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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR
1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/27/2026 has been entered.
Response to Amendment
The amendment filed 05/27/2026 has been entered. As directed, claim 9 has been amended, claims
11-14 has been canceled, claim 16 has been withdrawn, and no claim is added. Thus claims 9-10 remain pending in the application. However, new rejection under 35 U.S.C 112(b) has been made in the current office action based on the amendment.
Response to Arguments
With respect to the Applicant’s argued claim interpretation in “Applicant Arguments/Remarks Made in an Amendment,”
Applicant argues:
…
Applicant has amended claim 9 so that none of the limitations can be considered to fall within the Mental Process or the Mathematical Concept groupings of Abstract ideas.
As amended claim 9 sets forth "read the architectural model data stored in the storage unit, convert the read architectural model data into an executable format of elevator platform data, [and] estimate, by using the elevator platform data, a front-of- line position that is a position at which people who use elevators start to line up at an elevator platform, and a first position determined based on a distance measured by using the elevator platform data from an entrance to a location of the elevator platform. These limitations cannot be practically performed by the human mind because they require using executable format data (i.e., elevator platform data converted from the read architectural model data).
For example, the specification states:
Here, converting into data of an executable format means converting the architectural model data 111 into, for example, elevator platform data. That is, in the conversion into the data of an executable format, the architectural model data reading unit 101 extracts the elevator platform data from the architectural model data 111. Then, the architectural model data reading unit 101 inputs the extracted elevator platform data to the front-of-line position estimation unit 102.
Para. [0037] of the pre-grant publication corresponding to the present application (US 2022/0414278). The specification also explains:
The front-of-line position estimation unit 102 estimates the front-of-line position on the basis of the input elevator platform data. The front-of- line position information estimated by the front-of-line position on the basis of the input elevator data. The front-of-line position information estimated by the front-of-line position estimation unit 102 is input to the simulation execution unit 103. At this time, data obtained by converting the architectural model data 111 read by the architectural model data reading unit 101 into a simulation executable format is also input to the simulation execution unit 103.
Para. [0039]. A claim with limitation(s) that cannot practically be performed in the human mind does not recite a mental process." MPEP 2106.04(a)(2) III. A. Emphasis added.
Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, Cir. 2019).
Id. Emphasis added.
In addition, claim 9, when considered as a whole, does not recite a mathematical concept. While some limitations may be based on or involve a mathematical concept, claim 9 does not recite a mathematical concept. See MPEP 2106.04(a)(2), which states:
A claim does not recite mathematical concept (i.e., the claim limit do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept.
MPEP 2106.04(a)(2) (emphasis added); see, e.g., Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017).
Certain aspects of Applicant's claim 9 may be based on or involve a mathematical concept but claim 9 is not directed to mathematical relationships and calculations such that it merely amounts to an abstract idea. Rather, claim 9 is directed to estimating movements of people at an elevator platform.
For example, claim 1 does not include a relationship between a reaction rate and temperature, as in Diamond v. Diehr; 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981). Claim 1 is also not comparable to "a conversion between binary coded decimal and pure binary," as in Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972) and is not comparable to "a mathematical relationship between enhanced directional radio activity and antenna conductor arrangement (i.e., the length of the conductors with respect to the operating wave length and the angle between the conductors)," as in Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86, 91, 40 USPQ 199, 201 (1939).
For at least these reasons, claims 9 and 10 are not directed to abstract ideas under Step 2A Prong One. Therefore, the rejection of the claims under 35 U.S.C. § 101 should be withdrawn.
(see Response filed 05/27/2026 [pages 4-7]).
With respect to applicant's argument, the examiner respectfully disagrees that limitations cannot be practically performed by the human mind because they require the use of executable format data, i.e., elevator platform data converted from the read architectural model data. The rejection does not treat the reading and conversion operations themselves as the recited mental process. Rather, the mental process resides in the broadly recited determination and estimation operations performed using the resulting spatial information. The limitation of “executable format of elevator platform data” merely describes the form in which the architectural model information is provided for subsequent processing. Claim 9 does not recite a particular conversion algorithm, specialized executable data structure, or computer specific technique that changes how the first position, front-of-line position or movements of people are determined or estimated. Thus, requiring the spatial information to be represented in a computer usable format does not remove the later recited observation, evaluation, judgment, and reasoning process from the mental process grouping. Claim 10 likewise recites a mental process because the recited limitation merely further narrows the basis on which the front-of-line position is estimated and does not change the mental nature of the recited determination and estimation.
Further, regarding claimed additional limitations under Step 2A, Prong Two and Step 2B. Please refer to the current Office Action for the details of analysis under 35 U.S.C. § 101. The additional limitations are merely adding a recitation of insignificant extra-solution activities such as data gathering activity, and merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use, which do not integrate a judicial exception into practical application and amount to significantly more than judicial exception. See MPEP §§ 2106. 05 (f), (g), and (h).
Therefore, the claims 9 and 10 recite “mental process”, does not integrated judicial exception into practical application and amount to significantly more, and rejection under 35 U.S.C. § 101 is maintained.
Applicant’s arguments with respect to claim(s) 9 have been considered but are moot because the
new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The newly applied reference Li (US20210272314A1) teaches estimating a front-of-line position by identifying a queue head object relative to preset mark points associate with respective service locations and the surrounding passable area configuration. Marusco (US20170089709A1) teaches determining a first position based on a distance measured from an entrance to another location. Therefore, the combination of Mihashi (JP2014010659A) in view of Fujiwara (“Development of Pedestrian Flow Simulator for Smooth People Movement in Buildings,” published in 2018) and Li (US20210272314A1) and Marusco (US20170089709A1) teach or suggest the amended limitations of claim 9, and the rejection of claims 9-10 under 35 U.S.C. §103 is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The claim(s) 9-10 are rejected under 35 USC § 101 because the claimed invention is directed to
judicial exception an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated
the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance
published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates,
and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 9-10 are directed to system and fall within the statutory category of machine.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claim 9: The limitations of “estimate, by using the elevator platform data, a front-of-line position that is a position at which people who use elevators start to line up at an elevator platform, and a first position determined based on a distance measured by using the elevator platform data from an entrance to a location of the elevator platform, and … using the front-of-line position and the architectural model data to estimate movements of people at the elevator platform” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the human mind. For example, a person is capable of observing spatial information representing an elevator platform layout, including the locations of the elevators, maternally selecting a first position based on a distance measured from the entrance to a location of the elevator platform, mentally estimating a front-of-line position at which people start to line up at the elevator platform, and mentally estimating movement of people at the elevator platform base on the estimated front-of-line position. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
Examiner note: the limitation is recited at a high level of generality and does not require any specific technological mechanism, specialized algorithm, or particular computer operation for performing the determination and estimation. Therefore, the limitation does not include a constraint that would preclude performance in the human mind or with pen and paper, and is reasonably considered as a mental process. See MPEP § 2106.4(a)(2)(III).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Prong I step 2A.
Step 2A Prong 2: Claim 9: The judicial exception is not integrated into a practical application.
In particular, the claim recites the following additional elements: “An architectural model data assistance system comprising: a storage unit that stores architectural model data and simulation data; a processor; and a memory storing instructions, that when executed by the processor, configures the processor to:,” which are merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception, which does not integrate judicial exception into a practical application (see MPEP § 2106.05(f)).
Further, the following additional elements: “read the architectural model data stored in the storage unit,” are merely a recitation of insignificant extra-solution activity such as data gathering (i.e., receiving/extracting model data), which do not integrate a judicial exception into practical application. See MPEP § 2106.05(g).
Further, the following additional elements: “convert the read architectural model data into an executable format of elevator platform data” and “execute a simulation using the front-of-line position and the architectural model data to estimate …,” which is merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). In particular, the claim does not recite a specific data conversion technique, executable data structure, simulation algorithm, or computer operation that improves the functioning of a computer or another technology. Rather, the architectural model data is merely converted into a form usable by the simulation, and the simulation merely uses the converted model data and the estimated positions to estimate movements of people. Therefore, these additional limitations merely use generic computer processing to apply the recited mental process and do not integrate the judicial exception into a practical application.
Alternatively, the limitations merely link the use of the judicial exception to a particular technological environment or field of use, such as field of elevator platform simulation. See MPEP § 2106.05(h). Accordingly, the additional limitations does not integrate the judicial exception into a practical application.
Therefore, "Do the claim recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claim 9 not only recites a judicial exception, but that the claim is directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claim 9: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea.
Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g));
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, … iii. Electronic recordkeeping, … (updating an activity log). iv. Storing and retrieving information in memory,…
Further, the instant specification describes the simulation as being performed using a known pedestrian flow simulation technique. For example, [0018], “Note that the simulation in the simulation execution unit 103 can be performed, for example, by using a known simulation technique disclosed in "Development of pedestrian flow simulator for smooth people movement in buildings" (FUJIWARA Masayasu, TORIYABE Satoru, and HATORI Takahiro) presented by the inventors in "The Proceedings of the Elevator, Escalator and Amusement Rides Conference" of The Japan Society of Mechanical Engineers held on January 19, 2018.” Therefore, the claim merely applies a known simulation technique to process estimated positional information, and does not represent an inventive concept sufficient to amount to significantly more than the judicial exception.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claim 9 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 10 is also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 10 is also rejected for incorporating the deficiency of their independent claim 9.
Claim 10 recites “The architectural model data assistance system according to claim 9, wherein the processor is configured to estimate the front-of-line position on a basis of a shape of the elevator platform and a positional relationship of the elevators.”
The limitation merely specifies that the shape of the elevator platform and the positional relationship of the elevators are considered when estimating the front-of-line position. This is an extension of recited mental process, for example, a persona is capable of observing platform layout and relative locations of the elevators, mentally estimating a front-of-line position at which people start to line up at the elevator platform. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). Therefore, the claim 10 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as
being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites “execute a simulation using the front-of-line position and the architectural model data to estimate movements …” which renders the claim indefinite because it is unclear whether “the architectural model data” refers to the architectural data originally stored in the memory, or to the architectural model data after being converted into an executable format of elevator platform data. For the purpose of substantive examination, the examiner interprets the recited “architectural model data” used in executing the simulation as referring to the converted architectural model data in the executable format.
The remaining claim 10 is dependent upon the claim 9 listed above and rejected for the same reason.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and
103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Mihashi
JP2014010659A in view of Fujiwara (“Development of Pedestrian Flow Simulator for Smooth People Movement in Buildings,” published in 2018) and Li US20210272314A1 and Marusco US20170089709A1.
Claim 9, Mihashi teaches An architectural model data assistance system (Fig.2, terminal device 100 and server device 200. [0012] As shown in FIG. 2, the BIM system … includes a server device 200 capable of providing information such as a three-dimensional model of an elevator (i.e., BIM parts of the elevator), and one or more servers.) comprising:
a storage unit that stores architectural model data and simulation data (Fig.2, storage unit 106; [0025], “The BIM model database 106a is a BIM model storage unit that stores a BIM model of a building.” [0026], “The object information database 106b is an object information storage unit that stores object information related to movable human objects in a BIM model of a building. …, the movement conditions include the number of human objects, transit points, moving means (for example, use of stairs, elevator (use of elevators, passenger conveyors (including escalators or moving walkways), etc.), departure time, transit time, arrival time, The object information may include at least one of a moving speed, a using moving path, and a moving pattern, …” [0033], “In addition, on the layout screen displayed by the screen display unit 102d, the motion analysis unit 102f simulates the movement of the human object and the motion of the BIM parts of the elevator based on the object information stored in the object information database 106b. It is an analysis means.” – Examiner note: the reference teaches movement conditions and human object simulation data (e.g., number of humans, movement speed, movement path, and movement pattern), which are input parameters used by motion analysis unit to perform simulation);
a processor; and a memory storing instructions, that when executed by the processor, configures the processor ([0017], “The control unit 202 also has a control program such as an operating system (OS), a program defining various processing procedures and the like, and an internal memory for storing necessary data. Then, the control unit 202 performs information processing for executing various processes according to these programs …”[0027] The control unit 102 also has a control program such as an operating system (OS), a program defining various processing procedures and the like, and an internal memory for storing necessary data. Then, the control unit 102 performs information processing for executing various processes according to these programs ...”) to:
read the architectural model data stored in the storage unit ([0029], “The integrated modeling unit 102b is integrated modeling means for creating an integrated BIM model in which the BIM parts of the elevator transmitted from the server device 200 are incorporated into the BIM model of the building stored in the BIM model database 106a.” [0043], “Then, the integrated modeling unit 102b of the terminal device 100 performs the BIM parts of the single or plural elevators received by the processing of the control unit 102 in step SA-5 with the BIM model of the building stored in the BIM model database 106a.” – Examiner note: the reference teaches retrieving BIM model data stored in the BIM model database (106a) and incorporating BIM parts received from the server device into the stored building model, which requires the system to access and read the architectural model data stored in the storage unit),
convert the read architectural model data into an executable format of elevator platform data ([0029] The integrated modeling unit 102 b is integrated modeling means for creating an integrated BIM model in which the BIM parts of the elevator transmitted from the server device 200 are incorporated into the BIM model of the building stored in the BIM model database 106a. [0032] In addition, the movement route generation unit 102e is a movement route generation unit that generates a movement route of the human object in the integrated BIM model based on the object information stored in the object information database 106b. Specifically, the movement route generation unit 102e uses the movement conditions and the network data in the BIM model included in the object information to determine the start point and destination point from the start point that includes at least the start point and the destination point of each human object. [0033] In addition, on the layout screen displayed by the screen display unit 102d, the motion analysis unit 102f simulates the movement of the human object and the motion of the BIM parts of the elevator based on the object information stored in the object information database 106b. [0061] For example, when human objects stay in the entrance of the elevator in the integrated BIM model, the user can determine that the conditions such as the number, capacity, and speed of the elevators are insufficient. Examiner note: the reference teaches creating an integrated BIM model by incorporating elevator BIM parts into a building BIM model stored in the BIM model database, and further teaches using the integrated BIM model to generate movement routes of human objects and to simulate movement of the human objects and operation of the elevator BIM parts. The reference also identifies an entrance of the elevator within the integrated BIM model as an area in which human objects may remain during the simulation. Therefore, the integrated BIM model corresponds to the architectural model data converted into an executable format of elevator platform data),
(See Mihashi, [0032]), (See Mihashi, [0032])
execute a simulation using ([0032], “… the movement route generation unit 102e is a movement route generation unit that generates a movement route of the human object in the integrated BIM model based on the object information stored in the object information database 106b. [0056] Referring back to FIG. 3 again, the operation analysis unit 102f of the terminal device 100 processes the movement route generation unit 102e in step SA-13 based on the movement conditions set by the process of the control unit 102 in step SA-12. The movement of the human object is simulated by moving the human object on the movement route generated by (step SA-14). [0057] Here, FIG. 8 is a view showing an example of the layout screen showing the movement of the human object around the elevator. Examiner note: the reference teaches generating movement routes for human objects in the integrated BIM model and executing a simulation by moving the human objects along the generated movement routes, and further teaches that the simulated movement occurs around the elevator, as illustrated in Figure 8. As discussed above, the integrated BIM model corresponds to the converted architectural model data, and the area around the elevator corresponds to the elevator platform. Therefore, the reference teaches executing a simulation using the architectural model data to estimate movements of people at the elevator platform).
However, Mihashi fails to teach estimate a front-of-line position that is a position at which people who use elevators start to line up at an elevator platform, and a first position determined based on a distance measured from an entrance to a location of the elevator platform; and execute a simulation using the front-of-line position.
Fujiwara teaches (Page.4, 2.5.1, “Figure 4 shows an example of conventional elevator layout data, and this layout data defines the elevator's installation location, the positions of the up and down hall call buttons, and the head position of a queue when waiting in line during peak hours … A person emerges from the exit area and moves to the head position of the queue in the hall.” Fig.4, “Layout data and behaviors of conventional system,” identifying the “Exist area,” “Head position of a queue,” “Elevator,” and “Hall call button.” Examiner note: the reference teaches that passengers enter the elevator hall from an Exit area and form queues near the elevators beginning at a defined head position. The head position of each queue is defined in the layout data. Accordingly, the disclosed head position of a queue corresponds to the claimed front-of-line position at which people who use elevators start to line up at an elevator platform. Figure 4 separately identifies the Exit area, the elevator installation positions and the hall call button locations. The Exit area corresponds to the claimed entrance, while an elevator installation position within the elevator hall corresponds to a location of the elevator platform. Therefore, the reference teaches a front-of-line position at which elevator users start to line up, and further teaches an entrance and a location of the elevator platform); and
execute a simulation using the front-of-line position (Abstract, “Our simulator has a detailed user behavior model of elevator that simulates queuing and operating buttons at elevator halls … The simulator enables qualitative and quantitative evaluation of building traffic planning effectively using 3D graphics of pedestrian movements and some indices of the elevator usage.” Page.3, 2.5, “In the simulation, people's movement is achieved by repeatedly moving them in a direction that decreases the distance to a destination set on the layout data. In particular, this elevator usage behavior simulation is achieved by dynamically updating the destination set for each person to local destinations such as button positions, terminal positions, and the last position in the queue.” Page.4, 2.5.1, “Figure 4 shows an example of conventional elevator layout data, and this layout data defines the elevator's installation location, the positions of the up and down hall call buttons, and the head position of a queue when waiting in line during peak hours. The usage behavior of a conventional elevator is simulated as follows: (1) A person emerges from the exit area and moves to the head position of the queue in the hall.” Examiner note: the reference teaches a pedestrian flow simulator that simulates queuing behavior at elevator hall. In the simulation, each person moves toward a destination defined in the layout data, and the destination is dynamically updated to queue related position. For the conventional system, the reference teaches that the layout data defines the head position of the queue and that a simulated person moves from the Exit area to that head position. Figure 4 and Section 2.5.1 show that the head position of the queue is used as a destination during the simulated passenger movement. Therefore, the disclosed head position of the queue corresponds to the claimed front-of-line position and is used in executing the pedestrian flow simulation).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mihashi to incorporate the teachings of Fujiwara by using elevator hall layout data that defines an Exit area, elevator installation locations, hall call button locations, and a head position of a queue, and by using the defined queue head position during simulation of passenger movement and queue formation at the elevator platform, in order to provide defined spatial locations governing where elevator passenger enter that elevator hall, begin lining up, and move during the simulation. In this case, Mihashi teaches executing a simulation using architectural model data and movement characteristics of people to estimate movement within a building environment. Fujiwara teaches simulating elevator passengers entering from an Exit area, moving toward a queue head position defined in the layout data, and forming a queue near the elevators. The combination of teachings would have predictably improved the accuracy and realism of Mihashi’s simulation by enabling the simulation to reproduce passenger entry, movement toward a predefined front-of-line position, and queue formation at an elevator platform.
However, Mihashi and Fujiwara fail to teach a front-of-line position that is a position at which people who use elevators start to line up.
Li teaches estimate a front-of-line position that is a position at which people who use elevators start to line up ([0077] Step 3021, recognizing queue heads [0078] … According to the preset mark point (uoi, voi) beside the cashier desk, a world coordinate corresponding to the point is solved, and then a head object Xi which is with the shortest distance from the point and falls within a range of ±90° from an Xw axis direction with X0 as a center is found to serve as a position of a head the queue. [0080] As shown in FIG. 3, by taking the position of the head of the queue as the first determined effective queuing object of the effective queue, whether there are other subsequent queuing objects is determined … Examiner note: the reference teaches determining the position of the head of a queue by calculating the world coordinate of a preset mark point and identifying the head object having the shortest distance from that point. The reference further teaches treating the determined head position as the first determined effective queuing object of the effective queue. Therefore, the determined queue head position corresponds to the claim estimated front-of-line position).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mihashi and Fujiwara to incorporate the teachings of Li by determining a queue head position based on a preset mark point and identifying the head object having the shortest distance from that point as the first effective queuing object, in order to automatically identify the beginning position of a queue for use in the pedestrian flow simulation. Mihashi teaches executing a simulation using architectural model data and movement characteristics of people to estimate movement within a building environment. Fujiwara teaches simulating elevator passengers entering from an Exit area, moving toward a queue head position defined in the layout data, and forming a queue near the elevators. Li teaches estimating the position of the head of a queue by determining the head object having the shortest distance from a preset mark point and treating the determined head position as the first effective queuing object. The combination of teachings would have predictably improved the accuracy and efficiency of the simulation by enabling the front-of-line position to be determined from detected queue information rather than requiring the position to be manually predefined.
However, Mihashi and Fujiwara and Li fail to teach a first position determined based on a distance measured from an entrance to a location.
Marusco teaches a first position determined based on a distance measured from an entrance to a location ([0041] …the navigation graph generator can determine the secondary waypoints using a shortest distance between an entrance of a unit and path 232. For example, the navigation graph generator can determine entrance waypoint 318. Entrance waypoint 318 can correspond to a location of an entrance to a unit … The navigation graph generator can determine shortest distance 320 between entrance waypoint 318 and path 232. The navigation graph generator can designate an intersection point between shortest distance 320 and path 232 as secondary waypoint 120. Examiner note: the reference teaches determining entrance waypoint 318 at the location of an entrance to a unit, determining shortest distance 320 between entrance waypoint 318 and path 232, and designating the intersection point between shortest distance 320 and path 232 as secondary waypoint 120. Entrance waypoint 318 corresponds to the claimed entrance, and path 232 corresponds to the claimed location. Shortest distance 320 corresponds to the claimed distance measure from the entrance to the location. Secondary waypoint 120 corresponds to the claimed first position because its location is determined by the intersection of shortest distance 320 with path 232. Accordingly, the reference teaches determining a first position based on a distance measured from an entrance to a location).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mihashi and Fujiwara and Li to incorporate the teachings of Marusco by determine a spatial reference position on a pedestrian movement path based on the shortest distance from an entrance to the path, in order to provide a consistent reference position for locating pedestrians within an indoor layout. Mihashi teaches executing a simulation using architectural model data and movement characteristics of people to estimate movement within a building environment. Fujiwara teaches simulating elevator passengers entering from an Exit area, moving toward a queue head position defined in the layout data, and forming a queue near the elevators. Li teaches estimating the position of the head of a queue by determining the head object having the shortest distance from a preset mark point and treating the determined head position as the first effective queuing object. Marusco teaches determining a shortest distance between an entrance waypoint and a pedestrian path and designating the intersection of the shortest distance with the path as a secondary waypoint. The combination of these teachings would predictably allow Marusco’s geometrically determined waypoint to provide the spatial reference position used with Li’s distance based queue head estimation technique in Fujiwara’s elevator hall layout, thereby providing a more consistent and adaptable determination of the queue head position and improving the spatial accuracy of the passenger flow simulation.
Claim 10, Mihashi fails to teach, but Fujiwara teaches The architectural model data assistance system according to claim 9,
wherein the processor is configured to (Page.4, 2.5.1, “Figure 4 shows an example of conventional elevator layout data, and this layout data defines the elevator's installation location, the positions of the up and down hall call buttons, and the head position of a queue when waiting in line during peak hours.” Page.5, 3.1.1, “Four elevators with these specifications are arranged in a line. See also Figure 4, “Layout data and behaviors of conventional system” Examiner note: Figure 4 illustrates the floor area configuration of the elevator platform and a plurality of elevators positioned adjacent to one another along one side of the platform. The illustrated boundary and configuration of the floor area in front of the elevators correspond to the claimed shape of the elevator platform. The defined installation locations of the elevators, together with the disclosure that four elevators are arranged in a single line, correspond to the claimed positional relationship of the elevators).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mihashi to incorporate the teachings of Fujiwara by using elevator platform layout data defining the shape of the elevator platform and the positional relationship of the elevators, in order to provide defined spatial information governing where elevator passenger enter form a queue, wait, and move within the elevator platform in the simulation. The combination of teachings would have predictably improved the accuracy and realism of Mihashi’s simulation by enabling the simulation to reproduce passenger movement and queue formation within an elevator platform layout having a defined platform shape and elevator arrangement.
However, Mihashi and Fujiwara fail to teach estimate the front-of-line position on a basis of a shape and a positional relationship.
Li teaches estimate the front-of-line position on a basis of a shape and a positional relationship ([0074] In scenes such as the supermarkets, contour positions pre-marked, namely obstacle positions in an environment near a queuing channel, of fixed equipment including a cashier window in the environment are obtained. Moreover, a position of a passable channel area can be calculated and stored by using a raster method path planning algorithm, and channel data are used for predicting a queue. [0078] … a mark point (uoi, voi), wherein i=1, 2, 3 . . . , is set at a passable center position of a side of each cashier desk firstly, for determining the head of the corresponding queue. According to the preset mark point (uoi, voi) beside the cashier desk, a world coordinate corresponding to the point is solved, and then a head object Xi which is with the shortest distance from the point and falls within a range of ±90° from an Xw axis direction with X0 as a center is found to serve as a position of a head the queue. A circle is made by taking X1 as a center and taking a set distance threshold d as a radius; and if no intersection point exists between the circle and pre-marked obstacle contour, it is considered that the surrounding is empty … Examiner note: the reference teaches pre-marked obstacle contours and calculated passable channel area that define the boundaries and passable configuration of the queuing area in which the queue forms. The reference further sets a respective mark point beside each cashier desk and identifies the queue head object based on its distance and angular relationship to the corresponding mark point. The respective cashier desk and mark point locations correspond to the positional relationship of service objects within the queuing environment. Therefore, the reference estimates the head of the queue on the basis of the shape of the queuing area and the positional relationship of the service objects).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mihashi and Fujiwara to incorporate the teachings of Li by treating the elevator platform of Mihashi and Fujiwara as the queuing area of Li and the elevators positioned along the elevator platform as the service objects of Li, and applying Li’s queue head estimation technique to the elevator platform, in order to determine the beginning position of a queue in accordance with the shape and passable area configuration of the elevator platform and the positional relationship of the elevators. The combination of teachings would have predictably improved the accuracy and efficiency of the simulation by enabling the front-of-line position to be estimated based on the shape of the platform area and the positional relationship of the elevators, rather than requiring the position to be manually predefined.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Witczak US20170190544 A1 teaches a measurement unit that measures a situation of the elevator platform in the elevator platform (Fig.6; [0013], [0058]); the processor is configured to estimate the front-of-line position on a basis of elevator platform data obtained by measurement by the measurement unit ([0065]); the measurement unit includes one or more sensors selected from the group consisting of an image sensor, a depth sensor, and an infrared sensor ([0013]; the processor is configured to calculate a density of people at each point of the elevator platform on a basis of measured data ( [0053], [0054], [0057], [0058]).
Gyger US20160224844A1 teaches sets a point where the density exceeds a preset predetermined threshold as the front-of-line position ([0057], [0060], [0122], [0132]); predetermined threshold is variably set depending on a time zone or the front-of-line position (Figure.4, [0057], [0062], [0132]).
Finn US20180329032 A1 discloses a self-calibrating sensor system is provided and includes one or more sensors supportively disposed on or proximate to a wall defining one or more apertures and a processor. Each of the one or more sensors is configured to track individuals proximate to at least one of the one or more apertures. The processor is configured to determine one or more of a location and an orientation of each of the one or more sensors relative to the at least one of the one or more apertures from analyses of the tracking of the individuals.
Gyger US20160224845 A1 discloses determining the spatial extent of a free queue, proceeding from position information, firstly a monitoring region comprising the free queue is subdivided into a plurality of positions. Proceeding from the position information, objects assigned to the free queue are identified and tracked. A current position of at least a portion of the tracked objects is periodically stored. An average speed of at least a portion of the objects is determined, wherein the average speed of an object is determined on the basis of a plurality of the stored positions of the respective object. Finally, a first map is created, which records, in relation to the positions in the monitoring region, an appearance density of objects at the corresponding positions. Objects having an average speed outside a predefined range are not taken into account when creating the first map. Proceeding from a predefined exit region of the free queue, a flood fill method is carried out for generating a continuous region corresponding to the extent of the free queue.
Loos US20120207350 A1 discloses a device 1 is proposed for identification of a queue 2 of objects 10 in a monitoring area, having an interface 6 which can be connected to an image source 7, with the interface 6 being designed to observe at least one monitoring image 3 of the monitoring area of the image source, wherein the monitoring image 3 shows a scene background of the monitoring area with possible objects 10, having an evaluation device 5 which is designed to identify the queue 2 of the objects 10 in the at least one monitoring image, wherein the evaluation device 5 has an object detector module 8 which is designed to detect a plurality of objects 10 on the basis of the monitoring image 3, wherein the plurality of the detected objects 10 forms the basis for identification of the queue 2 of the objects 10, wherein the object detector module 8 is designed to identify the objects 8 in the monitoring image with the scene background and/or wherein the object detector module 8 has content-sensitive detectors 9 for detection of the objects 10.
Paragios US20070031005 A1 discloses change detection and crowding/congestion density estimation are two sub-tasks in an effective subway monitoring video system. Events of interest in subway settings include, for example, people counting (or density estimation), crowdedness (congestion) detection, any anomalous presence (e.g., jumping or falling) of persons/objects onto the track, and people tracking. Crowding detection in subway platforms, for example, is of interest for closing certain passageways, dynamically scheduling additional trains, and to improve security and passenger safety in the subway environment.
Westmacott US20190392222 A1, discloses a system and method for analysing queues in frames of video enables operators to preferably draw three regions of interest overlaid upon the video as short, medium, and long queue regions that form a notional queue area within the video. Examples include retail point of sale locations or for automated teller machine (ATM) transactions. In conjunction with a video analytics system that analyses the movement of the foreground objects relative to the queue regions, the system determines the number of objects occupying each queue region, length of the queue, and other queue-related statistics.
“MassMotion Help Guide” by Oasys, published April. 2019 discloses MassMotion is developed to enable design and planning professionals to rapidly test and analyse the movement of people in many kinds of environments. To do this MassMotion provides users with a suite of tools for creating and modifying 3D environments, defining operational scenarios, executing dynamic simulations and developing powerful analyses.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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