DETAILED ACTION
This action is responsive to the claims filed on February 24, 2022. Claims 1-5 are under examination.
The specification is objected to for minor informalities.
Claims 1-5 are objected to for elements that lack antecedent basis.
Claims 1-5 are rejected under 35 USC 101 as directed to ineligible subject matter.
Claims 1, 3, and 5 are rejected under 35 USC 103 as obvious over Innan and Zheng.
Claims 2 and 4 are rejected under 35 USC 103 as obvious over Innan, Zheng, and Wirz.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement was filed prior to this action, so the reference referred to therein has been considered.
Specification
The disclosure is objected to because of the following informalities: The reference numeral 110 is assigned to both “agent generating unit” and “device.” The reference numeral 120 is assigned to both “position recording unit” and “agent generating unit.” Reference numeral 130 is assigned to both “edge selecting unit” and “position recording unit.” Reference numeral 140 is assigned to both “front density calculating unit” and “edge selection unit.” Reference numeral 150 is assigned to both “moving speed calculation unit” and “front density calculating unit.” Reference numeral 160 is assigned to both “position updating unit” and “moving speed calculating unit.” Reference numeral 170 is assigned to both “determination unit” and “position updating unit.”
Appropriate correction is required.
Claim Objections
Claims 1-5 are objected to because of the following informalities:
Lack of Antecedent Basis
Claims 1, 3, and 5 recite the following limitations that lack proper antecedent basis: “the mobile agent”; “the mobile agents”; “the other agent”; and “the operations.”
Claims 2 and 4 recite “the parameter.” It appears the Applicant intended to claim “the predetermined parameter.”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The claims are directed to an abstract idea without significantly more.
Claim 1
Independent Claims
Claim 1 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical concept, which are abstract ideas.
Claim 1 Recites:
select an edge on which the mobile agent moves for each of the mobile agents based on a position of the agent, the destination point of the agent information, and the edge information; (Mental Evaluation, Mental Process – Selecting an edge of a mathematical graph based on available data is an evaluation practically performable in the mind or with aid of pen, paper, and/or a calculator. Therefore, this is a mental process, an abstract idea.)
calculate a population density for each of the mobile agents based on an area of a section from the position of the mobile agent to a predetermined front length and the size magnification of the other agent in the section; (Mental Evaluation, Mental Process – calculating a population density for a mobile agent based on available data is an evaluation practically performable in the mind or with aid of pen, paper, and/or a calculator. Therefore, this is a mental process, an abstract idea.; Mathematical Calculation, Mathematical Concept – calculating a population density based on available data is also a mathematical calculation, a mathematical concept, an abstract idea.)
calculate a moving speed of the mobile agent for each of the mobile agents based on the free walking speed of the agent information, the calculated population density, and a predetermined parameter; (Mental Evaluation, Mental Process – calculating moving speeds for mobile agents based on available data is an evaluation practically performable in the mind or with aid of pen, paper, and/or a calculator. Therefore, this is a mental process, an abstract idea.; Mathematical Calculation, Mathematical Concept – calculating a moving speeds for mobile agents based on available data is also a mathematical calculation, a mathematical concept, an abstract idea.)
update the position of the mobile agent for each of the mobile agents based on the calculated moving speed; and (Mental Evaluation, Mental Process – Updating a position of an agent in a simulation based on available data is an evaluation practically performable in the mind or with aid of pen, paper, and/or a calculator. Therefore, this is a mental process, an abstract idea.)
repeat a simulation by each of the operations until a predetermined condition is satisfied. (Mental Evaluation, Mental Process – Running a simulation based on available data, whether for a first time or for subsequent times with modifications, is an evaluation practically performable in the mind or with aid of pen, paper, and/or a calculator. Therefore, this is a mental process, an abstract idea.)
Mental processes and mathematical concepts are abstract ideas.
Claim 1 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
The Additional limitations:
A movement predicting device including:
a memory; and
at least one processor connected to the memory, wherein the at least one processor is configured to:
[…] agent […]
These are generic computing elements recited a high level of generality and, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
receive, as an input, edge information on each edge indicating a path connecting each of nodes and agent information having a departure time, a departure point in each node, a destination point in each node, a free walking speed of an agent, and a size magnification for the agent determined for each of a plurality of the agents and record each mobile agent departing from the departure point in accordance with the departure time of the agent information;
The receive step is mere data gathering activity similar to the MPEP 2106.05(g) insignificant extra-solution activity examples: “e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.” “iv. Obtaining information about transactions using the Internet to verify credit card transactions” “vi. Determining the level of a biomarker in blood” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Because the receive step is insignificant extra-solution activity, under MPEP 2106.05(g), the step fails to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Also, any specific details about the parameters the data recited represent, the parameters merely limit the abstract idea to a particular technological environment and, under MPEP 2106.05(h), fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
The additional limitations recited in claim 1 fail to integrate the abstract idea into a practical application at Step 2A, Prong 2.
Claim 1 is directed to the Abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
The additional limitations:
A movement predicting device including:
a memory; and
at least one processor connected to the memory, wherein the at least one processor is configured to:
[…] agent […]
These are generic computing elements recited a high level of generality and, under MPEP 2106.05(f), fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
receive, as an input, edge information on each edge indicating a path connecting each of nodes and agent information having a departure time, a departure point in each node, a destination point in each node, a free walking speed of an agent, and a size magnification for the agent determined for each of a plurality of the agents and record each mobile agent departing from the departure point in accordance with the departure time of the agent information;
The receive step is well-understood, routine, and conventional activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network,” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “i. Determining the level of a biomarker in blood by any means” (sensors) “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Because the receive step is WURC and, as previously demonstrated, insignificant extra-solution activity, under MPEP 2106.05(d) and MPEP 2106.05(g), the step fails to combine with other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Also, any specific details about the parameters the data recited represent, the parameters merely limit the abstract idea to a particular technological environment and, under MPEP 2106.05(h), fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
The additional limitations fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept at Step 2B.
Claim 1 is ineligible.
Claim 3
Claim 3 (Statutory Category – Process)
Regarding claim 3, claim 3 recites the method steps executed by the movement predicting device of claim 1 and is rejected for at least the same reasons as claim 1.
Claim 3 is ineligible.
Claim 5
Claim 5 (Statutory Category – Machine)
Regarding claim 5, claim 5 recites a CRM that stores instructions for the method steps executed by the movement predicting device of claim 1, which is an embodiment of the memory taught in claim 1, so claim 5 is rejected for at least the same reasons as claim 1.
Claim 5 is ineligible.
Dependent Claims
The dependent claims fail to provide any additional limitations that would confer eligibility at Step 2A, Prong 2 and Step 2B.
NOTE: For all of the dependent claims, the parameters the data represents merely limit the abstract idea to a particular technological field and fail to confer eligibility under MPEP 2106.05(g). Also, all recited computing elements or the use thereof are recited at a high level of generality and represent generic computing processes, so, under MPEP 2106.05(f), these fail to confer eligibility.
Claims 2 and 4
wherein the size magnification is determined in accordance with attributes of the agent, and wherein the parameter is adjusted in accordance with the attributes for each edge changing in accordance with a time.
These features merely qualify the evaluations of the abstract idea, and are, therefore, merge with the abstract idea of the claim from which the claim depends.
Should it be found otherwise, the parameters the data represent merely limit the abstract idea to a particular field and, under MPEP 2106.05(h).
Claims 2 and 4 fail to provide any additional limitations that confer eligibility at Step 2A, Prong 2 and Step 2B
Claims 2 and 4 are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, and 5: Innan and Zheng
Claim(s) 1, 3, and 5 are rejected under 35 U.S.C. 103 as being unpatentable over NPL: “Examination of measures for evacuation guidance information around large-scale terminal stations using congestion simulation methods” by Innan et al. (Innan) in view of NPL: “Heterogeneous crowd behaviors simulation: a physiological perspective” by Zheng et al. (Zheng).
Regarding claim 1, Innan Teaches:
receive, as an input, edge information on each edge indicating a path connecting each of nodes and (Innan Page 3, Left Column, Last Paragraph “[n the network walking type, a road network in which the road shape of an object space is expressed by points (nodes) and lines (links) is used as a simulation space model (Figure 5), and a node corresponding to a destination is used as a goal point node.” See also Figures 5 and 6 – The problem area is simulated as a series of nodes and edges/links.)
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agent information having a departure time, a departure point in each node, a destination point in each node, a free walking speed of an agent, and record each mobile agent departing from the departure point in accordance with the departure time of the agent information; (Innan Page 3, Left Column, Second Paragraph “In this study, a relatively wide range of simulations needs to be carried out to investigate evac1rntion behavior from a large—scale terminal station and its surrounding facilities, so we used a network walking multi-agent model that can simulate a large number of people and a wide range of objects.” – Agents are used to model people moving along the nodes and edges. Page 7, left Column, Second Paragraph “By guiding evacuees to temporarily evacuate to an evacuation area, a time difference appears between evacuees who come out onto the sidewalk. and the concentration of evacuees on the sidewalk can be suppressed.” Page 3, Right Column, First Paragraph “Evacuees move on the node of this road network toward the Gonore site node. Since the road shape is expressed as a simplified network space, it is possible to simulate a relatively wide area. In addition, since evacuees move on the node one-dimensionally, the calculation cost is low, and it is possible to simulate in tens of thousands of people. The example of wide-area evacuation simulation for tsunami countermeasures shown in Fig. l was carried out by the network simulation described above. The target area is 6 Jun x 4 km, and the evacuation behavior of 100.000 people has been simulated” Page 4, Right Column, Second Paragraph “The earthquake is assumed to have occurred at 14:00, the peak population time of the target district For blocks with evacuation spaces within blocks, evacuation to a wide-area evacuation site is to be started when the evacuation spaces are full. For blocks without evacuation spaces within blocks. evacuation is to be started 20 minutes after the earthquake at 10) (14:20)” Page 5, Right Column, Last Paragraph “The route that takes the most time to complete evacuation is the mute that evacuees from many blocks pass through the north sidewalk and take refuge in the west wide evacuation arm. Jn this mule, evacuees from many blocks pass through the north sidewalk and head toward the entrance of the west wide evacuation area, forming a long retention period (Fig. J 3, purple circle).” “Comparing the 100 minutes after lhe earthquake, in Scenario 2, the slagnation in the center portion was eliminated, but the stagnation in the north portion was prolonged, and it look approximately 25 minutes for the stagnation to be eliminated. In Scenario 3, the stagnation in both the center portion and the north portion remained, and these stagnation was eliminated after approximately 20 minutes. 1n Scenario 3, the evacuation completion time was reduced by approximately 5 minutes compared to the other two scenarios. ln Scenario 3, the evacuees were appropriately allocated to both sidewalks, because the stagnation time on the north sidewalk and the central sidewalk were eliminated almost simultaneously.” – The behavior of each agent/evacuee is tracked including the locations and directions of motion at any given time, including times for each agent at nodes and edges and at any departures or arrivals therefrom or thereto. Page 4, Left Column, Second Paragraph “where V represents the free walking speed of the evacuees i The free walking speed was given by a normal distribution with a mean of 1.2 m/s and a standard deviation of 0.2 for each evacuee” See Also, Equation 1 on Page 4. – The free walking speed of each agent is modeled based on a normal distribution of walking speeds.)
select an edge on which the mobile agent moves for each of the mobile agents based on a position of the agent, the destination point of the agent information, and the edge information; (Innan Page 3, Right Column, Third Paragraph “The links in the network space used in this study are indicated by blue lines and the nodes are indicated by red dots. Because many vehicles are expected to be on the road even after an earthquake, and because it is necessary to secure roads for emergency vehicles, only sidewalks are set up as roads that can be used by evacuees.” Page 4, Right Column, First Paragraph “Evacuees in the block were first evacuated to an open area within the block, and those who could not evacuate to the open area were evacuated to a wide-area evacuation site in the east and the west. Based on the assumption in Chiyoda Ward that the density at which evacuees could evacuate to the open area was 2 persons per 1.3 po, the upper limit of the density at which evacuees could evacuate to the open area was simplified to 2.0 persons per po in this simulation. When this upper limit was exceeded, evacuees were to flow from the block to 1he sidewalk” – By moving agents along “links” that are modeled sidewalks between nodes at intersections, the agents are essentially all on one link or another in the model, and their time, location, direction (showing departure and destination points), and free walking speed are determined.)
calculate a population density for each of the mobile agents based on an area of a section from the position of the mobile agent to a predetermined front length ; (Innan Page 4, Left Column, Third Paragraph “Here, the forward density P represents the density of the evacuees in the range of the forward distance Lm X road width Wm, and is calculated by counting the number of pedestrians within the range of L m in front of the evacuees and determining the damage Q based on the area of the target area ahead. In this simulation, the forward dis1ance L of the pedestrian was set at 6.0 m.”
calculate a moving speed of the mobile agent for each of the mobile agents based on the free walking speed of the agent information, the calculated population density, and a predetermined parameter; (Innan Page 4, Left Column, First Paragraph Below The Bullets “Figure 7 shows the relationship between the equation for determining the walking speed and the velocity forward density. The upper limit of the walking speed is the free walking speed, and the speed is dynamically reduced according to the density of the pedestrian forward 5). Figure 7 shows the equation for the velocity V, .. of a certain evacuee i.
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where V represents the free walking speed of the evacuees i. The free walking speed was given by a normal distribution with a mean of 1.2 m/s and a standard deviation of 0.2 for each evacuee 8), where ρ is the density of the pedestrian ahead (person/po) and ρi is the density that satisfies [equation (1)]. The equation for velocity reduction in the range of density ρi < ρ < 6.0 is set to a function for velocity 0 m/s at a density of 6.0 people/m2.” See Also Figure 7 on Page 4 – The moving speed of each mobile agent is calculated based on equation 1, which accounts for the free walking speed and the calculated population density and a predetermined parameter (e.g., -.3, 6, 1.8).)
update the position of the mobile agent for each of the mobile agents based on the calculated moving speed; and (Innan Page 4, Right Column, Second-To-Last Paragraph “In crowd flow, it is said tlrnt when 1he density becomes 4 persons/po or more, the crowd becomes stagnant, such as slow or stopping l J) 12) Therefore, when the forward density is 0 persons/po, the color is blue, and when the fo1ward density is ,j persons/po or more, the color is red, and when the density is between them, the color of the evacuees is expressed by the color continuously connected from blue to red” – The simulation is commenced with the people moving and the simulation updating position, location, direction, and speed information to inform the population density at any given edge or node. The speed at a given time will affect the agents’ current and future positions.)
repeat a simulation by each of the operations until a predetermined condition is satisfied. (Innan Page 6, Right Column, First-Fourth Paragraphs “In the case of no guidance (scenario 1), evacuation to the nearest evacuation site was conducted to prevent the occurrence of countercurrent ( scenario 2), and retention at the point where countercurrent occurred was reduced. However, in scenario 2, evacuation to the west via the north sidewalk where the longest retention occurred was not suppressed. Therefore, in Scenario 3, a simulation was carried out with a setup to guide evacuees evacuating from the north side to another entrance of the western wide evacuation area or the eastern wide evacuation area. […] Table 3 shows a comparison of the evacuation situations in Scenario 2 and Scenario 3. The orange circle in the figure indicates the evacuation completion poin1. A comparison of 1he snapshot~ 1aken 70 minutes after the earthquake shows that in Scenario 3, the evacuees were guided to avoid the congestion on the north sidewalk and moved to the south side. Because the evacuees were directed to the evacuation point in the center of the west side, their residence in the center was prolonged. Comparing the 100 minutes after the earthquake, in Scenario 2, the stagnation in the center portion was eliminated, but the stagnation in the north portion was prolonged, and it look approximately 25 minutes for the stagnation to be eliminated. In Scenario 3, the stagnation in both the center portion and the north portion remained, and these stagnation was eliminated after approximately 20 minutes. In Scenario 3, the evacuation completion time was reduced by approximately 5 minutes compared to the other two scenarios. ln Scenario 3, the evacuees were appropriately allocated to both sidewalks, because the stagnation time on the north sidewalk and the central sidewalk were eliminated almost simultaneously.” – The Innan reference repeats the scenario three times under different circumstances, the authors determined that three scenarios was sufficient to demonstrate the principle of the paper, the satisfaction of a condition. Note that the recited condition is not tied to anything in the claim, nor is there any indication in the claim of how the scenario would be varied from one scenario run to another such that results would be expected to change.)
Innan teaches that the density of people in an area in front of an agent is considered in determining agent speed (Equation 1 and Figure 7 on Page 4), but Innan does not appear to explicitly teach, but Innan in view of Zheng teaches:
A movement predicting device including: a memory; and at least one processor connected to the memory, wherein the at least one processor is configured to: (Zheng Page 3, Right Column, First Paragraph “All experiments are done on a PC with 3.4 GHz Intel i7-2600 CPU, 8GB memory and NVIDIA GTX 560 Ti display card. All simulation experiments of this paper are executed in real time.” – While Innan appears to implicitly teach using a computer, Zheng explicitly teaches a movement prediction device with a processor and memory.)
[…] agent information having a departure time, a departure point in each node, a destination point in each node, a free walking speed of an agent, and a size magnification for the agent determined for each of a plurality of the agentsand thesize magnification of the other agent in the section; (Zheng Page 196, Left Column, Fourth Paragraph “Also weight can produce effect on their walking space, and health to walking speed. According to the above analysis, we present a qualitative mapping from person's main 4 physiological characteristics, including gender, age, weight, and health, to the RVO's 6 parameters as Fig.1.” See Also Table 3 (shown below) – The “weight” classifier does not address weight itself, but size/radius, which is what the magnification of the instant application means. The people who are fat are set to have a 40% larger radius, and the people who are skinny are set to have a 40% smaller radius than the people who are normal. Innan would use Zheng to calculate the forward area population density to determine the speed of agents to better account for the size of each agent.)
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It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the crowd behavior system of Innan by the agent considerations and hardware computing of Zheng because the person of ordinary skill in the art would be motivated, based on Innan’s attempt to efficiently model agent heterogeneity using a walking speed/population density model with a normal distribution, to look to Zheng that efficiently and validly uses a computer to model agent heterogeneity as it affects crowd simulation by simple, efficient mapping. (Innan Page 4, Left Column, First Paragraph Below The Bullets “The free walking speed was given by a normal distribution with a mean of 1.2 m/s and a standard deviation of 0.2 for each evacuee 8), where ρ is the density of the pedestrian ahead (person/po) and ρi is the density that satisfies [equation (1)].”; Zheng Abstract “Most of existing approaches to simulate heterogeneous crowd behaviors focus on the aspect of psychology. From a human's physiological characteristics perspective, this paper presents a method to generate different crowd behaviors. We choose RVO library as navigation method and four basic physiological characteristics including gender, age, weight, and health, and determine a mapping from a single physiological feature to RVO parameters through user studies and statistical method. Then by combining these four single characteristics, a comprehensive mapping can generate various parameters for agents to exhibit heterogeneous behaviors. Through a number of simulation and validation experiments, we demonstrate the proposed method is valid and efficient.” Page 1, Right Column, Second Paragraph “The main contribution of this paper is a new physiological perspective to simulate heterogeneous behaviors of a crowd while most current works focus on the aspect of psychology. Also, a simple, but efficient mapping method, at both single factor and combination level, is proposed to implement various motion planning based on RVO library.”)
Regarding claim 3, claim 3 recites the method steps executed by the movement predicting device of claim 1 and is rejected for at least the same reasons as claim 1.
Regarding claim 5, claim 5 recites a CRM that stores instructions for the method steps executed by the movement predicting device of claim 1, which is an embodiment of the memory taught in claim 1, so claim 5 is rejected for at least the same reasons as claim 1.
Claims 2 and 4: Innan, Zheng, and Wirz
Claim(s) 2 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over NPL: “Examination of measures for evacuation guidance information around large-scale terminal stations using congestion simulation methods” by Innan et al. (Innan) in view of NPL: “Heterogeneous crowd behaviors simulation: a physiological perspective” by Zheng et al. (Zheng) and “Probing crowd density through smartphones in city-scale mass gatherings” by Wirz et al. (Wirz).
Claims 2 and 4
Regarding claims 2 and 4, Innan in view of Zheng teaches the features of claims 1 and 3 and further teaches:
wherein the size magnification is determined in accordance with attributes of the agent, and (Zheng Page 196, Left Column, Fourth Paragraph “Also weight can produce effect on their walking space, and health to walking speed. According to the above analysis, we present a qualitative mapping from person's main 4 physiological characteristics, including gender, age, weight, and health, to the RVO's 6 parameters as Fig.1.” See Also Table 3 (shown below) – The “weight” classifier does not address weight itself, but size/radius, which is what the magnification of the instant application means. Attributes include skinny, fat, and normal. The people who are fat are set to have a 40% larger radius, and the people who are skinny are set to have a 40% smaller radius than the people who are normal.)
While Zheng teaches several factors that can be accounted for in determining heterogeneity in the speed of agents along an Innan link/edge, Innan in view of Zheng does not appear to explicitly teach, but Innan in view of Zheng and Wirz teaches:
wherein the parameter is adjusted in accordance with the attributes for each edge changing in accordance with a time. (Wirz Page 7 “He proposed to describe the relation between local density and speed as follows:
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where v0 = 1.34 ms-1 is the free speed at low densities (free flow), ρmax 5.4m–2 the maximal pedestrian density from which onward movement is not possible anymore and γ = 1.913 m–1 a fit parameter. Figure 1 shows a plot of the fundamental diagram given by Equation 3 and the listed parameters. The work of Weidmann stimulated successive contributions focusing on verifying and understanding this relationship.” – Wirz uses actual experimental data derived from cell phones to correctly correlate the population density with velocity along paths/edges by tuning a parameter, γ.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the velocity heterogeneity estimate of Innan by the fitting parameter and real world data of Wirz because the person of ordinary skill in the art would be motivated by the aim of Innan to better simulate practical disaster prevention management systems, including by using its heterogenous velocity model that depends on density of the crowd ahead of each agent to look to Wirz which uses real data to determine its velocity based on the density of the crowd and a fit parameter, γ. (Innan Page 1, Right Column, First-Second Paragraph “Although there are some examples of validation, the safety verification from large--scale multiple facilities in central Tokyo to wide-area evacuation sites has been studied by simulation methods from the viewpoint of information management measures, and few examples 5) have been incorporated into practical disaster prevention management systems. Therefore, in this study, we have expanded the functions of information management as scenario control in the wide-area evacuation simulation technology we have developed so far for tsunami countermeasures in coastal areas 5) (Figure 1) and fire spread countermeasures in densely populated wooden houses 6) 7), and have conducted a basic study of evacuation information measures around large--scale terminal stations.”; Wirz Abstract “In this work, we consider location-aware smartphones for monitoring crowds during mass gatherings as an alternative to established video-based solutions. We follow a participatory sensing approach in which pedestrians share their locations on a voluntary basis. As participation is voluntarily, we can assume that only a fraction of all pedestrians shares location information. This raises a challenge when concluding about the crowd density. We present a methodology to infer the crowd density even if only a limited set of pedestrians share their locations. Our methodology is based on the assumption that the walking speed of pedestrians depends on the crowd density. By modeling this behavior, we can infer a crowd density estimation. We evaluate our methodology with a real-world data set collected during the Lord Mayor’s Show 2011 in London. This festival attracts around half a million spectators and we obtained the locations of 828 pedestrians. With this data set, we first verify that the walking speed of pedestrians depends on the crowd density. In particular, we identify a crowd density-dependent upper limit speed with which pedestrians move through urban spaces. We then evaluate the accuracy of our methodology by comparing our crowd density estimates to ground truth information obtained from video cameras used by the authorities. We achieve an average calibration error of 0.36 m–2 and confirm the appropriateness of our model. With a discussion of the limitations of our methodology, we identify the area of application and conclude that smartphones are a promising tool for crowd monitoring.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL: “A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance” by Kumar et al. (Teaches using digital twins to analyze traffic congestion)
NPL: “How simple rules determine pedestrian behavior and crowd disasters” by Moussaid et al. (Teaches heuristics for simulating crowd control)
NPL: “Crowd Simulation Incorporating Agent Psychological Models, Roles and Communication” by Pelechano et al. (Teaches the effects of human psychology and inter-agent interaction on crowd simulations)
NPL: “Transporttechnik der Fussgänger” by Wiedmann (Teaches a velocity equation that depends on crowd concentration and uses a fitted parameter)
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188