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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on April 18, 2024 and July 3, 2025 is considered by the examiner.
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 and 15 are rejected under 35 U.S.C. 101 because the claim invention is directed to an abstract idea with significantly more.
Regarding claim 1,
101 Analysis – Step 1
Claim 1 is directed toward a traffic information analysis method for a building a traffic digital twin via an apparatus which involves the processes of collecting traffic data for one or more intersections, setting a time section for each intersection by using the traffic information, matching a vehicle group for a respective intersection to a time section for each of the intersections, and estimating movement of the group of vehicles (a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, 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 claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A traffic information analysis method for building a traffic digital twin, the traffic information analysis method being performed by a traffic information analysis apparatus, the traffic information analysis method comprising:
collecting traffic information for one or more intersections;
setting a time section for each of the intersections by using the traffic information;
matching at least one vehicle group for the respective intersections to the time section for each of the intersections;
and estimating movement of the vehicle group.
The examiner submits that the foregoing bolded limitation constitutes a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “collecting”, “setting”, “matching”, and “estimating” in the context of this claim encompasses a person (operator) looking at information collected and forming a simple judgment. Accordingly, the claim recites at one abstract idea.
101 Analysis - Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into the practical application. As noted in the 2019 PEG, 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.” In the present case, therefore since there are no additional limitations beyond the above-noted abstract idea above, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent claim 1 does 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, there are no additional limitations that amount to significantly more.
Dependent claims 2-3 and 5-14 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claim are directed toward additional aspects of the judicial exception and/or well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application.
Claim 2 uses the limitation of “collecting the traffic information comprises obtaining sectional average speed information, sectional average travel time information, or a combination thereof by using public data from an external database”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 3 uses the limitation of “collecting the traffic information comprises obtaining vehicle identification information, vehicle type information, time attribute information, vehicle state information, or a combination thereof by using vehicle recognition devices installed at or near the intersections”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 5 uses the limitations of “setting a first time section corresponding to a first intersection” and “setting a second time section corresponding to a second intersection connected to the first intersection through a road”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 6 uses the limitation of “setting the time section for each of the intersections comprises setting the first start time and first end time of the first time section based on an intersection signal cycle of the first intersection”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 7 uses the limitation of “setting the time section for each of the intersections comprises setting the second start time and second end time of the second time section based on sectional average travel time information between the first and second intersections using public data in an external database”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 8 uses the limitations of “setting the time section for each of the intersections comprises: selecting a first vehicle and a second vehicle from among the vehicles belonging to the vehicle group based on their sequence by using the vehicle recognition devices”, “setting the second start time of the second time section based on an arrival time of the first vehicle”, and “setting the second end time of the second time section based on an arrival time of the second vehicle”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 9 uses the limitations of “analyzing movement of the vehicle group obtained in a detection area of the intersections” and “simulating movement of the vehicle group in an non-detection area outside the detection area”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 10 uses the limitation of “outputting information about the vehicle group, collected in real time, as a digital twin by using information extracted from public data in an external database, information obtained through vehicle recognition devices, or a combination thereof”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 11 uses the limitation of “estimating movement for each vehicle type, movement for each direction of movement, movement for each vehicle, or a combination thereof by mapping the vehicle identification information, the vehicle type information, the time attribute information, the vehicle state information, or a combination thereof to the vehicle group using the vehicle recognition devices”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 12 uses the limitation of “adjusting the time attribute information based on the vehicle state information or adjusting the vehicle state information based on the time attribute information”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 13 uses the limitation of “estimating traffic information corresponding to an entry path, an exit path, or a combination thereof through at least one sub-road branched off from the road based on traffic information of the first intersection and traffic information of the second intersection”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 14 uses the limitation of “estimating the movement of the vehicle group comprises correcting the movement of the vehicle group by using traffic information obtained through a vehicle recognition device installed on the road between the first and second intersections”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Regarding claim 15,
101 Analysis – Step 1
Claim 15 is directed toward a traffic information analysis apparatus in which the controller sets the time section for each intersection through the use of extracted intersection signal cycle information via a public database or a vehicle recognition device and outputs information about the vehicle group as a digital twin through the use of the extracted public data (a machine). Therefore, claim 15 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, 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 claim 15 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 15 recites:
The traffic information analysis apparatus of claim 15, wherein the controller:
sets the time section for each of the intersections by using intersection signal cycle information of an intersection signal device, information extracted from public data in an external database, information obtained through a vehicle recognition device, or a combination thereof;
and outputs information about the vehicle group, collected in real time, as a digital twin by using the information extracted from the public data in the external database, the information obtained through the vehicle recognition device, and/or the combination thereof.
The examiner submits that the foregoing bolded limitation constitutes a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “sets”, and “outputs” in the context of this claim encompasses a person (operator) looking at information collected and forming a simple judgment. Accordingly, the claim recites at one abstract idea.
101 Analysis - Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into the practical application. As noted in the 2019 PEG, 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.” In the present case, therefore since there are no additional limitations beyond the above-noted abstract idea above, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent claim 15 does 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, there are no additional limitations that amount to significantly more.
Dependent claim 16 does not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claim are directed toward additional aspects of the judicial exception and/or well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application.
Claim 16 uses the limitations of “sets the time section for each of the intersections by using intersection signal cycle information of an intersection signal device, information extracted from public data in an external database, information obtained through a vehicle recognition device, or a combination thereof”, and “outputs information about the vehicle group, collected in real time, as a digital twin by using the information extracted from the public data in the external database, the information obtained through the vehicle recognition device, and/or the combination thereof”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over D'Andre (U.S. Patent Application Publication No. 20230097373) in view of Lee, et al. (U.S. Patent No. 12437508).
Regarding claim 1, D’Andre teaches: A traffic information analysis method for building a traffic digital twin, the traffic information analysis method being performed by a traffic information analysis apparatus, the traffic information analysis method comprising: collecting traffic information for one or more intersections; (Paragraph [0014]: "…the digital twin includes multiple intersections [multiple intersections]." ; Step (210), Paragraph [0073]: "At operation (210), traffic data may be obtained.")
setting a time section for each of the intersections by using the traffic information; (Paragraph [0140]: "…an intersection view that presents various specific metrics for an intersection, which may indicate various information about the intersection's health, performance, and so on [each of intersection]. By way of illustration, this second example dashboard display may depict arrival on green over a period of time (which may be adjustable) versus arrival on red over a period of time (which may be adjustable), average speed over a period of time (which may be adjustable), arrival phase, and so on [period of time as it relates to traffic information].")
and estimating movement of the vehicle group (Fig. 2, Method (200), Paragraph [0072]: "FIG. 2 depicts a flow chart illustrating a first example method (200) for traffic [...] prediction [estimation process]. This method may be performed by the system of FIG. 1." ; Paragraph [0483]: "Example uses include, but are not limited to, [...] predicting [...] simulating traffic, using structured data and/or metrics to simulate how changes to traffic (and/or traffic signals, traffic conditions, and so on) will change traffic patterns [estimating movement of group of vehicles]").
D’Andre does not teach matching at least one vehicle group for the respective intersections to the time section for each of the intersections.
In a similar field of endeavor (moving object information analysis using digital twin platform), Lee, et al. teaches: matching at least one vehicle group for the respective intersections to the time section for each of the intersections; (Col. 10, lines 11-15: "The multimodal sensor data analysis module (300) combines the objectification data and traffic information matching the objectification data, and generates moving object information through situation analysis [matching vehicle data]." ; Step (S800), Col. 16, line 65 to Col. 17, lines 1-3: "Step (S800) is a situation analysis step. This step is a step of classifying basic actions of the object at a specific point in time on the basis of the objectification data, classifying complex actions of the object through situation analysis based on the basic actions of the object, and generating object information [matching based on time section].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify D’Andre to include the teaching of Lee, et al. based on a reasonable expectation of success and motivation to improve the process of analyzing collected moving vehicle data using a digital twin platform (Lee, et al. Col. 2, lines 48-54).
Regarding claim 2, D' Andre and Lee, et al. remain as applied to claim 1, and in a further embodiment, teach: The traffic information analysis method of claim 1, wherein collecting the traffic information comprises obtaining sectional average speed information, sectional average travel time information, or a combination thereof by using public data from an external database (Lee, et al. Col. 9, lines 1-12: "when traffic signal information collected from a public database [...] in accordance with the present disclosure is used, other traffic-related laws (for example, lanes and speed limit) [sectional average speed information] [...] The urban digital twin platform system (10) may collect data and information from various external sensors, systems, and databases [external database].").
Regarding claim 3, D' Andre and Lee, et al. remain as applied to claim 1, and in a further embodiment, teach: The traffic information analysis method of claim 1, wherein collecting the traffic information comprises obtaining vehicle identification information, vehicle type information, time attribute information, vehicle state information, or a combination thereof by using vehicle recognition devices installed at or near the intersections The traffic information analysis method of claim 1, wherein collecting the traffic information comprises obtaining vehicle identification information, vehicle type information, time attribute information, vehicle state information, or a combination thereof by using vehicle recognition devices installed at or near the intersections (D’Andre Paragraph [0058]: “Traffic data may be obtained, such as via the gateway from one or more traffic monitoring devices (104) [recognition devices installed at or near intersection] […]The one or more data processing pipeline devices (101) may perform object detection and classification using the data. For example, objects may be detected and classified as cars, trucks buses, pedestrians, light vehicles, heavy vehicles, non-motor vehicles, and so on [vehicle type information]."
Regarding claim 4, D' Andre and Lee, et al. remain as applied to claim 3, and in a further embodiment, teach: The traffic information analysis method of claim 3, wherein: the vehicle recognition devices recognize the vehicle identification information and also obtain the vehicle type information by using image information acquired by capturing a license plate of each vehicle, image information acquired by capturing an appearance of the vehicle, wireless information obtained through communication with a terminal located in the vehicle, or a combination thereof (D’Andre Paragraph [0058]: “Traffic data may be obtained, such as via the gateway from one or more traffic monitoring devices (104) [recognition devices installed at or near intersection] (such as one or more intersection and/or other still image and/or video cameras, Light Detection and Ranging sensors ( or "LiDAR") [image information acquired by capturing appearing of vehicle]")
the vehicle identification information is unique identification information recognized for the vehicle; (D’Andre Paragraph [0058]: "Objects may be assigned individual identifiers, identifiers by type, and so on [unique ID information recognized for vehicle].")
the time attribute information is attribute information related to time including movement speed of the vehicle, travel time of the vehicle, or a combination thereof; (D’Andre Paragraph [0058]: "For example, the metrics may involve […] average speed [time metric of movement speed]")
and the vehicle state information is movement state information including whether the vehicle is traveling or stopped, a direction of movement of the vehicle, a lane in which the vehicle is located, a queue to which the vehicle belongs, a length of the queue, a number of vehicles in the queue, or a combination thereof (D’Andre Paragraph [0058]: "For example, the metrics may involve […] queue length [vehicle state information - length of the queue]").
Regarding claim 5, D' Andre and Lee, et al. remain as applied to claim 1, and in a further embodiment, teach: The traffic information analysis method of claim 1, wherein setting the time section for each of the intersections comprises: setting a first time section corresponding to a first intersection; (D’Andre Paragraph [0140]: "…an intersection view that presents various specific metrics for an intersection, which may indicate various information about the intersection's health, performance, and so on [specified intersection]. By way of illustration, this second example dashboard display may depict arrival on green over a period of time (which may be adjustable) versus arrival on red over a period of time (which may be adjustable), average speed over a period of time (which may be adjustable), arrival phase, and so on [setting a first time section].")
and setting a second time section corresponding to a second intersection connected to the first intersection through a road; (D’Andre Paragraph [0473]: "…structured data from multiple frames may be used to determine a status of the object (such as an approach associated with the object, how an object moved through an intersection, an approach an object used to enter an intersection, the approach an object used to exit an intersection, and so on), a time or number of frames since the object was last detected (and/or since first detected and so on) [time related moving from one intersection to another and roads between intersections; determination of measurement of movement in specific regions out of frame in particular intersection]")
wherein the first time section has a first start time and a first end time; (D’Andre Paragraph [0567]: "…For example, a last stop time may be calculated based on a last time stamp that an object stopped at an approach. By way of another example, a last start time may be calculated based on a last time stamp that an object moved into the intersection at a particular approach [defined start and end time for selected intersection]." ; Paragraph [0598]: "In a number of examples, the digital twin may include multiple intersections [can be start or end time section for either intersection].")
and wherein the second time section has a second start time and a second end time (D’Andre Paragraph [0567]: "…For example, a last stop time may be calculated based on a last time stamp that an object stopped at an approach. By way of another example, a last start time may be calculated based on a last time stamp that an object moved into the intersection at a particular approach [defined start and end time for selected intersection]." ; D’Andre Paragraph [0598]: "In a number of examples, the digital twin may include multiple intersections [can be start or end time section for either intersection].").
Regarding claim 6, D' Andre and Lee, et al. remain as applied to claim 5, and in a further embodiment, teach: The traffic information analysis method of claim 5, wherein setting the time section for each of the intersections comprises setting the first start time and first end time of the first time section based on an intersection signal cycle of the first intersection (D’Andre Paragraph [0367]: "Binary, "True" or "False." Whether vehicle is forced to go through more than 1 light cycle (2 red lights or more). Determination may involve subtracting outflow from one intersection versus inflow from a feeder between light cycles. If the vehicle arrives at the intersection and is present for more than 1 cycle change [determining time interval relative to intersection signal cycle]." ; D'Andre Paragraph [0369]: "Total time a vehicle spends in intersection. May be measured in seconds or other units. Determination may use age of object and fps to convert to time units [determining start time and end time]").
Regarding claim 7, D’Andre and Lee, et al. remain as applied to claim 5, and in a further embodiment, D’Andre teaches: The traffic information analysis method of claim 5, wherein setting the time section for each of the intersections comprises setting the second start time and second end time of the second time section based on sectional average travel time information between the first and second intersections (Paragraph [0473]: "Such difference in position between frames, along with times respectively associated with the frames (such as from one or more time-stamps) may be used to calculate one or more metrics associated with the speed of the object (such as an average speed of the object during the video feed (such as in miles per hour and/or other units), cumulative speed, and so on) [determining average travel time through speed analysis].").
D’Andre does not teach using public data in an external database.
In a similar field of endeavor (moving object information analysis using digital twin platform), Lee, et al. teaches: using public data in an external database (Col. 9, lines 1-12: "when traffic signal information collected from a public database [...] The urban digital twin platform system (10) may collect data and information from various external sensors, systems, and databases [external database].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify D’Andre to include the teaching of Lee, et al. based on a reasonable expectation of success and motivation to improve the process of analyzing collected moving vehicle data using a digital twin platform (Lee, et al. Col. 2, lines 48-54).
Regarding claim 8, D' Andre and Lee, et al. remain as applied to claim 5, and in a further embodiment, teach: The traffic information analysis method of claim 5, wherein setting the time section for each of the intersections comprises: selecting a first vehicle and a second vehicle from among the vehicles belonging to the vehicle group based on their sequence by using the vehicle recognition devices; (D’Andre Paragraph [0481]: "For example, structured data and/or metrics related to one or more vehicles and/or other objects may be stored in one or more vehicle tables (such as the example vehicle table of FIG. 9) [selecting first and second vehicles from among other vehicles], structured data and/or metrics related to one or more intersections may be stored in one or more intersection tables (such as the example intersection table of FIG. 8), structured data and/or metrics related to one or more approaches may be stored in one or more approach tables (such as the example approach table of FIG. 10) [vehicle group sequence by using vehicle recognition devices]")
setting the second start time of the second time section based on an arrival time of the first vehicle; (D’Andre Paragraphs [0528]: "The analysis device (2301) and/or one or more other devices may perform object detection and classification using the data. […] For example, the metrics may involve […] light status on arrival, arrival phase [start time based on arrival]")
and setting the second end time of the second time section based on an arrival time of the second vehicle (D’Andre Paragraph [0567]: "Other structured data and/or metrics associated with approaches may be calculated. For example, a last stop time may be calculated based on a last time stamp that an object stopped at an approach. By way of another example, a last start time may be calculated based on a last time stamp that an object moved into the intersection at a particular approach [second end time based on arrival of second vehicle].").
Regarding claim 9, D' Andre and Lee, et al. remain as applied to claim 1, and in a further embodiment, teach: The traffic information analysis method of claim 1, wherein estimating the movement of the vehicle group comprises: analyzing movement of the vehicle group obtained in a detection area of the intersections; (D’Andre Step (230), Paragraph [0073]: "At operation (230), structured data may be determined and/or output [analyzing data]" ; D’Andre Paragraph [0058]: "The one or more data processing pipeline devices (101) may calculate one or more metrics using the structured data. For example, the metrics may involve [...] movement status [analyzing vehicle movement]")
and simulating movement of the vehicle group in an non-detection area outside the detection area (D’Andre Paragraph [0013]: "…the at least one service simulates traffic via the at least one dashboard using the processed data. In some implementations of such examples, the at least one service simulates how a change affects traffic patterns. In various implementations of such examples, the change alters at least one of a simulation of the traffic, a traffic signal, or a traffic condition [simulating movement of vehicles to other regions].").
Regarding claim 10, D' Andre and Lee, et al. remain as applied to claim 1, and in a further embodiment, teach: The traffic information analysis method of claim 1, wherein estimating the movement of the vehicle group comprises outputting information about the vehicle group, collected in real time, as a digital twin by using information extracted from public data in an external database, information obtained through vehicle recognition devices, or a combination thereof (D’Andre Step (230), Paragraph [0073]: "At operation (230), structured data may be determined and/or output [step outputting data]." ; D’Andre Paragraph [0077]: "…a data pipeline may begin with a raw, real-time video feed from an intersection camera that is in use by a city department of transportation [real-time data about vehicles; recognition device]." ; D’Andre Paragraph [0079]: "After this detection and classification layer has run, the pipeline may output structured data, such as the position, trajectory, count, and type of motorized and non-motorized road users [outputted digital twin information].").
Regarding claim 11, D' Andre and Lee, et al. remain as applied to claim 3, and in a further embodiment, teach: The traffic information analysis method of claim 3, wherein estimating the movement of the vehicle group comprises estimating movement for each vehicle type, movement for each direction of movement, movement for each vehicle, or a combination thereof by mapping the vehicle identification information, the vehicle type information, the time attribute information, the vehicle state information, or a combination thereof to the vehicle group using the vehicle recognition devices (D’Andre Paragraph [0088]: "FIGS. 3A and 3B depict a first example data pipeline structure that may be used for traffic monitoring, analysis, and prediction [estimation movement procedure]. […] As further illustrated, the camera stage [vehicle recognition devices] is where video footage may be collected at intersections (this stage may be hosted by cities or other population centers), the security layer stage and the object detection and classification stage may run one or more algorithms for object detection and classification, the video pipe stage may stream data from one or more algorithm processes run by the object detection and classification stage and store it [mapping vehicle ID information to vehicle group]").
Regarding claim 12, D' Andre and Lee, et al. remain as applied to claim 3, and in a further embodiment, teach: The traffic information analysis method of claim 3, wherein estimating the movement of the vehicle group comprises adjusting the time attribute information based on the vehicle state information or adjusting the vehicle state information based on the time attribute information (D’Andre Paragraph [0140]: "…an intersection view that presents various specific metrics for an intersection, which may indicate various information about the intersection's health, performance, and so on. By way of illustration, this second example dashboard display may depict arrival on green over a period of time (which may be adjustable) versus arrival on red over a period of time (which may be adjustable), average speed over a period of time (which may be adjustable), arrival phase, and so on [adjusting vehicle state information based on time attribute information]").
Regarding claim 13, D' Andre and Lee, et al. remain as applied to claim 5, and in a further embodiment, teach: The traffic information analysis method of claim 5, wherein estimating the movement of the vehicle group comprises estimating traffic information corresponding to an entry path, an exit path, or a combination thereof through at least one sub-road branched off from the road based on traffic information of the first intersection and traffic information of the second intersection (D’Andre Paragraph [0473]: "By way of another example, structured data from multiple frames may be used to determine a status of the object (such as an approach associated with the object, how an object moved through an intersection, an approach an object used to enter an intersection, the approach an object used to exit an intersection, and so on) [combination of entry and exit paths with regards to intersection], a time or number of frames since the object was last detected (and/or since first detected and so on) [determination if the vehicle has left the road since last approaching intersection], whether or not the object is moving, and so on." ; D’Andre Paragraph [0598]: "In a number of examples, the digital twin may include multiple intersections. In various such examples, the at least one dashboard may include indicators selectable to display information for each of the multiple intersections [multiple intersections].").
Regarding claim 14, D' Andre and Lee, et al. remain as applied to claim 5, and in a further embodiment, teach: The traffic information analysis method of claim 5, wherein estimating the movement of the vehicle group comprises correcting the movement of the vehicle group by using traffic information obtained through a vehicle recognition device installed on the road between the first and second intersections (D’Andre Paragraph [0473]: "Such difference in position between frames may also be used to calculate various metrics about the travel of the object (such as the direction of travel between frames, how the object left an intersection, whether or not the object made a right on red, and so on) [corrections of vehicle movement estimation based on sensors calculating movement between intersections]." ; D’Andre Paragraph [0543]: "In various implementations, other data sources beyond data extracted from intersection video feeds may be used. This may include weather, Internet of Things sensors, LiDAR sensors, fleet vehicles, city suppliers (e.g., traffic controller), navigation app data, connected vehicle data, and so on [can integrate multiple sources of data in various locations outside of intersection]").
Regarding claim 15, D’Andre teaches: A traffic information analysis apparatus for building a traffic digital twin, the traffic information analysis apparatus comprising: (Paragraph [0012]: "…a system for traffic monitoring, analysis [traffic information analysis], and prediction includes a memory allocation configured to store at least one executable asset and a processor allocation configured to access the memory allocation and execute the at least one executable asset to instantiate at least one service. The at least one service constructs a digital twin of an area of interest, retrieves structured data determined from traffic data for the area of interest [traffic digital twin]")
a communication interface configured to collect traffic information for one or more intersections; (Paragraph [0014]: "…the digital twin includes multiple intersections [multiple intersections]." ; Paragraph [0069]: "The system (100) may instead communicate with the traffic monitoring device (104) [communication interface collecting data for intersections]")
and a controller configured to set a time section for each of the intersections by using the traffic information, (Paragraph [0140]: "…an intersection view that presents various specific metrics for an intersection, which may indicate various information about the intersection's health, performance, and so on [each of intersection]. By way of illustration, this second example dashboard display may depict arrival on green over a period of time (which may be adjustable) versus arrival on red over a period of time (which may be adjustable), average speed over a period of time (which may be adjustable), arrival phase, and so on [period of time as it relates to traffic information].")
and estimate movement of the vehicle group (Paragraph [0483]: "Example uses include, but are not limited to, [...] predicting [...] simulating traffic, using structured data and/or metrics to simulate how changes to traffic (and/or traffic signals, traffic conditions, and so on) will change traffic patterns [estimating movement of group of vehicles]").
D’Andre does not teach match at least one vehicle group for the respective intersections to the time section for each of the intersections.
In a similar field of endeavor (moving object information analysis using digital twin platform), Lee, et al. teaches: match at least one vehicle group for the respective intersections to the time section for each of the intersections (Col. 10, lines 11-15: "The multimodal sensor data analysis module (300) combines the objectification data and traffic information matching the objectification data, and generates moving object information through situation analysis [matching vehicle data]." ; Col. 16, line 65 to Col. 17, lines 1-3: "...This step is a step of classifying basic actions of the object at a specific point in time on the basis of the objectification data, classifying complex actions of the object through situation analysis based on the basic actions of the object, and generating object information [matching based on time section].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify D’Andre to include the teaching of Lee, et al. based on a reasonable expectation of success and motivation to improve the process of analyzing collected moving vehicle data using a digital twin platform (Lee, et al. Col. 2, lines 48-54).
Regarding claim 16, D' Andre and Lee, et al. remain as applied to claim 15, and in a further embodiment, teach: The traffic information analysis apparatus of claim 15, wherein the controller: sets the time section for each of the intersections by using intersection signal cycle information of an intersection signal device, information extracted from public data in an external database, information obtained through a vehicle recognition device, or a combination thereof; (D’Andre Paragraph [0367]: "Binary, "True" or "False." Whether vehicle is forced to go through more than 1 light cycle (2 red lights or more). Determination may involve subtracting outflow from one intersection versus inflow from a feeder between light cycles. If the vehicle arrives at the intersection and is present for more than 1 cycle change [determining time interval relative to intersection signal cycle using intersection signal device]." ; D’Andre Paragraph [0369]: "Total time a vehicle spends in intersection. May be measured in seconds or other units. Determination may use age of object and fps to convert to time units [determining start time and end time]" ; D’Andre Paragraph [0529]: "The analysis device (2301) may be any kind of electronic device. [...] The analysis device (2301) may include one or more processors (2303) and/or other processing units and/or controllers [controller]" ; D’Andre Paragraph [0598]: "…the digital twin may include multiple intersections. In various such examples, the at least one dashboard may include indicators selectable to display information for each of the multiple intersections [setting for each intersection].")
and outputs information about the vehicle group, collected in real time, as a digital twin by using the information extracted from the public data in the external database, the information obtained through the vehicle recognition device, and/or the combination thereof (D’Andre Paragraph [0077]: "…a data pipeline may begin with a raw, real-time video feed from an intersection camera that is in use by a city department of transportation [real-time data about vehicles; recognition device]." ; Paragraph [0079]: "After this detection and classification layer has run, the pipeline may output structured data, such as the position, trajectory, count, and type of motorized and non-motorized road users [outputted digital twin information].").
Regarding claim 17, D' Andre and Lee, et al. remain as applied to claim 1, and in a further embodiment, teach: A non-transitory computer-readable storage medium having stored thereon a program that, when executed by a processor, causes the processor to execute the traffic information analysis method set forth in claim 1 (D’Andre Paragraph [0601]: "The described disclosure may be provided as a computer program product, or software, that may include a non-transitory machine-readable medium having stored thereon instructions [non-transitory computer readable storage medium], which may be used to program a computer system (or other electronic devices) to perform a process [program executed by processor to conduct analysis] according to the present disclosure.").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lin, et al. (U.S. Patent No. 11043122) teaches embodiments for managing a traffic flow of vehicles through an intersection in which a roadside device near a roadway intersection retrieves twin data (e.g. sensor data) describing one or more digital twins of a vehicle located near the intersection and in which this information is subsequently applied to the vehicle’s Advanced Driver Assistance System (ADAS) for the purpose of achieving proper traffic flow.
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/TORRENCE S MARUNDA II/ Examiner, Art Unit 3663
/ANGELA Y ORTIZ/ Supervisory Patent Examiner, Art Unit 3663