DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a Final Action in response to the claims filed on 04/03/2026.
Claims 1 – 4, have been amended.
Claims 9 – 16 are new claims.
Claims 1 – 16 are pending in this application.
Response to Remarks
Examiner’s Response to Remarks
Response to 35 U.S.C. 101
Response to 35 U.S.C. 102
Examiner’s Response to 35 U.S.C. § 101
Applicant argues claim 1 does not recite certain methods of organizing human activity and does not recite mathematical concepts; and the claims solves the problem of predicting traffic demand associated with holding various events.
Examiner respectfully disagrees. Applicant’s claim 1 is not directed towards a statutory category, as the claim does not recite any structure. Furthermore, claim 1 recites mental processes related to observation, judgment, and evaluation of data where the claim merely uses a computer as a tool to perform mental processes. Claim 1 further recites mathematical concepts, and particularly recites mathematical calculations as we have predict a number of people in an area from a predicted number of people obtained in advance for each of areas including at least one of a departure area and a return area of visitors based on at least a numerical value regarding a scale of use of each in each of areas obtained in advance; predict a number of people per route in an area obtained from the number of people in the area obtained by prediction; and predict a number of users based on the number of people per route obtained where the claim gathers data, predicts a number of users by a route search algorithm, and merely provides a prediction of a number of users per event nearest depot or per nearest route based on the number of people per route and performs acts of calculating using mathematical methods to determine a number. Accordingly claim 1 recites certain methods of organizing human activity and mathematical concepts.
The judicial exceptions are not integrated into a practical application, as the additional elements are merely generic computer components performing mathematical calculations. There are no additional elements recited that are significantly more than the judicial exception, as the claim merely resolves a marketing and sales business problem. The dependent claims inherit the same deficiencies as claim 1 and recite the same abstract ideas. Accordingly, all pending claims remain rejected under 35 U.S.C. § 101.
Examiner’s Response to 35 U.S.C. § 102
Applicant argues the present amendments overcome rejection under 35 U.S.C. 102(a)(2).
Examiner respectfully disagrees. Applicant argues amendments to independent claims. Further search and consideration is needed to respond to Applicant’s arguments. For these reasons, rejection under 35 U.S.C. § 102 remains for claims 1 – 8.
Claim Rejections – 35 U.S.C. §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 16, are rejected under 35 U.S.C. 101 because the claimed invention is directed towards non-statutory subject matter.
Claim 1 recites:
predict a number of people in an area from a predicted number of people obtained in advance for each of areas including at least one of a departure area and a return area of visitors to a target event based on at least a numerical value regarding a scale of use of each of depots in each of areas obtained in advance;
predict a number of people per route in an area obtained from the number of people in the area;
predict a number of users based on the number of people per route;
output the number of users per event nearest to a venue;
output the number of people per route between each of a plurality of combinations.
The limitations of claim 1, under its broadest reasonable interpretation, recites mental processes related to observation, judgment, and evaluation of data, but for the recitation of generic computer components; and uses a computer as a tool to perform mental processes. For example, claim 1 recites evaluate a number of people in an area from a predicted number of people observed in advance for each of areas including at least one of a departure area and a return area of visitors based on at least a numerical value regarding a scale of use of each in each of areas obtained in advance; evaluate a number of people per route in an area obtained from the number of people in the area obtained by prediction; evaluate a number of users based on the number of people per route observed; evaluate the number of users per event nearest to a venue; and evaluate the number of people per route between each of a plurality of combinations all involve evaluation and observation of data. Claim 9 is similar to claim 1. Accordingly, claims 1 and 9 recite mental processes.
Claim 1 further recites mathematical concepts, and particularly recites mathematical calculations as we have predict a number of people in an area from a predicted number of people obtained in advance for each of areas including at least one of a departure area and a return area of visitors based on at least a numerical value regarding a scale of use of each in each of areas obtained in advance; predict a number of people per route in an area obtained from the number of people in the area obtained by prediction; and predict a number of users based on the number of people per route obtained where the claim gathers data, predicts a number of users by a route search algorithm, and merely provides a prediction of a number of users per event nearest depot or per nearest route based on the number of people per route and performs acts of calculating using mathematical methods to determine a number. Accordingly claims 1 and 9 recite mathematical concepts.
The dependent claims encompass the same abstract ideas as well. For instance, claim 2 is directed towards evaluating the number of people in the area based on population data during a past event in addition to the numerical value regarding the scale of use in each area; claim 3 is directed towards evaluating the number of people in the area based on the numerical value regarding the scale of use in each area and an arithmetic average of population data during the past event; claim 4 is directed towards evaluating the number of people in the area from the numerical value regarding a scale of use of each in each area and population data during the past event based on a weighted average using a weight set to become greater as events have a greater number of users across an entire area; and claims 5 – 8 and 13 - 16 are directed towards evaluating an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; claim 10 is directed towards evaluating the number of people in the area based on population data during a past event in addition to the numerical value regarding the scale of use in each area; claim 11 is directed towards evaluating the number of people in the area based on the numerical value regarding the scale of use in each area and an arithmetic average of population data during the past event; and claim 12 is directed towards evaluating the number of people in the area from the numerical value regarding a scale of use of each in each area and population data during the past event based on a weighted average using a weight set to become greater as events have a greater number of users across an entire area where all are evaluating data. Thus, the dependent claims further limit the abstract ideas found in the independent claims.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of traffic demand prediction device, per depot, aaddition to reciting the additional elements of claim 1, claim 9 also recites the additional elements of to cause a terminal to display the number of users per event nearest depot, and to cause the terminal to display the number of people per route. The additional elements of a traffic demand prediction device, per depot, a
“[0032] For example, the traffic demand prediction device in an embodiment of the present disclosure may function as a computer that executes processing in the present embodiment. FIG. 7 is a diagram illustrating a hardware configuration example of the traffic demand prediction device 10 according to the embodiment of the present disclosure. The above-described traffic demand prediction device 10 may be physically configured as a computer apparatus including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like. [0033] In the following description, the term” apparatus" can be replaced with a circuit, a device, a unit, or the like. The hardware configuration of the traffic demand prediction device 10 may be configured to include one or a plurality of devices among the devices15 illustrate in the drawing or may be configured without including part of the devices.”
and thus are not practically integrated nor significantly more.
Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., traffic demand prediction device). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., traffic demand prediction device). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea. Therefore, the claims are directed to an abstract idea. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 8 and 10 – 16, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract ideas.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements
of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 16, are not patent eligible.
Claim Rejections – 35 U.S.C. §103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claims 1 – 3, 5 – 11, and 13 – 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Kato, Manabu et al. (JP-2018103924-A) hereinafter “Kato” in view of Otsuka, Rieko et al. (JP-2019177760-A) hereinafter “Otsuka” in view of Abe, Yuichi et al. (JP-2011075392-A) hereinafter “Abe”.
Claim 1:
Kato teaches the following:
A traffic demand prediction device comprising: processing circuitry configured to predict a number of people per depot in an area from a predicted number of people obtained in advance for each of areas including at least one of a departure area and a return area of visitors to a target event based on at least a numerical value regarding a scale of use of each of depots in each of areas obtained in advance; Kato teaches in Pg. 3, ¶¶ 11 – 13, the congestion prediction unit 108 is a device for predicting the degree of congestion in a predetermined area of the station using the prediction information acquired by the congestion data search unit 107 and the result of the number of people measurement unit 103; a flowchart illustrating a processing procedure of the congestion prediction unit 108; and the number of people measurement result is read from the number of people counting unit 103.
predict a number of people per route between each depot in an area obtained by a route search algorithm and an event nearest depot from the predicted number of people per depot in the area; Kato, teaches in Pg. 7, ¶ 17 congestion prediction process is executed using the input information determined in step 704 or 705 and the spatial information 400 stored in the spatial information database 106. Kato teaches in Pg. 7, ¶ 17, and Pg. 8, ¶ 1 as a prediction method, a method of predicting pedestrian flow by a known cellular automaton model is conceivable. In the congestion prediction process, the number of people present in each area defined by the partial space information 400 is output.
predict a number of users per event nearest depot or per nearest route based on the predicted number of people per route; Kato teaches in Pg. 2, ¶ 2, “a detection unit that detects arrival of a train, a storage unit that stores train arrival/departure information history and congestion information history, and a detection unit” Based on this information, the history data close to the current situation is retrieved from the storage unit and output as prediction information, a crowd data retrieval unit, a number counting unit that measures how many people have passed in which direction, and a number counting. And a congestion prediction unit that outputs a congestion prediction result based on the number of people measurement result and the prediction information output by the congestion data search unit;
Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station, Kato and Otsuka are similar where Kato and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
output the number of users per event nearest depot for a plurality of depots that are nearest to a venue of the target event; Otsuka teaches in Pg. 3, ¶ 6, the boarding train estimation program 134 reads the passenger demand information 124 stored in the data server 111, refers to the route information of each trip data, links the available trains, and thereby sets the train waiting time and the final arrival time. And the user behavior prediction information 127 is output. The boarding train estimation program 134 is automatically executed at regular intervals such as 5 minutes, 10 minutes, and 30 minutes, or is executed at a timing instructed by the system operator. Otsuka teaches in Pg. 3, ¶ 7 – 10, the passenger demand information aggregation program 135 reads the passenger demand information 124 stored in the data server 111 and aggregates according to the aggregation level to compress the number of records to be processed in order to shorten the calculation time of the boarding train estimation program. About the processing time when the boarding train estimation program is executed for the original passenger demand information before counting can be roughly estimated from the specifications (CPU and memory) of the execution environment. Further, in order to output the passenger number prediction information 430, the value of the number of trip data is added using "train ID, route ID, boarding station ID, and boarding station ID" as a key (step 1306). a flowchart of the station staying person counting program 137. The station staying person count program 137 reads the user behavior prediction information 127 output as a result of the boarding train estimation program 134 and outputs the station congestion prediction information 126. The station staying person count program 137 is automatically executed after completion of the boarding train estimation program 134 in association with the execution timing of the boarding train estimation program. Otsuka teaches in Pg. 7, ¶ 1, a train / station congestion prediction system including a processor that executes a program and a storage device that stores the program is described. This system is organically coupled with a measuring means for detecting the number of passengers installed in a boarding facility or a part of a vehicle for using transportation means. The storage device stores the number of passengers partially measured by the measuring means, and the processor aggregates the passenger data according to the specified estimated end time of calculation, and all the vehicles and all the getting-on / off facilities of the target transportation network Calculate the staying number of people at high speed.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, and Abe are similar where Kato, Otsuka, Abe teach predicting traffic information, train station passenger information and predicting arrival and departure information at train stations, and Abe further teaches the following:
between each of a plurality of combinations of (i) an event nearest depot as a departure depot, from the plurality of depots that are nearest to the venue and (ii) an arrival depot of a return area of visitors from a plurality of arrival depots; Abe teaches in Pg. 6, ¶ 14, and Pg. 7, ¶ 1, in the invention according to claim 4, in the navigation device according to claim 2, when the searched route is an advantageous route, the control means inputs the input from the parking lot that is the starting point of the searched route. A route to the determined destination is searched, and the parking lot that is the starting point of the route having the minimum required time or distance of the searched route is guided. According to such a configuration, when returning home from the destination or moving to the next destination, there is a low possibility of being involved in a traffic jam, and the minimum time from the parking lot to the input destination or It becomes possible to guide a parking lot that can move at a distance. Abe teaches in Pg. 7, ¶¶ 2 - 3, according to a fifth aspect of the present invention, in the navigation apparatus according to any one of the first to fourth aspects, the control means refers to event information including an event venue and is input by the input means. It is determined whether or not the place is an event venue, and when it is determined that the destination is an event venue, a parking lot that is the departure place of the searched route is guided. According to such a configuration, when the destination is an event venue, returning to the home after the end of the event, or moving to the next destination may cause a traffic jam caused by the event. Will be able to guide you through the low parking. In the invention concerning Claim 6, in the navigation apparatus concerning any one of Claim 1 thru | or 5, the said periphery of the destination is within the predetermined range centering on the said destination, and the nearest station of the said destination And a range including a predetermined range centered on a plurality of stations before and after the nearest station, the plurality of parking lots can be extracted in this range.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka with guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe to assist businesses with moving people from home, events, and destinations (Abe Spec. Pg. 5, ¶ 10).
Claim 2:
Kato, Otsuka, and Abe teach claim 1. Kato further teaches the following:
wherein the processing circuitry is configured to predict the number of people per depot in the area based on population data at each depot during a past event in addition to the numerical value regarding the scale of use of each of the depots in each area; Kato teaches in Pg. 2, ¶ 10, the train arrival/departure information history database 104 is a database that stores a history of past train arrivals and departures at the station. Kato teaches in Pg. 3, ¶ 1, a diagram showing an example of the data structure of the train arrival / departure information history database 104. Train arrival/departure information history data 200 is composed of arrival / departure information data ID 201, direction 202, train ID 203, arrival time 204, departure time 205, and situation 206. The data ID 201 is an ID for uniquely identifying data. The direction 202 is information for identifying the train number where the train arrives and departs. The train ID 203 is information for identifying a train. The arrival time 204 and the departure time 205 are information recording train arrival/departure information determined by the arrival/departure determination unit 102. The situation 206 is information that indicates how much the actual departure/arrival of the train has deviated from the plan diagram by comparing with the plan diagram information 110. Kato teaches in Pg. 3, ¶ 4, an example of the partial space information 500 recorded in the spatial information database 106. The partial space information 500 is information obtained by dividing the space information 400 formed of unit lattices into partial spaces representing the home 1 and the stairs. The congestion prediction unit 108 described later uses this partial space as an area, predicts the number of visitors for each area, and predicts the degree of congestion.
Claim 3:
Kato, Otsuka, and Abe teach claim 1. Kato further teaches the following:
The traffic demand prediction device according to wherein the processing circuitry is configured to predict the number of people per depot in the area based on the numerical value regarding the scale of scale of each of the depots in each area and an arithmetic average of population data at each depot during the past event; Kato teaches in Pg. 7, ¶ 17, and Pg. 8, ¶ 1, in step 706, the congestion prediction process is executed using the input information determined in step 704 or 705 and the spatial information 400 stored in the spatial information database 106. As a prediction method, a method of predicting pedestrian flow by a known cellular automaton model is conceivable. In the congestion prediction process, the number of people present in each area defined by the partial space information 400 is output.
Claim 5:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, and Abe are similar where Kato, Abe, and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
wherein the route search algorithm is an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; Otsuka teaches in Pg. 2, ¶ 6, the route information 406 may set only one route for the combination of the departure station ID and the arrival station ID when the actual route for each passenger is unknown in the acquired data. However, a plurality of routes may be distributed and stored by some method. With regard to route allocation, there are methods that use actual values obtained by tracing the travel route of each passenger using connection information with wireless access points installed in the station premises, and multiple route candidates are listed by route search. There is a method of allocating using functions. Alternatively, route candidates may be listed for each time zone using a train timetable, and specified by changing the distribution rate; Otsuka teaches in Pg. 12, ¶ 11, first, the route ID to be used first is obtained from the route information, and the train that travels on the relevant route is searched for the earliest arrival at the boarding station after time t (step 907). Otsuka teaches in Pg. 15, ¶ 7, the arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information; Otsuka teaches in Pg. 15, ¶ 6, an example in which the display is changed using the input interface; an example in which the map screen is enlarged and displayed by a zoom-in operation on the map screen; the congestion degree display area 1502 surrounding the map screen 1501 is not limited to the annular form, and only one of the sides may be used. The arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information, or may be arranged in order of name. Moreover, since there are many routes in a large-scale metropolitan area and it can be assumed that it is difficult to enumerate all routes on the screen space, the route to be displayed may be selected. Alternatively, only the routes displayed in the map screen 1501 may be displayed in the congestion level display area 1502. In that case, the list of routes displayed in the congestion degree display area 1502 dynamically changes according to the viewpoint position of the map screen 1501.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Claim 6:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, and Abe are similar where Kato, Abe, and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
wherein the route search algorithm is an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; Otsuka teaches in Pg. 2, ¶ 6, the route information 406 may set only one route for the combination of the departure station ID and the arrival station ID when the actual route for each passenger is unknown in the acquired data. However, a plurality of routes may be distributed and stored by some method. With regard to route allocation, there are methods that use actual values obtained by tracing the travel route of each passenger using connection information with wireless access points installed in the station premises, and multiple route candidates are listed by route search. There is a method of allocating using functions. Alternatively, route candidates may be listed for each time zone using a train timetable, and specified by changing the distribution rate; Otsuka teaches in Pg. 12, ¶ 11, first, the route ID to be used first is obtained from the route information, and the train that travels on the relevant route is searched for the earliest arrival at the boarding station after time t (step 907). Otsuka teaches in Pg. 15, ¶ 7, the arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information; Otsuka teaches in Pg. 15, ¶ 6, an example in which the display is changed using the input interface; an example in which the map screen is enlarged and displayed by a zoom-in operation on the map screen; the congestion degree display area 1502 surrounding the map screen 1501 is not limited to the annular form, and only one of the sides may be used. The arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information, or may be arranged in order of name. Moreover, since there are many routes in a large-scale metropolitan area and it can be assumed that it is difficult to enumerate all routes on the screen space, the route to be displayed may be selected. Alternatively, only the routes displayed in the map screen 1501 may be displayed in the congestion level display area 1502. In that case, the list of routes displayed in the congestion degree display area 1502 dynamically changes according to the viewpoint position of the map screen 1501.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Claim 7:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, and Abe are similar where Kato, Abe, and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
wherein the route search algorithm is an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; Otsuka teaches in Pg. 2, ¶ 6, the route information 406 may set only one route for the combination of the departure station ID and the arrival station ID when the actual route for each passenger is unknown in the acquired data. However, a plurality of routes may be distributed and stored by some method. With regard to route allocation, there are methods that use actual values obtained by tracing the travel route of each passenger using connection information with wireless access points installed in the station premises, and multiple route candidates are listed by route search. There is a method of allocating using functions. Alternatively, route candidates may be listed for each time zone using a train timetable, and specified by changing the distribution rate; Otsuka teaches in Pg. 12, ¶ 11, first, the route ID to be used first is obtained from the route information, and the train that travels on the relevant route is searched for the earliest arrival at the boarding station after time t (step 907). Otsuka teaches in Pg. 15, ¶ 7, the arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information; Otsuka teaches in Pg. 15, ¶ 6, an example in which the display is changed using the input interface; an example in which the map screen is enlarged and displayed by a zoom-in operation on the map screen; the congestion degree display area 1502 surrounding the map screen 1501 is not limited to the annular form, and only one of the sides may be used. The arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information, or may be arranged in order of name. Moreover, since there are many routes in a large-scale metropolitan area and it can be assumed that it is difficult to enumerate all routes on the screen space, the route to be displayed may be selected. Alternatively, only the routes displayed in the map screen 1501 may be displayed in the congestion level display area 1502. In that case, the list of routes displayed in the congestion degree display area 1502 dynamically changes according to the viewpoint position of the map screen 1501.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Claim 8:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, and Abe are similar where Kato, Abe, and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
wherein the route search algorithm is an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; Otsuka teaches in Pg. 2, ¶ 6, the route information 406 may set only one route for the combination of the departure station ID and the arrival station ID when the actual route for each passenger is unknown in the acquired data. However, a plurality of routes may be distributed and stored by some method. With regard to route allocation, there are methods that use actual values obtained by tracing the travel route of each passenger using connection information with wireless access points installed in the station premises, and multiple route candidates are listed by route search. There is a method of allocating using functions. Alternatively, route candidates may be listed for each time zone using a train timetable, and specified by changing the distribution rate; Otsuka teaches in Pg. 12, ¶ 11, first, the route ID to be used first is obtained from the route information, and the train that travels on the relevant route is searched for the earliest arrival at the boarding station after time t (step 907). Otsuka teaches in Pg. 15, ¶ 7, the arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information; Otsuka teaches in Pg. 15, ¶ 6, an example in which the display is changed using the input interface; an example in which the map screen is enlarged and displayed by a zoom-in operation on the map screen; the congestion degree display area 1502 surrounding the map screen 1501 is not limited to the annular form, and only one of the sides may be used. The arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information, or may be arranged in order of name. Moreover, since there are many routes in a large-scale metropolitan area and it can be assumed that it is difficult to enumerate all routes on the screen space, the route to be displayed may be selected. Alternatively, only the routes displayed in the map screen 1501 may be displayed in the congestion level display area 1502. In that case, the list of routes displayed in the congestion degree display area 1502 dynamically changes according to the viewpoint position of the map screen 1501.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Claim 9:
Kato teaches the following:
processing circuitry configured to predict a number of people per depot in an area from a predicted number of people obtained in advance for each of areas including at least one of a departure area and a return area of visitors to a target event based on at least a numerical value regarding a scale of use of each of depots in each of areas obtained in advance; Kato teaches in Pg. 3, ¶¶ 11 – 13, the congestion prediction unit 108 is a device for predicting the degree of congestion in a predetermined area of the station using the prediction information acquired by the congestion data search unit 107 and the result of the number of people measurement unit 103; a flowchart illustrating a processing procedure of the congestion prediction unit 108; and the number of people measurement result is read from the number of people counting unit 103;
predict a number of people per route between each depot in an area obtained by a route search algorithm and an event nearest depot from the predicted number of people per depot in the area; Kato, teaches in Pg. 7, ¶ 17 congestion prediction process is executed using the input information determined in step 704 or 705 and the spatial information 400 stored in the spatial information database 106. Kato teaches in Pg. 7, ¶ 17, and Pg. 8, ¶ 1 as a prediction method, a method of predicting pedestrian flow by a known cellular automaton model is conceivable. In the congestion prediction process, the number of people present in each area defined by the partial space information 400 is output;
predict a number of users per event nearest depot or per nearest route based on the predicted number of people per route output the number of users per event nearest depot for a plurality of depots that are nearest to a venue of the target event; Kato teaches in Pg. 2, ¶ 2, “a detection unit that detects arrival of a train, a storage unit that stores train arrival / departure information history and congestion information history, and a detection unit” Based on this information, the history data close to the current situation is retrieved from the storage unit and output as prediction information, a crowd data retrieval unit, a number counting unit that measures how many people have passed in which direction, and a number counting. And a congestion prediction unit that outputs a congestion prediction result based on the number of people measurement result and the prediction information output by the congestion data search unit;
Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; and Kato and Otsuka are similar where Kato and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
output the number of users per event nearest depot for a plurality of depots that are nearest to a venue of the target event, to cause a terminal to display the number of users per event nearest depot; Otsuka teaches in Pg. 3, ¶ 6, the boarding train estimation program 134 reads the passenger demand information 124 stored in the data server 111, refers to the route information of each trip data, links the available trains, and thereby sets the train waiting time and the final arrival time. And the user behavior prediction information 127 is output. The boarding train estimation program 134 is automatically executed at regular intervals such as 5 minutes, 10 minutes, and 30 minutes, or is executed at a timing instructed by the system operator; Otsuka teaches in Pg. 3, ¶ 7 – 10, the passenger demand information aggregation program 135 reads the passenger demand information 124 stored in the data server 111 and aggregates according to the aggregation level to compress the number of records to be processed in order to shorten the calculation time of the boarding train estimation program. About the processing time when the boarding train estimation program is executed for the original passenger demand information before counting can be roughly estimated from the specifications (CPU and memory) of the execution environment. Further, in order to output the passenger number prediction information 430, the value of the number of trip data is added using "train ID, route ID, boarding station ID, and boarding station ID" as a key (step 1306). a flowchart of the station staying person counting program 137. The station staying person count program 137 reads the user behavior prediction information 127 output as a result of the boarding train estimation program 134 and outputs the station congestion prediction information 126. The station staying person count program 137 is automatically executed after completion of the boarding train estimation program 134 in association with the execution timing of the boarding train estimation program. Otsuka teaches in Pg. 7, ¶ 1, a train / station congestion prediction system including a processor that executes a program and a storage device that stores the program is described. This system is organically coupled with a measuring means for detecting the number of passengers installed in a boarding facility or a part of a vehicle for using transportation means. The storage device stores the number of passengers partially measured by the measuring means, and the processor aggregates the passenger data according to the specified estimated end time of calculation, and all the vehicles and all the getting-on / off facilities of the target transportation network Calculate the staying number of people at high speed. Otsuka teaches in Pg. 16, ¶ 4, Regarding the display contents of the congestion degree display area 1502, as described above, the average value of the congestion degree of the stations constituting each partial section, the average value and the maximum value of the passengers of the trains traveling in each section, and the like can be mentioned. Moreover, you may use a boarding rate instead of the boarding person of a train. At this time, the display size of each partial section may be adjusted according to the size of the indicator to be displayed such as the total number of people staying in each partial section and the average congestion level.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, and Abe are similar where Kato, Otsuka, and Abe teach predicting traffic information, train station passenger information and predicting arrival and departure information at train stations, and Abe further teaches the following:
and output the number of people per route between each of a plurality of combinations of (i) an event nearest depot as a departure depot, from the plurality of depots that are nearest to the venue and (ii) an arrival depot of a return area of visitors from a plurality of arrival depots, to cause the terminal to display the number of people per route; Abe teaches in Pg. 6, ¶ 14, and Pg. 7, ¶ 1, in the invention according to claim 4, in the navigation device according to claim 2, when the searched route is an advantageous route, the control means inputs the input from the parking lot that is the starting point of the searched route. A route to the determined destination is searched, and the parking lot that is the starting point of the route having the minimum required time or distance of the searched route is guided. According to such a configuration, when returning home from the destination or moving to the next destination, there is a low possibility of being involved in a traffic jam, and the minimum time from the parking lot to the input destination or It becomes possible to guide a parking lot that can move at a distance. Abe teaches in Pg. 7, ¶¶ 2 - 3, according to a fifth aspect of the present invention, in the navigation apparatus according to any one of the first to fourth aspects, the control means refers to event information including an event venue and is input by the input means. It is determined whether or not the place is an event venue, and when it is determined that the destination is an event venue, a parking lot that is the departure place of the searched route is guided. According to such a configuration, when the destination is an event venue, returning to the home after the end of the event, or moving to the next destination may cause a traffic jam caused by the event. Will be able to guide you through the low parking. In the invention concerning Claim 6, in the navigation apparatus concerning any one of Claim 1 thru | or 5, the said periphery of the destination is within the predetermined range centering on the said destination, and the nearest station of the said destination And a range including a predetermined range centered on a plurality of stations before and after the nearest station, the plurality of parking lots can be extracted in this range.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka with guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe to assist businesses with moving people from home, events, and destinations (Abe Spec. Pg. 5, ¶ 10).
Claim 10:
Kato, Otsuka, and Abe teach claim 1. Kato further teaches the following:
wherein the processing circuitry is configured to predict the number of people per depot in the area based on population data at each depot during a past event in addition to the numerical value regarding the scale of use of each of the depots in each area; Kato teaches in Pg. 2, ¶ 10, the train arrival / departure information history database 104 is a database that stores a history of past train arrivals and departures at the station. Kato teaches in Pg. 3, ¶ 1, a diagram showing an example of the data structure of the train arrival / departure information history database 104. Train arrival / departure information history data 200 is composed of arrival / departure information data ID 201, direction 202, train ID 203, arrival time 204, departure time 205, and situation 206. The data ID 201 is an ID for uniquely identifying data. The direction 202 is information for identifying the train number where the train arrives and departs. The train ID 203 is information for identifying a train. The arrival time 204 and the departure time 205 are information recording train arrival / departure information determined by the arrival / departure determination unit 102. The situation 206 is information that indicates how much the actual departure / arrival of the train has deviated from the plan diagram by comparing with the plan diagram information 110. Kato teaches in Pg. 3, ¶ 4, an example of the partial space information 500 recorded in the spatial information database 106. The partial space information 500 is information obtained by dividing the space information 400 formed of unit lattices into partial spaces representing the home 1 and the stairs. The congestion prediction unit 108 described later uses this partial space as an area, predicts the number of visitors for each area, and predicts the degree of congestion.
Claim 11:
Kato, Otsuka, and Abe teach claim 1. Kato further teaches the following:
wherein the processing circuitry is configured to predict the number of people per depot in the area based on the numerical value regarding the scale of scale of each of the depots in each area and an arithmetic average of population data at each depot during the past event; Kato teaches in Pg. 7, ¶ 17, and Pg. 8, ¶ 1, in step 706, the congestion prediction process is executed using the input information determined in step 704 or 705 and the spatial information 400 stored in the spatial information database 106. As a prediction method, a method of predicting pedestrian flow by a known cellular automaton model is conceivable. In the congestion prediction process, the number of people present in each area defined by the partial space information 400 is output.
Claims 13:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, Abe are similar where Kato, Abe, and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
wherein the route search algorithm is an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; Otsuka teaches in Pg. 2, ¶ 6, the route information 406 may set only one route for the combination of the departure station ID and the arrival station ID when the actual route for each passenger is unknown in the acquired data. However, a plurality of routes may be distributed and stored by some method. With regard to route allocation, there are methods that use actual values obtained by tracing the travel route of each passenger using connection information with wireless access points installed in the station premises, and multiple route candidates are listed by route search. There is a method of allocating using functions. Alternatively, route candidates may be listed for each time zone using a train timetable, and specified by changing the distribution rate; Otsuka teaches in Pg. 12, ¶ 11, first, the route ID to be used first is obtained from the route information, and the train that travels on the relevant route is searched for the earliest arrival at the boarding station after time t (step 907). Otsuka teaches in Pg. 15, ¶ 7, the arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information; Otsuka teaches in Pg. 15, ¶ 6, an example in which the display is changed using the input interface; an example in which the map screen is enlarged and displayed by a zoom-in operation on the map screen; the congestion degree display area 1502 surrounding the map screen 1501 is not limited to the annular form, and only one of the sides may be used. The arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information, or may be arranged in order of name. Moreover, since there are many routes in a large-scale metropolitan area and it can be assumed that it is difficult to enumerate all routes on the screen space, the route to be displayed may be selected. Alternatively, only the routes displayed in the map screen 1501 may be displayed in the congestion level display area 1502. In that case, the list or routes displayed in the congestion degree display area 1502 dynamically changes according to the viewpoint position of the map screen 1501.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Claim 14:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, Abe are similar where Kato, Abe, and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
wherein the route search algorithm is an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; Otsuka teaches in Pg. 2, ¶ 6, the route information 406 may set only one route for the combination of the departure station ID and the arrival station ID when the actual route for each passenger is unknown in the acquired data. However, a plurality of routes may be distributed and stored by some method. With regard to route allocation, there are methods that use actual values obtained by tracing the travel route of each passenger using connection information with wireless access points installed in the station premises, and multiple route candidates are listed by route search. There is a method of allocating using functions. Alternatively, route candidates may be listed for each time zone using a train timetable, and specified by changing the distribution rate; Otsuka teaches in Pg. 12, ¶ 11, first, the route ID to be used first is obtained from the route information, and the train that travels on the relevant route is searched for the earliest arrival at the boarding station after time t (step 907). Otsuka teaches in Pg. 15, ¶ 7, the arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information; Otsuka teaches in Pg. 15, ¶ 6, an example in which the display is changed using the input interface; an example in which the map screen is enlarged and displayed by a zoom-in operation on the map screen; the congestion degree display area 1502 surrounding the map screen 1501 is not limited to the annular form, and only one of the sides may be used. The arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information, or may be arranged in order of name. Moreover, since there are many routes in a large-scale metropolitan area and it can be assumed that it is difficult to enumerate all routes on the screen space, the route to be displayed may be selected. Alternatively, only the routes displayed in the map screen 1501 may be displayed in the congestion level display area 1502. In that case, the list of routes displayed in the congestion degree display area 1502 dynamically changes according to the viewpoint position of the map screen 1501.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Claim 15:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, and Abe are similar where Kato, Abe, and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
wherein the route search algorithm is an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; Otsuka teaches in Pg. 2, ¶ 6, the route information 406 may set only one route for the combination of the departure station ID and the arrival station ID when the actual route for each passenger is unknown in the acquired data. However, a plurality of routes may be distributed and stored by some method. With regard to route allocation, there are methods that use actual values obtained by tracing the travel route of each passenger using connection information with wireless access points installed in the station premises, and multiple route candidates are listed by route search. There is a method of allocating using functions. Alternatively, route candidates may be listed for each time zone using a train timetable, and specified by changing the distribution rate; Otsuka teaches in Pg. 12, ¶ 11, first, the route ID to be used first is obtained from the route information, and the train that travels on the relevant route is searched for the earliest arrival at the boarding station after time t (step 907). Otsuka teaches in Pg. 15, ¶ 7, the arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information; Otsuka teaches in Pg. 15, ¶ 6, an example in which the display is changed using the input interface; an example in which the map screen is enlarged and displayed by a zoom-in operation on the map screen; the congestion degree display area 1502 surrounding the map screen 1501 is not limited to the annular form, and only one of the sides may be used. The arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information, or may be arranged in order of name. Moreover, since there are many routes in a large-scale metropolitan area and it can be assumed that it is difficult to enumerate all routes on the screen space, the route to be displayed may be selected. Alternatively, only the routes displayed in the map screen 1501 may be displayed in the congestion level display area 1502. In that case, the list of routes displayed in the congestion degree display area 1502 dynamically changes according to the viewpoint position of the map screen 1501.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Claim 16:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; and Kato, Otsuka, Abe are similar where Kato, Abe, and Otsuka teach predicting train station passenger information and predicting arrival and departure information at train stations, and Otsuka further teaches the following:
wherein the route search algorithm is an algorithm that performs route search based on geographical route map information, operation information along a time axis, and priority matters in a route search; Otsuka teaches in Pg. 2, ¶ 6, the route information 406 may set only one route for the combination of the departure station ID and the arrival station ID when the actual route for each passenger is unknown in the acquired data. However, a plurality of routes may be distributed and stored by some method. With regard to route allocation, there are methods that use actual values obtained by tracing the travel route of each passenger using connection information with wireless access points installed in the station premises, and multiple route candidates are listed by route search. There is a method of allocating using functions. Alternatively, route candidates may be listed for each time zone using a train timetable, and specified by changing the distribution rate; Otsuka teaches in Pg. 12, ¶ 11, first, the route ID to be used first is obtained from the route information, and the train that travels on the relevant route is searched for the earliest arrival at the boarding station after time t (step 907). Otsuka teaches in Pg. 15, ¶ 7, the arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information; Otsuka teaches in Pg. 15, ¶ 6, an example in which the display is changed using the input interface; an example in which the map screen is enlarged and displayed by a zoom-in operation on the map screen; the congestion degree display area 1502 surrounding the map screen 1501 is not limited to the annular form, and only one of the sides may be used. The arrangement of routes in the congestion degree display area 1502 may be determined in consideration of actual spatial position information, or may be arranged in order of name. Moreover, since there are many routes in a large-scale metropolitan area and it can be assumed that it is difficult to enumerate all routes on the screen space, the route to be displayed may be selected. Alternatively, only the routes displayed in the map screen 1501 may be displayed in the congestion level display area 1502. In that case, the list of routes displayed in the congestion degree display area 1502 dynamically changes according to the viewpoint position of the map screen 1501.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka to assist businesses with displaying routes and stations on a map (Otsuka Spec. Pg. 16, ¶ 4).
Claims 4 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Kato, Manabu et al. (JP-2018103924-A) hereinafter “Kato” in view of Otsuka, Rieko et al. (JP-2019177760-A) hereinafter “Otsuka” in view of Abe, Yuichi et al. (JP-2011075392-A) hereinafter “Abe” in view of Yoshida, Kazuki et al. (U.S. Publication No. 2023/0162103) hereinafter “Yoshiba”.
Claim 4:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; Yoshida teaches transportation time between points, predicting waypoints, and predicting people flow; and Kato, Otsuka, Abe, and Yoshida are similar where Kato, Otsuka, and Yoshida teach predicting the movement of people of passengers and Yoshida further teaches the following:
the traffic demand prediction device according to wherein the processing circuitry is configured to predict the number of people per depot in the area from the numerical value regarding a scale of use of each of the depots in each area and population data at each depot during the past event based on a weighted average using a weight set to become greater as events have a greater number of users across an entire area. Yoshida, ¶ 0007, a people flow prediction device according to the disclosure is a people flow prediction device predicting, in a facility including a waypoint where people pass, the number of people passing through the waypoint at a predetermined time in a future and includes a predictive value acquisition unit acquiring at least one of a first predictive value, which is a predictive value of the number of people located at a first point upstream from the waypoint in a movement direction of people before the predetermined time, or a second predictive value, which is a predictive value of the number of people located at a second point downstream from the waypoint in the movement direction of the people after the predetermined time, and a predictive waypoint-pass-through value calculation unit calculating a predictive waypoint-pass-through value, which is a predictive value of the number of people passing through the waypoint at the predetermined time, based on at least one of the first predictive value or the second predictive value; Yoshiba teaches in ¶ 0044, the predictive waypoint-pass-through value calculation unit 22 assigns a weight to the first predictive value for each past time and assigns a weight to the second predictive value for each future time, based on the influence degree, and then calculates the average value of the weighted values as the predictive waypoint-pass-through value at the predetermined time t. Specifically, the predictive waypoint-pass-through value calculation unit 22 calculates, for each past time, the product of the total value of the first predictive values of the respective first points A at the past time and the influence degree set for the past time and calculates the total of these calculated values as a first total value. Then, the predictive waypoint-pass-through value calculation unit 22 calculates, for each future time, the product of the total value of the second predictive values of the respective second points C at the future time and the influence degree set for the future time and calculates the total of these calculated values as a second total value. Then, the predictive waypoint-pass-through value calculation unit 22 calculates the total value of the first total value and the second total value as the predictive waypoint-pass-through value at the predetermined time t. That is, in the present embodiment, the predictive waypoint-pass-through value calculation unit 22 calculates a predictive waypoint-pass-through value Nt at the time t using Equation 1.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with a calculation unit calculating a predictive waypoint-pass-through value as a predictive value of the number of people passing through a waypoint at a predetermined time in a future of Yoshida to assist businesses with assigning weights and calculating averages for the number of people passing through points at different times (Yoshida, Spec. ¶ 0009).
Claim 12:
Kato, Otsuka, and Abe teach claim 1. Kato teaches a congestion prediction unit, arrival and departure determination, a number of people measurement unit, an output unit, and arrival and departure history; and Otsuka teaches user behavior predictions, boarding train estimation, passenger demand information, arrival and destination train station; Abe teaches advantageous route for returning home, next destination, returning home after the end of the event; Yoshida teaches transportation time between points, predicting waypoints, and predicting people flow; and Kato, Otsuka, Abe, and Yoshida are similar where Kato, Otsuka, Abe and Yoshida teach predicting traffic information, train station passenger information and predicting arrival and departure information at train stations, and Yoshida further teaches the following:
wherein the processing circuitry is configured to predict the number of people per depot in the area from the numerical value regarding a scale of use of each of the depots in each area and population data at each depot during the past event based on a weighted average using a weight set to become greater as events have a greater number of users across an entire area; Yoshida, ¶ 0007, a people flow prediction device according to the disclosure is a people flow prediction device predicting, in a facility including a waypoint where people pass, the number of people passing through the waypoint at a predetermined time in a future and includes a predictive value acquisition unit acquiring at least one of a first predictive value, which is a predictive value of the number of people located at a first point upstream from the waypoint in a movement direction of people before the predetermined time, or a second predictive value, which is a predictive value of the number of people located at a second point downstream from the waypoint in the movement direction of the people after the predetermined time, and a predictive waypoint-pass-through value calculation unit calculating a predictive waypoint-pass-through value, which is a predictive value of the number of people passing through the waypoint at the predetermined time, based on at least one of the first predictive value or the second predictive value; Yoshiba teaches in ¶ 0044, the predictive waypoint-pass-through value calculation unit 22 assigns a weight to the first predictive value for each past time and assigns a weight to the second predictive value for each future time, based on the influence degree, and then calculates the average value of the weighted values as the predictive waypoint-pass-through value at the predetermined time t. Specifically, the predictive waypoint-pass-through value calculation unit 22 calculates, for each past time, the product of the total value of the first predictive values of the respective first points A at the past time and the influence degree set for the past time and calculates the total of these calculated values as a first total value. Then, the predictive waypoint-pass-through value calculation unit 22 calculates, for each future time, the product of the total value of the second predictive values of the respective second points C at the future time and the influence degree set for the future time and calculates the total of these calculated values as a second total value. Then, the predictive waypoint-pass-through value calculation unit 22 calculates the total value of the first total value and the second total value as the predictive waypoint-pass-through value at the predetermined time t. That is, in the present embodiment, the predictive waypoint-pass-through value calculation unit 22 calculates a predictive waypoint-pass-through value Nt at the time t using Equation 1.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a device for predicting congestion at a station from information that can be acquired by a station alone without needing timetable information and for supporting a countermeasure against congestion of Kato and predicting and visualizing the congestion state of trains and stations at high speed for a large-scale route network of Otsuka and guiding a parking lot that can avoid a traffic jam due to event holding when participating in an event where a large number of people gather of Abe with a calculation unit calculating a predictive waypoint-pass-through value as a predictive value of the number of people passing through a waypoint at a predetermined time in a future of Yoshida to assist businesses with assigning weights and calculating averages for the number of people passing through points at different times (Yoshida, Spec. ¶ 0009).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Faaborg; Alexander (U.S. Publication No. 2015/0185016) discloses predicting transit calculations.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
06/10/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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