DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first
inventor to file provisions of the AIA .
Status of Claims
This is the first Office action on the merits. Claims 1-20 are currently pending and addressed below.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in
CN on 09/06/2023. It is noted, however, that applicant has not filed a certified copy of the CN 202311146352.3 application as required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process without significantly more.
101 Analysis – Step 1
Claims 1, 8, and 15 are directed to a method (i.e., a process), an electronic device (i.e., a machine), and non-transitory computer-readable storage medium (i.e., a machine). Therefore, claims 1, 8, and 15 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claims 1, 8, and 15 include limitations that recite an abstract idea and will be used as a representative claim for the remainder of the 101 rejection.
Independent claims 1, 8, and 15 recite the following information:
An electronic device, comprising:
a non-transitory computer-readable storage medium storing computer instructions configured to, when executed, cause a processor to perform a road section information determination method, the method comprising:
acquiring first running state information of a vehicle in a traffic road section and second running state information transmitted by an associated assistance system in the traffic road section;
in response to an associated signal indicator light satisfying an information determination condition, determining road section information of the traffic road section based on the first running state information, the second running state information, and preset road section attribute information; and
sending the road section information to an assistance vehicle corresponding to the assistance system.
The examiner submits that the foregoing bolded limitation(s) constitute an abstract idea of a mental process that gathers information obtained using sensors related to the operating states of a vehicle and information of a road section layout based on observation of the road section and analyzes the obtained information to determine traffic conditions at the road section during a traffic signal condition.
Each of the limitations can be performed in the mental realm or by using pen and paper to gather information based on visual observation of displayed sensor data obtained while a vehicle is driven at a road section and visual observation of the road section layout configuration, and evaluates the gathered information to estimate traffic congestion, queue, waiting times, etc. at the road section during a traffic signal condition.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
Claims 1, 8, and 15 do contain additional elements of an electronic device, a non-transitory computer-readable storage medium storing computer instructions configured to, when executed, cause a processor to perform a road section information determination method, acquiring first running state information of a vehicle in a traffic road section and second running state information transmitted by an associated assistance system in the traffic road section, and sending the road section information to an assistance vehicle corresponding to the assistance system. However, these additional elements do not add to significantly more than the abstract idea of a mental process.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional elements of an electronic device, a non-transitory computer-readable storage medium storing computer instructions configured to, when executed, cause a processor to perform a road section information determination method, acquiring first running state information of a vehicle in a traffic road section and second running state information transmitted by an associated assistance system in the traffic road section, and sending the road section information to an assistance vehicle corresponding to the assistance system, the examiner submits that these limitations merely describe how to generally apply the otherwise mental judgements in a generic or general-purpose vehicle traffic monitoring system environment. The electronic device, a non-transitory computer-readable storage medium storing computer instructions configured to, when executed, cause a processor to perform a road section information determination method, acquiring first running state information of a vehicle in a traffic road section and second running state information transmitted by an associated assistance system in the traffic road section, and sending the road section information to an assistance vehicle corresponding to the assistance system are recited at a high level of generality and merely automate the vehicle running state information receiving, data analyzing to determine road section information, and sending of road section information components of the system. As for the additional elements specifying acquiring first running state information of a vehicle in a traffic road section and second running state information transmitted by an associated assistance system in the traffic road section, and sending the road section information to an assistance vehicle corresponding to the assistance system, the examiner submits that these limitations are recited at a high level of generality (i.e., describe general means of the acquiring running state information of a vehicle and sending the road section information to an assistance vehicle steps) and therefore amount to mere transmission of data between computer processing components which is a form of insignificant extra-solution activity that merely uses computing components to perform the process.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B, representative independent claims 1, 8, and 15 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an electronic device, a non-transitory computer-readable storage medium storing computer instructions configured to, when executed, cause a processor to perform a road section information determination method, acquiring first running state information of a vehicle in a traffic road section and second running state information transmitted by an associated assistance system in the traffic road section, and sending the road section information to an assistance vehicle corresponding to the assistance system amount to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of gathering/transmitting data, the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of gathering/transmitting data are well-understood, routine, and conventional activities because the specification does not provide any indication that the computer is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claims are not patent eligible.
Dependent claims 2-7 (method claims), 9-14 (electronic device claims), and 16-20 (non-transitory computer-readable storage medium claims) do not recite and further limitations that cause the claims to be patent eligible. The limitations of the dependent claims are directed towards additional aspects of the judicial exception that do not integrate the judicial exception into a practical application. The dependent claims further narrow the scope of independent claims 1, 8, and 15, however, the identified additional limitations and elements still do not impose any meaningful limits on practicing the identified abstract ideas. Therefore, dependent claims 2-7, 9-14, and 16-20 are not patent eligible under the same rationale as provided for in the rejection of claims 1, 8, and 15. Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Caballero De Ita et al. US 20180126995 A1 (“Caballero De Ita”) in view of Mintz US 20020082767 A1 (“Mintz”).
For claim 1, Caballero De Ita discloses a road section information determination method, the method being applied to an assistance system (See at least [0007] of Caballero De Ita – “… A traffic light sensor identifies a plurality of vehicles predicted to pass through an intersection… server assigns an acceleration and start time for each of the vehicles based on the actuation of the traffic light from a red light … to a green light … each vehicle is actuated to move according to the specified acceleration and start time… maintaining the spacing between the vehicles through the intersection…”) and comprising:
acquiring first running state information of a vehicle in a traffic road section (See at least [0019]-[0020] of Caballero De Ita – “… The traffic light sensors 145 can detect spacing 210 between the vehicles 101… by maintaining or decreasing the spacing 210 between the vehicles 101 before the green light actuates, more vehicles 101 can move through the intersection 200…”) and second running state information transmitted by an associated assistance system in the traffic road section (See at least [0024] of Caballero De Ita – “… Each of the vehicles 101 can send a notification to the server 130 indicating a route that the respective vehicle 101 will follow through the intersection 200…”);
in response to an associated signal indicator light satisfying an information determination condition, determining road section information of the traffic road section based on the first running state information, the second running state information, and preset road section attribute information (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”).
Caballero De Ita fails to specifically disclose sending the road section information to an assistance vehicle corresponding to the assistance system.
However, Mintz, in the same field of endeavor teaches sending the road section information to an assistance vehicle corresponding to the assistance system (See at least [0270]-[0275] of Mintz – “… Central station 206 then rebroadcasts the information (either as raw information or as maps or utilizing any other suitable format) to all the vehicles… estimations of future traffic jams and slowdowns …uses current and predicted information… gives each vehicle the information required to make a distributed route calculation… This means that when vehicles receive predicted information about numbers of vehicles that are expected to pass a road or intersection in a given time… the overall result may be that traffic jams do not result or are less severe…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mintz teaches a vehicle traffic monitoring system that sends predicted traffic and slowdown information to vehicles in order for the vehicles to calculate routes to avoid future traffic jams.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of sending the road section information to an assistance vehicle corresponding to the assistance system as taught by Mintz, with a reasonable expectation of success, in order to send traffic information to vehicles and calculate routes for the vehicles that avoid future problems and traffic jams as specified in at least [0274]-[0275] of Mintz.
For claim 8, Caballero De Ita discloses an electronic device, comprising:
at least one processor (See at least [0016] of Caballero De Ita – “The system 100 further includes at least one traffic light 140 including a computer, i.e., a processor and a memory…”); and
a memory communicatively connected to the at least one processor, wherein the memory stores a computer program executable by the at least one processor to enable the electronic device to perform a road section information determination method (See at least claim 1 of Caballero De Ita – “… a computer including a processor and a memory, the memory storing instructions executable by the processor to: identify a plurality of vehicles predicted to pass through an intersection …”);
wherein the method comprising:
acquiring first running state information of a vehicle in a traffic road section (See at least [0019]-[0020] of Caballero De Ita – “… The traffic light sensors 145 can detect spacing 210 between the vehicles 101… by maintaining or decreasing the spacing 210 between the vehicles 101 before the green light actuates, more vehicles 101 can move through the intersection 200…”) and second running state information transmitted by an associated assistance system in the traffic road section (See at least [0024] of Caballero De Ita – “… Each of the vehicles 101 can send a notification to the server 130 indicating a route that the respective vehicle 101 will follow through the intersection 200…”);
in response to an associated signal indicator light satisfying an information determination condition, determining road section information of the traffic road section based on the first running state information, the second running state information, and preset road section attribute information (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”).
Caballero De Ita fails to specifically disclose sending the road section information to an assistance vehicle corresponding to the assistance system.
However, Mintz, in the same field of endeavor teaches sending the road section information to an assistance vehicle corresponding to the assistance system (See at least [0270]-[0275] of Mintz – “… Central station 206 then rebroadcasts the information (either as raw information or as maps or utilizing any other suitable format) to all the vehicles… estimations of future traffic jams and slowdowns …uses current and predicted information… gives each vehicle the information required to make a distributed route calculation… This means that when vehicles receive predicted information about numbers of vehicles that are expected to pass a road or intersection in a given time… the overall result may be that traffic jams do not result or are less severe…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mintz teaches a vehicle traffic monitoring system that sends predicted traffic and slowdown information to vehicles in order for the vehicles to calculate routes to avoid future traffic jams.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of sending the road section information to an assistance vehicle corresponding to the assistance system as taught by Mintz, with a reasonable expectation of success, in order to send traffic information to vehicles and calculate routes for the vehicles that avoid future problems and traffic jams as specified in at least [0274]-[0275] of Mintz.
For claim 15, Caballero De Ita discloses a non-transitory computer-readable storage medium storing computer instructions configured to, when executed, cause a processor to perform a road section information determination method (See at least claim 1 of Caballero De Ita – “… a computer including a processor and a memory, the memory storing instructions executable by the processor to: identify a plurality of vehicles predicted to pass through an intersection …”);
wherein the method comprising:
acquiring first running state information of a vehicle in a traffic road section (See at least [0019]-[0020] of Caballero De Ita – “… The traffic light sensors 145 can detect spacing 210 between the vehicles 101… by maintaining or decreasing the spacing 210 between the vehicles 101 before the green light actuates, more vehicles 101 can move through the intersection 200…”) and second running state information transmitted by an associated assistance system in the traffic road section (See at least [0024] of Caballero De Ita – “… Each of the vehicles 101 can send a notification to the server 130 indicating a route that the respective vehicle 101 will follow through the intersection 200…”);
in response to an associated signal indicator light satisfying an information determination condition, determining road section information of the traffic road section based on the first running state information, the second running state information, and preset road section attribute information (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”).
Caballero De Ita fails to specifically disclose sending the road section information to an assistance vehicle corresponding to the assistance system.
However, Mintz, in the same field of endeavor teaches sending the road section information to an assistance vehicle corresponding to the assistance system (See at least [0270]-[0275] of Mintz – “… Central station 206 then rebroadcasts the information (either as raw information or as maps or utilizing any other suitable format) to all the vehicles… estimations of future traffic jams and slowdowns …uses current and predicted information… gives each vehicle the information required to make a distributed route calculation… This means that when vehicles receive predicted information about numbers of vehicles that are expected to pass a road or intersection in a given time… the overall result may be that traffic jams do not result or are less severe…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mintz teaches a vehicle traffic monitoring system that sends predicted traffic and slowdown information to vehicles in order for the vehicles to calculate routes to avoid future traffic jams.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of sending the road section information to an assistance vehicle corresponding to the assistance system as taught by Mintz, with a reasonable expectation of success, in order to send traffic information to vehicles and calculate routes for the vehicles that avoid future problems and traffic jams as specified in at least [0274]-[0275] of Mintz.
Claims 2-3, 9-10, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Caballero De Ita in view of Mintz, as applied to claim 1 above, and further in view of Kim et al. US 20250299563 A1 (“Kim”).
For claim 2, Caballero De Ita discloses wherein determining the road section information of the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining vehicle accommodation information of the traffic road section based on the first running state information and the second running state information (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”);
determining the vehicle accommodation information as the road section information of the traffic road section (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”).
Caballero De Ita fails to specifically disclose determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information; and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section as the road section information of the traffic road section.
However, Kim, in the same field of endeavor teaches determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information (See at least [0144] – “it can be seen that as the red signal time R increases or the number of vehicles Q.sub.b in the initial waiting queue increases, the equal congestion indicator d.sub.1 increases, and as a result, the travel time of congestion of the vehicles increases, and thus the composite congestion index may increase...” and [0166] of Kim – “… in order for the user to easily recognize an average congestion time of the vehicles at the intersection, the evaluation index may be written in the detailed information layer …to easily recognize the average congestion travel time of the vehicles at the corresponding intersection…” and Fig. 4A of Kim – congestion scenario with all three lanes occupied by vehicles at an intersection); and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section as the road section information of the traffic road section (See at least [0144] – “it can be seen that as the red signal time R increases or the number of vehicles Q.sub.b in the initial waiting queue increases, the equal congestion indicator d.sub.1 increases, and as a result, the travel time of congestion of the vehicles increases, and thus the composite congestion index may increase...” and [0166] of Kim – “… in order for the user to easily recognize an average congestion time of the vehicles at the intersection, the evaluation index may be written in the detailed information layer …to easily recognize the average congestion travel time of the vehicles at the corresponding intersection…” and Fig. 4A of Kim – congestion scenario with all three lanes occupied by vehicles at an intersection). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Kim teaches a system for evaluating congestion times for vehicles waiting in queue at a red light in a road intersection.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information as taught by Kim, with a reasonable expectation of success, in order to determine a congestion time of vehicles at an intersection as specified in at least [0166] of Kim.
For claim 3, Caballero De Ita discloses wherein determining the vehicle accommodation information of the traffic road section based on the first running state information and the second running state information comprises:
determining a first target vehicle in each lane of the traffic road section based on the first running state information and the second running state information (See at least [0027] of Caballero De Ita – “… server 130 can determine the spacing 210 between the vehicles 101a-101c and determine the acceleration and start time for each of the vehicles 101a-101c to move through the intersection 200 and from the first roadway lane 205a to the second roadway lane 205b…”);
determining a distance between the first target vehicle in each lane and a preset road section start reference line to obtain first distance information of a respective lane (See at least [0026] of Caballero De Ita – “… the server 130 can determine a distance from the traffic light 140. The vehicles 101 within the distance from the traffic light 140 can be predicted to move through the intersection 200 when they move at the predetermined acceleration and maintain the spacing 210…”); and
determining the vehicle accommodation information of the traffic road section based on the first distance information of each lane (See at least [0026] of Caballero De Ita – “… the server 130 can determine a distance from the traffic light 140. The vehicles 101 within the distance from the traffic light 140 can be predicted to move through the intersection 200 when they move at the predetermined acceleration and maintain the spacing 210…”).
For claim 9, Caballero De Ita discloses wherein determining the road section information of the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining vehicle accommodation information of the traffic road section based on the first running state information and the second running state information (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”);
determining the vehicle accommodation information as the road section information of the traffic road section (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”).
Caballero De Ita fails to specifically disclose determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information; and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section as the road section information of the traffic road section.
However, Kim, in the same field of endeavor teaches determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information (See at least [0144] – “it can be seen that as the red signal time R increases or the number of vehicles Q.sub.b in the initial waiting queue increases, the equal congestion indicator d.sub.1 increases, and as a result, the travel time of congestion of the vehicles increases, and thus the composite congestion index may increase...” and [0166] of Kim – “… in order for the user to easily recognize an average congestion time of the vehicles at the intersection, the evaluation index may be written in the detailed information layer …to easily recognize the average congestion travel time of the vehicles at the corresponding intersection…” and Fig. 4A of Kim – congestion scenario with all three lanes occupied by vehicles at an intersection); and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section as the road section information of the traffic road section (See at least [0144] – “it can be seen that as the red signal time R increases or the number of vehicles Q.sub.b in the initial waiting queue increases, the equal congestion indicator d.sub.1 increases, and as a result, the travel time of congestion of the vehicles increases, and thus the composite congestion index may increase...” and [0166] of Kim – “… in order for the user to easily recognize an average congestion time of the vehicles at the intersection, the evaluation index may be written in the detailed information layer …to easily recognize the average congestion travel time of the vehicles at the corresponding intersection…” and Fig. 4A of Kim – congestion scenario with all three lanes occupied by vehicles at an intersection). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Kim teaches a system for evaluating congestion times for vehicles waiting in queue at a red light in a road intersection.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information as taught by Kim, with a reasonable expectation of success, in order to determine a congestion time of vehicles at an intersection as specified in at least [0166] of Kim.
For claim 10, Caballero De Ita discloses wherein determining the vehicle accommodation information of the traffic road section based on the first running state information and the second running state information comprises:
determining a first target vehicle in each lane of the traffic road section based on the first running state information and the second running state information (See at least [0027] of Caballero De Ita – “… server 130 can determine the spacing 210 between the vehicles 101a-101c and determine the acceleration and start time for each of the vehicles 101a-101c to move through the intersection 200 and from the first roadway lane 205a to the second roadway lane 205b…”);
determining a distance between the first target vehicle in each lane and a preset road section start reference line to obtain first distance information of a respective lane (See at least [0026] of Caballero De Ita – “… the server 130 can determine a distance from the traffic light 140. The vehicles 101 within the distance from the traffic light 140 can be predicted to move through the intersection 200 when they move at the predetermined acceleration and maintain the spacing 210…”); and
determining the vehicle accommodation information of the traffic road section based on the first distance information of each lane (See at least [0026] of Caballero De Ita – “… the server 130 can determine a distance from the traffic light 140. The vehicles 101 within the distance from the traffic light 140 can be predicted to move through the intersection 200 when they move at the predetermined acceleration and maintain the spacing 210…”).
For claim 16, Caballero De Ita discloses wherein determining the road section information of the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining vehicle accommodation information of the traffic road section based on the first running state information and the second running state information (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”);
determining the vehicle accommodation information as the road section information of the traffic road section (See at least [0026] of Caballero De Ita – “… Based on the route for each vehicle … server 130 can use the duration of the green light, the current spacing 210 of the vehicles 101, and a predetermined acceleration to predict a number of vehicles 101 that can pass through the intersection 200 while the light is green… Based on whether the routes indicate that the vehicles 101 will remain in their respective roadway lanes 205 or move … the server 130 can adjust the number of vehicles 101 predicted to move through the intersection 200…”).
Caballero De Ita fails to specifically disclose determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information; and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section as the road section information of the traffic road section.
However, Kim, in the same field of endeavor teaches determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information (See at least [0144] – “it can be seen that as the red signal time R increases or the number of vehicles Q.sub.b in the initial waiting queue increases, the equal congestion indicator d.sub.1 increases, and as a result, the travel time of congestion of the vehicles increases, and thus the composite congestion index may increase...” and [0166] of Kim – “… in order for the user to easily recognize an average congestion time of the vehicles at the intersection, the evaluation index may be written in the detailed information layer …to easily recognize the average congestion travel time of the vehicles at the corresponding intersection…” and Fig. 4A of Kim – congestion scenario with all three lanes occupied by vehicles at an intersection); and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section as the road section information of the traffic road section (See at least [0144] – “it can be seen that as the red signal time R increases or the number of vehicles Q.sub.b in the initial waiting queue increases, the equal congestion indicator d.sub.1 increases, and as a result, the travel time of congestion of the vehicles increases, and thus the composite congestion index may increase...” and [0166] of Kim – “… in order for the user to easily recognize an average congestion time of the vehicles at the intersection, the evaluation index may be written in the detailed information layer …to easily recognize the average congestion travel time of the vehicles at the corresponding intersection…” and Fig. 4A of Kim – congestion scenario with all three lanes occupied by vehicles at an intersection). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Kim teaches a system for evaluating congestion times for vehicles waiting in queue at a red light in a road intersection.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining a time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information as taught by Kim, with a reasonable expectation of success, in order to determine a congestion time of vehicles at an intersection as specified in at least [0166] of Kim.
For claim 17, Caballero De Ita discloses wherein determining the vehicle accommodation information of the traffic road section based on the first running state information and the second running state information comprises:
determining a first target vehicle in each lane of the traffic road section based on the first running state information and the second running state information (See at least [0027] of Caballero De Ita – “… server 130 can determine the spacing 210 between the vehicles 101a-101c and determine the acceleration and start time for each of the vehicles 101a-101c to move through the intersection 200 and from the first roadway lane 205a to the second roadway lane 205b…”);
determining a distance between the first target vehicle in each lane and a preset road section start reference line to obtain first distance information of a respective lane (See at least [0026] of Caballero De Ita – “… the server 130 can determine a distance from the traffic light 140. The vehicles 101 within the distance from the traffic light 140 can be predicted to move through the intersection 200 when they move at the predetermined acceleration and maintain the spacing 210…”); and
determining the vehicle accommodation information of the traffic road section based on the first distance information of each lane (See at least [0026] of Caballero De Ita – “… the server 130 can determine a distance from the traffic light 140. The vehicles 101 within the distance from the traffic light 140 can be predicted to move through the intersection 200 when they move at the predetermined acceleration and maintain the spacing 210…”).
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Caballero De Ita in view of Mintz and Kim, as applied to claim 2 above, and further in view of Ota et al. US 20140046581 A1 (“Ota”).
For claim 4, Caballero De Ita fails to specifically disclose wherein determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining a total number of vehicles in the traffic road section and a second target vehicle in each lane of the traffic road section based on the first running state information and the second running state information;
determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane; and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the total number of vehicles, the preset road section attribute information, and the second distance information of each lane.
However, Ota, in the same field of endeavor teaches wherein determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining a total number of vehicles in the traffic road section and a second target vehicle in each lane of the traffic road section based on the first running state information and the second running state information (See at least [0086]-[0087] of Ota – “… it is determined that the different vehicles d and e stop, and the number of vehicles is estimated to be two… it is possible to estimate the number of stopping vehicles for each traffic lane..”);
determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane (See at least [0120] of Ota – “…based on the vehicle line end position information at the current time, a distance between the different vehicle at the end and the traffic light is calculated… this distance may be calculated as the length of the stopping vehicle line…”); and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the total number of vehicles, the preset road section attribute information, and the second distance information of each lane (See at least [0125]-[0133] of Ota – “… Thus, the traffic information obtaining unit 5 obtains the traffic volume along the estimated driving route … travel time information (traffic jam information) is obtained … includes a predetermined section and travel time indicating how long it takes to pass through this predetermined section… dividing the travel time by a distance of this section reflects the traffic volume… at the end of the red/yellow light time zone (the point output of the green light starts), the number of stopping vehicles takes a maximum value … positive correlation is established between the traffic volume (for example, the value dividing the travel time by the section) and the maximum number of stopping vehicles nmax… the function of obtaining the traffic volume (the travel time information) or such from the center is mounted on a common car navigation system…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Ota teaches a system that determines a time length taking for vehicles to accumulate in a line of stopping vehicles at a red light and then for a vehicle pass through an intersection based on the number of vehicles in each lane of a road and a distance between the vehicle line end position and the traffic light existing in the road section.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane as taught by Ota, with a reasonable expectation of success, in order to obtain traffic volume information for a road section as specified in at least [0133] of Ota.
For claim 11, Caballero De Ita fails to specifically disclose wherein determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining a total number of vehicles in the traffic road section and a second target vehicle in each lane of the traffic road section based on the first running state information and the second running state information;
determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane; and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the total number of vehicles, the preset road section attribute information, and the second distance information of each lane.
However, Ota, in the same field of endeavor teaches wherein determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining a total number of vehicles in the traffic road section and a second target vehicle in each lane of the traffic road section based on the first running state information and the second running state information (See at least [0086]-[0087] of Ota – “… it is determined that the different vehicles d and e stop, and the number of vehicles is estimated to be two… it is possible to estimate the number of stopping vehicles for each traffic lane..”);
determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane (See at least [0120] of Ota – “…based on the vehicle line end position information at the current time, a distance between the different vehicle at the end and the traffic light is calculated… this distance may be calculated as the length of the stopping vehicle line…”); and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the total number of vehicles, the preset road section attribute information, and the second distance information of each lane (See at least [0125]-[0133] of Ota – “… Thus, the traffic information obtaining unit 5 obtains the traffic volume along the estimated driving route … travel time information (traffic jam information) is obtained … includes a predetermined section and travel time indicating how long it takes to pass through this predetermined section… dividing the travel time by a distance of this section reflects the traffic volume… at the end of the red/yellow light time zone (the point output of the green light starts), the number of stopping vehicles takes a maximum value … positive correlation is established between the traffic volume (for example, the value dividing the travel time by the section) and the maximum number of stopping vehicles nmax… the function of obtaining the traffic volume (the travel time information) or such from the center is mounted on a common car navigation system…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Ota teaches a system that determines a time length taking for vehicles to accumulate in a line of stopping vehicles at a red light and then for a vehicle pass through an intersection based on the number of vehicles in each lane of a road and a distance between the vehicle line end position and the traffic light existing in the road section.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane as taught by Ota, with a reasonable expectation of success, in order to obtain traffic volume information for a road section as specified in at least [0133] of Ota.
For claim 18, Caballero De Ita fails to specifically disclose wherein determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining a total number of vehicles in the traffic road section and a second target vehicle in each lane of the traffic road section based on the first running state information and the second running state information;
determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane; and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the total number of vehicles, the preset road section attribute information, and the second distance information of each lane.
However, Ota, in the same field of endeavor teaches wherein determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the first running state information, the second running state information, and the preset road section attribute information comprises:
determining a total number of vehicles in the traffic road section and a second target vehicle in each lane of the traffic road section based on the first running state information and the second running state information (See at least [0086]-[0087] of Ota – “… it is determined that the different vehicles d and e stop, and the number of vehicles is estimated to be two… it is possible to estimate the number of stopping vehicles for each traffic lane..”);
determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane (See at least [0120] of Ota – “…based on the vehicle line end position information at the current time, a distance between the different vehicle at the end and the traffic light is calculated… this distance may be calculated as the length of the stopping vehicle line…”); and
determining the time length taking for vehicles waiting at a red light to fully occupy the traffic road section based on the total number of vehicles, the preset road section attribute information, and the second distance information of each lane (See at least [0125]-[0133] of Ota – “… Thus, the traffic information obtaining unit 5 obtains the traffic volume along the estimated driving route … travel time information (traffic jam information) is obtained … includes a predetermined section and travel time indicating how long it takes to pass through this predetermined section… dividing the travel time by a distance of this section reflects the traffic volume… at the end of the red/yellow light time zone (the point output of the green light starts), the number of stopping vehicles takes a maximum value … positive correlation is established between the traffic volume (for example, the value dividing the travel time by the section) and the maximum number of stopping vehicles nmax… the function of obtaining the traffic volume (the travel time information) or such from the center is mounted on a common car navigation system…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Ota teaches a system that determines a time length taking for vehicles to accumulate in a line of stopping vehicles at a red light and then for a vehicle pass through an intersection based on the number of vehicles in each lane of a road and a distance between the vehicle line end position and the traffic light existing in the road section.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining a distance between the second target vehicle in each lane and a preset road section end reference line to obtain second distance information of a respective lane as taught by Ota, with a reasonable expectation of success, in order to obtain traffic volume information for a road section as specified in at least [0133] of Ota.
Claims 5-6, 12-13, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Caballero De Ita in view of Mintz, as applied to claim 1 above, and further in view of Mimeault et al. US 20140159925 A1 (“Mimeault”).
For claim 5, Caballero De Ita fails to specifically disclose further comprising:
allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles.
However, Mimeault, in the same field of endeavor teaches further comprising:
allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mimeault teaches a traffic detection system that uses lidar detection channels to detect vehicles on a road and assign a unique identifier to each vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles as taught by Mimeault, with a reasonable expectation of success, in order to track and record an updated position of each vehicle and an updated time at which the updated position is detected as specified in at least [0041] of Mimeault.
For claim 6, Caballero De Ita fails to specifically disclose wherein allocating the lidar channels to the associated vehicles in the traffic road section and determining the lidar channel information of the associated vehicles comprises:
acquiring a first entry sequence of the associated vehicles in the traffic road section;
dividing the associated vehicles into target vehicles satisfying an allocation condition and to-be-allocated vehicles based on the first entry sequence and a preset number of lidar channels and determining a second entry sequence of the to-be-allocated vehicles;
determining first lidar channel information of the target vehicles based on the first entry sequence;
determining second lidar channel information of a to-be-allocated vehicle of the to-be-allocated vehicles based on the second entry sequence when a target vehicle of the target vehicles exits the traffic road section; and
determining the first lidar channel information and the second lidar channel information as the lidar channel information.
However, Mimeault, in the same field of endeavor teaches wherein allocating the lidar channels to the associated vehicles in the traffic road section and determining the lidar channel information of the associated vehicles comprises:
acquiring a first entry sequence of the associated vehicles in the traffic road section (See at least [0118] of Mimeault – “… Detectors can also be installed at the entry … of a point-to-point enforcement system allowing the measurement of the average speed of a vehicle …”);
dividing the associated vehicles into target vehicles satisfying an allocation condition and to-be-allocated vehicles based on the first entry sequence and a preset number of lidar channels and determining a second entry sequence of the to-be-allocated vehicles (See at least [0058] of Mimeault – “FIG. 11 is a top view of the example installation of FIG. 10 on which four vehicle detections are visible in some of the 16 separate channels with simultaneous acquisition capability…”);
determining first lidar channel information of the target vehicles based on the first entry sequence (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected”);
determining second lidar channel information of a to-be-allocated vehicle of the to-be-allocated vehicles based on the second entry sequence when a target vehicle of the target vehicles exits the traffic road section (See at least [0041] – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected…”, [0058] – “FIG. 11 is a top view of the example installation of FIG. 10 on which four vehicle detections are visible in some of the 16 separate channels with simultaneous acquisition capability…” and [0118] of Mimeault – “… Detectors can also be installed… at the exit of a point-to-point enforcement system allowing the measurement of the average speed of a vehicle …”); and
determining the first lidar channel information and the second lidar channel information as the lidar channel information (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mimeault teaches a traffic detection system that uses lidar detection channels to detect vehicles on a road, assign a unique identifier to each vehicle, track and record an updated position of each vehicle and an updated time at which the updated position is detected.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining first lidar channel information of the target vehicles based on the first entry sequence as taught by Mimeault, with a reasonable expectation of success, in order to track and record an updated position of each vehicle and an updated time at which the updated position is detected as specified in at least [0041] of Mimeault.
For claim 12, Caballero De Ita fails to specifically disclose wherein the method further comprising:
allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles.
However, Mimeault, in the same field of endeavor teaches wherein the method further comprising:
allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mimeault teaches a traffic detection system that uses lidar detection channels to detect vehicles on a road and assign a unique identifier to each vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles as taught by Mimeault, with a reasonable expectation of success, in order to track and record an updated position of each vehicle and an updated time at which the updated position is detected as specified in at least [0041] of Mimeault.
For claim 13, Caballero De Ita fails to specifically disclose wherein allocating the lidar channels to the associated vehicles in the traffic road section and determining the lidar channel information of the associated vehicles comprises:
acquiring a first entry sequence of the associated vehicles in the traffic road section;
dividing the associated vehicles into target vehicles satisfying an allocation condition and to-be-allocated vehicles based on the first entry sequence and a preset number of lidar channels and determining a second entry sequence of the to-be-allocated vehicles;
determining first lidar channel information of the target vehicles based on the first entry sequence;
determining second lidar channel information of a to-be-allocated vehicle of the to-be-allocated vehicles based on the second entry sequence when a target vehicle of the target vehicles exits the traffic road section; and
determining the first lidar channel information and the second lidar channel information as the lidar channel information.
However, Mimeault, in the same field of endeavor teaches wherein allocating the lidar channels to the associated vehicles in the traffic road section and determining the lidar channel information of the associated vehicles comprises:
acquiring a first entry sequence of the associated vehicles in the traffic road section (See at least [0118] of Mimeault – “… Detectors can also be installed at the entry … of a point-to-point enforcement system allowing the measurement of the average speed of a vehicle …”);
dividing the associated vehicles into target vehicles satisfying an allocation condition and to-be-allocated vehicles based on the first entry sequence and a preset number of lidar channels and determining a second entry sequence of the to-be-allocated vehicles (See at least [0058] of Mimeault – “FIG. 11 is a top view of the example installation of FIG. 10 on which four vehicle detections are visible in some of the 16 separate channels with simultaneous acquisition capability…”);
determining first lidar channel information of the target vehicles based on the first entry sequence (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected”);
determining second lidar channel information of a to-be-allocated vehicle of the to-be-allocated vehicles based on the second entry sequence when a target vehicle of the target vehicles exits the traffic road section (See at least [0041] – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected…”, [0058] – “FIG. 11 is a top view of the example installation of FIG. 10 on which four vehicle detections are visible in some of the 16 separate channels with simultaneous acquisition capability…” and [0118] of Mimeault – “… Detectors can also be installed… at the exit of a point-to-point enforcement system allowing the measurement of the average speed of a vehicle …”); and
determining the first lidar channel information and the second lidar channel information as the lidar channel information (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mimeault teaches a traffic detection system that uses lidar detection channels to detect vehicles on a road, assign a unique identifier to each vehicle, track and record an updated position of each vehicle and an updated time at which the updated position is detected.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining first lidar channel information of the target vehicles based on the first entry sequence as taught by Mimeault, with a reasonable expectation of success, in order to track and record an updated position of each vehicle and an updated time at which the updated position is detected as specified in at least [0041] of Mimeault.
For claim 19, Caballero De Ita fails to specifically disclose wherein the method further comprising:
allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles.
However, Mimeault, in the same field of endeavor teaches wherein the method further comprising:
allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mimeault teaches a traffic detection system that uses lidar detection channels to detect vehicles on a road and assign a unique identifier to each vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of allocating lidar channels to associated vehicles in the traffic road section and determining lidar channel information of the associated vehicles as taught by Mimeault, with a reasonable expectation of success, in order to track and record an updated position of each vehicle and an updated time at which the updated position is detected as specified in at least [0041] of Mimeault.
For claim 20, Caballero De Ita fails to specifically disclose wherein allocating the lidar channels to the associated vehicles in the traffic road section and determining the lidar channel information of the associated vehicles comprises:
acquiring a first entry sequence of the associated vehicles in the traffic road section;
dividing the associated vehicles into target vehicles satisfying an allocation condition and to-be-allocated vehicles based on the first entry sequence and a preset number of lidar channels and determining a second entry sequence of the to-be-allocated vehicles;
determining first lidar channel information of the target vehicles based on the first entry sequence;
determining second lidar channel information of a to-be-allocated vehicle of the to-be-allocated vehicles based on the second entry sequence when a target vehicle of the target vehicles exits the traffic road section; and
determining the first lidar channel information and the second lidar channel information as the lidar channel information.
However, Mimeault, in the same field of endeavor teaches wherein allocating the lidar channels to the associated vehicles in the traffic road section and determining the lidar channel information of the associated vehicles comprises:
acquiring a first entry sequence of the associated vehicles in the traffic road section (See at least [0118] of Mimeault – “… Detectors can also be installed at the entry … of a point-to-point enforcement system allowing the measurement of the average speed of a vehicle …”);
dividing the associated vehicles into target vehicles satisfying an allocation condition and to-be-allocated vehicles based on the first entry sequence and a preset number of lidar channels and determining a second entry sequence of the to-be-allocated vehicles (See at least [0058] of Mimeault – “FIG. 11 is a top view of the example installation of FIG. 10 on which four vehicle detections are visible in some of the 16 separate channels with simultaneous acquisition capability…”);
determining first lidar channel information of the target vehicles based on the first entry sequence (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected”);
determining second lidar channel information of a to-be-allocated vehicle of the to-be-allocated vehicles based on the second entry sequence when a target vehicle of the target vehicles exits the traffic road section (See at least [0041] – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected…”, [0058] – “FIG. 11 is a top view of the example installation of FIG. 10 on which four vehicle detections are visible in some of the 16 separate channels with simultaneous acquisition capability…” and [0118] of Mimeault – “… Detectors can also be installed… at the exit of a point-to-point enforcement system allowing the measurement of the average speed of a vehicle …”); and
determining the first lidar channel information and the second lidar channel information as the lidar channel information (See at least [0041] of Mimeault – “… According to another broad aspect of the present invention, there is provided a method for tracking and characterizing a plurality of vehicles simultaneously in a traffic control environment… acquiring an individual digital full-waveform LIDAR trace for each detection channel of the 3D optical receiver; using the individual digital full-waveform LIDAR trace and the emitted light waveform, detecting a presence of a plurality of vehicles, a position of at least part of each vehicle and a time at which the position is detected; assigning a unique identifier to each vehicle; repeating the steps of driving, receiving, acquiring and detecting, at a predetermined frequency; tracking and recording an updated position of each vehicle and an updated time at which the updated position is detected…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Mimeault teaches a traffic detection system that uses lidar detection channels to detect vehicles on a road, assign a unique identifier to each vehicle, track and record an updated position of each vehicle and an updated time at which the updated position is detected.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of determining first lidar channel information of the target vehicles based on the first entry sequence as taught by Mimeault, with a reasonable expectation of success, in order to track and record an updated position of each vehicle and an updated time at which the updated position is detected as specified in at least [0041] of Mimeault.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Caballero De Ita in view of Mintz and Mimeault, as applied to claim 5 above, and further in view of Dulberg et al. US 20200242922 A1 (“Dulberg”).
For claim 7, Caballero De Ita fails to specifically disclose wherein the method further comprising determining the associated vehicles, wherein the determining the associated vehicles comprises:
determining, by using emitted lidar radio waves, whether a to-be-determined vehicle in the traffic road section is provided with a lidar electronic tag; and
in response to determining that the to-be-determined vehicle is provided with the lidar electronic tag, acquiring a vehicle identifier of the to-be-determined vehicle and determining the to-be-determined vehicle as an associated vehicle of the associated vehicles.
However, Dulberg, in the same field of endeavor teaches wherein the method further comprising determining the associated vehicles (See at least [0261] of Dulberg – “… FIG. 27 is a diagrammatic illustration of an exemplary system 2700 for managing and prioritizing traffic... The system may comprise receivers… configured to detect a plurality of electromagnetic emissions originating from vehicles in a vicinity of the intersection…”), wherein the determining the associated vehicles comprises:
determining, by using emitted lidar radio waves, whether a to-be-determined vehicle in the traffic road section is provided with a lidar electronic tag (See at least [0261] of Dulberg – “... The system may comprise receivers… configured to detect a plurality of electromagnetic emissions originating from vehicles in a vicinity of the intersection… based on received electromagnetic emissions, a frequency of a vehicle beacon …lidar… a type of beacon may be determined…”); and
in response to determining that the to-be-determined vehicle is provided with the lidar electronic tag, acquiring a vehicle identifier of the to-be-determined vehicle and determining the to-be-determined vehicle as an associated vehicle of the associated vehicles (See at least [0261] of Dulberg – “... The system may comprise receivers … configured to detect a plurality of electromagnetic emissions originating from vehicles in a vicinity of the intersection…The at least one processor may determine substantially in real-time locations of a plurality of vehicles … based on the received signal information… may identify a vehicle type for each of the vehicles… the vehicle type may be identified based on a unique identifier transmitted together with or as part of the electromagnetic emissions… the vehicle type code may be transmitted via a dedicated transmitter for conveying information regarding the host vehicle on which the transmitter is deployed (e.g., vehicle type, vehicle size, plate number, serial number… the vehicle type information may be inferred based on characteristics of the received electromagnetic emissions… based on received electromagnetic emissions, a frequency of a vehicle beacon … lidar… a type of beacon may be determined, and from this information a vehicle type may be determined … based on a lookup table, access to a database, etc…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Dulberg teaches road traffic monitoring system that is capable of detecting electromagnetic emissions from a lidar vehicle beacon to determine the vehicle types approaching a road intersection.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of in response to determining that the to-be-determined vehicle is provided with the lidar electronic tag, acquiring a vehicle identifier of the to-be-determined vehicle and determining the to-be-determined vehicle as an associated vehicle of the associated vehicles as taught by Dulberg, with a reasonable expectation of success, in order to identify a vehicle type for each of the vehicles approaching an intersection as specified in at least [0261] of Dulberg.
For claim 14, Caballero De Ita fails to specifically disclose wherein the method further comprising determining the associated vehicles, wherein the determining the associated vehicles comprises:
determining, by using emitted lidar radio waves, whether a to-be-determined vehicle in the traffic road section is provided with a lidar electronic tag; and
in response to determining that the to-be-determined vehicle is provided with the lidar electronic tag, acquiring a vehicle identifier of the to-be-determined vehicle and determining the to-be-determined vehicle as an associated vehicle of the associated vehicles.
However, Dulberg, in the same field of endeavor teaches wherein the method further comprising determining the associated vehicles (See at least [0261] of Dulberg – “… FIG. 27 is a diagrammatic illustration of an exemplary system 2700 for managing and prioritizing traffic... The system may comprise receivers… configured to detect a plurality of electromagnetic emissions originating from vehicles in a vicinity of the intersection…”), wherein the determining the associated vehicles comprises:
determining, by using emitted lidar radio waves, whether a to-be-determined vehicle in the traffic road section is provided with a lidar electronic tag (See at least [0261] of Dulberg – “... The system may comprise receivers… configured to detect a plurality of electromagnetic emissions originating from vehicles in a vicinity of the intersection… based on received electromagnetic emissions, a frequency of a vehicle beacon …lidar… a type of beacon may be determined…”); and
in response to determining that the to-be-determined vehicle is provided with the lidar electronic tag, acquiring a vehicle identifier of the to-be-determined vehicle and determining the to-be-determined vehicle as an associated vehicle of the associated vehicles (See at least [0261] of Dulberg – “... The system may comprise receivers … configured to detect a plurality of electromagnetic emissions originating from vehicles in a vicinity of the intersection…The at least one processor may determine substantially in real-time locations of a plurality of vehicles … based on the received signal information… may identify a vehicle type for each of the vehicles… the vehicle type may be identified based on a unique identifier transmitted together with or as part of the electromagnetic emissions… the vehicle type code may be transmitted via a dedicated transmitter for conveying information regarding the host vehicle on which the transmitter is deployed (e.g., vehicle type, vehicle size, plate number, serial number… the vehicle type information may be inferred based on characteristics of the received electromagnetic emissions… based on received electromagnetic emissions, a frequency of a vehicle beacon … lidar… a type of beacon may be determined, and from this information a vehicle type may be determined … based on a lookup table, access to a database, etc…”). Thus, Caballero De Ita discloses a vehicle traffic system that acquires the spacing between vehicles, at an intersection, routes being travelled by each vehicle, and the status color of a traffic signal in order to predict a number of vehicles that can pass through the intersection while the light is green, while Dulberg teaches road traffic monitoring system that is capable of detecting electromagnetic emissions from a lidar vehicle beacon to determine the vehicle types approaching a road intersection.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, electronic device, and non-transitory computer-readable storage medium as disclosed in Caballero De Ita to include the feature of in response to determining that the to-be-determined vehicle is provided with the lidar electronic tag, acquiring a vehicle identifier of the to-be-determined vehicle and determining the to-be-determined vehicle as an associated vehicle of the associated vehicles as taught by Dulberg, with a reasonable expectation of success, in order to identify a vehicle type for each of the vehicles approaching an intersection as specified in at least [0261] of Dulberg.
Conclusion
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/M.J.H./Examiner, Art Unit 3668 /Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668