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 .
Detailed Action
Claims 1-20 are pending.
Drawings
The drawings filed on 12/20/2022 are accepted.
Oath/Declaration
4. For the record, the Examiner acknowledges that the Oath/Declaration submitted on 12/20/2022 has been received.
Information Disclosure Statement
5. The information disclosure statements (IDS) submitted on 12/20/2022 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, an initialed and dated copy of Applicant's IDS form SB08 filed 12/20/2022 is attached to the instant Office action.
Examiner Notes
6. Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Objections
7. Claim 13 is objected to because of the following informalities:
Claim 13 recites: “The non-transitory machine-readable medium of claim 8”. Examiner assumes a typo in the preamble of claim 13 has been occurred, because claim 8 is dependent on claim 1, which is a method claim. However, the preamble of independent claim 9 is “non-transitory machine-readable medium”, therefore claim 13 is dependent on claim 9, instead of claim 8. Therefore, 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.
8. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite a mental process, see MPEP 2106.04(a)(2)(III).
Step 1
The claims under Step 1 are directed towards a method (claims 1-8) machine-readable medium (article of manufacture, claims 9-14) and a system (claims 15-20).
Claim 1 recites:
A method for managing traffic flow in an environment based on macro trends, ((field of use) (See Step 2A Prong 2 and Step 2B)
the method comprising: obtaining a graph, the graph representing the environment, the graph comprising: nodes that are associated with different locations in the environment, and edges connecting the nodes, the edges representing traversal times between the different locations; (Mere data gathering activity)
obtaining a macro trend of the macro trends, the macro trend indicating a level of foot traffic between two of the different locations, and the macro trend being obtained using computer vision; (Mere data gathering activity)
adjusting a travel time estimate of one or more travel time estimates between two nodes of the graph using the macro trend to obtain an updated graph, the two nodes of the graph representing the two of the different locations; (Mental Processes using evaluation or judgement)
performing a fastest route analysis using the updated graph to obtain a fastest route; (Mental Processes using evaluation or judgement)
and managing the traffic flow using the fastest route. (insignificant post-solution activity)
Step 2A, prong 1:
The limitations of claim 1: “adjusting a travel time estimate of one or more travel time estimates between two nodes of the graph …”; and “performing a fastest route analysis using the updated graph …” are recitations of evaluation or judgement that fall within the Mental Processes enumerated category of abstract ideas because it could be "performed by human without a computer", i.e. mental processes that require human to perform the claim abovementioned limitations. The Specification of current Application at para [0072] stated: "The fastest route analysis may be performed by summing the adjusted travel time estimates of each possible sequence of route traversal from node 210 to node 219". This is an evaluation performed in the human mind, capable of doing summing, moreover no specific algorithm being used for the fastest route analysis as per the Specification of current application. Therefore, under BRI, the abovementioned limitations can be performed evaluation or judgement which is reasonable to perform mentally. Accordingly, at step 2A, prong one, claim 1 as a whole is found to recite a judicial exception and is drawn to an abstract idea.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application because the claim language only recites elements that can practically be performed in the human mind, the limitations fall within the mental processes grouping. Therefore, the claim 1 recites an abstract idea because it does not impose any meaningful limitations on practicing the abstract idea. Claim 1 has no additional limitations that integrate the abstract idea into a practical application. The preamble stated: “A method for managing traffic flow in an environment based on macro trends” is recitation of field of use, i.e., amount to merely indicating a field of use or technological environment and cannot integrate a judicial exception into a practical application. Additionally, the limitations “obtaining a graph, the graph representing the environment, the graph comprising: nodes that are associated with different locations in the environment …; obtaining a macro trend of the macro trends, the macro trend indicating a level of foot traffic …” these limitations recite insignificant extra-solution activity because it involves Mere data gathering (See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv): Obtaining information about transactions). Further, the last limitation: “managing the traffic flow using the fastest route” is recitation of insignificant post-solution activity, performing a task/job (managing traffic flow) by using an obtained outcome/output (e.g., fastest route after performing fastest route analysis). Therefore, the abovementioned limitations do not integrate a judicial exception into a practical application.
Step 2B:
The claim 1 as a whole does not include any further additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with in the Step 2A, Prong Two analysis, with respect to integration of the abstract idea into a practical application. The additional element: “A method for managing traffic flow in an environment based on macro trends” is recitation of field of use, i.e., amount to merely indicating a field of use or technological environment and does not amount to significantly more than the judicial exception. Additionally, the limitations “obtaining a graph, the graph representing the environment, the graph comprising: nodes that are associated with different locations in the environment …; obtaining a macro trend of the macro trends, the macro trend indicating a level of foot traffic …” are recitations of Mere data gathering activity (See MPEP 2106.04(d) referencing MPEP 2106.05(g)) and does not amount to significantly more than the judicial exception. Further, the last limitation: “managing the traffic flow using the fastest route” is recitation of insignificant post-solution activity and does not amount to significantly more than the judicial exception.
Therefore, the claim 1 is not patent eligible under 35 USC 101.
Independent claims 9 and 15 are substantially similar to claim 1 and therefore are rejected under the same rationale as stated above. The additional elements in claims 9 and 15: “A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations …” and “A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations …”, are recited at a high-level of generality (i.e., as a generic computer/hardware) such that it amounts no more than mere instructions to apply the exception using a generic computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Claims 2-8 are rejected as a Judicial Exception (JE) since they do not add significantly more than the abstract idea or a practical application.
Claims 2, 3 and 5 are dependent on independent claim 1 and includes all the limitations of claim 1. The 1st limitation of claim 2: “obtaining an image of a scene …” is recitation of data gathering activity (See MPEP 2106.04(d) referencing MPEP 2106.05(g)), 2nd limitation of claim 2: “ingesting the image into an inference model …” is recitation of insignificant extra-solution activity. The limitations of claim 3 are recitations of non-functional data descriptions which do not add anything more to overcome the abstract idea. Further, the limitations of claim 5 are recitations of insignificant extra-solution activity because it involves Mere data gathering (See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv): Obtaining information about transactions). Therefore, the abovementioned limitations do not amount to significantly more than the abstract idea.
Claims 4 and 6-8 are dependent on independent claim 1 and includes all the limitations of claim 1. The limitations of claims 4, 6 and 8 are recitations of Mental Processes using evaluation or judgement, because under BRI, the limitations of claim 4 (e.g., identifying a number of people), claim 6 (e.g., replacing a weight of an edge of the edges using the adjusted travel time estimate) and claim 8 (e.g., performing a fastest route analysis comprises: using the updated edge) can be performed evaluation or judgement in the human brain. Further, limitation of claim 7 is recitation of Mental Processes using pen and paper, since any person can draw or connect two nodes using edge. Therefore, the abovementioned limitations do not amount to significantly more than the abstract idea.
Dependent claims 10-14 are substantially similar to claims 2-6 respectively and therefore are rejected under the same rationale as stated above.
Further dependent claims 16-20 are substantially similar to claims 2-6 respectively and therefore are rejected under the same rationale as stated above.
Therefore, the claims 1-20 are not patent eligible.
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.
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 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
9. Claims 1,5-9,13-15,19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over a Journal “Predicting pedestrian flow along city streets: A comparison of route choice estimation approaches in downtown San Francisco” by Andres Sevtsuk et al. (hereinafter Sevtsuk, published online in 2021) and in view of an Article “Dynamic Guidance Strategy for Pedestrian Travel in Large-Scale Activity under Harsh Environment” by Wenbo Huang et al. (hereinafter Huang, IDS provided on 12/20/2022).
Regarding Claim 1, Sevtsuk teaches a method for managing traffic flow in an environment based on macro trends, (Sevtsuk disclosed in page 1 heading ‘Abstract’: “This paper explores a simpler method of route choice prediction, implemented in the Urban Network Analysis toolbox, which assigns probabilities to available route options based on distance alone. We compare the accuracy of distance-weighted approaches to the more detailed utility-weighted approach using a large dataset of observed GPS pedestrian traces that include numerous trips between same inter sections pairs in downtown San Francisco as a benchmark.” In page 1-2 heading ‘Introduction’: “Using data from anonymous GPS traces collected from a smartphone app, we examine pedestrian flows between node pairs in the street network that are traversed by a relatively large number of pedestrians. Diverging from the more common revealed route choice preference surveys that record routes for spatially heterogeneous origin–destination pairs, the data we use enable us to focus on individual users walking between the exact same origin–destination nodes in the street network, collectively producing a route-choice pattern within a fixed environment.”
The disclosure above “pedestrian flows between node pairs in the street network that are traversed by a relatively large number of pedestrians; the individual users walking between the exact same origin–destination nodes in the street network” correspond to claim limitation “managing traffic flow in an environment based on macro trends”).
Sevtsuk teaches the method comprising: obtaining a graph, the graph representing the environment, the graph comprising: nodes that are associated with different locations in the environment, and edges connecting the nodes, (Sevtsuk disclosed in page 3-4 section 3 (3rd para and 5th para): “In this study, we obtained data from an activity-based smartphone app in San Francisco, which features hundreds of thousands of unique pedestrian trips, recorded between June 2014 and June 2015. ... These origin–destination pairs are not necessarily trip starting and ending points, but rather pairs of nodes in the street network that happened to be traversed by a large number of different pedestrians. … We also constrained the shortest path distance between the node-pairs to no less than 200m so as to maximize available route options. Within these constraints, we identified ten intersection pairs, shown in Figure 1, which have 50 or more unique pedestrian trajectories. … Black line weights in Figure 1 indicate the number of observed pedestrians on each street segment between the origin–destination pairs from our GPS data and red numbers next to diagonals indicate their corresponding total numbers for the given node pair.”).
Sevtsuk teaches obtaining a macro trend of the macro trends, the macro trend indicating a level of foot traffic between two of the different locations, and the macro trend being obtained using computer vision; (Sevtsuk disclosed in page 6 section 4 (right col.): “the “highest utility” approach only chooses the route with the highest utility score—the route with most positive pedestrian qualities (Figure 2(f)). This is conceptually similar to the “shortest route”, with the difference that utility maximization is not only performed on distance alone, but on the nine different route attributes simultaneously. An attractive feature of the first three prediction approaches (Figure 2(b–d)) is that they rely on relative route length as the sole attribute for probability assignment and can thus be predicted for any origin–destination pair ... From a practical perspective, such approaches favor a broader adoption of pedestrian flow prediction in practice … we compared observed pedestrian flows from GPS traces between the ten origin-destination pairs to each of the five prediction approaches across all ten node pairs. This comparative analysis took two forms. First, we compared the results at the street segment level. This is analogous to comparing the predicted flows against pedestrian counts collected on each street segment.”
The disclosure “observed pedestrian flows from GPS traces between the ten origin-destination pairs” correspond to claim limitation “the macro trend indicating a level of foot traffic between two of the different locations, and the macro trend being obtained using computer vision” (e.g., GPS traces/data)).
Sevtsuk teaches the two nodes of the graph representing the two of the different locations; (Sevtsuk disclosed in page 7 section 5: “Upon checking our data and juxtaposing the timestamps from GPS walking trajectories with historic Google Street View images, we discovered that there were two large construction projects underway in 2014–15 along the routes between nodes J–J`, obstructing pedestrian access on Freemont St, Baele St and Mission St (Figures 1 and 3)”).
Sevtsuk teaches performing a fastest route analysis using the updated graph to obtain a fastest route; (Sevtsuk disclosed in page 7-8 section 5: “Table 2 presents the results for both segment-level analysis and route-level analysis. It shows the root mean square errors (RMSE) for predicted models compared to observed flows, where smaller RMSE denotes a better fit with actual data. … Cells in Table 2 are shaded horizontally across different prediction methods using darker tones to denote a lower RMSE and better fit. … The only exception was found between intersections J–J`, where the shortest path prediction actually matched the actual route distribution the best. … Accordingly, GPS traces show that 127out of 139 pedestrians took Market St and Main St, avoiding the disruptions along Freemont, Baele and Mission streets. This explains why the “shortest-path” assumption fit the data best here … Route-level analysis in the bottom half of Table 2 … The “shortest-path” prediction is equally low (mean RMSE 0.289, mean pseudo R2 of 0.461). … At node-pair J–J`, where we found the construction disruptions above, the best route-level fit is achieved not by the shortest path approach, but the distance-weighted approach (Figure 1).”).
and Sevtsuk teaches managing the traffic flow using the fastest route. (Sevtsuk disclosed in page 10 section 6: “At the route-level, the utility-weighted approach produced a pseudo R2 of 0.784, compared to a distance-weighted R2 of 0.759 and an equal-probability R2 of 0.755. It is possible that the differences in means could become significant if a much large sample of segments and routes were used, but the accuracy levels would likely still remain close under similar environmental conditions. We thus conclude that both the “distance-weighed” probability assignment and “equal probability” assignment provide reasonable alternatives for pedestrian flow prediction. When investing significant efforts to modeling route choice according to specific route qualities and parameters is not feasible, these two considerably simpler models can be used to predict pedestrian flow. … Therefore, in urban environments, where pedestrian qualities vary starkly from one street to another—i.e. a vibrant main street surrounded by quiet suburban streets; a pleasant community path winding through an otherwise highly trafficked industrial district—a utility-weighted model will likely perform significantly better than a distance-weighted model. … While the utility-weighted model was calibrated on a large dataset of actual walking trajectories, its application at a concrete location can mask a significant error margin, depending on how closely the specific site users behave like “average” users in the city, on whom the utility coefficients were calibrated.”).
However, Sevtsuk doesn’t explicitly teach the limitation “the edges representing traversal times between the different locations; adjusting a travel time estimate of one or more travel time estimates between two nodes of the graph using the macro trend to obtain an updated graph,”
Huang teaches the edges representing traversal times between the different locations; (Huang disclosed in page 3 section 2.2.1: “In this paper, the layout formed by the lo cations of VMSs that release information during a certain period of time in road network is called the releasing layout of VMS information during the period. Different passenger flow states correspond to the different optimal releasing layouts of VMS information. Therefore, the releasing locations of the VMS information in the network are regarded as variables… In the model, the first objective considers the travel impedance of the network and the releasing cost of VMSs in the network within a certain period of time. After the information is released, the smaller the average travel impedance of the roads in the network and the fewer the road sections that release information in a certain period of time, the better the objective.”).
Huang teaches adjusting a travel time estimate of one or more travel time estimates between two nodes of the graph using the macro trend to obtain an updated graph, (Huang disclosed in page 3-4 section 2.2.1: “In this paper, the layout formed by the lo cations of VMSs that release information during a certain period of time in road network is called the releasing layout of VMS information during the period. Different passenger flow states correspond to the different optimal releasing layouts of VMS information. Therefore, the releasing locations of the VMS information in the network are regarded as variables… In the model, the first objective considers the travel impedance of the network and the releasing cost of VMSs in the network within a certain period of time. After the information is released, the smaller the average travel impedance of the roads in the network and the fewer the road sections that release information in a certain period of time, the better the objective. If the starting time of information releasing is t0, the objective can be expressed … where C`ij (t) is the travel impedance of road section (i,j) at time t, which can be expressed by the generalized travel cost ΔT is the calculation time after the information is released; Sij(t) is 1 if there is information released in road section (i,j); otherwise, it is 0; a is the cost of the information release for single VMS per unit time, which can be expressed by time cost; and E is the collection of road sections. Firstly, considering the factor of passenger congestion, the generalized travel cost function of passenger flow in a road section(i,j) at time t is established, … where t0,ij is the average time cost of pedestrians walking freely in road section (i, j); gij (t) is the total passenger volume on road section (i,j) at time t after the information is released; cij is the capacity of road section (i,j); ... In this paper, the second objective in the model is built to minimize the cumulative duration of high congestion in the road sections. If the releasing time of VMS information is started at t0, the objective can be expressed by (5). Within a certain period of time, the smaller the cumulative duration of the road congestion and the smaller the amplitude above the threshold, the better the objective.”).
Sevtsuk and Huang are analogous art because they are related to evaluate pedestrian flow along urban road network. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sevtsuk and Huang, before him or her, to modify managing traffic flow using the fastest route in Sevtsuk’s teaching, to include managing traffic flow by adjusting travel time between two locations in a road network graph in Huang’s teaching. The suggestion/motivation for doing so would have been obvious by Huang because “In order to improve the travel efficiency and reduce the safety risk of pedestrians, an adaptive information-distribution strategy of VMS (variable message sign) for road networks is proposed to guide the pedestrians. Through numerical simulation, it is found that the guidance strategy can improve the movement efficiency by adjusting releasing duration of VMS information or improving the information obedience rate of pedestrians. The results show that the average walking time and the road congestion can be significantly reduced in the road network with the strategy, and the proportion of pedestrians with shorter travel time can be increased. Therefore, the research can provide theoretical foundation and data support for managers to guide passenger flows and improve the spectating experience. (Huang disclosed in page 1 at 1st para (on top of section 1)).
Regarding Claim 5, Sevtsuk and Huang teach the method of claim 1, however, Sevtsuk doesn’t not explicitly teach the limitation “adjusting the travel time estimate comprises: obtaining, using the macro trend, a variable level of delay; and obtaining the travel time estimate based on the variable level of delay and a static level of delay”.
wherein Huang teaches adjusting the travel time estimate comprises: obtaining, using the macro trend, a variable level of delay; (Huang disclosed in page 4 section 2.2.1: “Firstly, considering the factor of passenger congestion, the generalized travel cost function of passenger flow in a road section(i, j) at time t is established, … where t0,ij is the average time cost of pedestrians walking freely in road section (i, j); gij (t) is the total passenger volume on road section (i,j) at time t after the information is released; …and αc,βc are the delay coefficients caused by the crowded passengers on the road section.” In page 8 section 3.2.1: “Pedestrian Speed. In the simulation, the pedestrians are regarded as multiple agents. Pedestrian speed changes adaptively with the changes of congestion degree in the remaining road section in front of the pedestrian’s location, and the calculation method is given in (13). The pedestrian speed is a dynamic variable. When the congestion of the remaining section in front of the pedestrian is large, the speed becomes slower. In particular, when a pedestrian’s speed is 0, the capacity of the remaining road section in front of the pedestrian reaches its maximum.”).
and Huang teaches obtaining the travel time estimate based on the variable level of delay and a static level of delay. (Under BRI, Examiner would construe the claim term “variable level of delay” as dynamic level of delay.
Huang disclosed in page 8 section 3.2.1: “Pedestrian Speed. In the simulation, the pedestrians are regarded as multiple agents. Pedestrian speed changes adaptively with the changes of congestion degree in the remaining road section in front of the pedestrian’s location, and the calculation method is given in (13). The pedestrian speed is a dynamic variable. When the congestion of the remaining section in front of the pedestrian is large, the speed becomes slower. In particular, when a pedestrian’s speed is 0, the capacity of the remaining road section in front of the pedestrian reaches its maximum. As the congestion dissipates, the pedestrian speed gradually increases until it returns to the speed under free flow. … where vp(t) is the walking speed of pedestrian p at time t; vp0,ij is the walking speed of pedestrian p in a free state;
The disclosures above “when the congestion of the remaining section in front of the pedestrian is large, the speed becomes slower. In particular, when a pedestrian’s speed is 0, the capacity of the remaining road section in front of the pedestrian reaches its maximum” correspond to claim element “static level of delay”. Further, the disclosure “As the congestion dissipates, the pedestrian speed gradually increases until it returns to the speed under free flow. … where vp(t) is the walking speed of pedestrian p at time t” correspond to claim element “variable level of delay”. From Equation (13), in above disclosure travel time can be obtained/determined).
Sevtsuk and Huang are analogous art because they are related to evaluate pedestrian flow along urban road network. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sevtsuk and Huang, before him or her, to modify managing traffic flow using the fastest route in Sevtsuk’s teaching, to include managing traffic flow by adjusting travel time between two locations in a road network graph in Huang’s teaching. The suggestion/motivation for doing so would have been obvious by Huang because “In order to improve the travel efficiency and reduce the safety risk of pedestrians, an adaptive information-distribution strategy of VMS (variable message sign) for road networks is proposed to guide the pedestrians. Through numerical simulation, it is found that the guidance strategy can improve the movement efficiency by adjusting releasing duration of VMS information or improving the information obedience rate of pedestrians. The results show that the average walking time and the road congestion can be significantly reduced in the road network with the strategy, and the proportion of pedestrians with shorter travel time can be increased. Therefore, the research can provide theoretical foundation and data support for managers to guide passenger flows and improve the spectating experience. (Huang disclosed in page 1 at 1st para (on top of section 1)).
Regarding Claim 6, Sevtsuk and Huang teach the method of claim 5, however, Sevtsuk doesn’t not explicitly teach the limitation “obtaining the updated graph comprises replacing a weight of an edge of the edges using the adjusted travel time estimate to obtain an updated edge of the edges”.
wherein Huang teaches obtaining the updated graph comprises replacing a weight of an edge of the edges using the adjusted travel time estimate to obtain an updated edge of the edges. (Huang disclosed in page 2-3 section 2.1: “The release of VMS information and the state of passenger flow can form a benign interactive feedback, and the feedback can provide support for realizing the guidance and travel guarantee for pedestrians in harsh environment. … the information content in the VMSs and the road sections chosen to release the information can be dynamically adjusted according to the current passenger flow state and emergency conditions, so that the optimal information guidance effect with the minimum release cost can be achieved.” In page 10 section 4.3.1. (left col.): “Figures 8 and 9 show the impact of the releasing duration of VMS information on the pedestrians’ average travel time and the continuous congestion time of road sections, respectively. As can be seen, when the duration is set to 6–8 minutes, the average travel time and the continuous congestion time reach the minimum.” In same page section 4.3.2. (right col.): “Figures 10 and 11 show the pedestrians’ average travel time and the maximum congestion of road sections under different information obedience rates, respectively. As can be seen, there is an inverse relationship between the information obedience rate and the average travel time. That is, the lower the information obedience rate Po, the worse the information guidance effect with longer average travel time. The results show that network congestion can be effectively reduced and the personal travel time can be saved by increasing information obedience rate Po.”
The disclosure above “the road congestion is about 0.43, 0.71, and 1, the compliance rate of pedestrians to VMS information is about 0.3946, 0.6906, and 0.861” corresponds to claim element “replacing a weight of an edge”. The disclosure “Figures 10 and 11 show the pedestrians’ average travel time and the maximum congestion of road sections under different information obedience rates” corresponds to claim limitation “using the adjusted travel time estimate to obtain an updated edge of the edges”).
Sevtsuk and Huang are analogous art because they are related to evaluate pedestrian flow along urban road network. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sevtsuk and Huang, before him or her, to modify managing traffic flow using the fastest route in Sevtsuk’s teaching, to include managing traffic flow by adjusting travel time between two locations in a road network graph in Huang’s teaching. The suggestion/motivation for doing so would have been obvious by Huang because “In order to improve the travel efficiency and reduce the safety risk of pedestrians, an adaptive information-distribution strategy of VMS (variable message sign) for road networks is proposed to guide the pedestrians. Through numerical simulation, it is found that the guidance strategy can improve the movement efficiency by adjusting releasing duration of VMS information or improving the information obedience rate of pedestrians. The results show that the average walking time and the road congestion can be significantly reduced in the road network with the strategy, and the proportion of pedestrians with shorter travel time can be increased. Therefore, the research can provide theoretical foundation and data support for managers to guide passenger flows and improve the spectating experience. (Huang disclosed in page 1 at 1st para (on top of section 1)).
Regarding Claim 7, Sevtsuk and Huang teach the method of claim 6, wherein Sevtsuk teaches the edge connects the two nodes. (Sevtsuk disclosed in page 3-4 section 3 (3rd para and 5th para): “In this study, we obtained data from an activity-based smartphone app in San Francisco, which features hundreds of thousands of unique pedestrian trips, recorded between June 2014 and June 2015. ... These origin–destination pairs are not necessarily trip starting and ending points, but rather pairs of nodes in the street network that happened to be traversed by a large number of different pedestrians. … We also constrained the shortest path distance between the node-pairs to no less than 200m so as to maximize available route options. Within these constraints, we identified ten intersection pairs, shown in Figure 1, which have 50 or more unique pedestrian trajectories. … Black line weights in Figure 1 indicate the number of observed pedestrians on each street segment between the origin–destination pairs from our GPS data and red numbers next to diagonals indicate their corresponding total numbers for the given node pair.”).
Regarding Claim 8, Sevtsuk and Huang teach the method of claim 6, wherein Huang teaches performing a fastest route analysis comprises: using the updated edge to obtain a travel time estimate for a candidate fastest route. (Huang disclosed in page 9 section 3.2.2. (left col.): “pedestrians only travel along the effective paths and the longer the travel time, the lower the path selection probability, the effective path between the starting place and the destination should meet the following condition: Crod ≤ (1 + H) Cod,min, where Crod is the generalized travel cost of the effective path r between the starting place and the destination; Cod,min is the generalized travel cost of the shortest path between the starting place and the destination; and H is the maximum detour coefficient of path r.” In page 14 section 5.3 (left col.): “Figures 17 and 18 show the comparison of travel time distribution of pedestrians for the scenarios of ingress and egress, respectively. As can be seen, in the both scenarios, the proportion of pedestrians’ short-term travel can increase with the VMS information releasing: the proportion of pedestrians with travel time less than 20 minutes increased by about 3.75% for the scenario of ingress, and the proportion of pedestrians with travel time less than 25 minutes increased by about 7.36% for the scenario of egress.”).
Sevtsuk and Huang are analogous art because they are related to evaluate pedestrian flow along urban road network. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sevtsuk and Huang, before him or her, to modify managing traffic flow using the fastest route in Sevtsuk’s teaching, to include managing traffic flow by adjusting travel time between two locations in a road network graph in Huang’s teaching. The suggestion/motivation for doing so would have been obvious by Huang because “In order to improve the travel efficiency and reduce the safety risk of pedestrians, an adaptive information-distribution strategy of VMS (variable message sign) for road networks is proposed to guide the pedestrians. Through numerical simulation, it is found that the guidance strategy can improve the movement efficiency by adjusting releasing duration of VMS information or improving the information obedience rate of pedestrians. The results show that the average walking time and the road congestion can be significantly reduced in the road network with the strategy, and the proportion of pedestrians with shorter travel time can be increased. Therefore, the research can provide theoretical foundation and data support for managers to guide passenger flows and improve the spectating experience. (Huang disclosed in page 1 at 1st para (on top of section 1)).
Regarding Claim 9, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sevtsuk and Huang as discussed above for substantially similar rationale. In addition, claim 9 recites following limitation:
Sevtsuk teaches a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations (Sevtsuk disclosed in page 2 section 1 (left col.): “In particular, we explore how the accuracy of utility-weighted pedestrian flow predictions compares against significantly simpler distance weighted pedestrian flow predictions, which network analysis software can produce without detailed data on route attributes.” Any person of ordinary skill in the art would understand that any action performed using software in a computer system must have machine-readable medium having instructions stored, which is executed by a processor, and cause the processor to perform the claimed operations (e.g., network analysis software can produce the accuracy of utility-weighted pedestrian flow predictions compares against distance weighted pedestrian flow predictions)).
Regarding Claims 13 and 14, Sevtsuk and Huang teach the non-transitory machine-readable medium of claim 9, are incorporating the rejections of claims 5 and 6 respectively, because claims 13 and 14 have substantially similar claim language as claims 5 and 6, therefore claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sevtsuk and Huang as discussed above for substantially similar rationale.
Regarding Claim 15, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Sevtsuk and Huang as discussed above for substantially similar rationale. In addition, claim 15 recites following limitation:
Sevtsuk teaches a data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations (Sevtsuk disclosed in page 2 section 1 (left col.): “In particular, we explore how the accuracy of utility-weighted pedestrian flow predictions compares against significantly simpler distance weighted pedestrian flow predictions, which network analysis software can produce without detailed data on route attributes.” Any person of ordinary skill in the art would understand that any action performed using software in a computer system must have a processor and a memory coupled to the processor to store instructions, which is executed by the processor, and cause the processor to perform the claimed operations (e.g., network analysis software can produce the accuracy of utility-weighted pedestrian flow predictions compares against distance weighted pedestrian flow predictions)).
Regarding Claims 19 and 20, Sevtsuk and Huang teach the data processing system of claim 15, are incorporating the rejections of claims 5 and 6 respectively, because claims 19 and 20 have substantially similar claim language as claims 5 and 6, therefore claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sevtsuk and Huang as discussed above for substantially similar rationale.
Claims 2-4, 10-12 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sevtsuk and Huang and further in view of an NPL paper “Path Detection in Video Surveillance” by Dimitrios Makris et al. (hereinafter Makris, IDS provided on 12/20/2022).
Regarding Claim 2, Sevtsuk and Huang teach the method of claim 1, however, Sevtsuk and Huang do not explicitly teach the limitation: “obtaining the macro trend comprises: obtaining an image of a scene associated with an edge of the edges; and ingesting the image into an inference model to obtain the macro trend”.
wherein Makris teaches obtaining the macro trend comprises: obtaining an image of a scene associated with an edge of the edges; (Makris disclosed in page 3-4 section 3: “We develop a spatial model for representing routes in the image. Each route is modelled with a central axis formed by a sequence of knot points (nodes) … The route nodes are spaced at equal separation distances equal to a resample distance. Each route has two terminator nodes (start and end) that typically correspond to entry/exit points in the image (fig. 2). … The paths are described with the following features: 1. entry/exit zones: regions where pedestrians enter or exit the image 2. junctions: regions where routes cross each other. Pedestrians enter and exit the scene in specific regions, following specific routes. A route describes the entire trajectory of a pedestrian from the time that he enters the scene till the time that he exits and can be described as a curve with specific start and end points. Junctions are the areas where routes cross or bifurcate. We use a graph to represent the topology of the network of nodes (entry/exit points and junctions) for the paths. The models are learnt from example trajectories extracted from an image sequence of pedestrian motion. Trajectories are grouped using a geometrical analysis that compares the separation distance between a trajectory and an evolving route description.”).
and Makris teaches ingesting the image into an inference model to obtain the macro trend. (Under BRI and conventional meaning in the art, Examiner would construe the claim element “ingest” as incorporate or integrate.
Makris disclosed in page 5 section 4: “Routes are learnt by grouping sets of trajectories. The route description is stored in a database. The following section describes how trajectories are selected for grouping, the criteria for matching a new trajectory to a route, the updating process of routes in the database, and finally, how routes are merged. A trajectory is derived from tracking an object across many frames extracted from an image sequence. It consists by a sequence of 2D points corresponding to a specific point on the target. … The route learning algorithm takes the next trajectory and attempts to match it with all existing routes in the database. If a match is detected, then the matched route will be updated. If not, then a new route will be initialised. After a route has been matched with a trajectory, it becomes a candidate for merging with other routes in the database. The database builds up a typically a small number of routes which represent the principal pathways taken by pedestrians in the scene.”
The disclosure above “the criteria for matching a new trajectory to a route, the updating process of routes in the database, and finally, how routes are merged; If a match is detected, then the matched route will be updated; database builds up a typically a small number of routes which represent the principal pathways taken by pedestrians in the scene” correspond to claim limitation “ingesting the image into an inference model to obtain the macro trend”).
Sevtsuk, Huang and Makris are analogous art because they are related to evaluate pedestrian flow along urban road network. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sevtsuk, Huang and Makris, before him or her, to modify managing traffic flow using Google Street View images in Sevtsuk’s teaching, to include managing traffic flow by ingesting or incorporating the image into an inference model to obtain pedestrian flow in Makris’s teaching. The suggestion/motivation for doing so would have been obvious by Makris because “This paper has demonstrated the practicality of building spatial models based on the analysis of trajectory data extracted from image sequences. The models have been shown to be valuable for economically encoding the route followed by an object in the scene, reducing the trajectory data down to a single label associated with each route. Although many surveillance tracking algorithms provide a local predictive step to aid the correspondence process in the next image frame, encoding the route and path data supports prediction over many time steps, and may be particularly useful for predicting across some types of occlusion in the scene” (Makris disclosed in page 17 section 6).
Regarding Claim 3, Sevtsuk, Huang and Makris teach the method of claim 2, however, Sevtsuk doesn’t not explicitly teach the limitation “the macro trend comprises one selected from a group consisting of: a number of people in a queue in the scene; and a number of people that pass by a point in the scene”.
wherein Huang teaches the macro trend comprises one selected from a group consisting of: a number of people in a queue in the scene; and a number of people that pass by a point in the scene. (Huang disclosed in page 4 section 2.2.1: “Firstly, considering the factor of passenger congestion, the generalized travel cost function of passenger flow in a road section(i,j) at time t is established, … where t0,ij is the average time cost of pedestrians walking freely in road section (i, j); gij (t) is the total passenger volume on road section (i,j) at time t after the information is released; cij is the capacity of road section (i,j); ...”. Further, in page 8 in Figure 5, disclosed “Calculate the passenger volume and congestion of each road section”.
The disclosure “passenger volume” corresponds to “number of people in a queue”. The disclosures “gij (t) is the total passenger volume on road section (i,j) at time t; and “calculate the passenger volume and congestion of each road section” correspond to claim limitation “a number of people that pass by a point in the scene”).
Sevtsuk and Huang are analogous art because they are related to evaluate pedestrian flow along urban road network. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sevtsuk and Huang, before him or her, to modify managing traffic flow using the fastest route in Sevtsuk’s teaching, to include managing traffic flow by adjusting travel time between two locations in a road network graph in Huang’s teaching. The suggestion/motivation for doing so would have been obvious by Huang because “In order to improve the travel efficiency and reduce the safety risk of pedestrians, an adaptive information-distribution strategy of VMS (variable message sign) for road networks is proposed to guide the pedestrians. Through numerical simulation, it is found that the guidance strategy can improve the movement efficiency by adjusting releasing duration of VMS information or improving the information obedience rate of pedestrians. The results show that the average walking time and the road congestion can be significantly reduced in the road network with the strategy, and the proportion of pedestrians with shorter travel time can be increased. Therefore, the research can provide theoretical foundation and data support for managers to guide passenger flows and improve the spectating experience. (Huang disclosed in page 1 at 1st para (on top of section 1)).
Regarding Claim 4, Sevtsuk, Huang and Makris teach the method of claim 2, however, Sevtsuk doesn’t not explicitly teach the limitation “the inference model is programmed to identify a number of people in the scene”.
where Huang teaches the inference model is programmed to identify a number of people in the scene. (Huang disclosed in page 4 section 2.2.1: “Firstly, considering the factor of passenger congestion, the generalized travel cost function of passenger flow in a road section(i,j) at time t is established, … where t0,ij is the average time cost of pedestrians walking freely in road section (i, j); gij (t) is the total passenger volume on road section (i,j) at time t after the information is released; cij is the capacity of road section (i,j); ...”. Further, in page 8 in Figure 5, disclosed “Calculate the passenger volume and congestion of each road section”. The disclosure “passenger volume” corresponds to “number of people in a queue”).
Sevtsuk and Huang are analogous art because they are related to evaluate pedestrian flow along urban road network. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Sevtsuk and Huang, before him or her, to modify managing traffic flow using the fastest route in Sevtsuk’s teaching, to include managing traffic flow by adjusting travel time between two locations in a road network graph in Huang’s teaching. The suggestion/motivation for doing so would have been obvious by Huang because “In order to improve the travel efficiency and reduce the safety risk of pedestrians, an adaptive information-distribution strategy of VMS (variable message sign) for road networks is proposed to guide the pedestrians. Through numerical simulation, it is found that the guidance strategy can improve the movement efficiency by adjusting releasing duration of VMS information or improving the information obedience rate of pedestrians. The results show that the average walking time and the road congestion can be significantly reduced in the road network with the strategy, and the proportion of pedestrians with shorter travel time can be increased. Therefore, the research can provide theoretical foundation and data support for managers to guide passenger flows and improve the spectating experience. (Huang disclosed in page 1 at 1st para (on top of section 1)).
Regarding Claims 10-12, Sevtsuk and Huang teach the non-transitory machine-readable medium of claim 9, are incorporating the rejections of claims 2-4 respectively, because claims 10-12 have substantially similar claim language as claims 2-4, therefore claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Sevtsuk, Huang and Makris as discussed above for substantially similar rationale.
Regarding Claims 16-18, Sevtsuk and Huang teach the data processing system of claim 15, are incorporating the rejections of claims 2-4 respectively, because claims 16-18 have substantially similar claim language as claims 2-4, therefore claims 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sevtsuk, Huang and Makris as discussed above for substantially similar rationale.
Conclusion
10. The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. Fowe et al. (Pub. No. US2020/0202386A1) disclosed a method includes accessing pedestrian location information for the given area. In response to the provided pedestrian location information and based on a position of a mobile robot, the method also includes deter mining a display location containing one or more pedestrians within the given area for the mobile robot to display a mobile message. In an example embodiment, the apparatus also includes means for selecting the at least one of the one or more pedestrians to display the mobile message based on at least one of facial recognition or gesture recognition. According to some example embodiments, the path segment and node data records or other data that may represent pedestrian paths or areas. The map database may contain road segment data records may be links or segments representing roads, streets, or paths, as may be used in calculating a route or recorded route information for determination of one or more personalized routes in addition to or instead of the pedestrian walkway record data.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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