DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/27/2026 has been entered.
Status of Claims
This is a Non-Final Action for Request for Continued Examination (RCE) application Serial No. 18/482,962. Claim(s) 1-5, 7, and 15-18 are amended and claim(s) 6, 12, 14, and 20 are canceled.
Claim(s) 21-24 are newly added.
Claim(s) 1-5, 7-11, 13, 15-19, and 21- 24 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 § 103 have been fully considered. Applicant has amended claims to overcome rejections under 35 USC § 103.
Claim Rejections - 35 U.S.C. @ 103
Claim(s) 1-3, 5, 15-17, and 19 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 Grimm et al. (Pub. No.: US 2021/0142658).
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) 1-2, 5, 7, 9, 11, 15-16,19, and 21-24 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 Grimm et al. (Pub. No.: US 2021/0142658), hereinafter, referred to as “Grimm”.
Regarding [claim 1], Mahler discloses a system (see, Abstract) comprising:
a system transceiver (“a receiver”) configured to:
receive real-time traffic signal information associated with the traffic light for a first predefined count of traffic signal cycles (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.”); and
a system processor communicatively coupled with the system transceiver (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.”; and [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.”), wherein the system processor is configured to:
obtain the real-time traffic signal information from the system transceiver (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
predict traffic signal information associated with a future traffic signal cycle based on the real-time traffic signal information and before the vehicle reaches the traffic light (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 [0063]);
correlate the traffic signal information associated with the future traffic signal cycle with the real-time vehicle information to determine whether the vehicle will cross the traffic light in a green phase (see, Paragraph [0026]; [0045]: “The analytics component 120 predicts at least one future light signal status for each traffic light based on the use of a machine learning algorithm applied to current signal status data and previously received signal status data (e.g., FIG. 2, block 1200). The at least one future signal status includes the expected point in time when the current signal status will switch from the current to the at least one future signal status. In other words, the future signal status as used herein is a value pair which includes the value of the expected future light signal and the point in time when the traffic light will switch to the future value.”; and [0047]: “For example, if the message indicates that the traffic light will switch from green to red in 6 seconds, the vehicles may automatically adjust their powertrain systems to the future situation. If a particular vehicle is able to reach the intersection under green light within allowed speed limits and traffic conditions, then the vehicle can adjust its speed to reach green light. If the vehicle is unlikely to reach the green light, the vehicle systems can notify the driver and prepare to slow down the vehicle speed and avoid crossing under red light while avoiding sudden and dangerous braking maneuvers. Such a Time To Green Scenario can build upon technology components enabling communication of traffic light state and intersection topology, and vehicles being equipped with the technology to receive that information.”; and [0062]: “While the status is green g, the detected signals of the detectors D1 to D4 can provide valuable information about the overall traffic situation. For example, the frequency of detector signals may indicate the density of the traffic. Other types of detectors (e.g., pressure detectors, video camera systems, etc.) can be used in alternative implementations to implement D1 to D4. In the example scenario shown in FIG. 6, a vehicle 401 is approaching traffic light S3 and its associated detector D3. The vehicle may go at speed V. However, the speed may vary over time and other vehicles may drive at a different speed.”; and [0048]: “Taking advantage of the signal phase information, drivers are notified about the remaining time before the signal changes, increasing drivers awareness of an upcoming traffic situation and preparedness to react accordingly. In autonomous car applications the TTG might be used to automatically start and stop the motor (engine) at the optimal point in time.”);
generate a command signal based on the traffic signal information and real-time vehicle information of a vehicle responsive to determining that the vehicle will not cross the traffic light in the green phase based on the correlation (see, Paragraphs [0026]; [0045]: “The analytics component 120 predicts at least one future light signal status for each traffic light based on the use of a machine learning algorithm applied to current signal status data and previously received signal status data (e.g., FIG. 2, block 1200). The at least one future signal status includes the expected point in time when the current signal status will switch from the current to the at least one future signal status. In other words, the future signal status as used herein is a value pair which includes the value of the expected future light signal and the point in time when the traffic light will switch to the future value.”; and [0047]: “For example, if the message indicates that the traffic light will switch from green to red in 6 seconds, the vehicles may automatically adjust their powertrain systems to the future situation. If a particular vehicle is able to reach the intersection under green light within allowed speed limits and traffic conditions, then the vehicle can adjust its speed to reach green light. If the vehicle is unlikely to reach the green light, the vehicle systems can notify the driver and prepare to slow down the vehicle speed and avoid crossing under red light while avoiding sudden and dangerous braking maneuvers. Such a Time To Green Scenario can build upon technology components enabling communication of traffic light state and intersection topology, and vehicles being equipped with the technology to receive that information.”; and [0062]: “While the status is green g, the detected signals of the detectors D1 to D4 can provide valuable information about the overall traffic situation. For example, the frequency of detector signals may indicate the density of the traffic. Other types of detectors (e.g., pressure detectors, video camera systems, etc.) can be used in alternative implementations to implement D1 to D4. In the example scenario shown in FIG. 6, a vehicle 401 is approaching traffic light S3 and its associated detector D3. The vehicle may go at speed V. However, the speed may vary over time and other vehicles may drive at a different speed.”, also see [0048]);
…
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 teaches predictive traffic signal information may be used to adjust an operation of an on-board system of a vehicle and Rolle teaches analytics component can be configured to predict at least one future signal status for each of the traffic lights based on the use of a machine learning algorithm. The outbound status provisioning component can be configured to send a message(s) to a vehicle. The sent message can influence the operation of the vehicle, however, neither Mahler nor Rolle explicitly 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.
Additionally, Grimm teaches
…
send the command signal to a vehicle control unit of the vehicle (see, Paragraphs [0013]: “The system also includes a processor to determine at least one of three types of information about the one or more traffic lights based on the vehicle data, the three types of information in ding a location of the one or more traffic lights, a signal phase and timing (SPAT) of the one or more traffic lights, and a lane correspondence of the one or more traffic lights, and to provide the at least one of the three types of information about the one or more traffic lights for the operation of autonomous vehicles”; [0032]: “The traffic signal management may be cloud-based according to the exemplary embodiment detailed herein. In the specific context of an intersection with a traffic light, three types of information may be provided to an approaching autonomous vehicle”; [0033]. “The first of the three types of information is the location of the traffic light in global coordinates (i.e., latitude, longitude, elevation). This information facilitates faster acquisition of the location of the traffic light in the field of view of the autonomous vehicle. Such information mitigates the time and computational resources it would take to search for the location of the traffic light. The second type of information is the timing model of the traffic light (e.g., signal phase and timing (SPaT) model). This information indicates the duration of each light and may aid in decision making (e.g., speed control in advance of an intersection approach) or facilitate quicker reaction to a light turning green, for example (e.g., engagement of start/stop function or resuming automated driving function from a stop). The third type of information indicates the specific traffic light (i.e., set of two or three lights) that corresponds with each lane at an intersection when there is more than one set of lights at a given intersection.”), wherein the command signal causes the vehicle control unit to autonomously control vehicle movement responsive to receiving the command signal (see, Abstract; and Paragraphs [0035]: “The vehicle controller 107 may be a collection of vehicle controllers 107 that communicate with each other to perform the functionality discussed herein. The vehicle controller 107 may perform or facilitate communication functions as well as autonomous control functions.”; also, see [0036] ).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to implement traffic signal management for autonomous vehicle operation as taught by Grimm and combining the elements of Mahler and Rolle. One would be motivated to make this modification in order to convey autonomous operation of a vehicle requires information gathering and decision making at rates that are sufficiently high to act in and react to real-time situations (see, Paragraph [0003]).
As to [claim 2], the combination of Mahler, Rolle, and Grimm teaches the system of claim 1. Mahler discloses wherein the real-time traffic signal information and the 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.”) 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 5], the combination of Mahler, Rolle, and Grimm teaches the system of claim 1. Mahler discloses wherein the system transceiver receives 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.”).
As to [claim 7], recites analogous limitations that are present in claim 1, the1refore claim 7 would be rejected for the same/similar premise above.
As to [claim 9], recites analogous limitations that are present in claim 2, the1refore claim 9 would be rejected for the same/similar premise above.
As to [claim 11], the combination of Mahler, Rolle, and Grimm teaches the vehicle of claim 7. Mahler discloses wherein the real-time vehicle information comprises a current vehicle speed and a direction of vehicle movement (see, Paragraph [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. For example, the internal information may include an engine map, a transmission map, a position of the vehicle, and/or a current vehicle speed. Optimal velocity profiles may be generated based on the predictive traffic signal information and the internal information at step 320.”; and [0032]: “Another example of an on-board system that may be adjusted is a navigation system. A related art navigation system makes a directional recommendation based on map data, and may also account for traffic flow information. An exemplary embodiment of the invention improves the navigation system by using the integrated predictive traffic signal information and the internal information, and/or the optimal velocity profiles, to modify the directional recommendation.”).
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 19], recites analogous limitations that are present in claim 5, therefore claim 19 would be rejected for the same/similar premise above.
As to [claim 21], the combination of Mahler, Rolle, and Grimm teaches the system of claim 1. Mahler in view of Rolle teaches wherein the system transceiver is further configured to receive a geolocation of the traffic light, and wherein the system processor is configured to correlate the traffic signal information associated with the future traffic signal cycle and the geolocation of the traffic light with the real-time vehicle information to determine whether the vehicle will cross the traffic light in the green phase (see, Paragraph [0026]; [0045]: “The analytics component 120 predicts at least one future light signal status for each traffic light based on the use of a machine learning algorithm applied to current signal status data and previously received signal status data (e.g., FIG. 2, block 1200). The at least one future signal status includes the expected point in time when the current signal status will switch from the current to the at least one future signal status. In other words, the future signal status as used herein is a value pair which includes the value of the expected future light signal and the point in time when the traffic light will switch to the future value.”; and [0047]: “For example, if the message indicates that the traffic light will switch from green to red in 6 seconds, the vehicles may automatically adjust their powertrain systems to the future situation. If a particular vehicle is able to reach the intersection under green light within allowed speed limits and traffic conditions, then the vehicle can adjust its speed to reach green light. If the vehicle is unlikely to reach the green light, the vehicle systems can notify the driver and prepare to slow down the vehicle speed and avoid crossing under red light while avoiding sudden and dangerous braking maneuvers. Such a Time To Green Scenario can build upon technology components enabling communication of traffic light state and intersection topology, and vehicles being equipped with the technology to receive that information.”; and [0062]: “While the status is green g, the detected signals of the detectors D1 to D4 can provide valuable information about the overall traffic situation. For example, the frequency of detector signals may indicate the density of the traffic. Other types of detectors (e.g., pressure detectors, video camera systems, etc.) can be used in alternative implementations to implement D1 to D4. In the example scenario shown in FIG. 6, a vehicle 401 is approaching traffic light S3 and its associated detector D3. The vehicle may go at speed V. However, the speed may vary over time and other vehicles may drive at a different speed.”; and [0048]: “Taking advantage of the signal phase information, drivers are notified about the remaining time before the signal changes, increasing drivers awareness of an upcoming traffic situation and preparedness to react accordingly. In autonomous car applications the TTG might be used to automatically start and stop the motor (engine) at the optimal point in time.”).
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 to [claim 22], the combination of Mahler, Rolle, and Grimm teaches the vehicle of claim 7. Rolle teaches wherein the command signal causes the vehicle control unit to modify a speed of the vehicle such that the vehicle crosses the traffic light in the green phase(see, Paragraph [0026]; [0045]: “The analytics component 120 predicts at least one future light signal status for each traffic light based on the use of a machine learning algorithm applied to current signal status data and previously received signal status data (e.g., FIG. 2, block 1200). The at least one future signal status includes the expected point in time when the current signal status will switch from the current to the at least one future signal status. In other words, the future signal status as used herein is a value pair which includes the value of the expected future light signal and the point in time when the traffic light will switch to the future value.”; and [0047]: “For example, if the message indicates that the traffic light will switch from green to red in 6 seconds, the vehicles may automatically adjust their powertrain systems to the future situation. If a particular vehicle is able to reach the intersection under green light within allowed speed limits and traffic conditions, then the vehicle can adjust its speed to reach green light. If the vehicle is unlikely to reach the green light, the vehicle systems can notify the driver and prepare to slow down the vehicle speed and avoid crossing under red light while avoiding sudden and dangerous braking maneuvers. Such a Time To Green Scenario can build upon technology components enabling communication of traffic light state and intersection topology, and vehicles being equipped with the technology to receive that information.”; and [0062]: “While the status is green g, the detected signals of the detectors D1 to D4 can provide valuable information about the overall traffic situation. For example, the frequency of detector signals may indicate the density of the traffic. Other types of detectors (e.g., pressure detectors, video camera systems, etc.) can be used in alternative implementations to implement D1 to D4. In the example scenario shown in FIG. 6, a vehicle 401 is approaching traffic light S3 and its associated detector D3. The vehicle may go at speed V. However, the speed may vary over time and other vehicles may drive at a different speed.”; and [0048]: “Taking advantage of the signal phase information, drivers are notified about the remaining time before the signal changes, increasing drivers awareness of an upcoming traffic situation and preparedness to react accordingly. In autonomous car applications the TTG might be used to automatically start and stop the motor (engine) at the optimal point in time.”).
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 to [claim 23], recites analogous limitations that are present in claim 21, therefore claim 23 would be rejected for the same/similar premise above.
As to [claim 24], the combination of Mahler, Rolle, and Grimm teaches the method of claim 15. Rolle teaches wherein generating the command signal comprises incorporating a maximum lawful speed based on a speed limit associated with a road on which the vehicle is traveling (See, Paragraph [0047]: “For example, if the message indicates that the traffic light will switch from green to red in 6 seconds, the vehicles may automatically adjust their powertrain systems to the future situation. If a particular vehicle is able to reach the intersection under green light within allowed speed limits and traffic conditions, then the vehicle can adjust its speed to reach green light.”).
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]).
Claim(s) 3, 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahler, Rolle, and Grimm, and in view of Ova et al. (Pub. No.: US 2018/0286223; previously recorded), hereinafter, referred to as “Ova”.
As to [claim 3], the combination of Mahler, Rolle, and Grimm teaches the system of claim 1.
Ova teaches wherein the system further comprises a system memory configured to store historical traffic signal information associated with the traffic light, 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]).
As to [claim 8], recites analogous limitations that are present in claim 3, the1refore claim 8 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.
Claim(s) 4, 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahler, Rolle, and Grimm, 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, and Grimm teaches the system of claim 1. Mahler and Rolle teaches and/or suggest
wherein the system further comprises 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 historical traffic signal information associated with the traffic light, and wherein the system processor is further configured to generate the trained machine model by using the training data (see, Paragraph [0022]: “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]: “…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.”) ….
Rolle teaches (see, Paragraph [0025]: “An analytics module (component) of the monitoring system makes use of machine learning algorithms to train a model for the signal phases of a traffic light. After the model has been trained, the module is able to predict signal states in the future.”; [0033]: “The analytics component may then use the delayed sensor data stream as a training stream for the machine learning algorithm, and the non-delayed data stream for online pre diction of signal status changes.”; and [0070]: “FIG. 8 illustrates example components of a monitoring system (e.g., the monitoring system 100) that may be used to implement a machine learning algorithm and to perform light signal prediction. The training and prediction of light signal states can be done while the monitoring system 100 is in operation (online) so that the prediction algorithm can adapt itself automatically to light signal program changes. The analytics component can be configured to compute light signal status predictions for different future points in time that are a multiple of the equidistant event time interval…”), however, neither reference 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 10], recites analogous limitations that are present in claim 4, therefore claim 10 would be rejected for the same/similar premise above.
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) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mahler, Rolle, and Grim, and in view of Mese et al. (Pub. No.: US 2005/0134478; previously recorded), hereinafter, referred to as “Meses”.
As to [claim 13], the combination of Mahler, Rolle, and Grimm teaches the vehicle of claim 7. Rolle teaches wherein the vehicle processor outputs the command signal to a vehicle infotainment system (see, “display 916”; Paragraph [0106]: “To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device ( e.g., a CRT ( cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user...”), however, does not explicitly teaches ….wherein the vehicle infotainment system outputs a predefined message responsive to receiving the command signal.
Additionally, Mese teaches
….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”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify traffic control systems and, more particularly, to a “Smart’ system that broadcasts traffic Signal Status to vehicle-based receivers by as taught by Mese One would be motivated to make this modification in order to convey a receiving System in a vehicle is con figured to receive the traffic signal data and display, to a user of the vehicle, visual display information and/or audible information informing the user of a speed range which, if followed, optimizes the use of the highway and minimizes the number of Starts and Stops that must be made (see, Paragraph [0008]).
Conclusion
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/B.U./Examiner, Art Unit 3663 /JAMES M MCPHERSON/Examiner, Art Unit 3663
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