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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/20/2026 has been entered.
Status of Claims
Claims 1-20 of U.S. Application No. 18/407886 filed on 01/20/2026 have been examined.
Office Action is in response to the Applicant's amendments and remarks filed01/20/2026. Claims 1, 5, & 15 are presently amended. Claims 1-20 are presently pending and are presented for examination.
Response to Arguments
In regards to the previous rejections under 35 U.S.C. § 112(b): the amendments to the claims overcome the previous 35 USC § 112(b) rejection. Therefore, the previous 35 USC § 112(b) rejection is withdrawn.
In regards to the previous rejection under 35 U.S.C. § 102: Applicant’s arguments with respect to the independent claim(s) 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. A new grounds of rejection is made in view of US 2022/0180745A1 (“Kashani”).
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 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 nonobviousness.
Claim(s) 1-2, 5-6, 8, 10-11, 13-16, & 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2021/073716 (“Tudoran”), in view of US 2022/0180745A1 (“Kashani”).
As per claim 1 Tudoran discloses
A method for control of real traffic regulation agents of a road network comprising at least one road intersection, the real traffic regulation agents comprising traffic lights and controllable traffic signs (see at least Tudoran, pg. 4 lines 7-15: The determined and/or predicted traffic flow patterns may be used to determine traffic lights plans for traffic signals located at the intersections. The system may therefore be used to control traffic signals at multiple locations, which may help to avoid traffic congestion in the 10 region. The system may be further configured to determine traffic light plans for traffic signals located at the intersections by optimizing at least one traffic metric at the intersection. The at least one traffic metric may be one or more of traffic throughput, queue length and wasted green time of the traffic signals. This allows traffic flow in the region to be optimised, which may help to 15 avoid traffic jams.), the method comprising:
automatically continuously monitoring in real time a traffic situation of the road network by capturing actual traffic data using sensors installed at the road network (see at least Tudoran, pg. 6 lines 11-24: Traffic metrics are received as data streams, shown generally at 101, which are sequences of events (for example, tuples containing various types of data, such as the number of cars, speed of cars etc.) that are collected from various sources. For example, data may be collected from sensors in cars, or from sensors such as cameras and induction loops embedded on the roads, or from other data sources such as WiFi-access points, pollution sensors and noise sensors, in a chronologically ordered fashion.),
the traffic data indicating at least actual inflow and outflow for all available traffic flow directions of the road intersection (see at least Tudoran, pg. 8 lines 9-10: The stream operator can receive the number of 10 vehicles arriving at each intersection from all of the neighbouring intersections in real time, computes the probability for stopping and continuing in each direction, and updates the previously observed flow patterns.),
continuously modelling the traffic situation by a computer simulation comprising a digital model of the road network with digital traffic regulation agents modelling the real traffic regulation agents in parallel to the monitoring the captured actual traffic data; therein simulating the outflow for said all available flow directions of the road intersection (see at least Tudoran, pg. 9 lines 29-31: The online multi-metric optimizer 111 collects traffic metrics, models them in search states, 30 makes predictions about the future traffic flow and selects from the search states the control sequence to enhance the road traffic flow. The online multi-metric traffic optimizer adjusts the offline optimized plans based on multi-metrics such as maximizing throughput, pg. 12 lines 20-27: In the second scenario, illustrated in Figure 8, the online multi-metric traffic light optimization system was applied on data from seven intersections of Tianjin city. The simulation was run during the morning rush hours using an offline optimized reinforcement learning model and a real time adjusted model of the offline optimized plans. In this small-scale scenario, seven traffic lights systems were considered controlling: a highway intersection ( 4 directions, 2-4 25 lanes per direction), a T-type intersection (3 directions 2-3 lanes per direction), and a regular intersection ( 4 directions, 2-4 lanes per direction). Use of the online multi-metric adjusted model provided a reduction of 13.82 % in the average delay time.); and
continuously adapting real-time control of the real traffic regulation agents according to the simulated optimized control (see at least Tudoran, pg. 10 lines 1-3: Therefore, the optimization of the traffic can be performed continuously and in real-time from an incoming stream of traffic metrics to adjust the offline optimized plans based on multiple metrics.).
However Tudoran does not explicitly disclose
optimizing control of the digital traffic regulation agents by updating the digital model using reinforcement learning, in particular deep reinforcement learning.
Kashani teaches
optimizing control of the digital traffic regulation agents by updating the digital model using reinforcement learning, in particular deep reinforcement learning (see at least Kashani, para. [0062]: One non-limiting goal of this disclosure is to use up-to-date models of how a given traffic network is likely to operate for a geographic area, populate at least one of the models with state data that reflects at least one state of the traffic network, and evaluate the overall traffic network as a system of agents that are continuously subject to changes in the traffic conditions, i.e., continually subject to changing states of the network. para. [0072]: With reference to FIG. 1, the method 100 can include maintaining a traffic model and updating102 that traffic model with the current congestion and network state and determining 104 a set of active vehicles implementing the desired route-finding algorithm. Embodiments of the present disclosure can be used in multiple different contexts. & para. [0084]: In some embodiments of the present disclosure, the Learning system-optimal assignments via multi-agent reinforcement learning can include multi-agent RL and QMIX (a Monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning), and sub-optimal route planning with QMIX. Again, different combinations of these techniques are contemplated by the present disclosure, and the present disclosure contemplates that reinforcement learning methods can be used in combination with the different embodiments of the present disclosure.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tudoran to incorporate the teaching of optimizing control of the digital traffic regulation agents by updating the digital model using reinforcement learning, in particular deep reinforcement learning of Kashani, with a reasonable expectation of success, in order for improving the speed of the route planning or replanning (see at least Kashani, para. [0083]).
As per claim 2 Tudoran discloses
characterized by determining a traffic flow interruption in course of the simulation and/or based on the captured actual traffic data and optimizing control of the real traffic regulation agents to prevent and/or counteract the flow interruption (see at least Tudoran, pg. 13 lines 30-33 & pg. 14 lines 1-2: Non-recurrent events such as accidents are a major cause of traffic jams. The system described herein allows for the continuous monitoring of flows across intersections that is necessary to detect these incidents in real-time and can adapt the traffic light sequences to decrease jam in the area of the event, trigger the re-routing process, and start a back-pressure mechanism to avoid spreading of the congestion.).
As per claim 5 Tudoran discloses
wherein the real traffic regulation agents are an initial set of real traffic regulation agents, characterized in that the simulation enables simulation of traffic intervention by a different set of traffic regulation agents than the initial set of real traffic regulation agents as an optimization option and puts out instructions to a user for altering the initial set of real traffic regulation agents and/or for altering the initial set of real traffic regulation agents automatically (see at least Tudoran, pg. lines 17-21: Figure 3 shows an overview of a smart traffic management system incorporating the traffic reasoner of Figure 1. As shown in Figure 3, using traffic data collected from a region 302, the traffic intelligence agent and the online multi-metric traffic optimization components of the 20 traffic reasoner system 100 communicate the refined perspective of traffic light timing with online controller agents 301 to adjust and deploy a new traffic light plan to the region 302.).
As per claim 6 Tudoran discloses
characterized in that the optimizing comprises determining of simulated sensor data and comparing the simulated sensor data with corresponding actual real sensor data (see at least Tudoran, pg. 6 lines 11-27: The aggregation of real time and historical traffic data in the aggregation service 106 and the extraction of traffic flow patterns in the flow detector 108 therefore enable the deployment of 15 a range of intelligent traffic services, such as AI analytics, delay computation, event or anomaly detection ( such as the detection of accidents) and traffic predictions, which may be used in a traffic lights simulator (shown at 112 in Figure 1), or in an autonomous driving simulator. The traffic intelligence agent, shown at 110, optimizes in real time the offline optimized plans 20 based on a global traffic optimization strategy. This can be done using city-level traffic data updates from the aggregation service and flow detection in addition to local and global perspectives and correlations from the added value services, such as region correlation and classification, intersection clustering, green waves and anomaly detection. The traffic intelligence agent can adjust the optimized offline traffic plan in real time based on single 25 traffic metrics (such as throughput, queue length or wasted green time). Anomaly patterns detected in real time by the added value services 109, such as accidents and jams, can also be used to adjust the plans.).
As per claim 8 Tudoran discloses
characterized in that at least one of the sensors is a laser scanner or stereo camera for monitoring real-time 3D-data, in particular location and/or dimension, of traffic participants (see at least Tudoran, pg. 6 lines 11-21: Traffic metrics are received as data streams, shown generally at 101, which are sequences of events (for example, tuples containing various types of data, such as the number of cars, speed of cars etc.) that are collected from various sources. For example, data may be collected from sensors in cars, or from sensors such as cameras and induction loops embedded on the roads, or from other data sources such as WiFi-access points, pollution sensors and noise sensors, in a chronologically ordered fashion. Data streams may be received from sensors at a location or intersection in a sector of the spatial region under consideration. Such sectors are illustrated at 102, 103 and 104 in Figure 1. The data received from each of the sensors therefore comprises a data series of values, which may in some examples be a timeseries. Video cameras and inductions loops may measure traffic flow (i.e. the number of cars passing a location in a certain time period) directly.).
As per claim 10 Tudoran discloses
characterized in that the digital model models a geometry of the road network, in particular precise dimensions and/or locations of lanes and/or agents, and the geometry is taken into account in simulating the outflow (see at least Tudoran, pg. 6 lines 25-30: The roads layout service 105 provides detailed information about the layout of the roads and intersections in the spatial region under consideration. This may include the type of intersection, the number of lanes per direction, the length of roads or lanes, the maximum speed on each lane, and the number of allowed driving directions for each lane or road. It also provides layout details of intersections, such as incoming and outgoing roads and the 30 neighbouring intersections for each intersection.).
As per claim 11 Tudoran discloses
characterized in that the digital model is adaptable on-the-fly by machine learning based on at least part of the traffic data provided by at least part of the sensors and/or by human input of road network changes, in particular an interruption of a road (see at least Tudoran, pg. 9 lines 21-27: This can be done using city-level traffic data updates from the aggregation service and flow detection in addition to local and global perspectives and correlations from the added value services, such as region correlation and classification, intersection clustering, green waves and anomaly detection. The traffic intelligence agent can adjust the optimized offline traffic plan in real time based on single 25 traffic metrics (such as throughput, queue length or wasted green time). Anomaly patterns detected in real time by the added value services 109, such as accidents and jams, can also be used to adjust the plans.).
As per claim 13 Tudoran discloses
characterized in that the simulation automatically takes into account a known future network condition known time stamps of the network condition for the optimizing, and/or weather condition data by automatic input from sensor data as actual weather data and/or as weather forecast data (see at least Tudoran, pg. 4 lines 22-24: Each of the sensors may comprise one of a camera, a weather sensor, a pollution sensor, a noise sensor and an induction loop. This may allow the number of vehicles at a particular location to be measured directly or inferred.).
As per claim 14 Tudoran discloses
characterized by optimizing control of the digital traffic regulation agents using reinforcement learning in course of the simulation such that traffic accident risk and/or energy consumption of vehicles actually using the road network is optimized (see at least Tudoran, pg. 6 lines 3-9: From this aggregated data, the system can determine and/or predict traffic flow patterns at the locations and analyse the received data to identify deviations between the received data and the determined and/or predicted traffic 5 flow patterns. This allows the system to detect anomalies in the patterns, such as accidents, which may cause traffic jams. The system can also analyse the determined and/or predicted traffic flow patterns to identify vehicle-level features, such as the number of vehicles at a location or intersection, or the queue length of vehicles at a location or intersection, in the future. Pg. 9 lines 13-18).
As per claim 15 Tudoran discloses
A non-transitory computer-readable medium having computer executable code thereon, the computer executable code, when executed by a computer system, causing the computer system to perform a method, the code comprising (see at least Tudoran, pg. 11 lines 28-32: The system may comprise a processor and a non-volatile memory. The system may comprise more than one processor and more than one memory. The memory may store data that is 30 executable by the processor. The processor may be configured to operate in accordance with a computer program stored in non-transitory form on a machine readable storage medium.):
code for automatically continuously monitoring in real time a traffic situation of the road network by capturing actual traffic data using sensors installed at the road network (see at least Tudoran, pg. 6 lines 11-24: Traffic metrics are received as data streams, shown generally at 101, which are sequences of events (for example, tuples containing various types of data, such as the number of cars, speed of cars etc.) that are collected from various sources. For example, data may be collected from sensors in cars, or from sensors such as cameras and induction loops embedded on the roads, or from other data sources such as WiFi-access points, pollution sensors and noise sensors, in a chronologically ordered fashion.),
the traffic data indicating at least actual inflow and outflow for all available traffic flow directions of the road intersection (see at least Tudoran, pg. 8 lines 9-10: The stream operator can receive the number of 10 vehicles arriving at each intersection from all of the neighbouring intersections in real time, computes the probability for stopping and continuing in each direction, and updates the previously observed flow patterns.),
code for continuously modelling the traffic situation by a computer simulation comprising a digital model of the road network with digital traffic regulation agents modelling the real traffic regulation agents in parallel to the monitoring with the captured actual traffic data; (see at least Tudoran, pg. 9 lines 29-31: The online multi-metric optimizer 111 collects traffic metrics, models them in search states, 30 makes predictions about the future traffic flow and selects from the search states the control sequence to enhance the road traffic flow. The online multi-metric traffic optimizer adjusts the offline optimized plans based on multi-metrics such as maximizing throughput, pg. 12 lines 20-27: In the second scenario, illustrated in Figure 8, the online multi-metric traffic light optimization system was applied on data from seven intersections of Tianjin city. The simulation was run during the morning rush hours using an offline optimized reinforcement learning model and a real time adjusted model of the offline optimized plans. In this small-scale scenario, seven traffic lights systems were considered controlling: a highway intersection ( 4 directions, 2-4 25 lanes per direction), a T-type intersection (3 directions 2-3 lanes per direction), and a regular intersection ( 4 directions, 2-4 lanes per direction). Use of the online multi-metric adjusted model provided a reduction of 13.82 % in the average delay time.), and
code for continuously adapting real-time control of the real traffic regulation agents according to the simulated optimized control (see at least Tudoran, pg. 10 lines 1-3: Therefore, the optimization of the traffic can be performed continuously and in real-time from an incoming stream of traffic metrics to adjust the offline optimized plans based on multiple metrics.).
However Tudoran does not explicitly disclose
code for optimizing control of the digital traffic regulation agents by updating the digital model using reinforcement learning, in particular deep reinforcement learning.
Kashani teaches
code for optimizing control of the digital traffic regulation agents by updating the digital model using reinforcement learning, in particular deep reinforcement learning (see at least Kashani, para. [0062]: One non-limiting goal of this disclosure is to use up-to-date models of how a given traffic network is likely to operate for a geographic area, populate at least one of the models with state data that reflects at least one state of the traffic network, and evaluate the overall traffic network as a system of agents that are continuously subject to changes in the traffic conditions, i.e., continually subject to changing states of the network. para. [0072]: With reference to FIG. 1, the method 100 can include maintaining a traffic model and updating102 that traffic model with the current congestion and network state and determining 104 a set of active vehicles implementing the desired route-finding algorithm. Embodiments of the present disclosure can be used in multiple different contexts. & para. [0084]: In some embodiments of the present disclosure, the Learning system-optimal assignments via multi-agent reinforcement learning can include multi-agent RL and QMIX (a Monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning), and sub-optimal route planning with QMIX. Again, different combinations of these techniques are contemplated by the present disclosure, and the present disclosure contemplates that reinforcement learning methods can be used in combination with the different embodiments of the present disclosure. & para. [0135]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tudoran to incorporate the teaching of code for optimizing control of the digital traffic regulation agents by updating the digital model using reinforcement learning, in particular deep reinforcement learning of Kashani, with a reasonable expectation of success, in order for improving the speed of the route planning or replanning (see at least Kashani, para. [0083]).
As per claim 16 Tudoran discloses
code for determining a traffic flow interruption in course of the simulation and/or based on the captured actual traffic data and optimizing control of the real traffic regulation agents to prevent and/or counteract the flow interruption (see at least Tudoran, pg. 13 lines 30-33 & pg. 14 lines 1-2: Non-recurrent events such as accidents are a major cause of traffic jams. The system described herein allows for the continuous monitoring of flows across intersections that is necessary to detect these incidents in real-time and can adapt the traffic light sequences to decrease jam in the area of the event, trigger the re-routing process, and start a back-pressure mechanism to avoid spreading of the congestion.).
As per claim 18 Tudoran discloses
wherein the optimizing comprises determining of simulated sensor data and comparing the simulated sensor data with corresponding actual real sensor data (see at least Tudoran, pg. 6 lines 11-27: The aggregation of real time and historical traffic data in the aggregation service 106 and the extraction of traffic flow patterns in the flow detector 108 therefore enable the deployment of 15 a range of intelligent traffic services, such as AI analytics, delay computation, event or anomaly detection ( such as the detection of accidents) and traffic predictions, which may be used in a traffic lights simulator (shown at 112 in Figure 1), or in an autonomous driving simulator. The traffic intelligence agent, shown at 110, optimizes in real time the offline optimized plans 20 based on a global traffic optimization strategy. This can be done using city-level traffic data updates from the aggregation service and flow detection in addition to local and global perspectives and correlations from the added value services, such as region correlation and classification, intersection clustering, green waves and anomaly detection. The traffic intelligence agent can adjust the optimized offline traffic plan in real time based on single 25 traffic metrics (such as throughput, queue length or wasted green time). Anomaly patterns detected in real time by the added value services 109, such as accidents and jams, can also be used to adjust the plans.).
As per claim 19 Tudoran discloses
wherein at least one of the sensors is a laser scanner or stereo camera for monitoring real-time 3D-data, in particular location and/or dimension, of traffic participants (see at least Tudoran, pg. 6 lines 11-21: Traffic metrics are received as data streams, shown generally at 101, which are sequences of events (for example, tuples containing various types of data, such as the number of cars, speed of cars etc.) that are collected from various sources. For example, data may be collected from sensors in cars, or from sensors such as cameras and induction loops embedded on the roads, or from other data sources such as WiFi-access points, pollution sensors and noise sensors, in a chronologically ordered fashion. Data streams may be received from sensors at a location or intersection in a sector of the spatial region under consideration. Such sectors are illustrated at 102, 103 and 104 in Figure 1. The data received from each of the sensors therefore comprises a data series of values, which may in some examples be a timeseries. Video cameras and inductions loops may measure traffic flow (i.e. the number of cars passing a location in a certain time period) directly.).
Claim(s) 3-4, & 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tudoran, in view of Kashani, in view of US 2022/0230540A1 (“Howell”).
As per claim 3 Tudoran does not explicitly disclose
characterized by determining optimized control parameters for the digital agents in course of the control optimizing and outputting the optimized control parameters to a human controller of at least part of the real agents.
Howell teaches
characterized by determining optimized control parameters for the digital agents in course of the control optimizing and outputting the optimized control parameters to a human controller of at least part of the real agents (see at least Howell, para. [0053-0054]: The agents may be neural-network-based agents. Preferably, the agents use deep neural networks to approximate highly non-linear and highly complex functions. This offers benefits in terms of scalability and accuracy. The agents may be software running on GPU (graphics processor unit)hardware….A user interface may be provided which enables an operator to monitor the status of the road network, including real time traffic conditions. Some facility for manual intervention may also be provided, for example allowing the system operator to select different priorities or optimization parameters in real time.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tudoran to incorporate the teaching of characterized by determining optimized control parameters for the digital agents in course of the control optimizing and outputting the optimized control parameters to a human controller of at least part of the real agents of Howell, with a reasonable expectation of success, in order for reducing cars in certain areas, promoting public transport (see at least Howell, para. [0008]).
As per claim 4 Tudoran does not explicitly disclose
characterized in that the computer simulation comprises simulation of human agent control, in particular using a machine learning algorithm trained on past human control of at least part of the real traffic regulation agents.
Howell teaches
characterized in that the computer simulation comprises simulation of human agent control, in particular using a machine learning algorithm trained on past human control of at least part of the real traffic regulation agents (see at least Howell, para. [0055]: Aspects of inverse reinforcement learning may be used. This involves agents learning to mimic an existing control strategy rather than learning their own. In practice, this involves telling the agents what decision humans (or existing products) would have made from each given state, and rewarding the agents for correctly choosing that action. In this context, choosing a “predictable” action can be of real benefit even if an alternative action might have been better optimised other goals.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tudoran to incorporate the teaching of characterized in that the computer simulation comprises simulation of human agent control, in particular using a machine learning algorithm trained on past human control of at least part of the real traffic regulation agents of Howell, with a reasonable expectation of success, in order for reducing cars in certain areas, promoting public transport (see at least Howell, para. [0008]).
As per claim 17 Tudoran does not explicitly disclose
code for determining optimized control parameters for the digital agents in course of the control optimizing and outputting said control parameters to a human controller of at least part of the real agents, wherein the computer simulation comprises simulation of human agent control, in particular using a machine learning algorithm trained on past human control of at least part of the real traffic regulation agents.
Howell teaches
code for determining optimized control parameters for the digital agents in course of the control optimizing and outputting said control parameters to a human controller of at least part of the real agents (see at least Howell, para. [0053-0054]: The agents may be neural-network-based agents. Preferably, the agents use deep neural networks to approximate highly non-linear and highly complex functions. This offers benefits in terms of scalability and accuracy. The agents may be software running on GPU (graphics processor unit)hardware….A user interface may be provided which enables an operator to monitor the status of the road network, including real time traffic conditions. Some facility for manual intervention may also be provided, for example allowing the system operator to select different priorities or optimization parameters in real time.),
wherein the computer simulation comprises simulation of human agent control, in particular using a machine learning algorithm trained on past human control of at least part of the real traffic regulation agents (see at least Howell, para. [0055]: Aspects of inverse reinforcement learning may be used. This involves agents learning to mimic an existing control strategy rather than learning their own. In practice, this involves telling the agents what decision humans (or existing products) would have made from each given state, and rewarding the agents for correctly choosing that action. In this context, choosing a “predictable” action can be of real benefit even if an alternative action might have been better optimised other goals.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tudoran to incorporate the teaching of code for determining optimized control parameters for the digital agents in course of the control optimizing and outputting said control parameters to a human controller of at least part of the real agents, wherein the computer simulation comprises simulation of human agent control, in particular using a machine learning algorithm trained on past human control of at least part of the real traffic regulation agents of Howell, with a reasonable expectation of success, in order for reducing cars in certain areas, promoting public transport (see at least Howell, para. [0008]).
Claim(s) 7, 9, 12, & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tudoran, in view of Kashani, in view of US 2018/0096595A1 (“Janzen”).
As per claim 7 Tudoran does not explicitly disclose
characterized in that optimized travel parameters for individual traffic participants are determined in course of the simulated outflow optimization and communicated to the respective traffic participant.
Janzen teaches
characterized in that optimized travel parameters for individual traffic participants are determined in course of the simulated outflow optimization and communicated to the respective traffic participant (see at least Janzen, para. [0143]: Another feature of the optimization system is the ability to provide selectively optimized routes for different types of road users. Because the sensor network provides data on the types of road users, an algorithm can selectively weight or optimize based on road user type. One embodiment of this would be for bicycle optimization on corridors specifically designed for bicycle traffic.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tudoran to incorporate the teaching of characterized in that optimized travel parameters for individual traffic participants are determined in course of the simulated outflow optimization and communicated to the respective traffic participant of Janzen, with a reasonable expectation of success, in order to improve overall traffic flow (see at least Janzen, para. [0130]).
As per claim 9 Tudoran does not explicitly disclose
characterized in that from the monitored real-time 3D-data a movement pattern for an individual traffic participant is automatically derived and based on the movement pattern as input for the simulation, a driving and/or navigation aid information for the individual traffic participant is determined in course of the simulation and communicated to the individual traffic participant.
Janzen teaches
characterized in that from the monitored real-time 3D-data a movement pattern for an individual traffic participant is automatically derived and based on the movement pattern as input for the simulation participant (see at least Janzen, para. [0129]: This can be handled, for example by estimating each vehicle's predicted current state for example by using a simulation, or alternatively by using a particle filter and conditional probabilities between the GIS platform-detections and the distributions. Using one of these techniques, the likelihood that a GIS detection matches a simulated vehicle position can be estimated. Sufficiently probable GIS detections can be treated as identical to the those found using the simulation technique. Detections which fail to match are injected into the simulation state as a new detection.),
a driving and/or navigation aid information for the individual traffic participant is determined in course of the simulation and communicated to the individual traffic (see at least Janzen, para. [0143]: Another feature of the optimization system is the ability to provide selectively optimized routes for different types of road users. Because the sensor network provides data on the types of road users, an algorithm can selectively weight or optimize based on road user type. One embodiment of this would be for bicycle optimization on corridors specifically designed for bicycle traffic.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tudoran to incorporate the teaching of characterized in that from the monitored real-time 3D-data a movement pattern for an individual traffic participant is automatically derived and based on the movement pattern as input for the simulation, a driving and/or navigation aid information for the individual traffic participant is determined in course of the simulation and communicated to the individual traffic of Janzen, with a reasonable expectation of success, in order to improve overall traffic flow (see at least Janzen, para. [0130]).
As per claim 20 Tudoran does not explicitly disclose
characterized in that the traffic data enables identification of a type of monitored vehicles and the computer simulation takes into account type specific vehicle data, in particular speed related data, retrieved from a stored database
Janzen teaches
characterized in that the traffic data enables identification of a type of monitored vehicles and the computer simulation takes into account type specific vehicle data, in particular speed related data, retrieved from a stored database (see at least Janzen, para. [0083]: Once a road user has been identified, it can be tracked (608) from frame to frame using visual features identified in the image. This information can extend even past the current intersection such that road users can be tracked through multiple intersections, providing robust measurement of transit times for various classes of objects such as vehicles of different types, bikes, and pedestrians. Information about vehicle speed and direction of travel (610) can be used to predict when a car will arrive at an adjacent intersection. & para. [0134-0136]: One straightforward approach for the "Error Function" would be to simulate the behavior of the intersection over the upcoming several minutes, provided a hypothetical timing plan. Various parameters in the outcome, such as vehicle wait times (potentially as a function of vehicle type), would be described as components in this error function…In many embodiments, the traffic signal phase control optimization criterion can assume that there are an equal number of people in each vehicle which is often not the case. Thus, certain vehicles such as buses can be weighted more highly by multiplying the wait time by the number of riders ( or expected number of riders) in the vehicle. This is very hard to measure from camera footage but fixed values can be given to certain types of vehicles such as busses to account for this effect. This can, for example, be accounted for by multiplying a weight by the actual wait time of each car. & para. [0153]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tudoran to incorporate the teaching of characterized in that the traffic data enables identification of a type of monitored vehicles and the computer simulation takes into account type specific vehicle data, in particular speed related data, retrieved from a stored database of Janzen, with a reasonable expectation of success, in order to improve overall traffic flow (see at least Janzen, para. [0130]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABDO ALGEHAIM whose telephone number is (571)272-3628. The examiner can normally be reached Monday-Friday 8-5PM EST.
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/MOHAMED ABDO ALGEHAIM/Primary Examiner, Art Unit 3668