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 .
Response to Amendment
The amendment filed 13 May, 2026 has been entered.
The amendment of claims 1 - 19 has been acknowledged.
The cancellation of claim 20 has been acknowledged.
The addition of new claim 21 has been acknowledged.
Response to Arguments
Applicant’s arguments, see page 2, section “Remarks”, filed 14 August, 2026 with respect to the notice of non-compliant amendment fully considered and are persuasive. The election and cancellation of claims 1 – 19 in view of the non-compliant amendment have been withdrawn.
Applicant’s arguments, see page 8, section “Claim Objections”, filed 13 May, 2026 with respect to the objections of claims 1, 10, and 19 have been fully considered and are persuasive. The objections of claims 1, 10, and 19 have been withdrawn.
Applicant’s arguments, see page 9, section “Rejections under 35 U.S.C. § 101”, filed 13 May, 2026 with respect to the rejection of claims 1 - 20 under 35 U.S.C. § 101 have been fully considered and are persuasive. The rejection of claims 1 - 20 under 35 U.S.C. § 101 have been withdrawn.
Applicant’s arguments, see page 10, section “Rejections under 35 U.S.C. § 112(a)”, filed 13 May, 2026 with respect to the rejection of claims 3 – 7 and 12 - 16 under 35 U.S.C. § 112(a) have been fully considered and are persuasive. The rejection of claims 3 – 7 and 12 - 16 under 35 U.S.C. § 112(a) have been withdrawn. However, upon further examination, a new rejection is made under 35 U.S.C. § 112(a).
Applicant’s arguments, see page 10, section “Rejections under 35 U.S.C. § 112(b)”, filed 13 May, 2026 with respect to the rejection of claims 1, 10, and 19 under 35 U.S.C. § 112(b) have been fully considered and are persuasive. The rejection of claims 1, 10, and 19 under 35 U.S.C. § 112(b) have been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7 and 16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 7 and 16 state “wherein the transmitted instructions cause graphical user interfaces (GUIs) of the group of vehicles to display updated navigation routes to avoid the roadway incident. When reviewing applicant’s specification filed 5 April, 2024, there appears to be no mention of any graphical user interfaces (GUI), the closest recitation to this would be the “human-machine interfaces” of ¶ 0031, however, this is a broad type of interfaces and does not explicitly describe any GUIs of any form. Additionally, when reviewing the specification for the display of updated navigation routes, ¶ 0019 and 0092 disclose “For example, affected drivers in connected vehicles are notified of the incident and, if necessary, given navigational assistance (e.g., rerouting). Additionally, ¶ 0080 discloses “roadway incident response system 310 may transmit instructions to vehicles 510, as available, to help guide vehicles 510 accordingly around the roadway incident 520.” While this describes a broad concept of transmitting information to a vehicle that can help guide vehicles, this is not indicated to be navigation instructions, nor is it displayed on a GUI.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 6, 9, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Takasaki et al (U.S. Patent Publication No. 2022/0292955 A1 B1, hereinafter “Takasaki”) in view of Weldemariam et al (U.S. Patent Publication No. 2021/0020038 A1, hereinafter “Weldemariam”).
Regarding claim 1, Takasaki teaches a system comprising:
A wireless transceiver circuit (¶ 0049: This continuous or periodic receipt of data from vehicles can be generated by vehicles and/or other sources connected using IoT (internet of things) technology.);
A traffic flow profile database storing data associated with traffic flow profiles for historical roadway incidents (¶ 0113: The event type database 406 can include information relating to the event types, such as expected durations of each of the event types, average size of areas of effect for each event type, historical data regarding number of vehicles selected for each event type, etc.);
Memory (¶ 0140: In some embodiments, each CPU 705 can retrieve and execute programming instructions stored in the memory 730 or storage 740.); and
on or more processor configured to execute machine readable instructions stored in the memory to cause the system to (¶ 0140: In some embodiments, each CPU 705 can retrieve and execute programming instructions stored in the memory 730 or storage 740.):
receive, via the wireless transceiver circuit, sensor data from one or more vehicles in a transportation network (¶ 0049: For example, a traffic event can be detected when a vehicle involved in a traffic event sends a signal to the traffic prediction system, which may occur if the vehicle is connected to the traffic prediction system by, for example, an IoT network… In some embodiments, the traffic prediction system can continuously or periodically receive data from vehicles including location information, trajectory information, acceleration/deceleration information, or other types of information);
Based on the received information, determining characteristics of the roadway incident causing traffic congestion in the transportation network (¶ 0049: In such embodiments, the traffic prediction system can use this received information in determining whether a traffic event is occurring.; ¶ 0050: At operation 104, the traffic prediction system calculates an affected area of the detected traffic event. More specifically, calculating the affected area includes identifying a geographical area, including roads and/or portions of roads near the traffic event, surrounding the geographical point of occurrence);
Correlate the determined characteristics of the roadway incident to the traffic flow profile database to select a suitable traffic flow profile for the roadway incident from the traffic flow profile database (¶ 0050: At least one of a number of factors related to the traffic event such as, for example, an event type and an event severity, is considered in the calculation of the affected area.; ¶ 0113: The event detector 404 can pull information from an event type database 406 in detecting traffic events. The event type database 406 can include event types such as accidents, traffic jams, lane closures (e.g., due to road construction or obstructions on a road), wild animal crossings, or any other type of event the traffic prediction system 400 is configured to detect. The event type database 406 can include information relating to the event types, such as expected durations of each of the event types, average size of areas of effect for each event type, historical data regarding number of vehicles selected for each event type, etc.), wherein the selected traffic flow profile comprises a curve plotting levels of traffic congestion along a timeline (¶ 0050: The event type database 406 can include information relating to the event types, such as expected durations of each of the event types, average size of areas of effect for each event type, historical data regarding number of vehicles selected for each event type, etc.; Examiner’s note: “number of vehicles selected for each type of event” is understood to refer to the traffic flow rate in view of ¶ 0071 of Takasaki. As such, by maintaining the duration of the event as well as the traffic flow rate of the event, it is understood that this data comprises the levels of congestion along a timeline.);
Takasaki does not explicitly teach based on the selected traffic flow profile, determine a traffic management strategy to reduce the traffic congestion caused by the roadway incident; and transmit, via the wireless transceiver circuit, instructions to a group of vehicles in accordance with the determined traffic management strategy.
However, Weldemariam does teach based on the selected traffic flow profile, determine a traffic management strategy to reduce the traffic congestion caused by the roadway incident (¶ 0031: The processor 120 can apply a bi-directional long short-term memory (Bi-LSTM) model on the historical clearance data 225 to generate a machine readable vectorized matrix, denoted as X3. In an example, the vectorized matrix X3 can be, for example, a column matrix including elements indicating clear zones and roadway agencies in the area 160.; ¶ 0037: The processor 120 can input the vectors X1, X2, X3, X4, X5, and X6 into the prediction engine 130, which can be running, for example, a deep neural network, to generate an output that is categorized into multiple threshold ranges of the predicted clearance time T.; ¶ 0039: In an example, if the congestion duration D is greater than the threshold T, the processor 120 can execute operations 151, 152, and 153, where for example, the operation 151 can be a traffic control operation, the operation 152 can be a clustering operation, and the operation 153 can be an optimization operation.); and
transmit, via the wireless transceiver circuit, instructions to a group of vehicles in accordance with the determined traffic management strategy (¶ 0023: In some examples, the processor 120 can send the determined congestion duration 140 to devices outside of the area 160, such as areas that are different from the area 160, to provide a recommendation to users regarding whether to enter the area 160 within the estimated congestion duration 140.; ¶ 0040: The system 100 may further provide recommendations and guidance to for assisting the charging service vehicles to reach the area 160, such as recommending alternative routes and requesting personnel to direct traffic leading into the area 160… In yet another embodiment, the processor 120 can recommend an alternate route to the desired destination where the vehicle will have a higher probability of reaching its destination with low fuel or battery level ( e.g. a route that has fewer hills, fewer requirements to stop and start such as fewer stop signs or traffic signals).).
Weldemariam is considered to be analogous art as it pertains to traffic incident prediction. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the predictive route congestion management system (as taught by Weldemariam) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Weldemariam can coordinate traffic flow to vehicle destinations so as to minimize congestion. (See ¶ 0044)
This motivation for the combination of Takasaki and Weldemariam is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 2, the Takasaki and Weldemariam combination teaches the system of claim 1.
Additionally, Takasaki teaches wherein the characteristics of the roadway incident comprise speed, density, and traffic flow (¶ 0049: In some embodiments, the traffic prediction system can continuously or periodically receive data from vehicles including location information, trajectory information, acceleration/deceleration information, or other types of information.; ¶ 0071: Determining a number of vehicles to be selected for measurement of traffic volume includes determining a number of vehicles that will be observed such that the observations of those vehicles can be used to a measure traffic flow rate.).
Additionally, Weldemariam teaches wherein the characteristics of the roadway incident comprise speed, density, and traffic flow (¶ 0016: For example, the code 126 can be installed on a device, and can be executed by the device to detect incidents (e.g., traffic incidents, weather incidents) in a geographical location or area, and to provide current context information such as traffic congestion indicators, vehicle speeds, presence of incidents, and/or other types of current context information, corresponding to the current time, to the processor 120.)
Weldemariam is considered to be analogous art as it pertains to traffic incident prediction. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the predictive route congestion management system (as taught by Weldemariam) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Weldemariam can coordinate traffic flow to vehicle destinations so as to minimize congestion. (See ¶ 0044).
Regarding claim 6, the Takasaki and Weldemariam combination teaches the system of claim 1.
Additionally, Weldemariam teaches wherein the selected traffic flow profile indicates a predicted duration of traffic congestion caused by the roadway incident (¶ 0031: The historical data 202 received from the historical data source 105 can also include historical clearance time data 225 indicating clearance time of historical incidents at roadway location or area 160. For example, the historical clearance data 225 can indicate durations, such as an amount of time, to clear historical incidents at the area 160. The historical clearance data 225 can be represented by numerical vectors arranged in a matrix.).
Regarding claim 9, the Takasaki and Weldemariam combination teaches the system of claim 1.
Additionally, Takasaki teaches wherein the one or more processors are configured to execute further machine readable instructions stored in the memory to cause the system to (¶ 0140: In some embodiments, each CPU 705 can retrieve and execute programming instructions stored in the memory 730 or storage 740.):
receive information associated with a roadway incident (¶ 0049: For example, a traffic event can be detected when a vehicle involved in a traffic event sends a signal to the traffic prediction system, which may occur if the vehicle is connected to the traffic prediction system by, for example, an IoT network… In some embodiments, the traffic prediction system can continuously or periodically receive data from vehicles including location information, trajectory information, acceleration/deceleration information, or other types of information); and
assess the information associated with the roadway incident (¶ 0050: At operation 104, the traffic prediction system calculates an affected area of the detected traffic event. More specifically, calculating the affected area includes identifying a geographical area, including roads and/or portions of roads near the traffic event, surrounding the geographical point of occurrence).
Regarding claim 21, the Takasaki and Weldemariam combination teaches the system of claim 1.
Additionally, Weldemariam teaches wherein the transmitted instructions comprise autonomous vehicle control instructions which cause the group of vehicles to maneuver around the roadway incident (¶ 0041: The operation 152 can include clustering and/or grouping entities (e.g., vehicles) within the area 160 into one or more time windows for navigating through the area 160. For example, in the example shown in FIGS. 1 and 2, the processor 120 can notify the entity 170 to navigate pass the incident 162 in the area 160 at a first time window, which may be a first range of times, and can notify the entity 171 to navigate pass the incident 162 in the area 160 at a second time window, which may also be a second range of times.; ¶ 0042: The plurality of points, or entities, being clustered by the operation 152 can include autonomous vehicles, electric or hybrid vehicles in the area 160.).
Weldemariam is considered to be analogous art as it pertains to traffic incident prediction. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the predictive route congestion management system (as taught by Weldemariam) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Weldemariam that can coordinate traffic flow to vehicle destinations so as to minimize congestion. (See ¶ 0044).
Claims 4, 10, 11, 13, 15, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Takasaki et al (U.S. Patent Publication No. 2022/0292955 A1 B1, hereinafter “Takasaki”) in view of Weldemariam et al (U.S. Patent Publication No. 2021/0020038 A1, hereinafter “Weldemariam”) and further in view of Donderici et al (U.S. Patent Publication No. 2024/0087450 A1, hereinafter “Donderici”).
Regarding claim 4, the Takasaki and Weldemariam combination teaches the system of claim 1.
Additionally, Takasaki teaches wherein the sensor data comprises image data capturing an (¶ 0112: In some embodiments, the event detector 404 can use machine learning technology to perform object recognition to detect a traffic event from images received from, for example, the camera and/or satellite technology.); and
Takasaki does not explicitly teach sensor data comprises image data capturing an emergency vehicle; and determining the characteristics of the roadway incident comprises identifying a count of the emergency vehicles.
However, Donderici does teach sensor data comprises image data capturing an emergency vehicle (¶ 0056: At step 506, the AV can receive sensor data pertaining to the environment. For example, AV sensors 402 such as cameras…; ¶ 0058: At step 510, the process 500 determines whether an emergency vehicle is identified. For example, EMV behavior classification 404 may use machine learning algorithms or classification systems to identify the presence of an EMV 302.); and
Determining the characteristics of the roadway incident comprises identifying a count of the emergency vehicles (¶ 0056: At step 506, the AV can receive sensor data pertaining to the environment. For example, AV sensors 402 such as cameras…; ¶ 0058: At step 510, the process 500 determines whether an emergency vehicle is identified. For example, EMV behavior classification 404 may use machine learning algorithms or classification systems to identify the presence of an EMV 302.; Examiner’s note: By detecting a single emergency vehicle in the images, the system identifies a count of 1, or the presence of an emergency vehicle.).
Donderici is considered to be analogous art as it pertains to vehicle sensor analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the emergency vehicle intent detection system (as taught by Donderici) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Donderici increases the frequency of sensors to improve and lower latency detection of emergency vehicles (See ¶ 0058).
This motivation for the combination of Takasaki, Weldemariam, and Donderici is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 10, Takasaki teaches a method comprising:
Receive sensor data from one or more vehicles in a transportation network (¶ 0049: For example, a traffic event can be detected when a vehicle involved in a traffic event sends a signal to the traffic prediction system, which may occur if the vehicle is connected to the traffic prediction system by, for example, an IoT network… In some embodiments, the traffic prediction system can continuously or periodically receive data from vehicles including location information, trajectory information, acceleration/deceleration information, or other types of information);
Based on the received sensor data, determining characteristics of the roadway incident causing traffic congestion in the transportation network (¶ 0049: In such embodiments, the traffic prediction system can use this received information in determining whether a traffic event is occurring.; ¶ 0050: At operation 104, the traffic prediction system calculates an affected area of the detected traffic event. More specifically, calculating the affected area includes identifying a geographical area, including roads and/or portions of roads near the traffic event, surrounding the geographical point of occurrence),
Based on the determined characteristics of the roadway incident, selecting traffic flow profile for the roadway incident from a traffic flow profile database (¶ 0050: At least one of a number of factors related to the traffic event such as, for example, an event type and an event severity, is considered in the calculation of the affected area.; ¶ 0113: The event detector 404 can pull information from an event type database 406 in detecting traffic events. The event type database 406 can include event types such as accidents, traffic jams, lane closures (e.g., due to road construction or obstructions on a road), wild animal crossings, or any other type of event the traffic prediction system 400 is configured to detect. The event type database 406 can include information relating to the event types, such as expected durations of each of the event types, average size of areas of effect for each event type, historical data regarding number of vehicles selected for each event type, etc.).
Takasaki does not explicitly teach wherein determining the characteristics of the roadway incident comprises at least one of: Identifying an emergency vehicle leaving a location of the roadway incident, Identifying a count of emergency vehicles at the location of the roadway incident, or Identifying emergency cones at the location of the roadway incident; based on the selected traffic flow profile, determine a traffic management strategy to reduce the traffic congestion caused by the roadway incident and transmit, via the wireless transceiver circuit, instructions to a group of vehicles in accordance with the determined traffic management strategy.
However, Weldemariam teaches based on the selected traffic flow profile, determine a traffic management strategy to reduce the traffic congestion caused by the roadway incident (¶ 0031: The processor 120 can apply a bi-directional long short-term memory (Bi-LSTM) model on the historical clearance data 225 to generate a machine readable vectorized matrix, denoted as X3. In an example, the vectorized matrix X3 can be, for example, a column matrix including elements indicating clear zones and roadway agencies in the area 160.; ¶ 0037: The processor 120 can input the vectors X1, X2, X3, X4, X5, and X6 into the prediction engine 130, which can be running, for example, a deep neural network, to generate an output that is categorized into multiple threshold ranges of the predicted clearance time T.; ¶ 0039: In an example, if the congestion duration D is greater than the threshold T, the processor 120 can execute operations 151, 152, and 153, where for example, the operation 151 can be a traffic control operation, the operation 152 can be a clustering operation, and the operation 153 can be an optimization operation.); and
transmit, via the wireless transceiver circuit, instructions to a group of vehicles in accordance with the determined traffic management strategy (¶ 0023: In some examples, the processor 120 can send the determined congestion duration 140 to devices outside of the area 160, such as areas that are different from the area 160, to provide a recommendation to users regarding whether to enter the area 160 within the estimated congestion duration 140.; ¶ 0040: The system 100 may further provide recommendations and guidance to for assisting the charging service vehicles to reach the area 160, such as recommending alternative routes and requesting personnel to direct traffic leading into the area 160.; ¶ 0040: In yet another embodiment, the processor 120 can recommend an alternate route to the desired destination where the vehicle will have a higher probability of reaching its destination with low fuel or battery level ( e.g. a route that has fewer hills, fewer requirements to stop and start such as fewer stop signs or traffic signals).).
Weldemariam is considered to be analogous art as it pertains to traffic incident prediction. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the predictive route congestion management system (as taught by Weldemariam) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Weldemariam can coordinate traffic flow to vehicle destinations so as to minimize congestion. (See ¶ 0044)
Additionally, Donderici teaches wherein determining the characteristics of the roadway incident comprises at least one of:
Identifying a count of emergency vehicles at the location of the roadway incident (¶ 0056: At step 506, the AV can receive sensor data pertaining to the environment. For example, AV sensors 402 such as cameras…; ¶ 0058: At step 510, the process 500 determines whether an emergency vehicle is identified. For example, EMV behavior classification 404 may use machine learning algorithms or classification systems to identify the presence of an EMV 302.; Examiner’s note: By detecting a single emergency vehicle in the images, the system identifies a count of 1, or the presence of an emergency vehicle.), or
Donderici is considered to be analogous art as it pertains to vehicle sensor analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the emergency vehicle intent detection system (as taught by Donderici) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Donderici increases the frequency of sensors to improve and lower latency detection of emergency vehicles (See ¶ 0058).
Regarding claim 11, claim 11 has been analyzed with regard to respective claim 2 and is rejected for the same reasons of obviousness as used above.
Regarding claim 13, claim 13 has been analyzed with regard to respective claim 4 and is rejected for the same reasons of obviousness as used above.
Regarding claim 15, claim 15 has been analyzed with regard to respective claim 6 and is rejected for the same reasons of obviousness as used above.
Regarding claim 18, claim 18 has been analyzed with regard to respective claim 9 and is rejected for the same reasons of obviousness as used above.
Regarding claim 19, Takasaki teaches a method comprising:
Receive information associated with a roadway incident causing traffic congestion in a transportation network (¶ 0049: For example, a traffic event can be detected when a vehicle involved in a traffic event sends a signal to the traffic prediction system, which may occur if the vehicle is connected to the traffic prediction system by, for example, an IoT network… In some embodiments, the traffic prediction system can continuously or periodically receive data from vehicles including location information, trajectory information, acceleration/deceleration information, or other types of information);
Based on the received information, determining characteristics of the roadway incident (¶ 0049: In such embodiments, the traffic prediction system can use this received information in determining whether a traffic event is occurring.; ¶ 0050: At operation 104, the traffic prediction system calculates an affected area of the detected traffic event. More specifically, calculating the affected area includes identifying a geographical area, including roads and/or portions of roads near the traffic event, surrounding the geographical point of occurrence),
Based on the determined characteristics of the roadway incident, selecting traffic flow profile for the roadway incident from a traffic flow profile database (¶ 0050: At least one of a number of factors related to the traffic event such as, for example, an event type and an event severity, is considered in the calculation of the affected area.; ¶ 0113: The event detector 404 can pull information from an event type database 406 in detecting traffic events. The event type database 406 can include event types such as accidents, traffic jams, lane closures (e.g., due to road construction or obstructions on a road), wild animal crossings, or any other type of event the traffic prediction system 400 is configured to detect. The event type database 406 can include information relating to the event types, such as expected durations of each of the event types, average size of areas of effect for each event type, historical data regarding number of vehicles selected for each event type, etc.).
Takasaki does not explicitly teach wherein determining the characteristics of the roadway incident comprises at least one of: Identifying an emergency vehicle leaving a location of the roadway incident, Identifying a count of emergency vehicles at the location of the roadway incident, or Identifying emergency cones at the location of the roadway incident; based on the selected traffic flow profile, determine a traffic management strategy to reduce the traffic congestion caused by the roadway incident and controlling a group of vehicles traversing the transportation network in accordance with the determined traffic management strategy.
However, Weldemariam teaches based on the selected traffic flow profile, determine a traffic management strategy to reduce the traffic congestion caused by the roadway incident (¶ 0031: The processor 120 can apply a bi-directional long short-term memory (Bi-LSTM) model on the historical clearance data 225 to generate a machine readable vectorized matrix, denoted as X3. In an example, the vectorized matrix X3 can be, for example, a column matrix including elements indicating clear zones and roadway agencies in the area 160.; ¶ 0037: The processor 120 can input the vectors X1, X2, X3, X4, X5, and X6 into the prediction engine 130, which can be running, for example, a deep neural network, to generate an output that is categorized into multiple threshold ranges of the predicted clearance time T.; ¶ 0039: In an example, if the congestion duration D is greater than the threshold T, the processor 120 can execute operations 151, 152, and 153, where for example, the operation 151 can be a traffic control operation, the operation 152 can be a clustering operation, and the operation 153 can be an optimization operation.); and
controlling a group of vehicles traversing the transportation network in accordance with the determined traffic management strategy (¶ 0040: The operation 151 can be an operation to control or manage entities in the area 160 from the occurrence time of the incident 162 until the area 160 is free from the congestion caused by the incident 162… In another example, the processor can deploy aerial drones to deliver items such as medicine, food, to entities in the area 160 that may need these items.).
Weldemariam is considered to be analogous art as it pertains to traffic incident prediction. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the predictive route congestion management system (as taught by Weldemariam) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Weldemariam can coordinate traffic flow to vehicle destinations so as to minimize congestion. (See ¶ 0044)
Additionally, Donderici teaches wherein determining the characteristics of the roadway incident comprises at least one of:
Identifying a count of emergency vehicles at the location of the roadway incident (¶ 0056: At step 506, the AV can receive sensor data pertaining to the environment. For example, AV sensors 402 such as cameras…; ¶ 0058: At step 510, the process 500 determines whether an emergency vehicle is identified. For example, EMV behavior classification 404 may use machine learning algorithms or classification systems to identify the presence of an EMV 302.; Examiner’s note: By detecting a single emergency vehicle in the images, the system identifies a count of 1, or the presence of an emergency vehicle.), or
Donderici is considered to be analogous art as it pertains to vehicle sensor analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the emergency vehicle intent detection system (as taught by Donderici) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Donderici increases the frequency of sensors to improve and lower latency detection of emergency vehicles (See ¶ 0058).
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Takasaki et al (U.S. Patent Publication No. 2022/0292955 A1 B1, hereinafter “Takasaki”) in view of Weldemariam et al (U.S. Patent Publication No. 2021/0020038 A1, hereinafter “Weldemariam”) and further in view of Donderici et al (U.S. Patent Publication No. 2024/0087450 A1, hereinafter “Donderici”) and further in view of Myshenin et al (U.S. Patent Publication No. 2022/0141616 A1, hereinafter “Myshenin”).
Regarding claim 3, the Takasaki and Weldemariam combination teaches the system of claim 1.
Additionally, Takasaki teaches the sensor data comprises image data capturing (¶ 0112: In some embodiments, the event detector 404 can use machine learning technology to perform object recognition to detect a traffic event from images received from, for example, the camera and/or satellite technology.); and
Takasaki does not explicitly teach an emergency vehicle leaving a location of the roadway incident; and determining the characteristics of the roadway incident comprises identifying the emergency vehicle leaving the location of the roadway incident.
However, Donderici does teach sensor data comprises image data capturing an emergency vehicle (¶ 0063: At block 606, the process 600 includes identifying, based on the sensor data, an emergency vehicle in the environment. For example, EMV (emergency vehicle) behavior classification 404 may identify the location, type, and the behavior of the EMV.); and
Determining the characteristics of the roadway incident comprises identifying the emergency vehicle (¶ 0056: At step 506, the AV can receive sensor data pertaining to the environment. For example, AV sensors 402 such as cameras…; ¶ 0058: At step 510, the process 500 determines whether an emergency vehicle is identified. For example, EMV behavior classification 404 may use machine learning algorithms or classification systems to identify the presence of an EMV 302.).
Donderici is considered to be analogous art as it pertains to vehicle sensor analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the emergency vehicle intent detection system (as taught by Donderici) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Donderici increases the frequency of sensors to improve and lower latency detection of emergency vehicles (See ¶ 0058).
This motivation for the combination of Takasaki, Weldemariam, and Donderici is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Additionally, Myshenin teaches identifying the emergency vehicle leaving the location of the roadway incident (¶ 0120: Also as shown in FIG. 4D, ambulance 450 is leaving the incident scene to take the injured victim 420 to a hospital. The electronic computing device may recognize that the task of providing medical care to the injured victim 420 has been completed (for example, by monitoring communications of the paramedics M1 and M2, by monitoring one or more cameras providing video of the incident 400, and/or the like)).
Myshenin is considered to be analogous art as it pertains to vehicle sensor analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the coordinating task zones at a public safety incident scene (as taught by Myshenin) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Myshenin reduces overlap in geofenced areas which define the zones of the traffic incident, improving safety for emergency responders by preventing them from entering a different geofenced area (See ¶ 0093).
This motivation for the combination of Takasaki, Weldemariam, Donderici, and Myshenin is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 12, claim 12 has been analyzed with regard to respective claim 3 and is rejected for the same reasons of obviousness as used above.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Takasaki et al (U.S. Patent Publication No. 2022/0292955 A1 B1, hereinafter “Takasaki”) in view of Weldemariam et al (U.S. Patent Publication No. 2021/0020038 A1, hereinafter “Weldemariam”) and further in view of Xu et al (U.S. Patent Publication No. 2020/0027333 A1, hereinafter “Xu”).
Regarding claim 5, the Takasaki and Weldemariam combination teaches the system of claim 1.
Additionally Takasaki teaches wherein the sensor data comprises image data capturing (¶ 0112: In some embodiments, the event detector 404 can use machine learning technology to perform object recognition to detect a traffic event from images received from, for example, the camera and/or satellite technology.); and
Takasaki does not explicitly teach wherein the sensor data comprises image data capturing emergency cones; and determining the characteristics of the roadway incident comprises identifying the emergency cones at the location of the roadway incident.
However Xu does teach the sensor data comprises image data capturing emergency cones at a location of the roadway incident (¶ 0045: For example, in some embodiments, the incident determination unit 40 may determine the occurrence of extreme road conditions, including major potholes, significant damage, or debris on the road/side of road, by analyzing images from the cameras 30 to detect features of the road or objects on the road that are unexpected. For example, the incident determination unit 40 may compare the images from the cameras 30 with images of an expected road or with images of known objects that may be on the road (for example road cones, barrels, signs, construction equipment, etc.).); and
Determining the characteristics of the roadway incident comprises identifying the emergency cones at the location of the roadway incident (¶ 0045: For example, in some embodiments, the incident determination unit 40 may determine the occurrence of extreme road conditions, including major potholes, significant damage, or debris on the road/side of road, by analyzing images from the cameras 30 to detect features of the road or objects on the road that are unexpected. For example, the incident determination unit 40 may compare the images from the cameras 30 with images of an expected road or with images of known objects that may be on the road (for example road cones, barrels, signs, construction equipment, etc.).).
Xu is considered to be analogous art as it pertains to vehicle sensor analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the automatic traffic incident detection and reporting system (as taught by Xu) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Xu can automatically identify a road surface and any debris or damage to the road and uses a cloud system to validate the data by comparing the findings from multiple sources (See ¶ 0046 and 0085).
This motivation for the combination of Takasaki, Weldemariam, and Xu is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Takasaki et al (U.S. Patent Publication No. 2022/0292955 A1 B1, hereinafter “Takasaki”) in view of Weldemariam et al (U.S. Patent Publication No. 2021/0020038 A1, hereinafter “Weldemariam”) and further in view of Filley et al (U.S. Patent Publication No. 2017/0098373 A1, hereinafter “Filley”).
Regarding claim 7, the Takasaki and Weldemariam combination teaches the system of claim 1.
Additionally, Weldemariam teaches wherein the transmitted instructions cause (¶ 0040: In yet another embodiment, the processor 120 can recommend an alternate route to the desired destination where the vehicle will have a higher probability of reaching its destination with low fuel or battery level (e.g. a route that has fewer hills, fewer requirements to stop and start such as fewer stop signs or traffic signals).).
Takasaki does not explicitly teach graphical user interfaces (GUIs) of the group of vehicles to display navigation routes.
However, Filley does teach graphical user interfaces (GUIs) of the group of vehicles to display navigation routes (¶ 0067: At act A201, the device 122 generates a first route. The device may be a navigation system or configured as a navigation service in a vehicle. The device uses positioning circuitry 207 to determine a current location. The controller 200 uses mapping software or navigation software to determine a first route to a destination. The device 122 may maintain a local map database stored in memory 204 which may be updated from a server. The device 122 may also request a route from a current location to a destination from a navigation service or server. A user may use the input device 203 to enter a destination. The user may also enter a starting location other than the current location. The route may be displayed on the output interface 211.)
Filley is considered to be analogous art as it pertains to traffic incident prediction. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the transmission of targeted roadway alerts (as taught by Filley) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Filley archives polygons, which define historical data for road segments, for future use which reduces latency around analytics necessary for generating polygons . (See ¶ 0042)
This motivation for the combination of Takasaki, Weldemariam, and Filley is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Takasaki et al (U.S. Patent Publication No. 2022/0292955 A1 B1, hereinafter “Takasaki”) in view of Weldemariam et al (U.S. Patent Publication No. 2021/0020038 A1, hereinafter “Weldemariam”) and further in view of Donderici et al (U.S. Patent Publication No. 2024/0087450 A1, hereinafter “Donderici”) and Xu et al (U.S. Patent Publication No. 2020/0027333 A1, hereinafter “Xu”).
Regarding claim 14, the Takasaki, Weldemariam, and Donderici combination teaches the system of claim 10.
Additionally Takasaki teaches wherein the sensor data comprises image data capturing (¶ 0112: In some embodiments, the event detector 404 can use machine learning technology to perform object recognition to detect a traffic event from images received from, for example, the camera and/or satellite technology.);
Takasaki does not explicitly teach the sensor data comprises image data capturing emergency cones.
However Xu does teach the sensor data comprises image data capturing emergency cones (¶ 0045: For example, in some embodiments, the incident determination unit 40 may determine the occurrence of extreme road conditions, including major potholes, significant damage, or debris on the road/side of road, by analyzing images from the cameras 30 to detect features of the road or objects on the road that are unexpected. For example, the incident determination unit 40 may compare the images from the cameras 30 with images of an expected road or with images of known objects that may be on the road (for example road cones, barrels, signs, construction equipment, etc.).);
Xu is considered to be analogous art as it pertains to vehicle sensor analysis. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the automatic traffic incident detection and reporting system (as taught by Xu) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Xu can automatically identify a road surface and any debris or damage to the road and uses a cloud system to validate the data by comparing the findings from multiple sources (See ¶ 0046 and 0085).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Takasaki et al (U.S. Patent Publication No. 2022/0292955 A1 B1, hereinafter “Takasaki”) in view of Weldemariam et al (U.S. Patent Publication No. 2021/0020038 A1, hereinafter “Weldemariam”) and further in view of Donderici et al (U.S. Patent Publication No. 2024/0087450 A1, hereinafter “Donderici”) and Filley et al (U.S. Patent Publication No. 2017/0098373 A1, hereinafter “Filley”).
Regarding claim 16, the Takasaki, Weldemariam, and Donderici combination teaches the system of claim 10.
Additionally, Weldemariam teaches wherein the transmitted instructions cause (¶ 0040: In yet another embodiment, the processor 120 can recommend an alternate route to the desired destination where the vehicle will have a higher probability of reaching its destination with low fuel or battery level (e.g. a route that has fewer hills, fewer requirements to stop and start such as fewer stop signs or traffic signals).).
Takasaki does not explicitly teach graphical user interfaces (GUIs) of the group of vehicles to display navigation routes.
However, Filley does teach graphical user interfaces (GUIs) of the group of vehicles to display navigation routes (¶ 0067: At act A201, the device 122 generates a first route. The device may be a navigation system or configured as a navigation service in a vehicle. The device uses positioning circuitry 207 to determine a current location. The controller 200 uses mapping software or navigation software to determine a first route to a destination. The device 122 may maintain a local map database stored in memory 204 which may be updated from a server. The device 122 may also request a route from a current location to a destination from a navigation service or server. A user may use the input device 203 to enter a destination. The user may also enter a starting location other than the current location. The route may be displayed on the output interface 211.)
Filley is considered to be analogous art as it pertains to traffic incident prediction. Therefore, it would have been obvious to one of ordinary skill in the art to combine the system for calculating traffic flow changes due to traffic events (as taught by Takasaki) and the transmission of targeted roadway alerts (as taught by Filley) before the effective filing date of the claimed invention. The motivation for this combination of references would be the system of Filley archives polygons, which define historical data for road segments, for future use which reduces latency around analytics necessary for generating polygons . (See ¶ 0042)
This motivation for the combination of Takasaki, Weldemariam, Donderici, and Filley is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Allowable Subject Matter
Claims 8 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 8 and 17, the prior arts, neither alone or in combination appear to teach wherein a start time of the bell curve and an end time of the bell curve correspond with speeds greater than a threshold value for a location of the roadway incident.
Conclusion
THIS ACTION IS MADE FINAL. 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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/ANDREW B. JONES/Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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