DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/18/2024 and 11/12/2024, have been acknowledged. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. The initialed and dated copies of Applicant’s IDS forms, 1449, are attached to the instant Office Action.
Status of Claims
This is a Non-Final Action on the merits in response to the application filed on 07/18/2024.
Claims 1 – 8 are pending in this application.
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 – 8, are rejected under 35 U.S.C. 101 because the claimed invention is directed towards non-statutory subject matter.
The claim(s) do not fall within at least one of the four categories of patent eligible subject matter (process, machine, manufacture, or composition of matter) because the claimed invention is directed to software per se. This means that no structure is recited in the claimed invention. For instance, all elements of claim 1 do not recite structural elements and under broadest reasonable interpretation are software. Dependent claims 2 – 8, encompass the same language as claim 1. The dependent claims, when analyzed both individually and in combination, are also held to be software because they do not recite any structural elements. Thus claims 1 – 8 are rejected and do not fall within at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101, e.g., the claim(s) are directed to software per se, mere information in the form of data. (See MPEP § 2106, subsection I).
Claims 1 – 8 are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
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 obtained by prediction;
predict a number of users based on the number of people per route obtained.
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; and evaluate a number of users based on the number of people per route observed all involve evaluation and observation of data. Accordingly, claim 1 recites an abstract idea of 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 mathematical concepts.
The dependent claims encompass the same abstract ideas as well. For instance, claim 2 recites wherein evaluate 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 recites wherein evaluate 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 recites wherein evaluate 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, recite 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 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, a depot headcount prediction unit, 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 illustred in the drawing or may be configured without including part of the devices.”
and thus are not practically integrated nor significantly more.
When considering the claim as a whole, claim 1 does not include additional elements alone or in combination that integrate the judicial exception into a practical application. 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, 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 – 8, are not patent eligible.
Claim Rejections – 35 U.S.C. § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 3, are rejected under 35 U.S.C. § 102(a)(2) as anticipated by Kato, Manabu et al. (JP 2018103924A) hereinafter “Kato”.
Claim 1:
Kato teaches claim 1. Kato further teaches the following:
A traffic demand prediction device comprising: a depot headcount prediction unit 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.
a route headcount prediction unit configured to 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 number of people per depot in the area obtained by prediction in the depot headcount prediction unit; 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.
and a use headcount prediction unit configured to predict a number of users per event nearest depot or per nearest route based on the number of people per route obtained by prediction in the route headcount prediction unit; 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.
Claim 2:
Kato teaches claim 1. Kato further teaches the following:
The traffic demand prediction device according to the traffic demand prediction device according to wherein the depot headcount prediction unit 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 teaches claim 1. Kato further teaches the following:
The traffic demand prediction device according to The traffic demand prediction device according to wherein the depot headcount prediction unit 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 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.
Claim 4, is rejected under 35 U.S.C. § 103 as being unpatentable over Kato, Manabu et al. (JP 2018103924A) hereinafter “Kato” in view of Yoshida, Kazuki et al. (U.S. Publication No. 2023/0162103) hereinafter “Yoshiba”.
Claim 4:
While Kato teaches claim 1, Kato does not explicitly teach assigning weights and calculating averages. However, Yoshiba teaches the following:
The traffic demand prediction device according to wherein the depot headcount prediction unit 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 with an operation condition setting device includes 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 and is calculated in consideration that an arrival time to the waypoint deviates from a reference arrival time to the waypoint 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).
Claims 5 – 7, are rejected under 35 U.S.C. § 103 as being unpatentable over Kato, Manabu et al. (JP 2018103924A) hereinafter “Kato” in view of Otsuka, Rieko et al. (JP 2019177760A) hereinafter “Otsuka”.
Claim 5:
While Kato teaches claim 1, Kato does not explicitly teach route search and a map with routes displayed. However, Otsuka 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 with predict and visualize 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 is rejected under 35 U.S.C. § 103 as being unpatentable over Kato, Manabu et al. (JP 2018103924A) hereinafter “Kato” in view of Yoshida, Kazuki et al. (U.S. Publication No. 2023/0162103) hereinafter “Yoshiba” in view of Otsuka, Rieko et al. (JP 2019177760A) hereinafter “Otsuka”.
Claim 8:
While Kato teaches claim 1, Kato does not explicitly teach Dijkstra’s method. However, Sekimura 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 with predict and visualize 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).
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 Keng, Brian et al. (U.S. Publication No. 2021/012,5073) discloses a system and method for individual forecasting of a future event for a subject using historical data.
- Reference Tokumaru, Makoto (U.S. Publication No. 2019/003,9634) discloses a train operation control system includes: a headcount estimation device that estimates the number of people in a train and the number of people on a platform.
- Reference Sekimura, Toshiyuki (JP 2007078527) hereinafter “Sekimura” discloses a route search device capable of performing a route search in consideration of a communication state between an access point and an in-vehicle device.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday. Examiner can be reached via Fax at 571-483-7338. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor Beth Boswell can be reached at (571) 272-6737.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
1/08/2026
/ROBERT D RINES/Primary Examiner, Art Unit 3625