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 .
Response to Amendment
This action is in response to amendments and remarks filed on 08/13/2025. Claim(s) 8 and 18-19 have been amended. Claim(s) 2 and 10 have been cancelled. Claim(s) 21-25 have been added. Claim(s) 1, 3-9, and 11-25 are pending examination. Objections to the specification, not related to drawings, have been withdrawn in light of the instant amendments. Objections to claim 10 has been withdrawn in light of the instant amendment. This action is made final.
Regarding the objections to the drawings, the examiner is maintaining them for now as it appears that the drawings were not submitted with the amendment. The amended drawings are seen in the applicant’s arguments so it appears that they have been amended, however, a clean sheet of drawings separate from the applicant’s remarks were not submitted. In light of this the objections to the spec relating to the drawings and objects to the drawings themselves will be maintained.
Regarding new claim 22, it appears this claim is a redone version of claim 10 to overcome the prior claim objection. In doing this the applicant did not amend the claim to depend on new claim 21, rather claim 22 depends on now cancelled claim 2. The claim has been examined as if it depends on claim 21, and has been objected to due to this error.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 101 rejection to the independent claims, 1, 14, and 18, is improper. Applicant asserts that the claims are directed to manipulation of existing computer data structures to generate new computer data structures, which is patent eligible.
Applicant asserts that the 35 USC 103 rejection to the independent claims 1, 14, and 18, is improper. Applicant asserts that the prior art fails to teach all limitations as claimed.
Applicant’s arguments, see Page 16, "A. Rejections under 35 USC 101," subsection, "1.01;", filed 08/13/2025, with respect to claims 1 have been fully considered and are persuasive. The 35 USC 101 rejection of claims 1, 14, and 18 has been withdrawn.
Regarding applicant’s argument A, the examiner finds it persuasive. In particular the examiner finds the citation of MPEP 2106.4(a)(2)(III)(A), and Research Corp. Techs.; to read on the currently claimed subject matter. The claims are directed towards elements that manipulates computer data structures to create a modified computer data structure. The claims generate new computer data structures, i.e. global traffic map, using existing computer data structures, i.e. tracklets. Accordingly the 35 USC 101 rejections has been removed.
Applicant's arguments filed 08/11/2025 have been fully considered but they are not persuasive.
Regarding applicant’s argument B, the examiner respectfully disagrees. Applicant presents two arguments, 1.01 “Lane level” Traffic Maps, and 1.02 Associating “Local Tracklets” with “Existing Global Tracklets.”
With respect to argument 1.01, applicant asserts that the prior art fails to teach, “constructing a global traffic map at lane level.” In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., road vs. lane level maps) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The independent claims 1, 14, and 18 do not require more than one lane to exist. Therefore it would be appropriate to determine that as long as a mapping element can provide road level information it would also cover lane level information. There are single lane roads, and if a traffic map is created that covers roads, it would be obvious to cover lanes. Additionally, as Lee recites in [0078], “generating a vehicle map by collecting, for each vehicle, the received driving information for each of the plurality of the vehicles and the received driving information for a surrounding vehicle of the plurality of the vehicles, a step S230 of extracting a vehicle map to be transmitted to each of a plurality of vehicles so as to include driving information on surrounding vehicles on a route.” (Emphasis added). Lee teaches a vehicle map that would have information for all “surrounding vehicles.” This would presumably include vehicles in neighboring lanes. In light of this the examiner has determined that Lee would teach the claim limitation of constructing a lane level traffic map.
With respect to argument 1.02, applicant asserts that the prior art fails to teach, “associating a respective local tracklet with a corresponding existing global tracklet when there is a match between the respective local tracklet and a corresponding global tracklet.” Looking at cited paragraph [0086], Lee teaches all collected data has a “vehicle ID or a license plate number.” This ID is used to determine when data relates to a specific vehicle in the global tracking system, [0087] “the vehicle map generating unit 220 collects the driving information for each vehicle based on the identifier.” Additionally, [0070] recites “As an obtaining method, there is a method of recognizing a license plate number of the surrounding vehicle from the image including the surrounding vehicle captured by the surrounding vehicle detecting unit 130. However, if the license plate number is not recognized from the image, the identifier may not be assigned to the corresponding driving information and the corresponding surrounding vehicle.” (Emphasis added). This clearly states that the global identifier is used by these systems and if there is a match and/or mismatch the system does not associate the data between the detections. As [0102]-[0103] further teach the system performs a feature point matching to ensure that the same vehicle is being detected. When the feature points detected by the system match it is sure that the vehicle detected is the same and the global vehicle map can track it as such. This clearly teaches the limitations recited in claims 1, 14, and 18.
In light of the above explanations the examiner finds that the claims are still subject to rejection under 35 USC 103. Lee and the associated prior art clearly teach all claim limitations as written/amended. Please see the section below titled, “Claim Rejections – 35 USC 103,” for further detailed mapping and explanation.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Fig. 1 has a “V” but there is no explanation of what V is in the spec
Fig. 1 has “136” but there is no explanation of what 136 is in the spec
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
[0045] describes “vehicle speed, NV” in Fig 1, but it is absent from the figure
[0045] describes “battery SOC” in Fig 1, but it is absent from the figure
[0091] describes 400 in Fig. 4 but it is absent from the figure
Appropriate correction is required.
Claim Objections
Claim(s) 22 objected to because of the following informalities:
Claim 22 recites, “the method of claim 2,” however claim 2 has been cancelled. Claim 22 should recite, “the method of claim 21,”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 3-4, 6-9, 11, 13-15, 17, and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US PG Pub 2021/0364321) in view of Shiota (US PG Pub 2022/0082407) and Zou (NPL Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy).
Regarding claim 1, Lee teaches a method implemented in a cloud server for tracking multiple objects detected by vehicles, (Fig. 1 and [0038]-0039], and [0045] teach a “vehicle providing map server” which receives driving detection information from a plurality of vehicles. The driving detection information is defined in [0045] as information relating to the objects around a vehicle) the method comprising: receiving a plurality of local tracklets, each local tracklet received from a respective vehicle, ([0038]-[0039] teach the server receiving tracking information from each of the vehicles in the system, said tracking information corresponds to each of a plurality of objects tracked by a plurality of vehicles. [0070] teaches the system providing a unique identifier to each tracked object) each local tracklet comprising sensor data corresponding to a respective object detected at lane level within a respective time period; ([0037] teaches the vehicles collecting sensor data of their surroundings at a given time at the lane level)
associating a respective local tracklet with a corresponding existing global tracklet when there is a match between the respective local tracklet and a corresponding existing global tracklet; ([0101]-[0103] teaches matching vehicle information between detected image results to ensure that the same vehicle is being tracked between vehicles. As [0070] further teaches the use of a vehicle ID allows the system to further match vehicles by ensuring that the vehicle’s ID matches an existing ID, if it does not then the system can determine to not match a detected vehicle with an existing detection. [0086]-[0087] further this idea to teach what the ID used to track the vehicles can be.)
updating each respective global tracklet with (1) all received local tracklets associated with the respective global tracklet within a same time period, ([0084]-[0089] teach updating the globally tracked information with newly detected information in the form of updating the vehicle map based on new information gathered by the plurality of vehicles) (2)
constructing a global traffic map at lane level from the updated global tracklets. (Fig 5 and [0078] teach generating an updated map based on all the received vehicle detection information. The examiner finds that the use of lane level is equivalent to a road level map in this instance as a road with one lane having a map would be a lane level map)
Lee does not teach assigning a priority score to each received local tracklet; the priority score assigned to each associated local tracklet; a weighted average location of the associated local tracklets based on the priority score assigned to each associated local tracklet; and removing redundant global tracklets.
However, Shiota teaches “assigning a priority score to each received local tracklet;” ([0050]-[0052] teaches assigning a bias and priority weighting to the respective collected data) “the priority score assigned to each associated local tracklet;” ([0052] teaches tracking the priority level of the probe data detected to be integrated) “a weighted average location of the associated local tracklets based on the priority score assigned to each associated local tracklet;” ([0049]-[0052] teach generating a position based on the determined weighting of the data based on the weighted priority. The higher the priority the more weight given to the determination of a map location of an object based on the detected data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee with Shiota; and have a reasonable expectation of success. Both relate to object tracking by vehicle systems. As Shiota teaches in [0016]-[0018] vehicle sensors can vary between vehicles. This may induce some form of bias in the sensors and some sensors may provide better data to a server for making a map, than others. Ensuring that the system can make sure the best data is used allows for the optimal creation of a map.
Neither Lee nor Shiota teach removing redundant global tracklets.
However Zou teaches “removing redundant global tracklets.” (Page 8, 3.3.1. teaches removing data for tracked objects after it is no longer necessary; Page 9, paragraph 1, further teaches the explicit removal of redundant tracked data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Shiota with Zou; and have a reasonable expectation of success. All relate to the tracking of objects by vehicle systems. As Zou teaches on Page 9, paragraph 1, the removal of redundant data allows for more efficient data processing.
Regarding claim 3, Lee teaches the method claim 1, further comprising transmitting the global tracklet map to one or more vehicles. ([0039] teaches transmitting the vehicle map to the plurality of connected vehicles)
Regarding claim 4, Lee teaches the method of claim 1, further comprising transmitting a control signal to an autonomous vehicle based on the global traffic map to control a route of the autonomous vehicle at lane level. ([0040] and [0071] teach controlling the vehicle through the ADAS of the vehicle based on the detected vehicle map)
Regarding claim 6, Lee teaches the method of claim 1, wherein each local tracklet includes identifying data comprising location, velocity, yaw, yaw rate, and acceleration of the respective object. ([0037] teaches the vehicles collecting the location, traveling direction, attitude angle, speed information, and trajectory of a surrounding vehicle, this would be analogous to the information collected by the current application)
Regarding claim 7, Lee teaches the method of claim 1, wherein each object corresponds to an observed vehicle. ([0037] teaches the vehicles tracking other vehicles)
Regarding claim 8, the combination of Lee and Zou teaches the method of claim 1.
The combination of Lee and Zou does not teach storing each received local tracklet in a database.
However, Shiota teaches, “storing each received local tracklet in a database.” ([0033] teaches storing vehicle surrounding detections in a storage database)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Zou with Shiota; and have a reasonable expectation of success. All relate to object tracking by vehicle systems. As [0024] teaches the use of servers allows for map and environmental information to be stored in an online device. This can be sent to various vehicles and ensures they have updated information at all times.
Regarding claim 9 the combination of Lee and Shiota teaches the method of claim 1.
The combination of Lee and Shiota does not teach storing a history of each global tracklet, and constructing a trajectory of each global tracklet based on the history.
However, Zou teaches “storing a history of each global tracklet,” (Page 7, 3.2.3. teaches the storing of historical track data for the tracked objects) and “constructing a trajectory of each global tracklet based on the history.” (Page 7, 3.2.3. teaches finding a set of trajectories of the stored objects based on stored track data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Shiota with Zou; and have a reasonable expectation of success. All relate to the tracking of objects by vehicle systems. As Zou teaches in 3.3.4., storing the tracked elements allows the system to ensure that all objects are accounted for and in the case of a re-emergence of a previously tracked vehicle its past elements aren’t lost. This cuts down on the total number of tracklets used and would lead to a more efficient system.
Regarding claim 11, the combination of Lee and Zou teaches the method of claim 1.
The combination of Lee and Zou does not teach calculating a weighted average of the priority scores of the plurality of local tracklets that are from within the same time period.
However, Shiota teaches “calculating a weighted average of the priority scores of the plurality of local tracklets that are from within the same time period.” ([0052] teaches the system integrating the weighted data to determine the location average of the item based on the priority data.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Zou with Shiota; and have a reasonable expectation of success. All relate to object tracking by vehicle systems. As Shiota teaches in [0016]-[0018] vehicle sensors can vary between vehicles. This may induce some form of bias in the sensors and some sensors may provide better data to a server for making a map, than others. Ensuring that the system can make sure the best data is used allows for the optimal creation of a map.
Regarding claim 13, the combination of Lee and Zou teaches the method of claim 1.
The combination of Lee and Zou does not teach wherein the priority score is assigned to each received local tracklet based on at least one of (1) an accuracy of one or more sensors that detected the respective object or (2) a distance from the one or more sensors to the respective object.
However, Shiota teaches “wherein the priority score is assigned to each received local tracklet based on at least one of (1) an accuracy of one or more sensors that detected the respective object” ([0062] teaches that the system can weight the results of the sensors based on the resolution, i.e. sensor accuracy) or “(2) a distance from the one or more sensors to the respective object.” ([0055]-[0056] teach the system biased towards sensor data that allows for the closer sensor to be favored in by weighting the resulting data higher)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Zou with Shiota; and have a reasonable expectation of success. All relate to object tracking by vehicle systems. As Shiota teaches in [0016]-[0018] vehicle sensors can vary between vehicles. This may induce some form of bias in the sensors and some sensors may provide better data to a server for making a map, than others. Ensuring that the system can make sure the best data is used allows for the optimal creation of a map.
Regarding claim 14, Lee teaches a system implemented in an edge/cloud server for tracking multiple objects detected by vehicles, (Fig. 1 and [0038]-0039], and [0045] teach a “vehicle providing map server” which receives driving detection information from a plurality of vehicles. The driving detection information is defined in [0045] as information relating to the objects around a vehicle) the system comprising: a memory storing instructions; (Claim 15, teaches the usage of a non-transitory computer readable storage medium that can store instructions) and
one or more processors communicably coupled to the memory and configured to execute the instructions to: ([0080] and claim 15 teach a processor connected to the memory)
receive a plurality of local tracklets, each local tracklet received from a respective vehicle, ([0038]-[0039] teach the server receiving tracking information from each of the vehicles in the system, said tracking information corresponds to each of a plurality of objects tracked by a plurality of vehicles. [0070] teaches the system providing a unique identifier to each tracked object) each local tracklet comprising sensor data corresponding to a respective object detected at lane level within a respective time period using one or more sensors communicating with the respective vehicle; ([0037] teaches the vehicles collecting sensor data of their surroundings at a given time at the lane level
associate a respective local tracklet with a corresponding existing global tracklet when there is a match between the respective local tracklet and a corresponding existing global tracklet; ([0101]-[0103] teaches matching vehicle information between detected image results to ensure that the same vehicle is being tracked between vehicles. As [0070] further teaches the use of a vehicle ID allows the system to further match vehicles by ensuring that the vehicle’s ID matches an existing ID, if it does not then the system can determine to not match a detected vehicle with an existing detection. [0086]-[0087] further this idea to teach what the ID used to track the vehicles can be.)
update each respective global tracklet with (1) all received local tracklets associated with the respective global tracklet within a same time period, ([0084]-[0089] teach updating the globally tracked information with newly detected information in the form of updating the vehicle map based on new information gathered by the plurality of vehicles) (2)
constructing a global traffic map at lane level from the updated global tracklets. (Fig 5 and [0078] teach generating an updated map based on all the received vehicle detection information. The examiner finds that the use of lane level is equivalent to a road level map in this instance as a road with one lane having a map would be a lane level map)
Lee does not teach assign a priority score to each received local tracklet; the priority score assigned to each associated local tracklet, a weighted average location of the associated local tracklets based on the priority score assigned to each associated local tracklet; removing redundant global tracklets.
However, Shiota teaches “assign a priority score to each received local tracklet;” ([0050]-[0052] teaches assigning a bias and priority weighting to the respective collected data) “the priority score assigned to each associated local tracklet;” ([0052] teaches tracking the priority level of the probe data detected to be integrated) “a weighted average location of the associated local tracklets based on the priority score assigned to each associated local tracklet;” ([0049]-[0052] teach generating a position based on the determined weighting of the data based on the weighted priority. The higher the priority the more weight given to the determination of a map location of an object based on the detected data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee with Shiota; and have a reasonable expectation of success. Both relate to object tracking by vehicle systems. As Shiota teaches in [0016]-[0018] vehicle sensors can vary between vehicles. This may induce some form of bias in the sensors and some sensors may provide better data to a server for making a map, than others. Ensuring that the system can make sure the best data is used allows for the optimal creation of a map.
Neither Lee nor Shiota teach removing redundant global tracklets.
However Zou teaches “removing redundant global tracklets.” (Page 8, 3.3.1. teaches removing data for tracked objects after it is no longer necessary; Page 9, paragraph 1, further teaches the explicit removal of redundant tracked data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Shiota with Zou; and have a reasonable expectation of success. All relate to the tracking of objects by vehicle systems. As Zou teaches on Page 9, paragraph 1, the removal of redundant data allows for more efficient data processing.
Regarding claim 15, Lee teaches the system of claim 14, wherein each local tracklet has a local ID assigned by the respective vehicle, ([0070] teaches determining a respective local identifier for a vehicle) and a respective local tracklet is associated with a corresponding existing global tracklet when there is a match between (1) the local ID of the respective local tracklet and a global ID of the corresponding existing global tracklet, ([0086]-[0087] and Claim 3 teaches the matching of identifiers to determine if a vehicle is the same vehicle) or (2) a position/direction of an object identified by the respective local tracklet and a position/direction of an object identified by the corresponding existing global tracklet. ([0088]-[0089] and Claim 4 teaches the matching of vehicle identifiers if the vehicles match a position and direction detected by multiple vehicles)
Regarding claim 17, the combination of Lee and Zou teaches the system of claim 14.
The combination of Lee and Zou does not teach wherein the priority score is assigned to each received local tracklet based on at least one of (1) an accuracy of one or more sensors that detected the respective object or (2) a distance from the one or more sensors to the respective object.
However, Shiota teaches “wherein the priority score is assigned to each received local tracklet based on at least one of (1) an accuracy of one or more sensors that detected the respective object” ([0062] teaches that the system can weight the results of the sensors based on the resolution, i.e. sensor accuracy) or “(2) a distance from the one or more sensors to the respective object.” ([0055]-[0056] teach the system biased towards sensor data that allows for the closer sensor to be favored in by weighting the resulting data higher)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Zou with Shiota; and have a reasonable expectation of success. All relate to object tracking by vehicle systems. As Shiota teaches in [0016]-[0018] vehicle sensors can vary between vehicles. This may induce some form of bias in the sensors and some sensors may provide better data to a server for making a map, than others. Ensuring that the system can make sure the best data is used allows for the optimal creation of a map.
Regarding claim 21, Lee teaches the method of claim 1, wherein each local tracklet has a local ID assigned by the respective vehicle, ([0070] teaches determining a respective local identifier for a vehicle) and a respective local tracklet is associated with a corresponding existing global tracklet when there is a match between (1) the local ID of the respective local tracklet and a global ID of the corresponding existing global tracklet, ([0086]-[0087] and Claim 3 teaches the matching of identifiers to determine if a vehicle is the same vehicle. Also, [0101]-[0103] teaches matching vehicle information between detected image results to ensure that the same vehicle is being tracked between vehicles. As [0070] further teaches the use of a vehicle ID allows the system to further match vehicles by ensuring that the vehicle’s ID matches an existing ID, if it does not then the system can determine to not match a detected vehicle with an existing detection. [0086]-[0087] further this idea to teach what the ID used to track the vehicles can be.) or (2) a position/direction of an object identified by the respective local tracklet and a position/direction of an object identified by the corresponding existing global tracklet. ([0088]-[0089] and Claim 4 teaches the matching of vehicle identifiers if the vehicles match a position and direction detected by multiple vehicles. Also, [0101]-[0103] teaches matching vehicle information between detected image results to ensure that the same vehicle is being tracked between vehicles. As [0070] further teaches the use of a vehicle ID allows the system to further match vehicles by ensuring that the vehicle’s ID matches an existing ID, if it does not then the system can determine to not match a detected vehicle with an existing detection. [0086]-[0087] further this idea to teach what the ID used to track the vehicles can be.)
Regarding claim 22, Lee teaches the method of claim 2, further comprising determining the position/direction of an object identified by the respective local tracklet and the position/direction of an object identified by a corresponding existing global tracklet based on ([0118]-[0119] teaches using the Mahalanobis distance to determine if two tracked vehicles are in fact the same vehicle) and
The combination of Lee and Shiota does not teach Intersection Over Union (IOU) values of each pair of unassociated local and global tracklets and using a Linear Assignment Problem (LAP) Solver to determine the match.
However, Zou teaches “Intersection Over Union (IOU) values of each pair of unassociated local and global tracklets” (Page 5, paragraph 1; teaches determining and IOU for a detected object and a global representation of the object) and “using a Linear Assignment Problem (LAP) Solver to determine the match.” (Page 4, 3. Methods; teaches solving a linear assignment problem to determine a match between a new detection and a historical detection)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Shiota with Zou; and have a reasonable expectation of success. All relate to the tracking of objects by vehicle systems. As Zou teaches on Page 4, section 3, using a linear assignment problem solver allows the system to link multiple detections and ensure that the detections were the same vehicle. Lee uses the Mahalanobis distance to link vehicles, Zou adds to it by using the IOU with linear assignment problem solving to do some more accurately.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, Shiota, and Zou in view of Fowe (US Pat 10,417,906).
Regarding claim 5, the combination of Lee, Shiota, and Zou teaches the method of claim 1.
The combination of Lee, Shiota, and Zou does not teach transmitting a signal to a connected vehicle to update an online navigation system of the vehicle based on the global traffic map.
However, Fowe teaches “transmitting a signal to a connected vehicle to update an online navigation system of the vehicle based on the global traffic map.” (Column 12, lines 15-59; teach the system having a computing entity, that can push notifications to the vehicle in order for the vehicle to determine that it needs an update/the computing entity can push the updates to the map information. Column 8, lines 3-34; further teach this kind of update between an apparatus and a vehicle where the system is able to push notifications regarding needs to update a mapping device)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee, Shiota, and Zou with Fowe; and have a reasonable expectation of success. All relate to the determination of tracked objects and providing an update. As Fowe teaches in Column 8, the system of providing updates from a central server and pushing notifications for updates allows a user to ensure that their vehicle system is as up to date as possible. This ensures routing of the vehicle is optimal.
Claim(s) 12, 16, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, Shiota, and Zou in view of Radha (US PG Pub 2022/0405513).
Regarding claim 12, the combination of Lee, Shiota, and Zou teaches the method of claim 1.
The combination of Lee, Shiota, and Zou does not teach associating a respective local tracklet with a corresponding new global tracklet when there is no match.
However Radha teaches “associating a respective local tracklet with a corresponding new global tracklet when there is no match.” (Fig. 4, and [0037]-[0040] teach the system creating a new ID for an object in the world map for which there is no existing match. If the object has a prior ID it will be assigned that but if there is no match the system creates a new ID)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee, Shiota, and Zou with Radha; and have a reasonable expectation of success. All relate to the tracking of multiple objects by vehicle sensor systems. As Radha teaches in [0007] the use of identifiers associated with object data allows the system to reduce the total amount of data by making sure only necessary data is kept. This reduces the amount of total data needed.
Regarding claim 16, the combination of Lee, Shiota, and Zou teaches the system of claim 14.
The combination of Lee, Shiota, and Zou does not teach a respective local tracklet is associated with a corresponding new global tracklet when there is no match.
However, Radha teaches “a respective local tracklet is associated with a corresponding new global tracklet when there is no match.” (Fig. 4, and [0037]-[0040] teach the system creating a new ID for an object in the world map for which there is no existing match. If the object has a prior ID it will be assigned that but if there is no match the system creates a new ID)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee, Shiota, and Zou with Radha; and have a reasonable expectation of success. All relate to the tracking of multiple objects by vehicle sensor systems. As Radha teaches in [0007] the use of identifiers associated with object data allows the system to reduce the total amount of data by making sure only necessary data is kept. This reduces the amount of total data needed.
Regarding claim 18, Lee teaches a vehicle, comprising: a memory storing instructions; (Fig. 1 and [0038]-0039], and [0045] teach a “vehicle providing map server” which receives driving detection information from a plurality of vehicles. The driving detection information is defined in [0045] as information relating to the objects around a vehicle. The memory being on the server vs. vehicle would be considered obvious as it is merely making portable.) and
one or more processors communicably coupled to the memory and configured to execute the instructions to: ([0080] and claim 15 teach a processor connected to the memory)
detect an object within a respective time period using one or more sensors, at least one sensor configured to detect lane-level traffic data; ([0037] teaches the vehicles collecting sensor data of their surroundings at a given time at the lane level)
create a local tracklet comprising sensor data corresponding to the detected object; ([0037] teaches the system generating the sensed data of for the local vehicle surroundings, [0038]-[0039] teaches processing this data to be sent)
assign a local ID to the local tracklet; ([0070] teaches the system providing a unique identifier to each tracked object)
transmit the local tracklet to an edge/cloud server; ([0038] teaches transmitting the collected data to the server) and
receive a global traffic map from the edge/cloud server, the global traffic map constructed from a plurality of global tracklets, each global tracklet comprising associated local tracklets received from respective vehicles, ([0101]-[0103] teaches matching vehicle information between detected image results to ensure that the same vehicle is being tracked between vehicles. As [0070] further teaches the use of a vehicle ID allows the system to further match vehicles by ensuring that the vehicle’s ID matches an existing ID, if it does not then the system can determine to not match a detected vehicle with an existing detection. [0086]-[0087] further this idea to teach what the ID used to track the vehicles can be.)
each associated local tracklet corresponding to a same object detected at a same time period, ([0039] teaches transmitting the vehicle map to the plurality of connected vehicles; this map was made using the collected sensor data with associated data elements)
wherein: a respective local tracklet was associated with a corresponding existing global tracklet when there was a match between (1) the local ID of the respective local tracklet and a global ID of the corresponding existing global tracklet, ([0086]-[0087] and Claim 3 teaches the matching of identifiers to determine if a vehicle is the same vehicle. Also, [0101]-[0103] teaches matching vehicle information between detected image results to ensure that the same vehicle is being tracked between vehicles. As [0070] further teaches the use of a vehicle ID allows the system to further match vehicles by ensuring that the vehicle’s ID matches an existing ID, if it does not then the system can determine to not match a detected vehicle with an existing detection. [0086]-[0087] further this idea to teach what the ID used to track the vehicles can be.) or (2) a position/direction of an object identified by the respective local tracklet and a position/direction of an object identified by the corresponding existing global tracklet. ([0088]-[0089] and Claim 4 teaches the matching of vehicle identifiers if the vehicles match a position and direction detected by multiple vehicles. Also, [0101]-[0103] teaches matching vehicle information between detected image results to ensure that the same vehicle is being tracked between vehicles. As [0070] further teaches the use of a vehicle ID allows the system to further match vehicles by ensuring that the vehicle’s ID matches an existing ID, if it does not then the system can determine to not match a detected vehicle with an existing detection. [0086]-[0087] further this idea to teach what the ID used to track the vehicles can be.)
each respective global tracklet was updated with (1) all local tracklets associated with the respective global tracklet within the same time period, ([0084]-[0089] teach updating the globally tracked information with newly detected information in the form of updating the vehicle map based on new information gathered by the plurality of vehicles) (2)
(Fig 5 and [0078] teach generating an updated map based on all the received vehicle detection information. The examiner finds that the use of lane level is equivalent to a road level map in this instance as a road with one lane having a map would be a lane level map)
Lee does not teach the respective local tracklet was associated with a corresponding new global tracklet when there was no match, a priority score was assigned to each local tracklet based on (1) an accuracy of the one or more sensors that detected the respective object and (2) a distance from the one or more sensors to the respective object; the priority score assigned to each associated local tracklet, and a weighted average location of the associated local tracklets based on the priority score assigned to each associated local tracklet; and redundant global tracklets were removed.
However, Shiota teaches “assign a priority score to each received local tracklet;” ([0050]-[0052] teaches assigning a bias and priority weighting to the respective collected data) “the priority score assigned to each associated local tracklet;” ([0052] teaches tracking the priority level of the probe data detected to be integrated) “a weighted average location of the associated local tracklets based on the priority score assigned to each associated local tracklet;” ([0049]-[0052] teach generating a position based on the determined weighting of the data based on the weighted priority. The higher the priority the more weight given to the determination of a map location of an object based on the detected data) “a priority score was assigned to each local tracklet based on (1) an accuracy of the one or more sensors that detected the respective object” ([0062] teaches that the system can weight the results of the sensors based on the resolution, i.e. sensor accuracy) and “a distance from the one or more sensors to the respective object;” ([0055]-[0056] teach the system biased towards sensor data that allows for the closer sensor to be favored in by weighting the resulting data higher)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee with Shiota; and have a reasonable expectation of success. Both relate to object tracking by vehicle systems. As Shiota teaches in [0016]-[0018] vehicle sensors can vary between vehicles. This may induce some form of bias in the sensors and some sensors may provide better data to a server for making a map, than others. Ensuring that the system can make sure the best data is used allows for the optimal creation of a map.
Neither Lee nor Shiota teach the respective local tracklet was associated with a corresponding new global tracklet when there was no match and removing redundant global tracklets.
However Zou teaches “removing redundant global tracklets.” (Page 8, 3.3.1. teaches removing data for tracked objects after it is no longer necessary; Page 9, paragraph 1, further teaches the explicit removal of redundant tracked data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee and Shiota with Zou; and have a reasonable expectation of success. All relate to the tracking of objects by vehicle systems. As Zou teaches on Page 9, paragraph 1, the removal of redundant data allows for more efficient data processing.
The combination of Lee, Shiota, and Zou does not teach the respective local tracklet was associated with a corresponding new global tracklet when there was no match.
However, Radha teaches “the respective local tracklet was associated with a corresponding new global tracklet when there was no match” (Fig. 4, and [0037]-[0040] teach the system creating a new ID for an object in the world map for which there is no existing match. If the object has a prior ID, it will be assigned that but if there is no match the system creates a new ID)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee, Shiota, and Zou with Radha; and have a reasonable expectation of success. All relate to the tracking of multiple objects by vehicle sensor systems. As Radha teaches in [0007] the use of identifiers associated with object data allows the system to reduce the total amount of data by making sure only necessary data is kept. This reduces the amount of total data needed.
Regarding claim 19, Lee teaches the connected vehicle of claim 18, wherein the connected vehicle is an autonomous vehicle and the one or more processors execute further instructions to: receive a control signal based on the global traffic map, to control a route of the connected vehicle at a lane level. ([0040] and [0071] teach controlling the vehicle through the ADAS of the vehicle based on the detected vehicle map)
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, Shiota, Zou, and Radha, in view of Fowe.
Regarding claim 20, the combination of Lee, Shiota, Zou, and Radha teaches the connected vehicle of claim 18.
The combination of Lee, Shiota, Zou, and Radha does not teach receive a signal to update an online navigation system of the connected vehicle based on the global traffic map.
However, Fowe teaches “receive a signal to update an online navigation system of the connected vehicle based on the global traffic map.” (Column 12, lines 15-59; teach the system having a computing entity, that can push notifications to the vehicle in order for the vehicle to determine that it needs an update/the computing entity can push the updates to the map information. Column 8, lines 3-34; further teach this kind of update between an apparatus and a vehicle where the system is able to push notifications regarding needs to update a mapping device)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee, Shiota, Zou, and Radha with Fowe; and have a reasonable expectation of success. All relate to the determination of tracked objects and providing an update. As Fowe teaches in Column 8, the system of providing updates from a central server and pushing notifications for updates allows a user to ensure that their vehicle system is as up to date as possible. This ensures routing of the vehicle is optimal. The use of the vehicle receiving the signal vs. the server sending the signal would be a simple rearrangement of parts. It is well understood to reverse the order of sending vs. receiving a signal as the system of Fowe is equipped for to do both.
Claim(s) 23-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, Shiota, and Zou in view of Allsop (GB-2579021-A).
Regarding claim 23, the combination of Lee, Shiota, and Zou teaches the method of claim 1.
The combination of Lee, Shiota, and Zou does not teach the global traffic map at the lane level comprises lane-level visual indicators of traffic congestion or lane closure on multi-lane roads represented in the global traffic map; a first lane-level visual indicator indicates a first level of traffic congestion for a first lane of a first multi-lane road represented in the global traffic map; and a second lane-level visual indicator indicates a second level of traffic congestion for a second lane of the first multi-lane road represented in the global traffic map.
However, Allsop teaches “the global traffic map at the lane level comprises lane-level visual indicators of traffic congestion or lane closure on multi-lane roads represented in the global traffic map;” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.) “a first lane-level visual indicator indicates a first level of traffic congestion for a first lane of a first multi-lane road represented in the global traffic map;” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway, i.e. a first lane. Page 39, lines 25-31, and Page 40, lines 1-3; teach the association of a specific traffic condition with a specific lane. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.) and “a second lane-level visual indicator indicates a second level of traffic congestion for a second lane of the first multi-lane road represented in the global traffic map.” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway, i.e. a first lane. Page 39, lines 25-31, and Page 40, lines 1-3; teach the association of a specific traffic condition with a specific lane. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee, Shiota, and Zou with Allsop; and have a reasonable expectation of success. All relate to vehicle systems. As Allsop teaches on Page 11, the specific granularity that is respective to each lane, “enables certain lanes to be avoided before a traffic queue is reached.” This allows a user and/or autonomous vehicle to avoid a specific lane or get off the road early.
Regarding claim 24, the combination of Lee, Shiota, and Zou teaches the system of claim 14.
The combination of Lee, Shiota, and Zou does not teach the global traffic map at the lane level comprises lane-level visual indicators of traffic congestion or lane closure on multi-lane roads represented in the global traffic map; a first lane-level visual indicator indicates a first level of traffic congestion for a first lane of a first multi-lane road represented in the global traffic map; and a second lane-level visual indicator indicates a second level of traffic congestion for a second lane of the first multi-lane road represented in the global traffic map.
However, Allsop teaches “the global traffic map at the lane level comprises lane-level visual indicators of traffic congestion or lane closure on multi-lane roads represented in the global traffic map;” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.) “a first lane-level visual indicator indicates a first level of traffic congestion for a first lane of a first multi-lane road represented in the global traffic map;” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway, i.e. a first lane. Page 39, lines 25-31, and Page 40, lines 1-3; teach the association of a specific traffic condition with a specific lane. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.) and “a second lane-level visual indicator indicates a second level of traffic congestion for a second lane of the first multi-lane road represented in the global traffic map.” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway, i.e. a first lane. Page 39, lines 25-31, and Page 40, lines 1-3; teach the association of a specific traffic condition with a specific lane. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee, Shiota, and Zou with Allsop; and have a reasonable expectation of success. All relate to vehicle systems. As Allsop teaches on Page 11, the specific granularity that is respective to each lane, “enables certain lanes to be avoided before a traffic queue is reached.” This allows a user and/or autonomous vehicle to avoid a specific lane or get off the road early.
Claim(s) 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, Shiota, Zou, and Radha, in view of Allsop (GB-2579021-A).
Regarding claim 25, the combination of Lee, Shiota, Zou, and Radha teaches the vehicle of claim 18.
The combination of Lee, Shiota, Zou, and Radha does not teach the global traffic map at the lane level comprises lane-level visual indicators of traffic congestion or lane closure on multi-lane roads represented in the global traffic map; a first lane-level visual indicator indicates a first level of traffic congestion for a first lane of a first multi-lane road represented in the global traffic map; and a second lane-level visual indicator indicates a second level of traffic congestion for a second lane of the first multi-lane road represented in the global traffic map.
However, Allsop teaches “the global traffic map at the lane level comprises lane-level visual indicators of traffic congestion or lane closure on multi-lane roads represented in the global traffic map;” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.) “a first lane-level visual indicator indicates a first level of traffic congestion for a first lane of a first multi-lane road represented in the global traffic map;” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway, i.e. a first lane. Page 39, lines 25-31, and Page 40, lines 1-3; teach the association of a specific traffic condition with a specific lane. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.) and “a second lane-level visual indicator indicates a second level of traffic congestion for a second lane of the first multi-lane road represented in the global traffic map.” (Page 11, lines 5-25; teaches the use of “dynamic traffic data,” which is understood to have specific data about an area’s traffic. It has a “sufficient resolution…to enable the traffic condition to be associated with a specific lane of a highway, i.e. a first lane. Page 39, lines 25-31, and Page 40, lines 1-3; teach the association of a specific traffic condition with a specific lane. Page 14, lines 16-26 further teach the use of a display screen to show outputs including map data/traffic data, this includes by each lane and the indications of closure/traffic queues/etc.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Lee, Shiota, Zou, and Radha with Allsop; and have a reasonable expectation of success. All relate to vehicle systems. As Allsop teaches on Page 11, the specific granularity that is respective to each lane, “enables certain lanes to be avoided before a traffic queue is reached.” This allows a user and/or autonomous vehicle to avoid a specific lane or get off the road early.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wray (US PG Pub 2025/0214609) teaches lane segment-level traversal information is obtained. The lane segment-level traversal information is converted into probabilities for a state transition function. A policy is derived from a decision model using the state transition function. The policy directs vehicle movement of a vehicle between neighboring lane segments based on a cost function integrating a user preference with respect to at least two objectives and a slack time for alternative routes. The slack time indicates an allowable deviation in travel time relative to the user preference. A destination is received. The vehicle is then autonomously controlled on a route to the destination using the policy for lane transitions based on current lane positions.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665