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 .
Status of Claims
This is a Final Rejection office action in response to application Serial No. 18/482,962. Claim(s) 1, 3, 7, 8, 15, and 17 are amended and claim(s) 6, 14, and 20 are canceled.
Claim(s) 1-5, 7-13, and 15-19 are pending in Instant Application.
Response to Arguments/Rejections
Applicant’s arguments, see Remarks, filed 02/11/2026, with respect to the rejection(s) of claim(s) 1, 7, 15 under 35 USC § 112 and 103 have been fully considered. Applicant has amended claims to overcome rejections under 35 USC § 112 and 103.
Claim Rejections - 35 U.S.C. § 112
Applicant states “Claims 1, 3, 7, 8, 15, and 17 stand rejected under 35 U.S.C. §112(b) as being indefinite. Specifically, the Examiner has alleged that the term "recent" in the phrase "a first predefined count of recent traffic signal cycles" is a relative term that renders the claims indefinite because the term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.”
The claims were amended, where “recent” was strikethrough, therefore the 35 U.S.C. @ 103 of 1, 3, 7, 8, 15, and 17 has been withdrawn.
Claim Rejections - 35 U.S.C. @ 103
Claim(s) 1, 7, 15 under 35 USC § 103 have been fully considered and are
persuasive. However, upon further consideration, a new ground(s) of rejection is made
in view of Ova et al. (Pub. No.: US 2018/0286223).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5, 15-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahler et al. (Pub. No.: US 2014/0277986; previously recorded), hereinafter, referred to as “Mahler” in view of Rolle et al. (Pub. No.: US 2017/0084172; previously recorded), hereinafter, referred to as “Rolle”, and in view of Ova et al. (Pub. No.: US 2018/0286223), hereinafter, referred to as “Ova”, and in further view of Mück et al. (Pub. No.: Pub. No.: US 2023/0249713; previously recorded), hereinafter, referred to as “Mück”.
Regarding [claim 1], Mahler discloses a system (see, Figure 1; Abstract; “Traffic management system”) comprising:
a system transceiver (“a receiver”) configured to:
receive historical traffic signal information associated with a traffic light; and
receive real-time traffic signal information associated with the traffic light (see, Paragraph [0021]: “The traffic center data 120 may include nearly real-time and/or historical data from a commercial or governmental traffic center. The traffic center data 120 typically include a collection of data from a plurality of traffic signals. For example, a city may collect raw data from traffic signals and send the raw data to vehicles, or a third party Such as Green DriverR) may obtain the raw data from the city, analyze the data, and send traffic signal predictions to vehicles. The traffic signal data 130 may include real-time data from a plurality of traffic signals. For example, each individual traffic signal may send real-time data regarding its status directly to vehicles.”; [0026]: “the predictive traffic signal information is received from outside of the vehicle, and is integrated with internal information about the vehicle, as described in more detail below.”; [0027]: “As shown in FIG.2, a receiver in the vehicle receives the predictive traffic signal information from outside of the vehicle at step 300. The communication may occur over any suitable network. The predictive traffic signal information is then integrated with internal information about the vehicle at step 310.”)…
a system memory configured to store a training data and a trained machine model, wherein the trained machine model is trained using the training data that comprises the historical traffic signal information;
a system processor communicatively coupled with the system transceiver and the system memory, wherein the system processor is configured to:
obtain the real-time traffic signal information from the system transceiver;
execute instructions stored in the trained machine model to predict traffic signal information associated with a future traffic signal cycle based on the real-time traffic signal information (see, Paragraphs [0022]: “FIG. 1 shows that data are obtained from various sources at step 200, and combined into a database at step 210. The data are then analyzed at step 220. Advanced modeling, data mining, and/or machine learning techniques may be used to predict traffic signal information, including signal phase and timing (SPAT) information. Due to the complexity of the algorithms, a computer processor is required to perform the data analysis at Step 220”; [0025]: “By using data from more than one type of source, exemplary embodiments of the invention may enable improved models that provide more accurate predictions of traffic signal information. Further, machine learning techniques are able to use historical data in order to further improve the accuracy of the predictions. The results are not limited to a localized area, and can be used to predict SPAT information along the driver's entire route.”)
Rolle, in a similar field of endeavor, additionally teaches
a system memory configured to store a training data and a trained machine model, wherein the trained machine model is trained using the training data that comprises the historical traffic signal information;
a system processor communicatively coupled with the system transceiver and the system memory, wherein the system processor is configured to:
obtain the real-time traffic signal information from the system transceiver;
execute instructions stored in the trained machine model to predict traffic signal information associated with a future traffic signal cycle based on the real-time traffic signal information (see, Paragraphs [0024]-[0026]: “An outbound information module (component) can publish the current status of traffic signals as well as predictions of future states. This data can be relevant for all types of Spat/MAP”; [0031]: “The analytics component can be configured to predict at least one future signal status for each traffic light of the plurality of traffic lights based on the use of a machine learning algorithm applied to current signal status data and previously received signal status data. The at least one future signal status can include the expected point in time when the current signal status will switch from the current to the future signal status.”; and [0038]: “The traffic control system in the example shown in FIG. 1 includes five traffic lights 301 to 305. Traffic control system as used throughout this disclosure can also refer to any sub-system of a large system. For example, a city may have a traffic control system which is managed by a traffic management system 300. Such a traffic management system can control the various programs running in the respective traffic lights. The traffic management system can be configured to receive data and information about a current status of the various programs running in the respective traffic lights” and [0032]: “The outbound status provisioning component can be configured to send at least one message to a vehicle wherein the at least one message includes the current signal status and the at least one future signal status of at least one traffic light. The sent message can be configured to influence the operation of the vehicle, which includes the ability to control the operation, by control signals/messages, without human interaction. For example, the sent message may include data relevant for a Signal Phase & Timing (Spat)” and [0034]: “the inbound interface can generate data frames of equal length from the received sensor data stream associated with one or more traffic lights of a signaling sub-system. This allows using the generated data frames as input for the machine learning algorithm. The length of a data frame has impact on the accuracy of the status prediction and on the time needed for training the prediction model. A reasonable frame length can be the cycle period of the signaling sub-system or a multiple thereof. The cycle period of the sub-system can be defined as the time interval it takes for a sub-system to arrive at the initial status again after the system has gone through a plurality of status changes” and [0036]: “the inbound interface may further receive traffic light program data for one or more traffic lights from a traffic management system. The traffic light program data includes information about programs controlling the signal switching of the respective one or more traffic lights. For example, the program ID of the program that is currently running to control the traffic light or the sub-system of traffic lights can be retrieved. Based on the program ID further program data may be retrieved, such as for example the current state of the program, the elapsed time since the program started, etc. In general, such further program data can include run-time data derived from the program in operation, such as data regarding the algorithm of the program as a sequence of typical traffic light switch patterns, including time between status changes. Such data can be available from traffic management systems that can be interfaced with the monitoring system to allow real-time program data retrieval. The analytics component can then use the received traffic light program data in the machine learning algorithm for the prediction of the future signal status of the respective one or more traffic lights which again may improve the accuracy of the traffic light status prediction results.”) and before the vehicle reaches the traffic light (see, Paragraph [0023]: “In some implementations, static visual sensors, such as for example cameras or photocells, can be used to detect the state or status of traffic lights belonging to the traffic control system. For example, it may be assumed that the status of a traffic light can be red [r], yellow [y], or green [g]. Of course, in a real scenario other states can occur. For example, a state [r/y] may occur before the status switches to [g]. There may be other states, such as for example, a blinking green light [g blinking].”; [0048]: “Taking advantage of the signal phase information, drivers are notified about the remaining time before the signal changes, increasing driver's awareness of an upcoming traffic situation and preparedness to react accordingly” [0085]-[0086]: “In a GLOSA scenario, the current status of a traffic light is broadcasted to vehicles being within a predefined distance and approaching the traffic light (e.g., at an intersection). In addition or in the alternative, information about the intersection topology itself and the expected light phase schedule of each traffic signal (predicted light signal status) at the intersection can be broadcasted to the vehicles. The information in the corresponding sent messages may be sent in a SPaT and/or a MAP message format.”);
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a system receiving the predictive traffic signal information from outside of the vehicle and integrating the predictive traffic signal information as taught by Mahler by combining predicting at least one future signal status for at least one of the plurality of traffic lights based on use of a machine learning algorithm applied to data when the current signal status with future signal status, and sending a message to a vehicle as taught by Rolle. One would be motivated to make this modification in order to convey retrieving information about traffic light status of traffic control systems and switching cycles in any geographical region (e.g., a city, a town, a district, or a countryside area). However, in order to access the information of traffic lights within a region, a connection to several traffic light infrastructure systems delivered by different technology providers may be needed, and such data can be available from traffic management systems that can be interfaced with the monitoring system to allow real-time program data retrieval. The analytics component can then use the received traffic light program data in the machine learning algorithm for the prediction of the future signal status of the respective one or more traffic lights which again may improve the accuracy of the traffic light status prediction results (see, Paragraphs [0003] and [0036]).
As Mahler and Rolle teaches traffic signal information, however, neither Mahler nor Rolle explicitly teaches
…
receive…the traffic light for a first predefined count of recent traffic signal cycles, wherein the historical traffic signal information is associated with a second predefined count of traffic signal cycles, and wherein the second predefined count of traffic signal cycles is greater than the first predefined count of traffic signal cycles;
…
However, Ova teaches
…
receive…the traffic light for a first predefined count of recent traffic signal cycles, wherein the historical traffic signal information is associated with a second predefined count of traffic signal cycles, and wherein the second predefined count of traffic signal cycles is greater than the first predefined count of traffic signal cycles(see, claims 3; Figure 1 and 8; Paragraphs [0024]: “The system 100 includes a roadside unit (RSU) 110 or a similar device capable of acquiring a signalized intersection's current traffic signal phase and timing data in real-time, the duration for which this state will persist for each approach and lane, the next signal state switch time, and transmitting the data to the computer system 130 to support performing one or more of the processes described herein. The data may be transmitted, for example, in the form of a Signal Phasing and Timing (SPT) message, as defined by the Society of Automotive Engineers (SAE) J2735 protocol. In some embodiments, the RSU 110 may acquire data from the traffic signal controller 120 on a second-by-second (or different frequency resolution) basis”; [0029]: “computer system 130 to acquire SPaT and GID message data for a signalized intersection in real-time is by interfacing directly with the computer systems residing within a Traffic Management Center (TMC) 150, that are connected to and capable of monitoring traffic signal controllers within a specific geographic boundary. In an embodiment, a suitable controller or switch 162 may be coupled to the TMC 150 and configured to transmit data via path 164 to the computer system 30”; and [0034]: “Based on the transit vehicle location and speed, the exemplary computer system 130 may calculate an expected arrival time at the next signal by comparing historical time-stamped traffic signal timing data against real-time signal status data to predict the signal interval and timing state for the next signal (or signals) which the transit vehicle (is approaching. The exemplary computer system 130 may transmit the relevant advisory message to the transit vehicle through the PED 170 device or to a Mobile Data Terminal MDT”);
…
generate a command signal based on the traffic signal information and real-time vehicle information of a vehicle (see, Paragraph [0032]: and [0034]);
…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to provide information and commands to the traffic controller, and/or to receive information and data from the traffic controller which controls the traffic signals at an intersection, such as through the traffic light signal phase and timing (Spat) as taught by Ova, in the combined traffic management system of Mahler and Rolle. One would be motivated to make this modification in order to mitigate transit vehicle delays and stops at traffic signals, transportation agencies have traditionally implemented a combination of engineering and policy measures focused on manipulating the environment outside of the transit vehicle, such as signal timing (transit signal priority, preemption), and/or the roadway network changes (queue jumps, transit-only lanes , yield-to-transit vehicle laws). The need remains for improvements to reduce delay, improve safety, mobility, economic competitiveness and environmental sustainability of transit vehicles (see, Paragraph [0004]).
Neither Mahler, Rolle or Ova teaches
…
send the command signal to a vehicle control unit of the vehicle, wherein the command signal causes the vehicle control unit to autonomously control vehicle movement responsive to receiving the command signal.
However, Mück teaches
…generate a command signal based on the traffic signal information and real-time vehicle information of a vehicle; send the command signal to a vehicle control unit of the vehicle, wherein the command signal causes the vehicle control unit to autonomously control vehicle movement responsive to receiving the command signal (see, Paragraph [0016]: “the SPaT message includes said predicted parameters for all signal groups of the traffic light system. However, dependent on the direction from which the vehicle approaches the traffic light system, only a subset of predictions associated with the signal groups affecting said direction may be included in the respective SPaT message. That is, the message composer may compose the SPaT messages in a manner which is specific for the various road directions which are controlled by the traffic light system. Finally, the interface component provides the composed SPaT message to a receiving device associated with the vehicle.”; [0017]: “In an autonomous car, it may be provided directly to a respective control unit of the autonomous car (e.g., a respective module of the On-Board Driving Computer). In vehicles equipped with driving support systems, the SPaT message may be provided to the board computer of the vehicle to be displayed on the user interface for the driver of the vehicle. Alternatively, it may be sent to a mobile communication device (e.g., a smartphone) of the driver which is associated with the vehicle (e.g., by Bluetooth coupling or the like). Once the vehicle is in possession of a reliable timing parameters, it can take operating decisions accordingly. For example, it may trigger an acceleration operation to pass the traffic light during the pass-state phase with certainty. The acceleration decision may of course depend on other constraints such as the maximum speed limit or the traffic density. Or it may trigger a breaking action if the predicted pass-state phase does not allow the vehicle to pass the traffic light under safe conditions”; [0098]: “For example, in a real-world implementation, the specific interface for receiving signal data is different from the specific interface used for SPaT message exchange. The message may be directly sent to a control unit of the vehicle, or it may be sent to any driving assistance system supporting the driver of the vehicle to safely navigate the vehicle 501 when approaching the traffic light system.” [0099]: “Once the SpaT message 402 with the minimum end-time is received by the vehicle 501, the receive information provides all the advantages as described in the summary above with regards to the operation of the vehicle. For example, based on the received minimum and maximum-end-times, the vehicle may take decisions about automatically breaking or accelerating in the context of the current traffic situation. This can be advantageous in an autonomous driving mode of the vehicle”).
Mahler and Rolle teaches traffic signal information, and Ova teaching providing information and commands to the traffic controller.
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify for use of the information for vehicle control and, thereby, improving traffic safety as taught by Mück. One would be motivated to make this modification in order to convey Signal Phase and Timing (SPaT) information can be communicated to vehicles in order to enable an improved driving behavior of the vehicle in that unnecessary acceleration or braking activities are avoided to reduce energy consumption of the vehicle and to improve overall traffic safety (see, Paragraph [0003]).
As to [claim 2], the combination of Mahler, Rolle, Ova and Mück teaches the system of claim 1. Mahler discloses wherein each of the historical traffic signal information, the real-time traffic signal information (see, Paragraph [0021]: “The traffic center data 120 may include nearly real time and/or historical data from a commercial or governmental traffic center. The traffic center data 120 typically include a collection of data from a plurality of traffic signals. For example, a city may collect raw data from traffic signals and send the raw data to vehicles, or a third party Such as Green DriverR) may obtain the raw data from the city, analyze the data, and send traffic signal predictions to vehicles. The traffic signal data 130 may include real-time data from a plurality of traffic signals. For example, each individual traffic signal may send real-time data regarding its status directly to vehicles.”) and the traffic signal information associated with the future traffic signal cycle comprises information associated with traffic signal phase and timing (Spat) associated with each traffic signal cycle (see, Paragraph [0022]: “FIG. 1 shows that data are obtained from various sources at step 200, and combined into a database at step 210. The data are then analyzed at step 220. Advanced modeling, data mining, and/or machine learning techniques may be used to predict traffic signal information, including signal phase and timing (SPAT) information. Due to the complexity of the algorithms, a computer processor is required to perform the data analysis at Step 220” and [0025]: “By using data from more than one type of source, exemplary embodiments of the invention may enable improved models that provide more accurate predictions of traffic signal information. Further, machine learning techniques are able to use historical data in order to further improve the accuracy of the predictions. The results are not limited to a localized area, and can be used to predict SPAT information along the driver's entire route. In addition, accounting for specific route information may reduce the volume of data that is analyzed and increase the overall accuracy of the predictions”).
As to [claim 3], the combination of Mahler, Rolle, Ova and Mück teaches the system of claim 1. Ova further teaches wherein the historical traffic signal information is associated with a second predefined count of traffic signal cycles, and wherein the second predefined count of traffic signal cycles is greater than the first predefined count of traffic signal cycles (see, claims 3; Figure 1 and 8; Paragraphs [0024]: “The system 100 includes a roadside unit (RSU) 110 or a similar device capable of acquiring a signalized intersection's current traffic signal phase and timing data in real-time, the duration for which this state will persist for each approach and lane, the next signal state switch time, and transmitting the data to the computer system 130 to support performing one or more of the processes described herein. The data may be transmitted, for example, in the form of a Signal Phasing and Timing (SPT) message, as defined by the Society of Automotive Engineers (SAE) J2735 protocol. In some embodiments, the RSU 110 may acquire data from the traffic signal controller 120 on a second-by-second (or different frequency resolution) basis”; [0029]: “computer system 130 to acquire SPaT and GID message data for a signalized intersection in real-time is by interfacing directly with the computer systems residing within a Traffic Management Center (TMC) 150, that are connected to and capable of monitoring traffic signal controllers within a specific geographic boundary. In an embodiment, a suitable controller or switch 162 may be coupled to the TMC 150 and configured to transmit data via path 164 to the computer system 30”; and [0034]: “Based on the transit vehicle location and speed, the exemplary computer system 130 may calculate an expected arrival time at the next signal by comparing historical time-stamped traffic signal timing data against real-time signal status data to predict the signal interval and timing state for the next signal (or signals) which the transit vehicle (is approaching. The exemplary computer system 130 may transmit the relevant advisory message to the transit vehicle through the PED 170 device or to a Mobile Data Terminal MDT”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to provide information and commands to the traffic controller, and/or to receive information and data from the traffic controller which controls the traffic signals at an intersection, such as through the traffic light signal phase and timing (Spat) as taught by Ova, in the combined traffic management system of Mahler and Rolle. One would be motivated to make this modification in order to mitigate transit vehicle delays and stops at traffic signals, transportation agencies have traditionally implemented a combination of engineering and policy measures focused on manipulating the environment outside of the transit vehicle, such as signal timing (transit signal priority, preemption), and/or the roadway network changes (queue jumps, transit-only lanes, yield-to-transit vehicle laws). The need remains for improvements to reduce delay, improve safety, mobility, economic competitiveness and environmental sustainability of transit vehicles (see, Paragraph [0004]).
As to [claim 5], the combination of Mahler, Rolle, Ova, and Mück teaches the system of claim 1. Mahler discloses wherein the system transceiver receives the historical traffic signal information and the real-time traffic signal information from a traffic light control server (see, Figure 1; [0022]: “FIG. 1 shows that data are obtained from various sources at step 200, and combined into a database at step 210. The data are then analyzed at step 220.”).
Regarding [claim 15], recites analogous limitations that are present in claim 1, therefore claim 15 would be rejected for the same/similar premise above.
As to [claim 16], recites analogous limitations that are present in claim 2, therefore claim 16 would be rejected for the same/similar premise above.
As to [claim 17], recites analogous limitations that are present in claim 3, therefore claim 17 would be rejected for the same/similar premise above.
As to [claim 19], recites analogous limitations that are present in claim 5, therefore claim 19 would be rejected for the same/similar premise above.
Claim(s) 4 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahler, Rolle, Ova, and Mück, and in view of Pittman et al. (Pub. No.: US 2022/0375340; previously recorded), hereinafter, referred to as “Pittman”.
As to [claim 4], the combination of Mahler, Rolle, Ova and Mück teaches the system of claim 1. Rolle teaches wherein the system processor is further configured to generate the trained machine model by using the training data and a Gaussian “function” (see, Rallo Paragraphs [0076]: “Therefore, in the example, the training support vector machine (SVM) 160 trains a decision boundary which serves as input to the prediction support vector machine 170 so that the prediction support vector machine 170 uses the trained decision boundary to predict future signal states. In other words, SVM 160 trains, for each new training set, a new decision boundary so that changes in the traffic flow are represented in the boundary after some time which is then injected into the prediction support vector machine 170 so that its predictions consider the changes in the traffic flow.”; [0077]: “For each time [-te, -2te, ... , -(tru,ure-l)te, !ruture tel a SVM is trained for each traffic light signal of the traffic control system. Each SVM is trained by using data frames and their corresponding future signal states as shown in FIG. 8. After training, a list of tfuture support vector machines is calculated for each traffic signal.”; and [0078]: “”.and [0079]: “The support vector machine might use a radial basis function, or any other kernel function (e.g., linear, polynomial, Gaussian basis function)”).
One would be motivated to make this modification in order to convey retrieving information about traffic light status of traffic control systems and switching cycles in any geographical region (e.g., a city, a town, a district, or a countryside area). However, in order to access the information of traffic lights within a region, a connection to several traffic light infrastructure systems delivered by different technology providers may be needed, and such data can be available from traffic management systems that can be interfaced with the monitoring system to allow real-time program data retrieval. The analytics component can then use the received traffic light program data in the machine learning algorithm for the prediction of the future signal status of the respective one or more traffic lights which again may improve the accuracy of the traffic light status prediction results (see, Paragraphs [0003] and [0036]).
Neither Mahler, Rolle or Ova explicitly teaches
…a Gaussian Process Regression (GPR) supervised machine learning algorithm.
However, Pittman teaches
…a Gaussian Process Regression (GPR) supervised machine learning algorithm (see, Paragraph [0038] “In some embodiments, the machine learning model 220 may employ machine learning algorithms and training of the machine learning model 220 may be supervised, unsupervised, or some combination thereof.” [0060]: “where a machine learning model is trained using historical traffic data. The historical traffic data may be indicative of traffic patterns over a historical time interval, such as a week, a month, a year, two years, or any other time `` before the machine learning model is implemented in relation to a given roadway traffic system, which may be obtained from a data storage. In some embodiments, the machine learning model may be trained using any processes for training machine learning models, such as using a Decision Tree, Naive Bayes Classifier, K-Nearest Neighbors, Support Vector Machines, Linear Regression, Logistic Regression, Dimensionality Reduction, and/or Artificial Neural Networks. In these and other embodiments, the machine learning model may be trained using an unsupervised learning process involving a multimodal Gaussian process regression.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement training a machine learning model using historical traffic data corresponding to a roadway traffic system in which the historical traffic data as taught by Pittman. One would be motivated to make this modification in order to provide improvements over existing traffic systems. For example, a highway traffic system including machine-learning based traffic operations according to the present disclosure may experience higher traffic throughput in shorter periods of time. Additionally or alternatively, vehicles may spend less time on the highway traffic system to travel a given distance relative to existing highway traffic systems. Such improvements to the traffic on the highway traffic system may increase fuel savings for vehicles traveling on the highway traffic system, reduce infrastructure degradation for the highway traffic system, and/or decrease the frequency with which vehicular accidents occur on the highway traffic system (see, Pittman [0017]).
Additionally and/or alternatively, Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the Gaussian Process Regression (GPR) supervised machine learning algorithm of Pittman for the Gaussian basis function of Rolle. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As to [claim 18], recites analogous limitations that are present in claim 4, therefore claim 18 would be rejected for the same/similar premise above.
Claim(s) 7-8 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mese et al. (Pub. No.: US 2005/0134478; previously recorded), hereinafter, referred to as “Meses” in view of Ova et al. (Pub. No.: US 2018/0286223), hereinafter, referred to as “Ova”, and in view of Kawaharada et al. (Pub. No.: US 2023/0106791; previously recorded), hereinafter, referred to as “Kawaharada”.
Regarding [claim 7], Mese discloses a vehicle (“Vehicle 100”) comprising:
a vehicle transceiver (“a receiver 106”) configured to receive traffic signal information associated with a future traffic signal cycle for a traffic light from a server (see, Paragraph [0017]: “traffic signal 102 transmits traffic Signal data identifying present and future traffic signal sequences, e.g., current information regarding the status of the light (red, greed, or yellow), timing data related to its cycle, and any schedule information regarding future cycles (e.g., if at a particular time of day, the timing of In addition, traffic signal 102 transmits traffic Signal data identifying present and future traffic signal sequences, e.g., current information regarding the status of the light (red, greed, or yellow), timing data related to its cycle, and any Schedule information regarding future cycles (e.g., if at a particular time of day, the timing of the Signal cycle changes due to changing traffic conditions, this information is also transmitted). Although the described embodiment illustrates a transmitter associated with each traffic signal, it is understood that data pertaining to multiple traffic signals, e.g., all traffic signals in a particular region or controlling a particular roadway, could be gathered in a central location using standard telemetry gathering techniques, and then broadcast regionally from a centralized, independent transmission source. In such an alternative embodiment, the traffic Signals, being connected via wires to central locations for power and control purposes, could use a wired or a wireless network to forward the traffic Signal data to the centralized transmission source, such as a collecting server”),
wherein the traffic signal information associated…traffic signal cycles (see, Paragraph [0017]: “In addition, traffic signal 102 transmits traffic Signal data identifying present and future traffic signal sequences, e.g., current information regarding the status of the light (red, greed, or yellow), timing data related to its cycle, and any schedule information regarding future cycles (e.g., if at a particular time of day, the timing of the signal cycle changes due to changing traffic conditions, this information is also transmitted). Although the described embodiment illustrates a transmitter associated with each traffic signal, it is understood that data pertaining to multiple traffic signals, e.g., all traffic Signals in a particular region or controlling a particular roadway, could be gathered in a central location using Standard telemetry gathering techniques, and t….The information transmitted may also include directional components associating the data elements with the light orientation, i.e., the cycle data for vehicles approaching the Signal in the north-south direction will be identifiably different from the cycle data for vehicles approaching the signal in the east-west direction. Further, the transmitted information may also include data related to right and left turn arrows, blinking lights, or any other”);
Mese does not explicitly disclose
…
wherein the historical traffic signal information is associated with a second predefined count of traffic signal cycles, and wherein the second predefined count of traffic signal cycles is greater than the first predefined count of traffic signal cycles;
a vehicle control unit configured to determine real-time vehicle information;.
a vehicle processor communicatively coupled with the vehicle transceiver and the vehicle control unit,
wherein the vehicle processor is configured to:
obtain, before the vehicle reaches the traffic light, the traffic signal information associated with the future traffic signal cycle from the vehicle transceiver and the real-time vehicle information from the vehicle control unit;
generate a command signal based on the traffic signal information and the real-time vehicle information; and
output the command signal to the vehicle control unit, wherein the vehicle control unit autonomously controls vehicle movement responsive to receiving the command signal.
However, Ova teaches
wherein the historical traffic signal information is associated with a second predefined count of traffic signal cycles, and wherein the second predefined count of traffic signal cycles is greater than the first predefined count of traffic signal cycles (see, claims 3; Figure 1 and 8; Paragraphs [0024]: “The system 100 includes a roadside unit (RSU) 110 or a similar device capable of acquiring a signalized intersection's current traffic signal phase and timing data in real-time, the duration for which this state will persist for each approach and lane, the next signal state switch time, and transmitting the data to the computer system 130 to support performing one or more of the processes described herein. The data may be transmitted, for example, in the form of a Signal Phasing and Timing (SPT) message, as defined by the Society of Automotive Engineers (SAE) J2735 protocol. In some embodiments, the RSU 110 may acquire data from the traffic signal controller 120 on a second-by-second (or different frequency resolution) basis”; [0029]: “computer system 130 to acquire SPaT and GID message data for a signalized intersection in real-time is by interfacing directly with the computer systems residing within a Traffic Management Center (TMC) 150, that are connected to and capable of monitoring traffic signal controllers within a specific geographic boundary. In an embodiment, a suitable controller or switch 162 may be coupled to the TMC 150 and configured to transmit data via path 164 to the computer system 30”; and [0034]: “Based on the transit vehicle location and speed, the exemplary computer system 130 may calculate an expected arrival time at the next signal by comparing historical time-stamped traffic signal timing data against real-time signal status data to predict the signal interval and timing state for the next signal (or signals) which the transit vehicle (is approaching. The exemplary computer system 130 may transmit the relevant advisory message to the transit vehicle through the PED 170 device or to a Mobile Data Terminal MDT”)
One would be motivated to make this modification in order to mitigate transit vehicle delays and stops at traffic signals, transportation agencies have traditionally implemented a combination of engineering and policy measures focused on manipulating the environment outside of the transit vehicle, such as signal timing (transit signal priority, preemption), and/or the roadway network changes (queue jumps, transit-only lanes, yield-to-transit vehicle laws). The need remains for improvements to reduce delay, improve safety, mobility, economic competitiveness and environmental sustainability of transit vehicles (see, Paragraph [0004]).
Additionally, Kawaharada teaches
a vehicle processor (“ECU 90”) communicatively coupled with the vehicle transceiver and the vehicle control unit (see, Paragraphs [0032] and [0053]: “FIG. 3 is a functional block diagram of the ECU 90 of the vehicle 3 in the first embodiment. In the embodiment, the ECU 90 includes a signal information acquisition unit 96 and a vehicle control unit 97. The signal information acquisition unit 96 and the vehicle control unit 97 are functional blocks to be realized when the processor 93 of the ECU 90 executes programs stored in the memory 92 of the ECU 90”), wherein the vehicle processor (“ECU 90”) is configured to:
obtain, before the vehicle reaches the traffic light, the traffic signal information associated with the future traffic signal cycle from the vehicle transceiver and the real-time vehicle information from the vehicle control unit (see, Paragraphs [0055]; “The vehicle control unit 97 controls the vehicle 3, based on the signal information acquired by the signal information acquisition unit 96. For example, using the actuator 35, the vehicle control unit 97 controls the vehicle 3 so as to observe traffic regulations, based on the signal information acquired by the signal information acquisition unit 96” and [0064]: “In this case, the server 2 acquires a schedule of control of each traffic light, from the traffic control center, and estimate the future signal information about a predetermined traffic light, based on the schedule. The server 2 may store the past signal information about the traffic light, and may estimate the future signal information about the traffic light, from the current signal information about the traffic light, based on the past signal information about the traffic
light.” );
generate a command signal based on the traffic signal information and the real-time vehicle information (see, Paragraph [0065]: “For example, the signal information acquisition unit 96 acquires the signal information about the forward traffic light when the vehicle 3 arrives at the forward traffic light, from the server 2. In this case, the signal information acquisition unit 96 sends the current position, movement direction and speed of the vehicle 3, to the server 2, and the server 2 identifies the forward traffic light and the arrival time of the vehicle 3 at the forward traffic light, based on the current position, movement direction and speed of the vehicle 3, and sends the signal information about the forward traffic light at the arrival time, to the vehicle 3. The speed of the vehicle 3 is detected by the sped sensor of the vehicle state detection device 32”); and
output the command signal to the vehicle control unit, wherein the vehicle control unit autonomously controls vehicle movement responsive to receiving the command signal (see, Paragraphs [0055]-[0060]; [0062]: “in step S102, the vehicle control unit 97 controls the vehicle 3, based on the signal information acquired by the signal information acquisition unit 96. For example, in the case where the lighting state of the forward traffic light is green, the vehicle control unit 97 controls the vehicle 3 such that the vehicle 3 passes through the forward traffic light. On the other hand, in the case where the lighting state of the forward traffic light is red, the vehicle control unit 97 controls the vehicle 3 such that the vehicle 3 stops at the forward traffic light. After step S102, the control routine ends”; [0066]: “For example, in the case where the lighting state of the forward traffic light when the vehicle 3 arrives at the forward traffic light is green, the vehicle control unit 97 maintains the speed of the vehicle 3, and in the case where the lighting state of the forward traffic light when the vehicle 3 arrives at the forward traffic light is red, the vehicle control unit 97 gradually decreases the speed of the vehicle 3.” ; and [0067])…
As Mese discloses traffic signal data is broadcast, for receipt by vehicles traversing the roadways controlled by the traffic signals prompt drivers to maintain speeds that minimize the amount of acceleration and stopping that they need to do, and encourage compliance with speed limits.
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a vehicle control unit that controls the vehicle, based on the signal information acquired by the signal information acquisition unit. as taught by Kawaharada. One would be motivated to make this modification in order to convey it is possible to reduce the amount of fuel consumption or electricity consumption by the vehicle (see, Kawaharada).
As to [claim 8], the combination of Mese, Ova and Kawaharada teaches the vehicle of claim 7. Mese discloses wherein the historical traffic signal information is associated with a second predefined count of traffic signal cycles, wherein the second predefined count of traffic signal cycles is greater than the first predefined count of recent traffic signal cycles (see, Paragraph [0017]: “Transmitter 104 broadcasts, on a regular basis, traffic Signal data identifying its location (e.g., by broadcasting, for example, GPS coordinates associated with its location). In addition, traffic Signal 102 transmits traffic signal data identifying present and future traffic signal Sequences, e.g., current information regarding the status of the light (red, greed, or yellow), timing data related to its cycle, and any schedule information regarding future cycles (e.g., if at a particular time of day, the timing of the signal cycle changes due to changing traffic conditions, this information is also transmitted).”).
As to [claim 11], the combination of Mese, Ova, and Kawaharada teaches the vehicle of claim 7. Mese discloses teaches wherein the real-time vehicle information comprises a current vehicle speed and a direction of vehicle movement (see, Paragraph [0018]: “The onboard receiver 106 of vehicle 100 captures the broadcast traffic signal data from traffic Signals (or independent transmission sources) within its vicinity. A traffic-signal processor is integrated into, or coupled to, receiver 106 and receives the captured traffic Signal data and, together with vehicle information (e.g., current vehicles speed, vehicle location, etc.) obtained from an onboard GPS system and/or a vehicle system processor associated with the vehicle, calculates an optimal pace to facilitate traffic flow.”; and [0021]: “The vehicle GPS information includes the vehicle location, the vehicle direction-of-travel, the speed of travel, and can even be as fine-grained as which lane on which particular roadway the vehicle is moving. Based upon this information, the processor of the present invention will filter out all but the data related to the next upcoming traffic signal”).
As to [claim 12], the combination of Mese, Ova, and Kawaharada teaches the vehicle of claim 7. Kawaharada teaches wherein the vehicle processor is further configured to:
correlate the traffic signal information associated with the future traffic signal cycle with the real-time vehicle information; and generate the command signal based on the correlation (see, Paragraphs [0061]-[0062]: “Next, in step S102, the vehicle control unit 97 controls the vehicle 3, based on the signal information acquired by the signal information acquisition unit 96. For example, in the case where the lighting state of the forward traffic light is green, the vehicle control unit 97 controls the vehicle 3 such that the vehicle 3 passes through the forward traffic light. On the other hand, in the case where the lighting state of the forward traffic light is red, the vehicle control unit 97 controls the vehicle 3 such that the vehicle 3 stops at the forward traffic light. After step S102, the control routine ends.”; and [0063]: “In step Sl0l, the signal information acquisition unit 96 may acquire future signal information about at least one traffic light, from the server 2, and in step S102, the vehicle control unit 97 may control the vehicle 3 based on the future signal information about at least one traffic light. Thereby, it is possible to realize an efficient control of the vehicle 3 in consideration of the future lighting state of the traffic light”).
As Mese discloses traffic signal data is broadcast, for receipt by vehicles traversing the roadways controlled by the traffic signals prompt drivers to maintain speeds that minimize the amount of acceleration and stopping that they need to do, and encourage compliance with speed limits.
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement a vehicle control unit that controls the vehicle, based on the signal information acquired by the signal information acquisition unit. as taught by Kawaharada. One would be motivated to make this modification in order to convey it is possible to reduce the amount of fuel consumption or electricity consumption by the vehicle (see, Kawaharada).
As to [claim 13], the combination of Mese, Ova, and Kawaharada teaches the vehicle of claim 7. Mese teaches wherein the vehicle processor outputs the command signal to a vehicle infotainment system (“Display 508”) , and wherein the vehicle infotainment system outputs a predefined message responsive to receiving the command signal (see, Paragraph [0021]: “Some or all of the information can also be acquired via on-board vehicle processors that are routinely used to, for example, display the vehicle speed to the driver on a dashboard display; the information from the vehicle processor can also be output to the traffic system processor for use in performing the calculations described herein.”; and [0025]: “FIG. 3 illustrates an example of a display that is displayed in the vehicle. As can be seen from FIG. 3, on the left side of a display, the speed limit on the road on which the vehicle is traveling is displayed. In the center, information regarding the appropriate speed range required to pass the upcoming light without stopping is identified. On the far right, a "countdown clock" provides the driver or passenger with information regarding the status of the upcoming light and when it is expected to change” and [0037]: “Display 508 can comprise any known display device, e.g., LED displays, LCD displays, CRT's and the like. Memory 510 is for storing programming information and received data, as well as any other data that might be used by traffic signal receiver processor 502. Any known memory device that can perform these functions can be used for memory 510”).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mese, Ova, Kawaharada, and in view of Mahler et al. (Pub. No.: US 2014/0277986; previously recorded).
As to [claim 9], the combination of Mese, Ova, Kawaharada teaches the vehicle of claim 7. Mese, Ova and Kawaharada teaches each of the historical traffic signal information, the real-time traffic signal information and the traffic signal information associated with the future traffic signal cycle (see, Mese Paragraph [0017]: “In addition, traffic Signal 102 transmits traffic Signal data identifying present and future traffic Signal Sequences, e.g., current information regarding the status of the light (red, greed, or yellow), timing data related to its cycle, and any schedule information regarding future cycles (e.g., if at a particular time of day, the timing of the signal cycle changes due to changing traffic conditions, this information is also transmitted)” and, see Kawaharada [0064]: “the server 2 acquires a schedule of control of each traffic light, from the traffic control center, and estimate the future signal information about a predetermined traffic light, based on the schedule. The server 2 may store the past signal information about the traffic light, and may estimate the future signal information about the traffic light, from the current signal information about the traffic light, based on the past signal information about the traffic light.”, respectfully,, …., however, neither references teaches where the information associated with traffic signal phase and timing (Spat) associated with each traffic signal cycle.
However, Mahler teaches
….the information associated with traffic signal phase and timing (Spat) associated with each traffic signal cycle. (see, Paragraph [0022]: “FIG. 1 shows that data are obtained from various sources at step 200, and combined into a database at step 210. The data are then analyzed at step 220. Advanced modeling, data mining, and/or machine learning techniques may be used to predict traffic signal information, including signal phase and timing (SPAT) information. Due to the complexity of the algorithms, a computer processor is required to perform the data analysis at Step 220” and [0025]: “By using data from more than one type of source, exemplary embodiments of the invention may enable improved models that provide more accurate predictions of traffic signal information. Further, machine learning techniques are able to use historical data in order to further improve the accuracy of the predictions. The results are not limited to a localized area, and can be used to predict SPAT information along the driver's entire route. In addition, accounting for specific route information may reduce the volume of data that is analyzed and increase the overall accuracy of the predictions”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to provide a predicted traffic signal information to adjust the operation of an on-board system of a motor vehicle as taught by Mahler. One would be motivated to make this modification in order to convey it would be desirable to provide an improved system and method for predicting traffic signal information that utilizes data from more than one source, and that uses advanced models and machine learning techniques that consider historical data. It would also be desirable to use the predictive traffic signal information to adjust the operation of an on-board system of a vehicle, particularly an on board system that can improve fuel consumption and reduce harmful emissions (see, Mahler, Paragraph [0007]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mese, Ova, Kawaharada, and in view of Pittman et al. (Pub. No.: US 2022/0375340; previously recorded), hereinafter, referred to as “Pittman”.
As to [claim 10], the combination of Mese, Ova, Kawaharada teaches the vehicle of claim 7. Neither Mese nor Kawaharada teaches wherein the traffic signal information associated with the future traffic signal cycle is determined using a Gaussian Process Regression (GPR) supervised machine learning algorithm.
However, Pittman teaches
wherein the traffic signal information associated with the future traffic signal cycle is determined using a Gaussian Process Regression (GPR) supervised machine learning algorithm (see, Paragraph [0060]: “where a machine learning model is trained using historical traffic data. The historical traffic data may be indicative of traffic patterns over a historical time interval, such as a week, a month, a year, two years, or any other time `` before the machine learning model is implemented in relation to a given roadway traffic system, which may be obtained from a data storage. In some embodiments, the machine learning model may be trained using any processes for training machine learning models, such as using a Decision Tree, Naive Bayes Classifier, K-Nearest Neighbors, Support Vector Machines, Linear Regression, Logistic Regression, Dimensionality Reduction, and/or Artificial Neural Networks. In these and other embodiments, the machine learning model may be trained using an unsupervised learning process involving a multimodal Gaussian process regression.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement training a machine learning model using historical traffic data corresponding to a roadway traffic system in which the historical traffic data as taught by Pittman. One would be motivated to make this modification in order to provide improvements over existing traffic systems. For example, a highway traffic system including machine-learning based traffic operations according to the present disclosure may experience higher traffic throughput in shorter periods of time. Additionally or alternatively, vehicles may spend less time on the highway traffic system to travel a given distance relative to existing highway traffic systems. Such improvements to the traffic on the highway traffic system may increase fuel savings for vehicles traveling on the highway traffic system, reduce infrastructure degradation for the highway traffic system, and/or decrease the frequency with which vehicular accidents occur on the highway traffic system (see, Pittman [0017]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.U./Examiner, Art Unit 3663 /JAMES M MCPHERSON/Examiner, Art Unit 3663