R={R1,R2,..,Rn} It is to meet one group of sets of lines of model constraints, R Middle kth line definitions are Rk=R1kR2k…Rik…Rnk, wherein Rik For website sequence number described in P.” (Xiaolei, p. 7)
Rationale:
Each generated line Rk is an ordered sequence of station numbers (R1k R2k … Rnk) that the customized bus serves, i.e., the path along which the vehicle moves. This teaching is express.
based on the at least one passenger movement flow vector for each time slot,
See at least:
“TD (Ri) represent circuit Ri Aggregate demand; The molecule of formula (1) represents whole demands of candidate line collection R.” (Xiaolei, p. 7)
Rationale:
Xiaolei’s first objective function selects the route set R by maximizing the share of total TD demand that R serves, so the movement path is determined based on the passenger flow quantities in TD. That the path is determined from TD is express; determining it from the hourly TD for each time slot is obvious for the reasons given above.
Claim Limitations Not Explicitly Disclosed by Xiaolei
Xiaolei does not explicitly disclose the following claim limitations, the limitation “based on the boarding/alighting information for each time slot” being treated below for the reason given for Claim 1:
a memory storing at least one program; and
a processor configured to execute the at least one program to perform an operation,
wherein the processor is further configured to
the boarding/alighting information being counted by sensors of boarding/alighting point terminals
respectively installed at the plurality of boarding/alighting points
and received via a communication network from the boarding/alighting point terminals,
based on the boarding/alighting information for each time slot,
update the at least one passenger movement flow vector at a first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
and at a second time interval different from the first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value, and
re-determine the movement path of the vehicle
based on the updated at least one passenger movement flow vector.
Disclosure by Gkiotsalitis (US 2018/0268705 A1)
Gkiotsalitis discloses:
a memory storing at least one program; and
See at least:
“The storage resources of the physical machines can, for example, include processor readable memory, e.g., random-access-memory (RAM) and/or cache memory.” (Gkiotsalitis, [0024])
“The physical machines can store, at the storage resources thereof, a set of processor executable instructions necessary to perform the processes executed by the DRAFD 102 and the VLG 103 and configured to be executed by the processing resources thereof.” (Gkiotsalitis, [0024])
Rationale:
Gkiotsalitis’s dispatcher includes processor-readable memory (e.g., RAM) that stores the processor-executable instructions for performing the dispatching processes. The stored instructions are at least one program. This disclosure is express.
a processor configured to execute the at least one program to perform an operation,
See at least:
“The compute resources of the physical machines can, for example, include one or more processors each having one or more processor cores.” (Gkiotsalitis, [0024])
Rationale:
Gkiotsalitis’s compute resources include one or more processors that execute the stored instructions ([0024]), i.e., a processor configured to execute the program to perform an operation. This disclosure is express.
wherein the processor is further configured to
See at least:
“The physical machines can store, at the storage resources thereof, a set of processor executable instructions necessary to perform the processes executed by the DRAFD 102 and the VLG 103 and configured to be executed by the processing resources thereof.” (Gkiotsalitis, [0024])
Rationale:
The instructions stored in memory are “configured to be executed by the processing resources” to perform the dispatching processes, so the processor is configured by the program to perform each function the program implements. This disclosure is express as to processor configuration; programming the processor to perform the combination’s functions is addressed in the motivation below.
update the at least one passenger movement flow vector at a first time interval
See at least:
“In performing demand-responsive automated fleet dispatching, the input database 101 provides updated spatio-temporal demand data to the DRAFD 102 at (1). At (2), the VLG 103 provides a set of virtual lines to the DRAFD 102. At (3), the DRAFD introduces on-demand virtual lines as new service lines and allocates vehicles to those lines. At (4), the DRAFD modifies the allocation of vehicles to the original lines in order to reduce operations costs through superior demand matching.” (Gkiotsalitis, [0026])
“The allocation of available vehicles to the original lines and virtual lines can be updated every time a significant demand variation is observed. For example, in public transport systems and logistics operations, a one-day period can be split into three to six time periods within which observed demand is relatively homogeneous and the allocation of available vehicles can be updated as one such time period transitions to another.” (Gkiotsalitis, [0025])
Rationale:
Gkiotsalitis expressly stores point-to-point demand “and variation of such demand over time” ([0023]), expressly supplies “updated spatio-temporal demand data” to the dispatcher ([0026]), and expressly performs its demand-responsive updating on a recurring basis, as each of three to six daily demand periods transitions to the next ([0025]). Gkiotsalitis does not use the term “passenger movement flow vector,” and [0025] itself speaks of updating the vehicle allocation. Applying Gkiotsalitis’s recurring updating of point-to-point demand to Xiaolei’s point-to-point passenger flow quantities TDij, so that those quantities are refreshed at a recurring (first) time interval, is PHOSITA-obvious for the reasons stated in the motivation below.
re-determine the movement path of the vehicle
See at least:
“The aforementioned method can be reapplied in a demand-responsive manner (e.g., when spatio-temporal demand changes significantly).” (Gkiotsalitis, [0021])
Rationale:
Gkiotsalitis teaches that its route and line optimization, which ends by “dispatching vehicles into different routes and lines” ([0021]), is reapplied when spatio-temporal demand changes. Reapplying the route optimization is re-determining the movement path of the vehicle. This teaching is express.
based on the updated at least one passenger movement flow vector.
See at least:
“In performing demand-responsive automated fleet dispatching, the input database 101 provides updated spatio-temporal demand data to the DRAFD 102 at (1). At (2), the VLG 103 provides a set of virtual lines to the DRAFD 102. At (3), the DRAFD introduces on-demand virtual lines as new service lines and allocates vehicles to those lines. At (4), the DRAFD modifies the allocation of vehicles to the original lines in order to reduce operations costs through superior demand matching.” (Gkiotsalitis, [0026])
Rationale:
The reapplied optimization operates on “updated spatio-temporal demand data” supplied by the input database, and the dispatcher modifies the vehicle allocation to the lines accordingly. As applied to Xiaolei, the re-determination is based on the updated passenger movement flow vectors. This teaching is express as to re-optimization on updated demand.
Motivation to Combine Xiaolei and Gkiotsalitis
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei and Gkiotsalitis before them, to implement Xiaolei’s route-generation method as a program stored in memory and executed by a processor, as disclosed by Gkiotsalitis, and to configure that processor to update Xiaolei’s time-slot passenger flow quantities on a recurring schedule and re-determine the routes based on the updated quantities. Xiaolei’s method consists of computational steps (K-means clustering and a 500-generation genetic algorithm, pp. 7–9) that are ordinarily and predictably carried out by a processor executing stored instructions, and Gkiotsalitis discloses exactly that architecture for the same kind of demand-responsive route computation ([0024]). The motivation to add recurring updating and re-determination is the same as stated for Claim 1. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A), (C).
Claim Limitations Not Explicitly Disclosed by the Combination of Xiaolei and Gkiotsalitis
After combining the teachings of Xiaolei and Gkiotsalitis, the following claim limitations are not explicitly disclosed:
the boarding/alighting information being counted by sensors of boarding/alighting point terminals
respectively installed at the plurality of boarding/alighting points
and received via a communication network from the boarding/alighting point terminals,
based on the boarding/alighting information for each time slot,
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
and at a second time interval different from the first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value, and
Disclosure by Koga (JP 2014-010493 A)
Koga discloses:
the boarding/alighting information being counted by sensors of boarding/alighting point terminals
See at least:
“Further, the bus stop device 20 analyzes the video taken by the camera 22 to determine the number of passengers (number of passengers) who get on the bus stopped at the bus stop and the number of passengers who get off the stopped bus (number of passengers getting off). Count each.” (Koga, p. 9)
Rationale:
Koga’s bus stop device 20 includes camera 22, which captures “how a person gets on and off the bus” (p. 8), and the device analyzes the camera video to count the number of passengers who get on and the number who get off the bus at the stop. The camera is a sensor, the bus stop device is a boarding/alighting point terminal, and the boarding and alighting counts are boarding/alighting information counted by that sensor. This disclosure is express.
respectively installed at the plurality of boarding/alighting points
See at least:
“The bus stop information providing system 10 includes a bus stop device 20 installed at each bus stop 3 and a central device 40 installed at an information center for managing bus operations.” (Koga, p. 7)
Rationale:
Koga installs a bus stop device 20 “at each bus stop 3,” i.e., the terminals are respectively installed at the plurality of boarding/alighting points. This disclosure is express.
and received via a communication network from the boarding/alighting point terminals,
See at least:
“When it is detected by video analysis that the bus has departed from the bus stop, the bus position information including the bus system number, the number of passengers, the number of passengers getting off, and the identification information of the bus stop is sent to the central device 40 in the mesh network. 12 through.” (Koga, p. 9)
Rationale:
After the bus departs, each bus stop device sends the boarding count, the alighting count, and the stop’s identification information to central device 40 over mesh network 12, which is formed by wirelessly connecting the access points of the bus stop devices (p. 8). The mesh network is a communication network, and central device 40 receives the counts from the bus stop devices through it. This disclosure is express.
Motivation to Combine Xiaolei, Gkiotsalitis, and Koga
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, and Koga before them, to configure the processor of the combination to obtain its boarding/alighting information from camera sensors of bus stop devices installed at the respective stops, which count the boarding and alighting passengers and report the counts over a communication network to a central device, as taught by Koga. Xiaolei identifies its problem as the inadequacy of existing demand data collection: demand data is “relatively single, in most cases Based on survey data, trip requirements can not be accurately captured,” and new collecting methods are needed (p. 5). Xiaolei’s solution is to combine multiple data sources to “extract customization bus trip demand to a greater extent” (p. 6). Koga supplies a known, automated source of stop-level boarding and alighting counts, delivered to a central processing location without on-board equipment (pp. 8–9). Adding Koga’s stop-counted data as a further source to Xiaolei’s multi-source demand extraction is technically compatible, because both operate on boarding and alighting events at identified bus stops, and it improves the completeness and timeliness of the demand data, which is Xiaolei’s stated goal. Koga’s counts are stop-level counts rather than individual trip records; how the departure/arrival information is obtained from such counts is addressed by Zhao below. KSR, 550 U.S. at 417; MPEP 2143(I)(C).
Claim Limitations Not Explicitly Disclosed by the Combination of Xiaolei, Gkiotsalitis, and Koga
After combining the teachings of Xiaolei, Gkiotsalitis, and Koga, the following claim limitations are not explicitly disclosed:
based on the boarding/alighting information for each time slot,
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
and at a second time interval different from the first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value, and
Disclosure by Zhao (CN 105868861 A)
Zhao discloses:
based on the boarding/alighting information for each time slot,
See at least:
“select any bus line l, and select the card-swipe information and vehicle arrival/departure information transmitted back in real time within the time slot [T1+(m−1)τ, T1+mτ], forming a new card-swipe information dataset ΔQ1 and a new arrival/departure record set ΔQ2, and forming a new boarding information dataset ΔQ3” (Zhao, [0080])
“calculate the real-time trip volume: for the real-time boarding flows Q_D^im and Q_U^jm, the station trip OD from the current boarding station to all downstream stations can be estimated according to the trip alighting probability” (Zhao, [0082])
“q_D^ijm is the real-time trip volume in the down direction from station s_D^i to station s_D^j” (Zhao, [0085])
Rationale:
Zhao collects boarding information within each time slot [T1+(m−1)τ, T1+mτ] ([0080]), accumulates the real-time boarding flow at each station ([0081]), and estimates from that boarding flow, using the trip alighting probability, the station-to-station trip OD from the boarding station to every downstream station ([0082]–[0083]). The result, q_D^ijm, is the trip volume from station i to station j in time slot m ([0085]). Zhao thus expressly teaches deriving origin-destination (departure/arrival) information from stop-level boarding information for each time slot, without requiring that each passenger’s alighting stop be individually recorded. Under the broadest reasonable interpretation consistent with the specification, “departure/arrival information for each passenger” encompasses origin-destination information estimated from boarding and alighting counts at the respective points: the specification itself obtains it by estimating the numbers of passengers travelling between pairs of points from aggregate counts ([0060]–[0062]). Applied to the combination, Zhao teaches obtaining the departure/arrival information based on the boarding/alighting information that Koga’s sensors count for each time slot.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, and Zhao
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, and Zhao before them, to configure the processor of the combination to obtain the departure/arrival information from the stop-level boarding/alighting counts for each time slot by estimating station-to-station origin-destination flows from those counts using alighting probabilities, as taught by Zhao. Zhao identifies the problem that arises when boarding is recorded at a stop but the alighting stop is not, and explains that its alighting-probability method “solves the problem that the alighting stops of passenger flows are difficult to estimate” ([0066]). That is precisely the condition presented when Koga’s stop-level counts are used as the source of boarding/alighting information. Zhao further explains that the resulting real-time OD provides data support for real-time intelligent bus dispatching and network optimization ([0023]), which is the purpose of Xiaolei’s and Gkiotsalitis’s route determination. Xiaolei itself recognizes the same need, supplementing IC-card records with GPS data when alighting stops are not recorded (p. 6). Zhao, Xiaolei, and Koga all address passenger boarding and alighting at bus stations, so the teachings are compatible, and applying Zhao’s known estimation technique to Koga’s counts yields predictable origin-destination estimates for Xiaolei’s demand model. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A), (C).
Claim Limitations Not Explicitly Disclosed by the Combination of Xiaolei, Gkiotsalitis, Koga, and Zhao
After combining the teachings of Xiaolei, Gkiotsalitis, Koga, and Zhao, the following claim limitations are not explicitly disclosed:
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
and at a second time interval different from the first time interval
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value, and
Disclosure by Nakamura (US 10,096,241 B2)
Nakamura discloses:
when the magnitude of the at least one passenger movement flow vector is greater than or equal to a first threshold value and less than a second threshold value,
See at least:
“The “transmission data number reduction correction” is to calculate a correction value to reduce the number of uploads of the probe data within a predetermined time by each vehicle 30, and is carried out when the total number of vehicles within the predetermined traveling area is equal to or greater than the predetermined value Th but is relatively small (when the total number of vehicles is equal to or greater than the first threshold value Th1 and less than a second threshold value Th2 (>Th1)). That is, in this “transmission data number reduction correction,” in which probe data are being transmitted from each vehicle 30 at 30-second intervals, a certain vehicle M is corrected to transmit at 60-second intervals (that is, the number of uploads is halved compared to before correction), and a different vehicle N is corrected to transmit at 90-second intervals (the number of uploads is set to 1/3 compared to before correction).” (Nakamura, col. 6, ll. 22–38)
Rationale:
Nakamura detects the total number of vehicles present in a traveling area, i.e., the count of entities moving through the area, and compares it with a first threshold value Th1 and a second threshold value Th2. When the count is equal to or greater than Th1 and less than Th2, Nakamura changes the recurring interval at which each source’s data is updated, from 30 seconds to 60 seconds (or 90 seconds); in the worked example, every vehicle’s upload interval is set to 60 seconds (col. 11, ll. 15–17). This is a first time interval applied when the count is greater than or equal to a first threshold value and less than a second threshold value. The threshold structure and the interval are express. Applying that count-band interval to the passenger count that forms the magnitude of the combination’s passenger movement flow vector is PHOSITA-obvious for the reasons stated in the motivation below.
and at a second time interval different from the first time interval
See at least:
“As a result, the upload times of vehicle B, vehicle D, vehicle G, and vehicle H are respectively corrected to “zero (none).” … As a result, the server 10 receives probe data at 00 seconds (vehicle A)→07 seconds (vehicle E)→15 seconds (vehicle F)→22 seconds (vehicle C)→30 seconds (vehicle A)→37 seconds (vehicle E)→45 seconds (vehicle F)→52 seconds (vehicle C)→00 seconds (vehicle A) . . . every minute.” (Nakamura, col. 12, ll. 31–43)
Rationale:
When the count is equal to or greater than Th2, Nakamura applies a different correction: it stops data transmission from some vehicles and leaves the remaining vehicles uncorrected. The remaining vehicles continue to report every 30 seconds; after the correction the server receives vehicle A’s data at 00 and 30 seconds of every minute. In the ≥ Th2 band the retained data is therefore updated at a 30-second interval, which is different from the 60-second interval of the [Th1, Th2) band. The aggregate reception interval also differs between the bands in the worked examples (five vehicles at 60 seconds versus four vehicles at 30 seconds). Either way, the interval in the second band is different from the interval in the first band. This teaching is express. The claim requires only that the second time interval be different from the first; it does not require that either be the longer one.
when the magnitude of the at least one passenger movement flow vector is greater than or equal to the second threshold value, and
See at least:
“Furthermore, the “data transmission vehicle reduction correction” is to calculate a correction value to reduce the number of vehicles that transmit probe data, and is carried out when the total number of vehicles within the predetermined traveling area is equal to or greater than the predetermined value Th, and is relatively large (when the total number of vehicles is equal to or greater than the second threshold value Th2).” (Nakamura, col. 6, ll. 42–49)
Rationale:
The second correction regime applies “when the total number of vehicles is equal to or greater than the second threshold value Th2,” i.e., when the count is greater than or equal to the second threshold value. Applied to the combination, a passenger movement flow vector whose magnitude is at or above the second threshold value is updated at the second, different time interval. This teaching is express as to the threshold condition.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura before them, to configure the processor of the combination to update each passenger movement flow vector at a time interval selected according to which of two count-defined bands the vector’s magnitude falls in, as taught by Nakamura, such that a vector whose magnitude is greater than or equal to a first threshold value and less than a second threshold value is updated at a first time interval and a vector whose magnitude is greater than or equal to the second threshold value is updated at a second, different time interval, and to re-determine the movement path based on the updated vectors. Nakamura teaches the reason for doing so: by changing data update frequency according to the counted number of moving entities, “[i]t is thereby possible to dynamically change the reception frequency of the probe data by the server 10 in accordance with the vehicle density for each traveling area, and to appropriately prevent an excessive communication load on the server 10 and secure the real-time nature of the probe data” (col. 9, ll. 23–28). The combination presents the same design pressure. Gkiotsalitis refreshes demand on a recurring cycle ([0025]) and emphasizes that its re-optimization is computationally intensive ([0029]: “finding a globally optimal solution for the allocation of vehicles to lines via a one-stage optimization is not possible with the use of state-of-the-art approaches”), and Koga’s distributed stop terminals report counts to a central device over a shared mesh network (p. 9). Nakamura is in the same field of endeavor (collection and processing of traffic flow data for traffic management) and is reasonably pertinent to the problem of how often count-based flow data should be refreshed. Applying Nakamura’s count-band update intervals to the passenger counts of the combination is the predictable use of a known technique according to its established function, balancing timeliness of the flow data against communication and processing load. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A), (D).
Regarding Claim 9,
The combination of Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura establishes the method of Claim 1, which is the basis for Claim 9.
Disclosure by Xiaolei (CN 108053062 A)
Xiaolei discloses:
for causing a computer to execute the method of claim 1.
See at least:
“The parameter of genetic manipulation is arranged to population scale 60, each circuit Line concentration way is 10, crossover probability 0.6, mutation probability 0.1.In 500 generation of iterations, the results are shown in Table 1 for each objective optimization.” (Xiaolei, p. 9)
Rationale:
Xiaolei’s method is executed by computation: K-means clustering of station coordinates and a genetic algorithm run for 500 generations over a population of 60 route sets. A computation of this kind is necessarily executed by a computer, so Xiaolei implicitly discloses a computer executing its method; the method of the combination, established for Claim 1, is likewise computer-executed. This disclosure is implicit.
Claim Limitations Not Explicitly Disclosed by Xiaolei
Xiaolei does not explicitly disclose the following claim limitations:
A non-transitory computer-readable recording medium
having recorded thereon a program
Disclosure by Gkiotsalitis (US 2018/0268705 A1)
Gkiotsalitis discloses:
A non-transitory computer-readable recording medium
See at least:
“The storage resources of the physical machines can, for example, include processor readable memory, e.g., random-access-memory (RAM) and/or cache memory.” (Gkiotsalitis, [0024])
Rationale:
Gkiotsalitis discloses processor-readable memory such as RAM and cache memory, which is a computer-readable recording medium. Because RAM and cache memory are physical storage devices, the medium is non-transitory. This disclosure is express.
having recorded thereon a program
See at least:
“The physical machines can store, at the storage resources thereof, a set of processor executable instructions necessary to perform the processes executed by the DRAFD 102 and the VLG 103 and configured to be executed by the processing resources thereof.” (Gkiotsalitis, [0024])
Rationale:
Gkiotsalitis’s memory stores the set of processor-executable instructions that perform the dispatching processes, i.e., the medium has a program recorded thereon. This disclosure is express.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura before them, to record the method of the combination, as established for Claim 1, as a program on a non-transitory computer-readable medium such as RAM, as disclosed by Gkiotsalitis, for causing a computer to execute it. Storing instructions for a computational method on a physical memory from which a processor executes them is the ordinary and predictable way to implement such a method, and Gkiotsalitis discloses that arrangement for the same kind of demand-responsive route computation ([0024]). The motivation to combine the references as to the method itself is as stated for Claim 1. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A).
Regarding Claim 3,
The combination of Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura establishes the method of Claim 1, which is the basis for Claim 3.
Disclosure by Xiaolei (CN 108053062 A)
Xiaolei teaches:
wherein the determining of the at least one passenger movement flow vector for each time slot comprises:
See at least:
“Passenger's trip terminus that the first step extracts is corresponded to the candidate stations in second step after Cluster-Fusion by the 3rd step Point has thus obtained the volume of the flow of passengers of each candidate's website.” (Xiaolei, p. 7)
Rationale:
Xiaolei determines its passenger flow quantities by mapping the extracted trip termini to clustered candidate stations, so the determination of the flow quantities for each time slot comprises the clustering steps mapped below. This teaching is express.
generating a boarding cluster by clustering a plurality of boarding/alighting points included in a first region
See at least:
“It is since same demand region is there are multiple bus platforms, i.e., identical Beginning or end, but nearby can there are multiple bus platforms, these platforms can serve the beginning or end, Cheng Kehui One of platform is selected to go on a journey, this has resulted in the overlapping of bus platform.Therefore in view of following to customizing bus platform Planning is, it is necessary to cluster these platforms.The present invention is by K-means methods by multiple bus stations adjacent in demand region Platform carries out Cluster-Fusion, determines candidate's website of customization public bus network” (Xiaolei, p. 7)
Rationale:
Xiaolei explains that multiple nearby bus platforms serve the same trip “Beginning or end,” and it clusters the adjacent platforms in the demand region with the K-means method into a single candidate station. A cluster of platforms that together serve a common trip beginning (origin) is a cluster of boarding/alighting points at which passengers board, i.e., a boarding cluster, formed from the platforms included in that region. That Xiaolei clusters the platforms of a region serving a common beginning is express. To the extent the claim is read to require that the boarding cluster be formed separately from the alighting cluster, doing so would have been obvious to a PHOSITA: Xiaolei itself identifies the two roles a platform cluster serves (“the beginning or end”), and running the same clustering on the origin side and on the destination side is the direct application of Xiaolei’s technique to each end of the trip.
where a large number of boarding passengers are concentrated during a certain time slot;
See at least:
“Fig. 3 be embodiment in resident trip spatial and temporal distributions schematic diagram, (a) departure place; (b) destination;” (Xiaolei, p. 6)
“In the present invention, monthly to get on or off the bus 5 times in same time period and identical platform and above for standard, system It counts in passenger's whole day period (using a hour as section) in the frequency of getting on or off the bus of all bus platforms, determines customization public transport demand OD.” (Xiaolei, pp. 6–7)
Rationale:
Xiaolei maps the spatial distribution of trip departure places separately from destinations (FIG. 3(a)) and identifies demand points by repeated boarding and alighting at the same platform in the same time period, counted in one-hour sections. The region whose platforms are clustered is a “demand region” (p. 7), i.e., a region of concentrated trip demand, and on the origin side that demand is boarding demand. Xiaolei does not expressly define the first region by a count of boarding passengers within a particular hour. Defining the region to be clustered as the area in which boarding passengers are concentrated during the time slot would have been obvious to a PHOSITA, because Xiaolei already maps where trips depart (FIG. 3(a)), already determines demand per hourly section, and clusters platforms precisely where demand is concentrated so that a single candidate station can serve it. The relative term “a large number” is met by the concentrations that Xiaolei’s demand regions represent.
generating an alighting cluster by clustering a plurality of boarding/alighting points included in a second region
See at least:
“It is since same demand region is there are multiple bus platforms, i.e., identical Beginning or end, but nearby can there are multiple bus platforms, these platforms can serve the beginning or end, Cheng Kehui One of platform is selected to go on a journey, this has resulted in the overlapping of bus platform.Therefore in view of following to customizing bus platform Planning is, it is necessary to cluster these platforms.The present invention is by K-means methods by multiple bus stations adjacent in demand region Platform carries out Cluster-Fusion, determines candidate's website of customization public bus network” (Xiaolei, p. 7)
Rationale:
For the reasons given for the boarding cluster, Xiaolei’s clustering of adjacent platforms that together serve a common trip “end” (destination) yields a cluster of boarding/alighting points at which passengers alight, i.e., an alighting cluster, formed from the platforms included in a second region. That Xiaolei clusters platforms serving a common end is express; forming the alighting cluster separately is obvious for the reasons stated above.
where a large number of alighting passengers are concentrated during the certain time slot; and
See at least:
“By the convergence analysis of several data, families in Hangzhou City trip origin and destination Spatial distribution is as shown in Figure 3.” (Xiaolei, p. 9)
Rationale:
Xiaolei maps the spatial distribution of trip destinations separately (FIG. 3(b)) and determines demand per hourly section. Defining the second region as the area in which alighting passengers are concentrated during the same time slot is obvious for the reasons given for the first region, applied to the destination end of the trip.
determining a passenger movement flow vector for the certain time slot based on the boarding cluster and the alighting cluster.
See at least:
“Make P={P1,P2,…,PN} It is the customization public transport candidate stations extracted in second step Point, any two website Pi To Pj Between passenger flow demand can be represented respectively with matrix TD and TC with hourage” (Xiaolei, p. 7)
Rationale:
The passenger flow quantities TD are defined between candidate stations Pi and Pj, which are the clustered stations. With the origin-side and destination-side clusters formed as discussed above, the flow from the boarding cluster to the alighting cluster for the time slot is determined from them. That TD is determined between clustered stations is express; determining it between the boarding cluster and the alighting cluster for the time slot follows directly from the obvious modification discussed above.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura before them, to determine the passenger movement flow vectors of the combination for each time slot by clustering the boarding/alighting points of the region where boarding passengers are concentrated into a boarding cluster, clustering those of the region where alighting passengers are concentrated into an alighting cluster, and determining the flow between the two clusters, as taught by and obvious from Xiaolei. Xiaolei explains why clustering is done: nearby platforms serving the same trip beginning or end cause overlapping stops, so they are clustered into a single candidate station for planning (p. 7). That reason applies independently to the origin end and the destination end of each trip, and Xiaolei already separates departure places from destinations (FIG. 3) and determines demand per hourly section (pp. 6–7). Applying Xiaolei’s clustering separately to the origin-concentrated and destination-concentrated regions for the time slot is the predictable use of Xiaolei’s own technique for its stated purpose, and it carries into the combined method the demand representation on which the routes of Claim 1 are determined and re-determined. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A), (G).
Regarding Claim 4,
The combination of Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura establishes the method of Claim 3, which is the basis for Claim 4.
Disclosure by Xiaolei (CN 108053062 A)
Xiaolei teaches:
wherein the determining of the at least one passenger movement flow vector for each time slot comprises:
See at least:
“Passenger's trip terminus that the first step extracts is corresponded to the candidate stations in second step after Cluster-Fusion by the 3rd step Point has thus obtained the volume of the flow of passengers of each candidate's website.” (Xiaolei, p. 7)
Rationale:
As for Claim 3, Xiaolei’s determination of the passenger flow quantities for each time slot comprises the steps mapped below. This teaching is express.
generating a plurality of pairs of clusters by matching boarding clusters with alighting clusters;
See at least:
“It connects Get off, judge whether each trip origin and destination are OD pairs of customization public transport demand; With monthly in same time period and identical in this example Platform gets on or off the bus 5 times and is above standard, counts in passenger's whole day period (using a hour as section) in all bus platforms The frequency of getting on or off the bus, determine customization public transport demand OD.” (Xiaolei, p. 9)
Rationale:
Xiaolei determines, for each trip, whether its origin and destination form an OD pair of customized-bus demand, and the origins and destinations are the clustered candidate stations (p. 7). With the boarding and alighting clusters formed as discussed for Claim 3, each OD pair matches a boarding (origin) cluster with an alighting (destination) cluster, and Xiaolei generates many such pairs. That Xiaolei generates OD pairs between clustered stations is express; their correspondence to pairs of boarding and alighting clusters follows from the modification established for Claim 3.
determining a passenger movement flow vector of each of the plurality of pairs of clusters;
See at least:
“Wherein, TDij It is vacancies of the website i to website j, the denominator in formula (1) represents whole trips of survey region Demand.” (Xiaolei, p. 7)
Rationale:
TDij is the passenger flow quantity from station i to station j, determined for each OD pair, i.e., for each pair of clusters. That a flow quantity is determined for each pair is express; its characterization as a vector is obvious for the reasons given for Claim 1.
allocating vehicles to pairs of clusters of which magnitudes of passenger movement flow vectors are greater than or equal to a preset value,
See at least:
“In this example, since website is excessive, 70 demand OD of passenger flow maximum are filtered out to carrying out example.” (Xiaolei, p. 9)
Rationale:
Xiaolei selects, for route generation, the 70 OD pairs whose passenger flow is largest, and generates routes to serve the selected pairs; the customized buses that operate those routes are thereby allocated to the selected pairs. Selecting the 70 largest flows serves exactly those pairs whose flow is at or above the flow of the 70th pair, which is a preset value in effect. Xiaolei also applies a fixed numerical criterion to qualify demand ODs (“5 times … and above for standard,” p. 9). Xiaolei does not expressly state the selection as a comparison of each pair’s flow against a fixed preset value. Doing so would have been obvious to a PHOSITA, because a fixed flow threshold and a top-N cut are interchangeable ways of selecting the high-demand pairs to be served, and Xiaolei already uses a fixed numerical standard for demand qualification; substituting one known selection criterion for the other yields predictable results.
Claim Limitations Not Explicitly Disclosed by Xiaolei
Xiaolei does not explicitly teach the following claim limitations:
obtaining information about the number of operable vehicles; and
considering the information about the number of operable vehicles.
Disclosure by Gkiotsalitis (US 2018/0268705 A1)
Gkiotsalitis teaches:
obtaining information about the number of operable vehicles; and
See at least:
“Furthermore, the input database 101 additionally stores information pertaining to the costs of operating vehicles, the total number of available vehicles, and drivers' availability, e.g. based on mandatory resting times between successive trips.” (Gkiotsalitis, [0023])
Rationale:
Gkiotsalitis’s input database stores “the total number of available vehicles,” which the dispatcher receives and uses ([0025]–[0026]), i.e., information about the number of operable vehicles is obtained. This teaching is express.
considering the information about the number of operable vehicles.
See at least:
“The second constraint Σl=1L nl≤γ requires that the total number of allocated vehicles not exceed the maximum number of available vehicles, denoted by γ.” (Gkiotsalitis, [0028])
Rationale:
Gkiotsalitis allocates vehicles to lines subject to the constraint that the total number allocated not exceed the number of available vehicles γ, and it notes that generated lines “might not be operated by vehicles if they do not match the demand properly” ([0046]). The allocation is therefore made considering the information about the number of operable vehicles. This teaching is express.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura before them, to allocate the available vehicles of the combination only to those pairs of boarding and alighting clusters whose passenger flow is greater than or equal to a preset value, considering the number of operable vehicles, as taught by Gkiotsalitis and obvious from Xiaolei. Gkiotsalitis teaches allocating a finite fleet γ among candidate lines and leaving unserved those lines that do not match demand ([0028], [0046]), in order to satisfy demand while reducing operational costs ([0018]). Xiaolei teaches serving the OD pairs with the largest passenger flows (p. 9) and qualifying demand by a fixed numerical standard (p. 9). Allocating a limited fleet only to the cluster pairs whose flow clears a preset value is the direct way to apply Gkiotsalitis’s fleet constraint to Xiaolei’s high-demand pairs, improving fleet utilization with predictable results. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A), (G).
Regarding Claim 6,
The combination of Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura establishes the method of Claim 1, which is the basis for Claim 6.
Disclosure by Xiaolei (CN 108053062 A)
Xiaolei teaches:
wherein the determining of the movement path of the vehicle comprises
See at least:
“4th step, on the basis of trip requirements extraction, consider gauze demand coverage rate and passenger's travel time into This structure customizes public bus network Optimized model.” (Xiaolei, p. 6)
Rationale:
Xiaolei’s route determination is performed by constructing and solving its route optimization model; that step comprises the sub-steps mapped below. This teaching is express.
determining the movement path of the vehicle further considering the passenger movement flow vector for each time slot, and
See at least:
“TD (Ri) represent circuit Ri Aggregate demand; The molecule of formula (1) represents whole demands of candidate line collection R.” (Xiaolei, p. 7)
Rationale:
Xiaolei’s first objective selects the route set based on the TD passenger flow quantities, which, as established for Claim 1, are the passenger movement flow vectors for each time slot. This teaching is express as to the flow quantities and obvious as to their characterization as vectors per time slot, for the reasons given for Claim 1.
at least one of a boarding period for each passenger and a waiting period for each passenger.
See at least:
“Wherein, rij(R) be in sets of lines R website i to the aggregate demand of website j, TCij It is the trip between website i to website j The Trip Costs of row time, i.e. passenger can be extracted by public transport GPS data and obtained.The object function is to optimize sets of lines R In all passengers average hourage, promoted customization public transport traffic efficiency.” (Xiaolei, p. 7)
Rationale:
Xiaolei’s second objective function determines the route set by optimizing “all passengers average hourage,” computed from rij(R), the demand from station i to station j served by route set R, and TCij, the travel time between station i and station j, which is the time each passenger travelling from i to j spends on board the bus (p. 7). The time a passenger spends on board between boarding and alighting is a boarding period for each passenger. Because Claim 6 requires only “at least one of” a boarding period and a waiting period, Xiaolei’s express use of the passengers’ on-board travel time satisfies the limitation. This teaching is express.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura before them, to determine the movement path of the combination further considering the passenger movement flow vectors for each time slot and each passenger’s on-board travel time, as taught by Xiaolei. Xiaolei’s own model uses both objectives together, demand coverage and passenger travel time, to generate routes that serve demand efficiently (p. 6: “considering demand coverage rate and time cost structure multiple target line optimization model”). Retaining Xiaolei’s travel-time objective in the combined method requires no modification of any reference and furthers the same purpose for which the references were combined for Claim 1. The results are predictable. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A).
Regarding Claim 7,
The combination of Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura establishes the method of Claim 1, which is the basis for Claim 7.
Disclosure by Xiaolei (CN 108053062 A)
Xiaolei teaches:
identifying a starting point, intermediate points, and a destination
See at least:
“Define R={R1,R2,..,Rn} It is to meet one group of sets of lines of model constraints, R Middle kth line definitions are Rk=R1kR2k…Rik…Rnk, wherein Rik For website sequence number described in P.” (Xiaolei, p. 7)
Rationale:
Each route Rk is an ordered sequence of station numbers R1k R2k … Rnk. The first station of the sequence is the starting point, the last is the destination, and the stations between them are intermediate points. Xiaolei’s example route includes four stations (p. 9). The ordering is express; identifying its first, last, and intermediate members is implicit in serving an ordered route and, at minimum, obvious to a PHOSITA.
from among the plurality of boarding/alighting points,
See at least:
“Make P={P1,P2,…,PN} It is the customization public transport candidate stations extracted in second step Point, any two website Pi To Pj Between passenger flow demand can be represented respectively with matrix TD and TC with hourage” (Xiaolei, p. 7)
Rationale:
The stations of each route are drawn from the set P of candidate stations, which are boarding/alighting points of the plurality established for Claim 1. This teaching is express.
based on the movement path of the vehicle; and
See at least:
“Fig. 4 is output One of public bus network scheme example, which includes 4 websites, 965 people of demand, 13 kilometers of line length, run time 45 minutes.” (Xiaolei, p. 9)
Rationale:
The starting point, intermediate points, and destination are those of the generated route, i.e., they are identified based on the movement path of the vehicle. This teaching is implicit in Xiaolei’s ordered route and, at minimum, obvious.
Claim Limitations Not Explicitly Disclosed by Xiaolei
Xiaolei does not explicitly teach the following claim limitations:
determining a return path
such that, after the vehicle arrives at the destination,
the vehicle returns to the starting point
without passing through the intermediate points.
Disclosure by Gkiotsalitis (US 2018/0268705 A1)
Gkiotsalitis teaches:
determining a return path
See at least:
“Service lines in a transportation network often start from an origin point, serve a pre-defined number of points (stops), reach a final destination point, and then return to the origin.” (Gkiotsalitis, [0018])
Rationale:
Gkiotsalitis teaches that service lines start from an origin, reach a final destination point, “and then return to the origin,” so the vehicle’s route includes a return portion. This teaching is express.
such that, after the vehicle arrives at the destination,
See at least:
“When generating virtual lines for a selected start point and break point of a selected original line, the process can determine whether a rest point is required for the virtual line, and if so, select a rest point from amongst possible rest points. The process can also determine whether deadhead time, i.e. time for a trip from the break point to return to the starting point with no passengers, is required for the virtual line.” (Gkiotsalitis, [0048])
Rationale:
For each generated line, Gkiotsalitis determines whether a deadhead trip is required “from the break point,” the point at which the generated line ends, i.e., after the vehicle arrives at the destination. This teaching is express.
the vehicle returns to the starting point
See at least:
“When generating virtual lines for a selected start point and break point of a selected original line, the process can determine whether a rest point is required for the virtual line, and if so, select a rest point from amongst possible rest points. The process can also determine whether deadhead time, i.e. time for a trip from the break point to return to the starting point with no passengers, is required for the virtual line.” (Gkiotsalitis, [0048])
Rationale:
The deadhead trip is a trip “to return to the starting point.” This teaching is express.
without passing through the intermediate points.
See at least:
“When generating virtual lines for a selected start point and break point of a selected original line, the process can determine whether a rest point is required for the virtual line, and if so, select a rest point from amongst possible rest points. The process can also determine whether deadhead time, i.e. time for a trip from the break point to return to the starting point with no passengers, is required for the virtual line.” (Gkiotsalitis, [0048])
Rationale:
Gkiotsalitis’s deadhead return is made “with no passengers,” so it serves none of the stops of the line. Gkiotsalitis does not expressly state that the deadhead trip avoids the intermediate stops. A PHOSITA would have found it obvious to route the empty return trip directly to the starting point without passing through the intermediate points, because the vehicle has no passengers to pick up or drop off there, and Gkiotsalitis’s objective function expressly penalizes “the operational cost in vehicle-kilometers traveled” ([0027]). Routing an out-of-service return trip along a direct path rather than back through intermediate stops it does not serve is the predictable result of minimizing that cost.
Motivation to Combine Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Xiaolei, Gkiotsalitis, Koga, Zhao, and Nakamura before them, to identify the starting point, intermediate points, and destination of each route of the combination and to determine a return path by which, after reaching the destination, the vehicle returns empty to the starting point directly, without passing through the intermediate points, as taught by and obvious from Gkiotsalitis. Gkiotsalitis teaches that service vehicles return to their origin ([0018]) and expressly plans the deadhead return from the end of a generated line to its starting point with no passengers ([0048]), and its objective penalizes vehicle-kilometers traveled ([0027]). Applying that known return-trip planning to the routes of the combination reduces non-revenue travel and operating cost, a recognized design incentive in the same field, with predictable results. KSR, 550 U.S. at 416–17; MPEP 2143(I)(A), (G).
Response to Arguments
Applicant’s arguments filed June 25, 2026 have been fully considered. They are not persuasive with respect to the rejection under 35 U.S.C. 101, and they are moot with respect to the rejections under 35 U.S.C. 103 in view of the new grounds of rejection necessitated by amendment. Each argument is addressed below.
A. Correction of the Record Regarding “Gkiotsalitis2”
Applicant correctly notes that the prior Office action identified the reference applied against original claim 5 (“Gkiotsalitis2”) by the same publication number as Gkiotsalitis (US 2018/0268705 A1). The examiner thanks applicant for identifying the error. That reference is not relied upon in this action, and the arguments directed to it (Remarks § 2.2) are moot, as explained in section C.1 below.
B. Rejection under 35 U.S.C. 101
Applicant’s arguments have been fully considered but are not persuasive. The rejection of claims 1–4 and 6–9 under 35 U.S.C. 101 is maintained, with the analysis updated above to address the claims as amended.
1. The “closed-loop” characterization and the “physically measured” sensor data. Applicant argues that claim 1 recites “a closed-loop system operation in which boarding/alighting information is physically measured by sensors.” The claim recites no closed loop with any physical system. Its only physical elements are the sensors of the boarding/alighting point terminals and the communication network, and both serve solely to supply the input data on which the mental process operates. The output of the claim, the determined and re-determined movement path, is not provided to or acted on by any sensor, terminal, or vehicle. The “loop” applicant describes is a repeated evaluation: re-tallying passenger flows and re-planning a route as new counts become available. Repeating an abstract evaluation on updated data does not make it less abstract. See Elec. Power Grp., 830 F.3d at 1353–55; Recentive Analytics, 134 F.4th 1205. Nor does it matter that the input data is physically measured. Gathering data with sensors for use in an abstract analysis is insignificant extra-solution activity (MPEP 2106.05(g)), and the claimed sensors and terminals are recited generically and only by function, so they are not a particular machine that integrates the abstract idea (MPEP 2106.05(b)).
2. The asserted improvement to the “operation and control” of a transportation system. Applicant argues that the claim improves “the real-time operation and control of a demand-responsive vehicle transportation system.” The claims recite no operation or control of any vehicle or transportation system. Claim 1 ends with “re-determining the movement path of the vehicle”; it does not recite dispatching a vehicle, transmitting the path to a vehicle or operator, or causing any vehicle to travel the path. An asserted improvement supports eligibility only if the claim itself reflects it. MPEP 2106.04(d)(1); MPEP 2106.05(a). The same applies to applicant’s statement that “vectors associated with larger passenger flows are updated less frequently to stabilize routing decisions, while vectors associated with moderate flows are updated more frequently.” The claims require only that the first and second time intervals be “different”; they do not specify which interval is longer, so the direction of the difference on which applicant relies is not reflected in the claims.
3. The asserted technical effects. Applicant asserts (Remarks § 2.4) that the claimed update scheme (i) optimizes computing resources, (ii) stabilizes vehicle operation control, and (iii) reduces latency in capturing changes in flow. Consistent with MPEP 2106.04(d)(1) and 2106.05(a), the examiner has reviewed the specification to determine whether it describes a technological improvement and whether the claims reflect any such improvement. The specification attributes the threshold-dependent update scheme to a characteristic of passenger flows, not of any computer or other technology: “as the magnitude of the passenger movement flow vector increases, the direction of the passenger movement flow vector may be maintained for a longer time” ([0081]). It identifies the resulting benefit as transportation throughput and profitability: “it is possible to carry more passengers within a unit time by determining a movement path of a vehicle based on an updated passenger movement flow vector, and scheduling vehicle allocation” ([0084]); and “it is possible to carry more passengers within a unit time, and thus increase the profitability of a transportation system” ([0011]).
The specification does not describe any reduction in computing resources, any change to the operation of a computer, sensor, terminal, or network, or any control of a vehicle. The assertion of computing-resource optimization appears only in counsel’s remarks, and attorney argument cannot take the place of evidence in the record. In re Geisler, 116 F.3d 1465, 1470–71 (Fed. Cir. 1997); MPEP 2145(I). No declaration under 37 CFR 1.132 has been submitted. Moreover, even as described, the asserted benefits are improvements to the route-planning process itself: deciding when to re-tally passenger flows and how to re-plan a route so that more passengers are carried. An improvement in the abstract idea, however beneficial, is not an improvement in technology. SAP Am., 898 F.3d at 1167–68; MPEP 2106.05(a), subsection II. Performing an abstract calculation less often when a flow is large is a feature of the calculation, not an improvement in the functioning of the computer that performs it.
4. Claim 9. The amendment reciting “a non-transitory computer-readable recording medium” places claim 9 within a statutory category of invention (Step 1). It does not affect the analysis at Step 2A or Step 2B, under which claim 9 remains ineligible for the reasons given in the rejection.
C. Rejections under 35 U.S.C. 103
1. Arguments moot in view of new grounds of rejection. Applicant’s arguments with respect to amended claims 1 and 8 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The limitation applicant emphasizes, updating the passenger movement flow vector at different time intervals depending on which of two threshold-defined magnitude ranges the vector falls within, is addressed by Nakamura, and the newly added sensor-terminal limitation is addressed by Koga. “Gkiotsalitis2” is not relied upon. Zhao, which was applied in the prior action, is relied upon in the present ground only for its separate teaching of estimating station-to-station departure/arrival information from boarding information using alighting probabilities (Zhao [0080]–[0085]). Applicant’s arguments regarding Zhao (Remarks § 2.3) are directed only to whether Zhao discloses updates at different time intervals based on a vector’s magnitude, a teaching for which Zhao is not relied upon. Racah (US 2017/0314948 A1) and Neiger (US 2017/0270790 A1), applied in the prior action against claims 6 and 7, are not relied upon in this action.
2. Arguments directed to Gkiotsalitis, Xiaolei, and Zhao individually. Applicant argues that Gkiotsalitis “does not disclose adjusting an update time interval of a passenger movement flow vector based on the vector’s magnitude,” that Xiaolei “does not disclose any concept of update time intervals,” and that Zhao’s flow updates are “not adaptive updates at different time intervals based on a vector’s magnitude” (Remarks § 2.3). The rejection does not rely on any of these references for that limitation. Gkiotsalitis is relied upon for updated demand data, recurring demand-responsive updating, and re-determination of routes; Xiaolei for per-passenger origin-destination information, time-slot demand, and route generation; and Zhao for estimating origin-destination information from boarding counts. Nonobviousness cannot be shown by attacking references individually where the rejection is based on a combination of references. In re Keller, 642 F.2d 413, 426 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 1097 (Fed. Cir. 1986); MPEP 2145(IV).
3. Applicant’s distinctions do not distinguish Nakamura. Applicant distinguished Gkiotsalitis2 on three grounds. For completeness of the record, none distinguishes the combination now applied. (1) Two-band threshold structure. Nakamura expressly defines a first threshold value Th1 and a second threshold value Th2 and applies a different update regime in each band: when the counted number is equal to or greater than Th1 and less than Th2, each source’s upload interval is changed from 30 seconds to 60 seconds (Nakamura, col. 6, ll. 22–38; col. 11, ll. 15–17); when the counted number is equal to or greater than Th2, the retained sources continue to report at 30-second intervals (col. 6, ll. 42–49; col. 12, ll. 31–43). (2) Magnitude versus variance. Nakamura’s thresholds are applied to “the total number of vehicles” in the area (col. 6, ll. 22–49), a count of moving entities, which corresponds to the claimed magnitude representing a number of passengers, not to a statistical dispersion measure. (3) Offline segmentation versus real-time adaptive updating. Nakamura changes the update frequency during operation, in response to the currently detected count, expressly “to dynamically change the reception frequency of the probe data … in accordance with the vehicle density” and to “secure the real-time nature of the probe data” (col. 9, ll. 23–28). It is not an offline, pre-operational segmentation technique.
4. Asserted technical effects and the direction of the intervals. Applicant relies on the effects of updating large-magnitude flows less frequently and moderate flows more frequently (Remarks § 2.4(ii)–(iii)). The claims require only that the two intervals be different, not that either be longer, so the asserted effects rest on an unclaimed feature. In re Self, 671 F.2d 1344, 1348 (CCPA 1982); MPEP 2145(VI). Nakamura applies different update intervals in the two count-defined bands, which is all the claims require. Further, the reason to combine references need not be the same as applicant’s reason. In re Kemps, 97 F.3d 1427, 1430 (Fed. Cir. 1996); MPEP 2144(IV). The rejection relies on Nakamura’s own teaching that changing update frequency according to the counted density balances real-time data against communication and processing load.
5. Dependent claims. Applicant argues that claims 2–4, 6, 7, and 9 are allowable by virtue of their dependency. This argument is not persuasive because claims 1 and 8 are not allowable for the reasons above. Claims 6 and 7 are rejected over the combination applied to claim 1 for the reasons set forth in their respective analyses above.
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Lang, “Optimization of customized bus green line network based on passenger travel time window,” IOP Conf. Series: Earth and Environmental Science, Vol. 791, 012155 (2021), DOI 10.1088/1755-1315/791/1/012155. Lang discloses dividing passengers’ starting and ending points into two non-overlapping areas, a boarding (departure) area and an alighting (destination) area, with the number of passengers boarding and alighting at each station determined (pp. 1–2, sections 2.1 and 2.2; p. 4, Table 1). A customized bus serves stations in the boarding area until its capacity is reached, delivers passengers to stations in the alighting area, and returns to the parking lot empty (p. 2, section 2.2). Lang determines routes by minimizing an objective that includes passenger waiting cost and the time passengers spend boarding and alighting, subject to vehicle capacity, using a fleet of a given number of vehicles (pp. 2–4, sections 3.1–4). Lang is pertinent to at least the boarding and alighting clusters of claim 3, the allocation of a limited number of vehicles of claim 4, the boarding and waiting periods of claim 6, and the return path of claim 7.
Basalamah, US 9,460,615 B2 (issued Oct. 4, 2016). Basalamah discloses counting vehicles and/or pedestrians in directional traffic flows using monitors at fixed locations and assigning an updating priority to each location according to the multiple of an updating threshold that the count reaches (a medium priority at two to four times the threshold and a high priority at five or more times), with higher-priority locations updated at an earlier time than lower-priority locations, to update a traffic transportation plan map. Basalamah is pertinent to at least the magnitude-dependent updating of claims 1 and 8.
Semertzidis US 11,797,946 B2. This reference discloses bus stops each having sensors that track the number of passengers waiting at the stop, the stop sensors communicating over a network with a bus scheduling system that determines routes and schedules to meet demand. It is pertinent to at least the boarding/alighting point terminals and sensors of claims 1 and 8.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this Office action. Claims 1 and 8 were amended to add limitations not previously presented in any claim, namely boarding/alighting information “counted by sensors of boarding/alighting point terminals respectively installed at the plurality of boarding/alighting points and received via a communication network from the boarding/alighting point terminals,” a passenger movement flow vector “having a direction representing a movement flow of passengers and a magnitude representing a number of passengers included in the movement flow,” and re-determination of the movement path based on the updated vector, and to incorporate the update-interval subject matter of canceled claim 5 in modified form. The resulting claims are of a scope not previously presented, and the combinations of references applied in this action were required to address the claims as amended. Dependent claims 2–4, 6, 7, and 9 incorporate the amended limitations.
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUWABUSAYO ADEBANJO AWORUNSE whose telephone number is (571)272-4311. The examiner can normally be reached M - F (8:30AM - 5PM).
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/OLUWABUSAYO ADEBANJO AWORUNSE/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
