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 . This office action is in response to an Amendment filed on 6/12/26. Claims 1-4, 6-8, 10- 16, 18-20 and 22-24 are pending.
Response to Amendments
Amendments filed on 6/12/26 are under consideration. Claims 1-4, 6, 10-16 and 19 are amended. Claim 5, 17, and 21 are cancelled. Claims 22-24 are newly added.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krivokon et al. (US 2020/0135030 Al) in view of Lei et al. (CN 115731709 A) and in further view of Thibaux (US 11,749,000 B2) and in further view of Qibin (CN 118097588 A)
Regarding Claim 1 Krivokon teaches A method (Pg. 15 – [0002] – “Aspects of the disclosure provide a method of training a model for determining states of lanes of interest” ) comprising: data associated with a plurality of drives of one or more first machines within an environment; (Pg. 15 – [0002] – “The method includes receiving, by one or more server computing devices, image data including an image and an associated label identifying at least one traffic light” & see Also Pg. 15 – [0005] – “In one example, identifying the image with the lane of interest includes projecting a road segment corresponding to a lane in which the vehicle is currently driving into the image.” & See Also Pg.19 – [0045] – “For instance, one or more computing devices 410 may include one or more server computing devices that are capable of communicating with computing device 110 of vehicle 100 or a similar computing device of vehicle 100A, 100B as well as computing devices 420, 430, 440 via the network 460. For example, vehicles 100, lOOA, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices 410” (equates data associated with a plurality of drives of one or more first machines within an environment; As the quote shows a detection of a traffic feature being a stop light and the second quote shows the data taken during a drive, and the last quote shows a plurality of first machines able to complete drives and take and send data.)) and sending the map data to one or more second machines for performing one or more control operations within the environment. (Pg. 15 - [0005] - "controlling the vehicle in the autonomous driving mode based on pre-stored map information " & See Also Pg.19 – [0045] – “For instance, one or more computing devices 410 may include one or more server computing devices that are capable of communicating with computing device 110 of vehicle 100 or a similar computing device of vehicle 100A, 100B as well as computing devices 420, 430, 440 via the network 460. For example, vehicles 100, lOOA, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices 410” (equates to and sending the map data to one or more second machines for performing one or more control operations within the environment. As thew first quote shows how the ma data is received to be used for control operations of the vehicle and the second quote shows the plurality of machines being used to send and receive said data.) )
Yet Krivoken fails to teach determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane; determining, based at least on the data, stopping locations of the one or more first machines within the environment during the plurality of drives; determining that a threshold number of the stopping locations is associated with a candidate line of the plurality of candidate lines; determining, based at least on the threshold number of the stopping locations being associated with the candidate line of the plurality of candidate lines that the candidate line includes the wait line for the traffic feature; updating the map data to indicate that the candidate line includes the wait line for the traffic feature.
Lei teaches determining, based at least on the data, stopping locations of the one or more first machines within the environment during the plurality of drives; (Pg. 3 - " Specifically, the initial vehicle is a calibrated vehicle capable of determining the number of stops of the vehicle in a signal period, that is, when the vehicle passes through the stop line without stopping the vehicle at the intersection, the vehicle stops zero, or when the vehicle stops once and passes through the stop line, the number of stops of the vehicle is one, for the type of vehicle capable of defining the number of stops of the vehicle, the vehicle attribute can be defined as the initial vehicle, and the number of stops of other vehicles can be calculated according to the initial vehicle "& See Also Pg. 5 – “Traffic is a fundamental industry and a service industry of national economy and social development, and road monitoring of urban traffic is more and more important. In order to ensure the traffic order of a city, related departments often need to analyze the traffic condition of each road section according to the number of stops (or the stop rate), delay and traffic capacity of vehicles passing through a signal lamp intersection, and then adjust the traffic facilities of the city to better ensure the traffic order” (equates to determining, based at least on the data, stopping locations of the one or more first machines within the environment during the plurality of drives; as the first and second quote show a plurality of vehicles and plurality of drives that vehicles go upon wherein the intersection contains stopping location and the vehicle that stop are cataloged in the number of stops.))) determining that a threshold number of the stopping locations is associated with a candidate line (Pg. 14 – “If inequality (17) is true, then signal period q releases vehicle p for the phase time 0 And (p) 0 + 1) the number of stops passing the stop line is 1, the process proceeds to step S402, and it is determined that the vehicle (p) is in the phase time of the signal period q being released 0 + 1) the number of vehicle stops past the stop line; if the inequality (17) does not hold, the process proceeds to step S403, where it is determined that the vehicle p is in the signal period q release phase time 0 The number of times the vehicle passes the stop line;” (equates to determining that a threshold number of the stopping locations is associated with a candidate line as the quote shows the number of stops the plurality of vehicles are making at the stop line associated with the intersection wherein the threshold number of number of stops is used to determine the state of the vehicle while in the intersection.) ) the threshold number of the stopping locations (Pg. 2 – “Pg. 14 – “If inequality (17) is true, then signal period q releases vehicle p for the phase time 0 And (p) 0 + 1) the number of stops passing the stop line is 1, the process proceeds to step S402, and it is determined that the vehicle (p) is in the phase time of the signal period q being released 0 + 1) the number of vehicle stops past the stop line; if the inequality (17) does not hold, the process proceeds to step S403, where it is determined that the vehicle p is in the signal period q release phase time 0 The number of times the vehicle passes the stop line;”)
Both fail to teach determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane; of the plurality of candidate lines; determining, based at least on stopping locations being associated with the candidate line of the plurality of candidate lines that the candidate line includes the wait line for the traffic feature; updating the map data to indicate that the candidate line includes the wait line for the traffic feature.
Thibaux teaches updating the map data to indicate that the candidate line includes the wait line for the traffic feature. (Pg. 15 – [0005] – “Regardless of whether it is a stop line or a no stop line situation, once the stop location is determined it is added to 15 the map (e.g., as a layer in a road graph).)
Yet all fail to teach determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane; of the plurality of candidate lines; determining, based at least on stopping locations being associated with the candidate line of the plurality of candidate lines that the candidate line includes the wait line for the traffic feature;
Qibin teaches determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane; (Pg. 1 – “and determining a road stop line from the at least one candidate recognition line based on map data and lane line data recognized during the driving of the vehicle, wherein the lane line data comprises lane lines indicating the driving direction of the vehicle on a driving road, and the lane line data does not comprise the candidate recognition line.” & See Also Fig. 3 (equates to determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane as the quote shows the determination of a stop line based on at least one candidate line which can be more than one and thus a plurality of candidate recognition lines may be determined wherein the quote specifically shows the candidate recognition line not including lane lines and thus are crossing a lane. )) of the plurality of candidate lines; (Pg. 2 – “In this embodiment, as shown in fig. 2, the line segment on the ground in the front of the vehicle driving direction is directly identified, and due to the diversity of the actual environment, there may be a plurality of stop line identification results”) determining, based at least on stopping locations being associated with the candidate line of the plurality of candidate lines that the candidate line includes the wait line for the traffic feature; (Pg. 2 – “According to the stop line identifying method provided in the first aspect of the embodiment of the present application, the determining a road stop line from the at least one candidate identifying line based on the map data and the lane line data identified during the driving of the vehicle includes: acquiring an intersection area of the next intersection in the vehicle driving direction based on the map data; determining the identification line to be selected located in the intersection area from the at least one identification line to be selected; and determining one of the identification lines to be selected from the identification lines to be selected located in the intersection area as the road stop line based on the lane line data.” & See Also Pg. 3 – “In this embodiment, first, one road stop line is selected from the respective candidate recognition lines by the map data and the lane line data.” (equates to determining, based at least on the one or more locations being associated with a candidate line of the plurality of candidate lines that the candidate line includes the wait line for the traffic feature; as the first quote shows the one or more location being the intersection area wherein the second quote shows the candidate recognition lines being acquired based on map data relating to the intersection detected. )) It would have been an advantageous addition to the method disclosed by Krivokon and Ding to include determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane; determining, based at least on the one or more locations being associated with a candidate line of the plurality of candidate lines that the candidate line includes the wait line for the traffic feature; as these limitations allow for a plurality of potential stop lines to be included in search for the stop line in which the vehicle needs to remain ensuring the vehicle has more potential stop lines considered in the map data.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane; determining, based at least on the one or more locations being associated with a candidate line of the plurality of candidate lines that the candidate line includes the wait line for the traffic feature; as this allows for more potential candidate lines to be generated ensuring that the scene encompassed by the map is best represented for an accurate stop line to be estimated.
Regarding Claim 6 Krivokon-Lei-Thibaux-Qibin teaches (Krivokon discloses the following limitations:) The method of claim 1, further comprising: determining, based at least on the map data, that the candidate lines and the traffic feature are associated with the lane, (Pg. 13 – [1210] – “Receive image data including an image and an associated label identifying at least one traffic light, a state of the at least one traffic light, and a lane controlled by the at least one traffic light” &see also Pg. 16 – [0021] – “Typically, such information is stored in a vehicle's map information (e.g. a roadgraph identifying lanes) and can be retrieved as needed to identify traffic lights and lane states.” (equates to determining, based at least on the map data, that the candidate lines and the traffic feature are associated with the lane as the first quote shows the traffic feature being associated with the lane controlled by it and the second quote showing map data being used to make this lane identification possible.)) wherein the determining the plurality of candidate lines associated with the traffic feature is based at least on the plurality of candidate lines and the traffic feature being associated with the lane. (Pg. 13 – [1210] – “Receive image data including an image and an associated label identifying at least one traffic light, a state of the at least one traffic light, and a lane controlled by the at least one traffic light” &see also Pg. 16 – [0021] – “Typically, such information is stored in a vehicle's map information (e.g. a roadgraph identifying lanes) and can be retrieved as needed to identify traffic lights and lane states.”)
Yet Krivokon-Lei fails to teach plurality of candidate lines.
Qibin teaches plurality of candidate lines (Pg. 3 – “In this embodiment, first, one road stop line is selected from the respective candidate recognition lines by the map data and the lane line data.” (equates to plurality of candidate lines as the art previously mapped shows the candidate recognition lines being separate from lane lines and the plurality of stop lines are determined. )) It would have been an advantageous addition to the method disclosed by Krivokon -Ding to include plurality of candidate lines as this allows for a plurality of potential stop lines to be included in search for the stop line in which the vehicle needs to remain ensuring the vehicle has more potential stop lines considered in the map data.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include plurality of candidate lines as this allows for more potential candidate lines to be generated ensuring that the scene encompassed by the map is best represented for an accurate stop line to be estimated.
Regarding Claim 8 Krivokon-Lei-Thibuax-Qibin (Krivokon discloses the following limitations: ) The method of claim 1, wherein: teaches the traffic feature comprises one or more of: a traffic signal; a stop sign; a crosswalk light; a crosswalk sign; a train crossing light; a train crossing sign; a yield sign; or a stop light; (Pg. 17 – [0035] – “In this example, the map information 200 includes information identifying the shape, location, and other characteristics of lane lines 210, 212, 214, traffic lights 220, 222, stop line 224, crosswalk 230, sidewalks 240, stop signs 250, 252.” (equates to wherein: teaches the traffic feature comprises one or more of: a traffic signal; a stop sign; a crosswalk light; a crosswalk sign; a train crossing light; a train crossing sign; a yield sign; or a stop light as the quote shows a stop sign being including in graph data.)) and the candidate line comprise one or more of: a stop line; a crosswalk line; an intersection entrance line; an intersection exit line; a train crossing line; or a yield line. (Pg. 17 – [0035] – “In this example, the map information 200 includes information identifying the shape, location, and other characteristics of lane lines 210, 212, 214, traffic lights 220, 222, stop line 224, crosswalk 230, sidewalks 240, stop signs 250, 252.”)
Yet Krivokon fails to teach plurality of candidate lines
Qibin teaches plurality of candidate lines (Pg. 3 – “In this embodiment, first, one road stop line is selected from the respective candidate recognition lines by the map data and the lane line data.” (equates to plurality of candidate lines as the art previously mapped shows the candidate recognition lines being separate from lane lines and the plurality of stop lines are determined. )) It would have been an advantageous addition to the method disclosed by Krivokon -Ding to include plurality of candidate lines as this allows for a plurality of potential stop lines to be included in search for the stop line in which the vehicle needs to remain ensuring the vehicle has more potential stop lines considered in the map data.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include plurality of candidate lines as this allows for more potential candidate lines to be generated ensuring that the scene encompassed by the map is best represented for an accurate stop line to be estimated.
Claim(s) 10 & 12 & 13 & 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krivokon and in further view of Thibaux (CN 118097588 A)
Regarding Claim 10 Krivokon teaches A system (Pg. 15 – [0001] – “important component of an autonomous vehicle is the perception system, which allows the vehicle to perceive and interpret its surroundings using sensors such as cameras, radar, LIDAR sensors, and other similar devices” ) obtain data representative of a plurality of drives associated with the traffic feature; (Pg. 15 – [0005] – “In one example, identifying the image with the lane of interest includes projecting a road segment corresponding to a lane in which the vehicle is currently driving into the image.” & See Also Pg. 15 – [0004] – “identifying, by the one or more processors, a lane of interest using, by the one or more processors, the image and the lane of interest as input into the model to output a state of the lane of interest according to a state of a traffic light in the image; ) determine, based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; (Pg. 20 – [0055] – “FIG. 6 is an example camera image 600 captured by a camera of perception system 172 of vehicle 100 as the vehicle approaches intersection 202 of the map information. In this example, traffic light 620 and part of an intersection 630 and a lane 640 are captured in camera image 600. This camera image may be processed and used to generate initial training data for the model.” & See Also Pg. 17 – [0035] – “Each feature may be stored as graph data and may be associated with information such as a geographic location and whether or not it is linked to other related features, for example, a stop sign may be linked to a road and an intersection,” (equates to determine, based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; as the quote shows the locations of the vehicles approaching the intersection containing stop line information being tracked and stored.) ) and update the map data to include a representation of the wait line and an indication that the wait line associated with the traffic feature. (Pg. 15 – [0005] – “controlling the vehicle in the autonomous driving mode based on pre-stored map information, and the image is input into the model when the vehicle is located in an area that is not up to date in the map information” & See Also Pg. 15 – [0003] – “in another example, the method also includes, training the model to identify stop lines in images relevant to the lane of interest. In this example, the training further includes using a label identifying a location of a stop line in the image.” (equates to and update the map data to include a representation of the wait line and an indication that the wait line associated with the traffic feature as the first quote shows the map data being updated with images based on the map data not having the image data being captured wherein the second quote shows how the image data is understood as a stop line and thus this image would be included for the map updating if not already pre-stored within the system.)) wherein the map data, as updated, is used by one or more second machines in performing one or more control operations. (Pg. 15 - [0005] - "controlling the vehicle in the autonomous driving mode based on pre-stored map information " )
Yet Krivokon fails to teach comprising: one or more processors to: determine, based at least on map data, that an environment does not include a line marking for stopping a traffic feature; determine, based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that associated with the traffic feature and update the map data to indicate that the wait line associated with the traffic feature
Thibaux teaches comprising: one or more processors to: determine, based at least on map data, that an environment does not include a line marking for stopping a traffic feature; (Pg. 1 – Abstract – “While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes” (equates to comprising: one or more processors to: determine, based at least on map data, that an environment does not include a line marking for stopping a traffic feature; as the quote shows the stop line not being included in the map data compared to the real time painted line )) determine, based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that associated with the traffic feature (Pg. 1 – Abstract – “While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes” (equates to determine, based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that associated with the traffic feature as the quote shows the absence of a stop line being detected and the neural network in conjunction with the taken in data from the vehicle being used to determine the location of the stop line. )) updating the map data to indicate that the candidate line includes the wait line for the traffic feature. (Pg. 15 – [0005] – “Regardless of whether it is a stop line or a no stop line situation, once the stop location is determined it is added to 15 the map (e.g., as a layer in a road graph).) It would have been an advantageous addition to the system disclosed by Kirvoken to include comprising: one or more processors to: determine, based at least on map data, that an environment does not include a line marking for stopping a traffic feature; determine, based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that associated with the traffic feature and update the map data to indicate that the wait line associated with the traffic feature as these limitations allow for a stop line to be included if it is not already being included in the physical environment or map data as well as when the vehicle is approaching to make that determination as to where the stop line would lie.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include comprising: one or more processors to: determine, based at least on map data, that an environment does not include a line marking for stopping a traffic feature; determine, based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that associated with the traffic feature and update the map data to indicate that the wait line associated with the traffic feature as this limitation allows for wait lines not already included in the environment to be sensed and included into the map data based on a lack of physical world existence.
Regarding Claim 12 Krivokon-Thibaux teaches (Krivokon disclose the following limitations: ) The system of claim 11, as previously mapped above.
Yet Krivokon fails to teach determine that a majority of the stopping locations are associated with a location within the environment, wherein the determination of the wait line associated with the traffic feature comprises determining the wait line as being projected at the location within the environment.
Thibaux teaches determine that a majority of the stopping locations are associated with a location within the environment (Pg. 36 – Fig. 14 – “Evaluating a set of training data to produce a classification channel to identify whether pixels in a subset of the data are located within a selected distance of a stop line of a roadway” &See Also Pg. 38 – [col. 1 – lines 37-44 - ] – “a set of training data to produce a classification channel to identify whether pixels in a subset of the sensor data are located within a selected distance of a stop line of a roadway; evaluating, by the one or more processors, the set of training data to produce a localization channel to determine where a closest point is on the stop line for each pixel in the subset;” (equates to determine that a majority of the stopping locations are associated with a location within the environment as the quote shows the training data being evaluated wherein a majority of the data being within the given threshold constitutes as a stopping location and thus allows the art to identify the location as a stopping location)) wherein the determination of the wait line associated with the traffic feature comprises determining the wait line as being projected at the location within the environment. (Pg. 46 – [Col. 17 – lines 22-26] – “the system only keeps stop line points within a region of interest around the lane endpoint (e.g., a mapped stop location associated with a stop sign or other applicable signage) ….As shown, it is wide along the lane direction to cover the distribution. Then, as shown in view 1230 of FIG. 12D, the remaining stop line points are projected to the lane tangent, and their weighted median will be the predicted stop location” (equates to wherein the determination of the wait line associated with the traffic feature comprises determining the wait line as being projected at the location within the environment as the quote shows the traffic feature, stop sign, being associated with the projection of the stop line.) ) It would have been an advantageous addition to the system disclosed to include determine that a majority of the stopping locations are associated with a location within the environment, wherein the determination of the wait line associated with the traffic feature comprises determining the wait line as being projected at the location within the environment as these limitations allow for large dataset to be used to determine a stop line location and ensure that erroneous data is filtered out from contributed to the generation of th estop line location.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include determine that a majority of the stopping locations are associated with a location within the environment, wherein the determination of the wait line associated with the traffic feature comprises determining the wait line as being projected at the location within the environment as this limitation allows for a large data amount to be used to confirm the existence of the stop line and then to project a stop line unto the map if its not already in the road.
Regarding Claim 13 Krivoken-Thibaux teaches (Krivokon discloses the following limitations: ) The system of claim 10, wherein the one or more processors are further to: determine, that at least a portion of the stopping locations, are located proximate to the wait line within the environment (Pg. 20 – [0061] – “This projection, for instance either the line or the area, and in some instances the distance and direction from the traffic light 620 in the image, may then be used to generate a label 1030 for instance that colors or otherwise identifies the location of the lane of interest. As such, label 1030 identifies a lane of interest, here lane 640 and/or road segment 218.” & See Also Pg. 20 – [0056] – “predetermined distance from and oriented towards intersections of the map information that are controlled by traffic lights,” & See Also Pg.19 – [0045] – “For instance, one or more computing devices 410 may include one or more server computing devices that are capable of communicating with computing device 110 of vehicle 100 or a similar computing device of vehicle 100A, 100B as well as computing devices 420, 430, 440 via the network 460. For example, vehicles 100, lOOA, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices 410” (equates to determine, that at least a portion of the stopping locations, are located proximate to the wait line within the environment as the image taken is shown to be a distance away from a traffic feature in which a stop line is determined to be proximate to the traffic feature. And the last quote shows the plurality of vehicle all sending an receiving the data from drives In which at least a portion of the data I used to determine the stopping location )) ) wherein the determination of the wait line that is associated with the traffic feature comprises determining based on the at least the portion of the stopping locations being located proximate to the wait line, the wait line that is associated with the traffic feature (Pg. 20 – [0061] – “This projection, for instance either the line or the area, and in some instances the distance and direction from the traffic light 620 in the image, may then be used to generate a label 1030 for instance that colors or otherwise identifies the location of the lane of interest. As such, label 1030 identifies a lane of interest, here lane 640 and/or road segment 218.” & See Also Pg. 20 – [0056] – “predetermined distance from and oriented towards intersections of the map information that are controlled by traffic lights,”)
Regarding Claim 18 Krivokon-Thibaux teaches (Krivokon discloses the following limitations:) The system of claim 10, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative Al operations; a system for performing operations using one or more large language models (LLMs); a system for performing one or more conversational Al operations; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. ((Pg. 21 – [0071] – “The vehicle may then be controlled in an autonomous driving mode” & See Also Pg. 18 – [0040] – “A control system software module of the computing devices 110 may be configured to control movement of the vehicle, for instance by controlling braking, acceleration and steering of the vehicle, in order to follow a trajectory” ) (equates to The system of claim 10, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative Al operations; a system for performing operations using one or more large language models (LLMs); a system for performing one or more conversational Al operations; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources as the quotes shows the existence of a control system for autonomous control of the vehicle. ))
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Krivokon-Thibaux and in further view of Lei (CN 115731709 A)
Regarding Claim 11 Krivokon-Thibaux teaches The system of claim 10, as previously mapped above.
Yet Krivoken fails to teach determine that a threshold number of the stopping locations are associated with a location within the environment, wherein the determination of the wait line associated with the traffic feature comprises determining the wait line as being projected at the location within the environment.
Thibaux teaches wherein the determination of the wait line associated with the traffic feature comprises determining the wait line as being projected at the location within the environment. (Pg. 46 – [Col. 17 – lines 22-26] – “the system only keeps stop line points within a region of interest around the lane endpoint (e.g., a mapped stop location associated with a stop sign or other applicable signage) ….As shown, it is wide along the lane direction to cover the distribution. Then, as shown in view 1230 of FIG. 12D, the remaining stop line points are projected to the lane tangent, and their weighted median will be the predicted stop location” (equates to wherein the determination of the wait line associated with the traffic feature comprises determining the wait line as being projected at the location within the environment as the quote shows the traffic feature, stop sign, being associated with the projection of the stop line.) )
Yet all fail to teach determine that a threshold number of the stopping locations are associated with a location within the environment,
Lei teaches determine that a threshold number of the stopping locations are associated with a location within the environment, ((Pg. 14 – “If inequality (17) is true, then signal period q releases vehicle p for the phase time 0 And (p) 0 + 1) the number of stops passing the stop line is 1, the process proceeds to step S402, and it is determined that the vehicle (p) is in the phase time of the signal period q being released 0 + 1) the number of vehicle stops past the stop line; if the inequality (17) does not hold, the process proceeds to step S403, where it is determined that the vehicle p is in the signal period q release phase time 0 The number of times the vehicle passes the stop line;” (equates to determine that a threshold number of the stopping locations are associated with a location within the environment, as the quote shows the number of stops the plurality of vehicles are making at the stop line associated with the intersection wherein the threshold number of number of stops is used to determine the state of the vehicle while in the intersection.) )) It would have been an advantageous addition to the system disclosed by _ to include determine that a threshold number of the stopping locations are associated with a location within the environment as tis allows for a count of vehicles passing through the region to be tabulated and used for the determination of a stop line within a given area.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include determine that a threshold number of the stopping locations are associated with a location within the environment as this allows for a total number of vehicles passing through the region to be used for the determination of the stop line within the region.
Claim(s) 19 and 20 and 23 is rejected under 35 U.S.C. 103 as being unpatentable over Krivokon-Qibin-Lei as mapped above and in view of Thibaux et al. (US 11,749,000 B2).
Regarding Claim 19 Krivoken teaches One or more processors comprising: processing circuitry to: obtain data associated with one or more drives of one or more first machines within an environment; (Pg. 15 – [0002] – “The method includes receiving, by one or more server computing devices, image data including an image and an associated label identifying at least one traffic light” & see Also Pg. 15 – [0005] – “In one example, identifying the image with the lane of interest includes projecting a road segment corresponding to a lane in which the vehicle is currently driving into the image.” & See Also Pg.19 – [0045] – “For instance, one or more computing devices 410 may include one or more server computing devices that are capable of communicating with computing device 110 of vehicle 100 or a similar computing device of vehicle 100A, 100B as well as computing devices 420, 430, 440 via the network 460. For example, vehicles 100, lOOA, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices 410” (equates data associated with a plurality of drives of one or more first machines within an environment; As the quote shows a detection of a traffic feature being a stop light and the second quote shows the data taken during a drive, and the last quote shows a plurality of first machines able to complete drives and take and send data.)) is used by one or more second machines in performing one or more control operations (Pg. 15 - [0005] - "controlling the vehicle in the autonomous driving mode based on pre-stored map information " & See Also Pg.19 – [0045] – “For instance, one or more computing devices 410 may include one or more server computing devices that are capable of communicating with computing device 110 of vehicle 100 or a similar computing device of vehicle 100A, 100B as well as computing devices 420, 430, 440 via the network 460. For example, vehicles 100, lOOA, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices 410” (equates to is used by one or more second machines in performing one or more control operations As the first quote shows how the ma data is received to be used for control operations of the vehicle and the second quote shows the plurality of machines being used to send and receive said data.) )
Yet Krivoken fails to teach determine, based at least on map data, a plurality of candidate lines that potentially correspond to a wait line for a traffic feature located within the environment, the plurality of candidate lines at least partially crossing a lane; determine, based at least on the data, one or more stopping locations of the one or more first machines within the environment during the one or more drives; determine that a majority of the one or more stopping locations is associated with a candidate line of the plurality of candidate lines; determine, based at least on the majority of the one or more stopping locations being associated with the candidate line of the plurality of candidate lines, that the candidate line includes the wait line for the traffic feature; and update the map data to indicate that the candidate line includes the wait line for the traffic feature, wherein the map data, as updated,
Qibin teaches determine, based at least on map data, a plurality of candidate lines that potentially correspond to a wait line for a traffic feature located within the environment, the plurality of candidate lines at least partially crossing a lane; (Pg. 1 – “and determining a road stop line from the at least one candidate recognition line based on map data and lane line data recognized during the driving of the vehicle, wherein the lane line data comprises lane lines indicating the driving direction of the vehicle on a driving road, and the lane line data does not comprise the candidate recognition line.” & See Also Fig. 3 (equates to determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane as the quote shows the determination of a stop line based on at least one candidate line which can be more than one and thus a plurality of candidate recognition lines may be determined wherein the quote specifically shows the candidate recognition line not including lane lines and thus are crossing a lane. )) that the candidate line includes the wait line for the traffic feature; (Pg. 1 – “and determining a road stop line from the at least one candidate recognition line based on map data and lane line data recognized during the driving of the vehicle, wherein the lane line data comprises lane lines indicating the driving direction of the vehicle on a driving road, and the lane line data does not comprise the candidate recognition line.”) of the plurality of candidate lines; (Pg. 2 – “there may be a plurality of stop line identification results,”) of the plurality of candidate lines, that the candidate (Pg. 2 – “there may be a plurality of stop line identification results,”) that the candidate line includes the wait line for the traffic feature; ((Pg. 1 – “and determining a road stop line from the at least one candidate recognition line based on map data and lane line data recognized during the driving of the vehicle, wherein the lane line data comprises lane lines indicating the driving direction of the vehicle on a driving road, and the lane line data does not comprise the candidate recognition line.” & See Also Pg. 2 – “in particular to a stop line identification method, a vehicle, equipment and a program product.”)
Yet Krivoken-Qibin fails to teach determine, based at least on the data, one or more stopping locations of the one or more first machines within the environment during the one or more drives; determine that a majority of the one or more stopping locations is associated with a candidate line determine, based at least on the majority of the one or more stopping locations being associated with the candidate line, and update the map data to indicate that the candidate line includes the wait line for the traffic feature, wherein the map data, as updated,
Lei teaches determine, based at least on the data, one or more stopping locations of the one or more first machines within the environment during the one or more drives; (Pg. 3 - " Specifically, the initial vehicle is a calibrated vehicle capable of determining the number of stops of the vehicle in a signal period, that is, when the vehicle passes through the stop line without stopping the vehicle at the intersection, the vehicle stops zero, or when the vehicle stops once and passes through the stop line, the number of stops of the vehicle is one, for the type of vehicle capable of defining the number of stops of the vehicle, the vehicle attribute can be defined as the initial vehicle, and the number of stops of other vehicles can be calculated according to the initial vehicle "& See Also Pg. 5 – “Traffic is a fundamental industry and a service industry of national economy and social development, and road monitoring of urban traffic is more and more important. In order to ensure the traffic order of a city, related departments often need to analyze the traffic condition of each road section according to the number of stops (or the stop rate), delay and traffic capacity of vehicles passing through a signal lamp intersection, and then adjust the traffic facilities of the city to better ensure the traffic order” (equates to determining, based at least on the data, stopping locations of the one or more first machines within the environment during the plurality of drives; as the first and second quote show a plurality of vehicles and plurality of drives that vehicles go upon wherein the intersection contains stopping location and the vehicle that stop are cataloged in the number of stops.)))
Yet all fails to teach determine that a majority of the one or more stopping locations is associated with a candidate line; determine, based at least on the majority of the one or more stopping locations being associated with the candidate line, and update the map data to indicate that the candidate line includes the wait line for the traffic feature, wherein the map data, as updated,
Thibaux teaches ; determine that a majority of the one or more stopping locations is associated with a candidate line (Pg. 36 – Fig. 14 – “Evaluating a set of training data to produce a classification channel to identify whether pixels in a subset of the data are located within a selected distance of a stop line of a roadway” &See Also Pg. 38 – [col. 1 – lines 37-44 - ] – “a set of training data to produce a classification channel to identify whether pixels in a subset of the sensor data are located within a selected distance of a stop line of a roadway; evaluating, by the one or more processors, the set of training data to produce a localization channel to determine where a closest point is on the stop line for each pixel in the subset;” (equates to determine that a majority of the stopping locations are associated with a location within the environment as the quote shows the training data being evaluated wherein a majority of the data being within the given threshold constitutes as a stopping location and thus allows the art to identify the location as a stopping location)) determine, based at least on the majority of the one or more stopping locations being associated with the candidate line (Pg. 36 – Fig. 14 – “Evaluating a set of training data to produce a classification channel to identify whether pixels in a subset of the data are located within a selected distance of a stop line of a roadway” &See Also Pg. 38 – [col. 1 – lines 37-44 - ] – “a set of training data to produce a classification channel to identify whether pixels in a subset of the sensor data are located within a selected distance of a stop line of a roadway; evaluating, by the one or more processors, the set of training data to produce a localization channel to determine where a closest point is on the stop line for each pixel in the subset;”) and update the map data to indicate that the candidate line includes the wait line for the traffic feature, wherein the map data, as updated, (Pg. 15 – [0005] – “Regardless of whether it is a stop line or a no stop line situation, once the stop location is determined it is added to 15 the map (e.g., as a layer in a road graph).) It would have been ana advantageous addition to the system disclosed to include determine that a majority of the one or more stopping locations is associated with a candidate line; determine, based at least on the majority of the one or more stopping locations being associated with the candidate line, and update the map data to indicate that the candidate line includes the wait line for the traffic feature, wherein the map data, as updated, as these limitations allow for a quality of data to be used and thus an evaluation to take place to ensure the data used for the bstop line determination is accurate.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include determine that a majority of the one or more stopping locations is associated with a candidate line; determine, based at least on the majority of the one or more stopping locations being associated with the candidate line, and update the map data to indicate that the candidate line includes the wait line for the traffic feature, wherein the map data, as updated, as this allows for updating of the map to be done based on the data gathered as well as an evaluation to take place to filter out data for the stop line determination.
Regarding Claim 20 Krivokon-Qibin-Lei-Thibaux teaches (Krivokon discloses the following limitations:)The one or more processors of claim 19, wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative Al operations; a system for performing operations using one or more large language models (LLMs); a system for performing one or more conversational Al operations; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. (Pg. 21 – [0071] – “The vehicle may then be controlled in an autonomous driving mode” & See Also Pg. 18 – [0040] – “A control system software module of the computing devices 110 may be configured to control movement of the vehicle, for instance by controlling braking, acceleration and steering of the vehicle, in order to follow a trajectory” & See Also Pg. 2 – [Fig. 1] – “110 & 120” (equates to The one or more processors of claim 19, wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative Al operations; a system for performing operations using one or more large language models (LLMs); a system for performing one or more conversational Al operations; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources as the quote shows the autonomous control of the vehicle via a control system which from figure 1 contains a processor 120.) )
Regarding claim 23 Krivokon-Qibin-Lei -Thibaux teaches The one or more processors of claim 19, wherein the processing circuitry is further to: determine, based at least on the map data, that the plurality of candidate lines and the traffic feature are associated with the lane, (Pg. 13 – [1210] – “Receive image data including an image and an associated label identifying at least one traffic light, a state of the at least one traffic light, and a lane controlled by the at least one traffic light” &see also Pg. 16 – [0021] – “Typically, such information is stored in a vehicle's map information (e.g. a roadgraph identifying lanes) and can be retrieved as needed to identify traffic lights and lane states.” (equates to determining, based at least on the map data, that the candidate lines and the traffic feature are associated with the lane as the first quote shows the traffic feature being associated with the lane controlled by it and the second quote showing map data being used to make this lane identification possible.)) wherein the plurality of candidate lines are determined to be associated with the traffic feature based at least on the plurality of candidate lines and the traffic feature being associated with the lane. (Pg. 13 – [1210] – “Receive image data including an image and an associated label identifying at least one traffic light, a state of the at least one traffic light, and a lane controlled by the at least one traffic light” &see also Pg. 16 – [0021] – “Typically, such information is stored in a vehicle's map information (e.g. a roadgraph identifying lanes) and can be retrieved as needed to identify traffic lights and lane states.”)
Yet Krivokon-Ding fails to teach plurality of candidate lines.
Qibin teaches plurality of candidate lines (Pg. 3 – “In this embodiment, first, one road stop line is selected from the respective candidate recognition lines by the map data and the lane line data.” (equates to plurality of candidate lines as the art previously mapped shows the candidate recognition lines being separate from lane lines and the plurality of stop lines are determined. )) It would have been an advantageous addition to the method disclosed by Krivokon -Ding to include plurality of candidate lines as this allows for a plurality of potential stop lines to be included in search for the stop line in which the vehicle needs to remain ensuring the vehicle has more potential stop lines considered in the map data.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include plurality of candidate lines as this allows for more potential candidate lines to be generated ensuring that the scene encompassed by the map is best represented for an accurate stop line to be estimated.
Claim(s) 2 & 22 are rejected under 35 U.S.C. 103 as being unpatentable over Krivokon-Lei-Thibaux-Qibin as mapped above and in view of Ding et al. (CN 110816544 A)).
Regarding Claim 2 Krivoken-Lei-Thibaux-Qibin teaches (Krivoken discloses the following: ) The method of claim 1, further comprising: determining one or more rules associated with the traffic feature; (Pg. 15 – [0003] – “In another example, the method also includes generating the associated label by projecting a three-dimensional location of the at least one traffic light into the image. In this example, the method also includes determining the state by processing the image to identify a blob of color within an area of the projection. In another example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution.” (equates to determining one or more rules associated with the traffic feature as the quote shows the detection and subsequent locating of a traffic feature and then determining a rule of go stop or caution to be associated with the traffic light being captured.)) and determining, based at least on the one or more rules, (Pg. 15 – [0003] – “In another example, the method also includes generating the associated label by projecting a three-dimensional location of the at least one traffic light into the image. In this example, the method also includes determining the state by processing the image to identify a blob of color within an area of the projection. In another example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution.”) wherein the determining that the candidate line includes the wait line for the traffic feature (Pg. 21 – [0065] – “The area of this projection 1110, including its location, and in some cases the distance and direction from the traffic light 620 in the image, may then be used to generate another label which can be used as training output to train the model. As a result, the model may also be trained to provide the location and distance information for a stop line, providing the vehicle's computing devices with more information about how the vehicle should respond to the traffic light.” (equates to wherein the determining that the candidate line includes the wait line for the traffic feature as the quote shows a determination of attributes of a stop line and thus a determination of a stop line itself.)) plurality of drives (Pg. 15 – [0002] – “The method includes receiving, by one or more server computing devices, image data including an image and an associated label identifying at least one traffic light” & see Also Pg. 15 – [0005] – “In one example, identifying the image with the lane of interest includes projecting a road segment corresponding to a lane in which the vehicle is currently driving into the image.” & See Also Pg.19 – [0045] – “For instance, one or more computing devices 410 may include one or more server computing devices that are capable of communicating with computing device 110 of vehicle 100 or a similar computing device of vehicle 100A, 100B as well as computing devices 420, 430, 440 via the network 460. For example, vehicles 100, lOOA, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices 410” (equates to plurality of drives as the quote shows the plurality of vehicle each on their own drives collecting data and able to transmit the data back and forth between one another ))
Yet Krivoken fails to teach one or more scores associated with the drives, further based at least on the one or more scores.
Ding teaches one or more scores associated with the one or more drives (Pg. 4 – “the driving behavior evaluation module is used for evaluating the driving behavior of the driver as a first score if the vehicle speed is less than a first vehicle speed;” (equates to one or more scores associated with the one or more drives as the quote shows the driving of the vehicle being associated with a score.)) further based at least on the one or more scores. (Pg. 7 – “when the vehicle stops between the first position and the target stop line, evaluating the driving behavior of the driver of the vehicle at this time according to the vehicle speed and a preset” & See Also Pg. 7 – “if the vehicle speed is greater than the third vehicle speed, the current driving behavior of the driver is evaluated as a third grade” & See Also Pg. 6 – “Optionally, the preset evaluation model is used for indicating a corresponding relationship between a vehicle speed and a driving score;” (equates to further based at least on the one or more scores as the first quote shows the vehicle’s relation with the stop line, second showing a speed being considered within the method wherein the last shows the speed correlating to the score associated with the vehicle stopping at or near the line.)) It would have been an advantageous addition to the method disclosed by Krivoken to include one or more scores associated with the one or more drives, further based at least on the one or more scores as this allows a numerical value to be associated with the determination of the stop line and thus the driving itself can be incorporated into the determination of a stop line.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include one or more scores associated with the one or more drives, further based at least on the one or more scores as this allows the driving rather than strictly imaging and map data to be incorporated into the determination of a stop line.
Regarding claim 22 Krivoken-Qibin-Lei-Thibaux teaches (Kriovokn teaches the following limitations: ) The one or more processors of claim 19, wherein the processing circuitry is further to: determine one or more rules associated with the traffic feature; (Pg. 15 – [0003] – “In another example, the method also includes generating the associated label by projecting a three-dimensional location of the at least one traffic light into the image. In this example, the method also includes determining the state by processing the image to identify a blob of color within an area of the projection. In another example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution.” (equates to determining one or more rules associated with the traffic feature as the quote shows the detection and subsequent locating of a traffic feature and then determining a rule of go stop or caution to be associated with the traffic light being captured.)) and determine, based at least on the one or more rules, (Pg. 15 – [0003] – “In another example, the method also includes generating the associated label by projecting a three-dimensional location of the at least one traffic light into the image. In this example, the method also includes determining the state by processing the image to identify a blob of color within an area of the projection. In another example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution.”) wherein the candidate line is further determined to include the wait line for the traffic feature (Pg. 21 – [0065] – “The area of this projection 1110, including its location, and in some cases the distance and direction from the traffic light 620 in the image, may then be used to generate another label which can be used as training output to train the model. As a result, the model may also be trained to provide the location and distance information for a stop line, providing the vehicle's computing devices with more information about how the vehicle should respond to the traffic light.” (equates to wherein the determining that the candidate line includes the wait line for the traffic feature as the quote shows a determination of attributes of a stop line and thus a determination of a stop line itself.))
Yet Krivoken fails to teach one or more scores associated with the drives, further based at least on the one or more scores.
Ding teaches one or more scores associated with the one or more drives (Pg. 4 – “the driving behavior evaluation module is used for evaluating the driving behavior of the driver as a first score if the vehicle speed is less than a first vehicle speed;” (equates to one or more scores associated with the one or more drives as the quote shows the driving of the vehicle being associated with a score.)) further based at least on the one or more scores. (Pg. 7 – “when the vehicle stops between the first position and the target stop line, evaluating the driving behavior of the driver of the vehicle at this time according to the vehicle speed and a preset” & See Also Pg. 7 – “if the vehicle speed is greater than the third vehicle speed, the current driving behavior of the driver is evaluated as a third grade” & See Also Pg. 6 – “Optionally, the preset evaluation model is used for indicating a corresponding relationship between a vehicle speed and a driving score;” (equates to further based at least on the one or more scores as the first quote shows the vehicle’s relation with the stop line, second showing a speed being considered within the method wherein the last shows the speed correlating to the score associated with the vehicle stopping at or near the line.)) It would have been an advantageous addition to the method disclosed by Krivoken to include one or more scores associated with the one or more drives, further based at least on the one or more scores as this allows a numerical value to be associated with the determination of the stop line and thus the driving itself can be incorporated into the determination of a stop line.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include one or more scores associated with the one or more drives, further based at least on the one or more scores as this allows the driving rather than strictly imaging and map data to be incorporated into the determination of a stop line.
Claim(s) 14 are rejected under 35 U.S.C. 103 as being unpatentable over Krivoken-Thibaux as mapped above previously and in view of Ding et al. (CN 110816544 A).
Regarding Claim 14 Krivoken-Thibaux teaches (Krivokon discloses the following limitations: )The system of claim 10, wherein the one or more processors are further to: determine one or more rules associated with the traffic feature; (Pg. 15 – [0003] – “In another example, the method also includes generating the associated label by projecting a three-dimensional location of the at least one traffic light into the image. In this example, the method also includes determining the state by processing the image to identify a blob of color within an area of the projection. In another example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution.” (equates to wherein the one or more processors are further to: determine one or more rules associated with the traffic feature as the traffic light correlates to rules the traffic light would provide , go ,stop, and caution.)) and determine, based at least on the one or more rules, , (Pg. 15 – [0003] – “In another example, the method also includes generating the associated label by projecting a three-dimensional location of the at least one traffic light into the image. In this example, the method also includes determining the state by processing the image to identify a blob of color within an area of the projection. In another example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution.”) wherein the the wait line that is associated with the traffic feature (Pg. 21 – [0065] – “The area of this projection 1110, including its location, and in some cases the distance and direction from the traffic light 620 in the image, may then be used to generate another label which can be used as training output to train the model. As a result, the model may also be trained to provide the location and distance information for a stop line, providing the vehicle's computing devices with more information about how the vehicle should respond to the traffic light.” (equates to wherein the determining that the candidate line includes the wait line for the traffic feature as the quote shows a determination of attributes of a stop line and thus a determination of a stop line itself.)) plurality of drives (Pg. 15 – [0002] – “The method includes receiving, by one or more server computing devices, image data including an image and an associated label identifying at least one traffic light” & see Also Pg. 15 – [0005] – “In one example, identifying the image with the lane of interest includes projecting a road segment corresponding to a lane in which the vehicle is currently driving into the image.” & See Also Pg.19 – [0045] – “For instance, one or more computing devices 410 may include one or more server computing devices that are capable of communicating with computing device 110 of vehicle 100 or a similar computing device of vehicle 100A, 100B as well as computing devices 420, 430, 440 via the network 460. For example, vehicles 100, lOOA, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices 410” (equates to plurality of drives as the quote shows the plurality of vehicle each on their own drives collecting data and able to transmit the data back and forth between one another ))
Yet Krivokon fails to teach one or more scores associated with the drives drives, further determined based at least on the one or more scores.
Ding teaches one or more scores associated with the one or more drives (Pg. 4 – “the driving behavior evaluation module is used for evaluating the driving behavior of the driver as a first score if the vehicle speed is less than a first vehicle speed;” (equates to one or more scores associated with the one or more drives as the quote shows the driving of the vehicle being associated with a score.)) further determined based at least on the one or more scores. (Pg. 7 – “when the vehicle stops between the first position and the target stop line, evaluating the driving behavior of the driver of the vehicle at this time according to the vehicle speed and a preset” & See Also Pg. 7 – “if the vehicle speed is greater than the third vehicle speed, the current driving behavior of the driver is evaluated as a third grade” & See Also Pg. 6 – “Optionally, the preset evaluation model is used for indicating a corresponding relationship between a vehicle speed and a driving score;” (equates to further based at least on the one or more scores as the first quote shows the vehicle’s relation with the stop line, second showing a speed being considered within the method wherein the last shows the speed correlating ot the score associated with the vehicle stopping at or near the line.)) It would have been an advantageous addition to the method disclosed by Krivoken to include one or more scores associated with the one or more drives, further based at least on the one or more scores as this allows a numerical value to be associated with the determination of the stop line and thus the driving itself can be incorporated into the determination of a stop line.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include one or more scores associated with the one or more drives, further based at least on the one or more scores as this allows the driving rather than strictly imaging and map data to be incorporated into the determination of a stop line.
Claim(s) 3, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Krivokon-Lei-Thibaux-Qibin-Ding as mapped above and in view of Olof et al. (CN 114118658 A).
Regarding Claim 3 Krivokon-Lei-Thibaux-Qibin-Ding teaches The method of claim 2, plurality of drives (Pg. 15 – [0002] – “The method includes receiving, by one or more server computing devices, image data including an image and an associated label identifying at least one traffic light” & see Also Pg. 15 – [0005] – “In one example, identifying the image with the lane of interest includes projecting a road segment corresponding to a lane in which the vehicle is currently driving into the image.” & See Also Pg.19 – [0045] – “For instance, one or more computing devices 410 may include one or more server computing devices that are capable of communicating with computing device 110 of vehicle 100 or a similar computing device of vehicle 100A, 100B as well as computing devices 420, 430, 440 via the network 460. For example, vehicles 100, lOOA, 100B may be a part of a fleet of vehicles that can send and receive information from the server computing devices 410” (equates to plurality of drives as the quote shows the plurality of vehicle each on their own drives collecting data and able to transmit the data back and forth between one another ))
Yet Krivokon-Ding fails to teach wherein the determining the one or more scores associated with the one or more drives comprises one or more of: determining a first score associated with a first drive based at least on a first machine, of the one or more first machines, associated with the first drive following the one or more rules; or determining a second score associated with a second drive based at least on a second machine, of the one or more second machines, with the second drive not following the one or more rules, wherein the second score is less than the first score.
Olof teaches wherein the determining the one or more scores associated with the one or more drives comprises one or more of: determining a first score associated with a first drive based at least on a first machine, of the one or more first machines, associated with the first drive following the one or more rules; or determining a second score associated with a second drive based at least on a third machine associated with the second drive not following the one or more rules, wherein the second score is less than the first score. (Pg. 3 – “In an embodiment, a method includes: generating, using one or more processors, a set of trajectories for a vehicle operating in an environment, each trajectory of the set of trajectories being associated with a traffic scenario; predicting, using the one or more processors, a rationality score for each trajectory of the set of trajectories, wherein the rationality score is obtained from a machine learning model trained using inputs obtained from a plurality of human annotators, and a loss function for penalizing predictions of rationality scores that violate a rule book structure” (equates to wherein the determining the one or more scores associated with the one or more drives comprises one or more of: determining a first score associated with a first drive based at least on a first machine, of the one or more first machines, associated with the first drive following the one or more rules; or determining a second score associated with a second drive based at least on a third machine associated with the second drive not following the one or more rules, wherein the second score is less than the first score as the quote shows the vehicle trajectories being assigned a score wherein the score is related to the following of a rule book structure. )) It would have been an advantageous addition to the method disclosed by Krivokon-Ding to include wherein the determining the one or more scores associated with the one or more drives comprises one or more of: determining a first score associated with a first drive based at least on a Second machine associated with the first drive following the one or more rules; or determining a second score associated with a second drive based at least on a third machine associated with the second drive not following the one or more rules, wherein the second score is less than the first score as this limitation allows for a following of rules to more accurately determine the existence of a stop line and thus ensure that law breakers are considered strongly into the data concerning decisions of safety for users.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the determining the one or more scores associated with the one or more drives comprises one or more of: determining a first score associated with a first drive based at least on a Second machine associated with the first drive following the one or more rules; or determining a second score associated with a second drive based at least on a third machine associated with the second drive not following the one or more rules, wherein the second score is less than the first score as this allows for only data of drivers following the rules of the road to be incorporated into a determination of the stop line.
Regarding Claim 15 Krivoken-Thibaux-Ding teaches The system of claim 14, as mapped above previously.
Yet Krivokon-Ding fails to teach wherein the determining the one or more scores associated with the plurality of drives comprises one or more of: determining a first score associated with a first drive based at least on the first drive being associated with following the one or more rules; or determining a second score associated with a second drive based at least on the second drive being associated with not following the one or more rules, wherein the second score is less than the first score.
Olof teaches wherein the determining the one or more scores associated with the plurality of drives comprises one or more of: determining a first score associated with a first drive based at least on the first drive being associated with following the one or more rules; or determining a second score associated with a second drive based at least on the second drive being associated with not following the one or more rules, wherein the second score is less than the first score. (Pg. 3 – “In an embodiment, a method includes: generating, using one or more processors, a set of trajectories for a vehicle operating in an environment, each trajectory of the set of trajectories being associated with a traffic scenario; predicting, using the one or more processors, a rationality score for each trajectory of the set of trajectories, wherein the rationality score is obtained from a machine learning model trained using inputs obtained from a plurality of human annotators, and a loss function for penalizing predictions of rationality scores that violate a rule book structure” (equates to determining the one or more scores associated with the one or more drives comprises one or more of: determining a first score associated with a first drive based at least on a first machine associated with the first drive following the one or more rules; or determining a second score associated with a second drive based at least on a second machine associated with the second drive not following the one or more rules, wherein the second score is less than the first score as the quote shows the vehicle trajectories being assigned a score wherein the score is related to the following of a rule book structure. )) It would have been an advantageous addition to the method disclosed by Krivokon-Ding to include wherein the determining the one or more scores associated with the one or more drives comprises one or more of: determining a first score associated with a first drive based at least on a first machine associated with the first drive following the one or more rules; or determining a second score associated with a second drive based at least on a second machine associated with the second drive not following the one or more rules, wherein the second score is less than the first score as this limitation allows for a following of rules to more accurately determine the existence of a stop line and thus ensure that law breakers are considered strongly into the data concerning decisions of safety for users.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the determining the one or more scores associated with the one or more drives comprises one or more of: determining a first score associated with a first drive based at least on a first machine associated with the first drive following the one or more rules; or determining a second score associated with a second drive based at least on a second machine associated with the second drive not following the one or more rules, wherein the second score is less than the first score as this allows for only data of drivers following the rules of the road to be incorporated into a determination of the stop line.
Claim(s) 4 are rejected under 35 U.S.C. 103 as being unpatentable over Krivokon-Ding-Thibaux-Lei-Qibin as mapped above and in view of Wolff et al. (GB 2636268 A).
Regarding Claim 4 Krivoken-Lei-Thibaux-Qibin-Ding teaches (Krivokon discloses the following limitations:) The method of claim 2, and the determining the stopping locations (Pg. 15 – [0003] – “In another example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution. In another example, the method also includes, training the model to identify stop lines in images relevant to the lane of interest” (equates to and the determining the stopping locations as the quote shows the lane in which the vehicle is supposed to make stops ain as well as the location of the stopping line being determined.))
Yet Krivokon-Ding fail to teach wherein: the one or more scores include a plurality of scores associated with the plurality of drives; the one or more processors are further to determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score, a second portion of the plurality of drives; based at least on the second portion of the plurality of drives.
Wolff teaches wherein: the one or more scores include a plurality of scores associated with the plurality of drives (Pg. 7 - [26] – “For example, the machine learning model may score individual trajectory features from the different trajectories and rank (or score) the trajectories based on the scores of the respective sets of trajectory features.”) the one or more processors are further to determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score (Pg. 18 – [113] – “In some cases, certain actions can be omitted from potential inclusion in a trajectory if they are not physically possible (e.g., the vehicle is not physically capable of increasing its acceleration and/or increasing its turning angle at that point in time) and/or would violate a safety threshold, comfort threshold, or other criteria. For example, if the trajectory generator 504 includes a safety threshold of not moving onto a sidewalk and the scene data 502 indicates that a sidewalk is immediately to the right of the vehicle 200, the vehicle action policy can indicate that turning right is not to be considered an available action and can omit it from inclusion in a trajectory” (equates to the one or more processors are further to determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score as the quote shows that trajectories that are less than a few different designated thresholds for vehicle safety and the likes are removed based on not being safe for the vehicle to travel on. )) , a second portion of the plurality of drives (Pg. 18 – [113] In some cases, certain actions can be omitted from potential inclusion in a trajectory if they are not physically possible” & See Also Pg. 19 – [122] – “identify a subset of the generated trajectories that most closely align with the traveled path”(equates to a second portion of the plurality of drives as the quote shows a trajectory failing to be included into a set and thus a second set is formed from omitting potentially hazardous trajectory recommendations. )) based at least on the second portion of the plurality of drives. (Pg. 18 – [113] In some cases, certain actions can be omitted from potential inclusion in a trajectory if they are not physically possible” & See Also Pg. 19 – [122] – “identify a subset of the generated trajectories that most closely align with the traveled path”) It would have been an advantageous addition to the system disclosed by Krivokon to include wherein: the one or more drives include a plurality of drives associated with the traffic feature; the one or more scores include a plurality of scores associated with the plurality of drives; the one or more processors are further to determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score, a second portion of the plurality of drives; based at least on the second portion of the plurality of drives as this limitation allows for a removal of trajectories that don’t fit the best the understanding of the scene and ensure an accurate scene is understood based on trajectories that obey the rules of the road.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein: the one or more drives include a plurality of drives associated with the traffic feature; the one or more scores include a plurality of scores associated with the plurality of drives; the one or more processors are further to determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score, a second portion of the plurality of drives; based at least on the second portion of the plurality of drives as these limitations allow for a numerical value to be associated with the drives that vehicle is or can take upon and best determine the scene based on feasible data from the drives being taken in.
Claim(s) 16 are rejected under 35 U.S.C. 103 as being unpatentable over Krivokon-Thibuax as mapped above and in view of Wolff et al. (GB 2636268 A).
Regarding Claim 16 Krivokon-Thibaux - Ding teaches (Krivoken teaches the following limitations: ) The system of claim 10, determination of the stopping locations of the one or more first machines within the environment when approaches the traffic feature comprises (Pg. 15 – [0005] – “In this example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution. In another example, the model further outputs a location of a stop line in the image that is relevant to the lane of interest,” ) the stopping locations of the one or more first machines within the environment when approaching the traffic feature(Pg. 15 – [0005] – “In this example, the lane state identifies whether a vehicle in that lane is required to go, stop, or use caution. In another example, the model further outputs a location of a stop line in the image that is relevant to the lane of interest,” )
Yet Krivokon-Ding fail to teach determine a plurality of scores associated with the plurality of drives, determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score, and determining, based at least on the second portion of the plurality of drives,
Wolff teaches determine a plurality of scores associated with the plurality of drives, (Pg. 7 - [26] – “For example, the machine learning model may score individual trajectory features from the different trajectories and rank (or score) the trajectories based on the scores of the respective sets of trajectory features.”) determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score, (Pg. 18 – [113] – “In some cases, certain actions can be omitted from potential inclusion in a trajectory if they are not physically possible (e.g., the vehicle is not physically capable of increasing its acceleration and/or increasing its turning angle at that point in time) and/or would violate a safety threshold, comfort threshold, or other criteria. For example, if the trajectory generator 504 includes a safety threshold of not moving onto a sidewalk and the scene data 502 indicates that a sidewalk is immediately to the right of the vehicle 200, the vehicle action policy can indicate that turning right is not to be considered an available action and can omit it from inclusion in a trajectory” (equates to determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score, as the quote shows that trajectories that are less than a few different designated thresholds for vehicle safety and the likes are removed based on not being safe for the vehicle to travel on. )) , a second portion of the plurality of drives; (Pg. 18 – [113] In some cases, certain actions can be omitted from potential inclusion in a trajectory if they are not physically possible” & See Also Pg. 19 – [122] – “identify a subset of the generated trajectories that most closely align with the traveled path”(equates to a second portion of the plurality of drives as the quote shows a trajectory failing to be included into a set and thus a second set is formed from omitting potentially hazardous trajectory recommendations. ) based at least on the second portion of the plurality of drives, (Pg. 18 – [113] In some cases, certain actions can be omitted from potential inclusion in a trajectory if they are not physically possible” & See Also Pg. 19 – [122] – “identify a subset of the generated trajectories that most closely align with the traveled path) It would have been an advantageous addition to the system disclosed by Krivokon to include determine a plurality of scores associated with the plurality of drives, determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score, and determining, based at least on the second portion of the plurality of drives, as this limitation allows for a removal of trajectories that don’t fit the best the understanding of the scene and ensure an accurate scene is understood based on trajectories that obey the rules of the road.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include determine a plurality of scores associated with the plurality of drives, determine, based at least on removing a first portion of the plurality of drives that are associated with a portion of the plurality of scores that are less than a threshold score, and determining, based at least on the second portion of the plurality of drives, as these limitations allow for a numerical value to be associated with the drives that vehicle is or can take upon and best determine the scene based on feasible data from the drives being taken in.
Claim(s) 7 & 24 is rejected under 35 U.S.C. 103 as being unpatentable over Krivokon-Lei-Thibaux-Qibin as mapped above and in view of Kuriyama (US 2021/0182576 Al).
Regarding Claim 7 Krivokon-Lei-Thibaux-Qibin teaches (Krivokon discloses the following limitations:) teaches The method of claim 1, further comprising: determining, based at least on the map data, that the candidate line are located within a distance to the traffic feature, (Pg. 21 – [0065] – “The area of this projection 1110, including its location, and in some cases the distance and direction from the traffic light 620 in the image, may then be used to generate another label which can be used as training output to train the model. As a result, the model may also be trained to provide the location and distance information for a stop line, providing the vehicle's computing devices with more information about how the vehicle should respond to the traffic light.” (equates to comprising: determining, based at least on the map data, that the one or more candidate lines are located within a threshold distance to the traffic feature as the quote shows a distance away a stop line is from a traffic light. ))
Yet Krivokon fails to teach plurality of candidate lines , within a threshold distance to the traffic feature, wherein the determining the plurality of candidate lines associated with the traffic feature is based at least on the one or more candidate lines being located within the threshold distance to the traffic feature.
Kuriyama teaches within a threshold distance to the traffic feature (Pg. 12 – [0004] – “recognizes that the traffic light just in front of the host vehicle on the host vehicle traveling lane is located within a preset distance L2 and the color of the traffic light is red, shifts to the stop line recognition mode and executes stop line recognition processing” (equates to within a threshold distance to the traffic feature as the quote shows the lane being within preset distance toa traffic light and thus a threshold distance to a traffic feature.) ) wherein the determining the candidate line associated with the traffic feature is based at least on the candidate line being located within the threshold distance to the traffic feature. (Pg. 12 – [0004] – “recognizes that the traffic light just in front of the host vehicle on the host vehicle traveling lane is located within a preset distance L2 and the color of the traffic light is red, shifts to the stop line recognition mode and executes stop line recognition processing” (equates to wherein the determining the one or more candidate lines associated with the traffic feature is based at least on the one or more candidate lines being located within the threshold distance to the traffic feature as the lane is shown to be within a preset distance to the traffic light and the determination of the stop line being made base on that distance and the vehicle stoppage.) )
Yet all fail to teach plurality of candidate lines.
Qibin teaches plurality of candidate lines (Pg. 3 – “In this embodiment, first, one road stop line is selected from the respective candidate recognition lines by the map data and the lane line data.” (equates to plurality of candidate lines as the art previously mapped shows the candidate recognition lines being separate from lane lines and the plurality of stop lines are determined. )) It would have been an advantageous addition to the method disclosed by Krivokon -Ding to include plurality of candidate lines as this allows for a plurality of potential stop lines to be included in search for the stop line in which the vehicle needs to remain ensuring the vehicle has more potential stop lines considered in the map data.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include plurality of candidate lines as this allows for more potential candidate lines to be generated ensuring that the scene encompassed by the map is best represented for an accurate stop line to be estimated.
Regarding claim 24 Krivokon-Qibin-Lei-Thibaux teaches The one or more processors of claim 19, wherein the processing circuitry is further to: determine, based at least on the map data, that the plurality of candidate lines are located within a distance to the traffic feature, (Pg. 21 – [0065] – “The area of this projection 1110, including its location, and in some cases the distance and direction from the traffic light 620 in the image, may then be used to generate another label which can be used as training output to train the model. As a result, the model may also be trained to provide the location and distance information for a stop line, providing the vehicle's computing devices with more information about how the vehicle should respond to the traffic light.” (equates to comprising: determining, based at least on the map data, that the one or more candidate lines are located within a threshold distance to the traffic feature as the quote shows a distance away a stop line is from a traffic light. ))
Yet Krikoven fails to teach within a threshold distance to the traffic feature, wherein the plurality of candidate lines are determined to be associated with the traffic feature based at least on the plurality of candidate lines being located within the threshold distance to the traffic feature.
Kuriyama teaches within a threshold distance to the traffic feature (Pg. 12 – [0004] – “recognizes that the traffic light just in front of the host vehicle on the host vehicle traveling lane is located within a preset distance L2 and the color of the traffic light is red, shifts to the stop line recognition mode and executes stop line recognition processing” (equates to within a threshold distance to the traffic feature as the quote shows the lane being within preset distance toa traffic light and thus a threshold distance to a traffic feature.) ) wherein the determining the candidate line associated with the traffic feature is based at least on the candidate line being located within the threshold distance to the traffic feature. (Pg. 12 – [0004] – “recognizes that the traffic light just in front of the host vehicle on the host vehicle traveling lane is located within a preset distance L2 and the color of the traffic light is red, shifts to the stop line recognition mode and executes stop line recognition processing” (equates to wherein the determining the one or more candidate lines associated with the traffic feature is based at least on the one or more candidate lines being located within the threshold distance to the traffic feature as the lane is shown to be within a preset distance to the traffic light and the determination of the stop line being made base on that distance and the vehicle stoppage.) )
Yet all fail to teach plurality of candidate lines.
Qibin teaches plurality of candidate lines (Pg. 3 – “In this embodiment, first, one road stop line is selected from the respective candidate recognition lines by the map data and the lane line data.” (equates to plurality of candidate lines as the art previously mapped shows the candidate recognition lines being separate from lane lines and the plurality of stop lines are determined. )) It would have been an advantageous addition to the method disclosed by Krivokon -Ding to include plurality of candidate lines as this allows for a plurality of potential stop lines to be included in search for the stop line in which the vehicle needs to remain ensuring the vehicle has more potential stop lines considered in the map data.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include plurality of candidate lines as this allows for more potential candidate lines to be generated ensuring that the scene encompassed by the map is best represented for an accurate stop line to be estimated.
Response to Arguments
Response to 35 U.S.C. § 103 rejection of claims 1-8 & 10-21 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are not persuasive.
Applicant argues on Pages 1-4, “Claims 1, 2, 6, 8, 10, 11, 13, 18-21 are rejected under 35 U.S.C. § 103 as allegedly being unpatentable over U.S. Publication No. 2020/0135030 to Krivokon (hereinafter "Krivokon") in view of Chinese Application No. 110816544 to Ding (hereinafter "Ding") in view of U.S. Patent No. 11,749,000 to Thibaux (hereinafter "Thibaux") and further in view of Chinese Application No. 118097588 to Qibin (hereinafter "Qibin"). However, it then appears that the Office does not in fact reject claims 19 and 20 using this combination of references while also rejecting claim 14 using this combination of references. As such, Applicant believes that the Office meant to reject claims 1, 2, 6, 8, 10, 11, 13, 14, 18, and 21. Additionally, Applicant cancels claim 21 by the present response, without prejudice. Applicant respectfully traverses these rejections for the remaining claims.
Applicant respectfully submits that Krivokon, Ding, Thibaux, and Qibin, whether taken alone or in combination, do not teach or suggest, at least, "determining, based at least on the data, stopping locations of the one or more first machines within the environment during the plurality of drives; determining that a threshold number of the stopping locations is associated with a candidate line of the plurality of candidate lines; [and] determining, based at least on the threshold number of the stopping locations being associated with the candidate line of the plurality of candidate lines, that the candidate line includes the wait line for the traffic feature," as amended claim 1 recites.
In the rejection of previously presented independent claim 1, the Office acknowledges that the combination of Krivokon, Ding, and Thibaux does not teach or suggest, "determining, based at least on map data, a plurality of candidate lines that potentially correspond to a wait line for a traffic feature located within the environment, the plurality of candidate lines at least partially crossing a lane" or "determining, based at least on the one or more locations being associated with a candidate line of the plurality of candidate lines, that the candidate line includes the wait line for the traffic feature." Office Action, p. 4. Consequently, the combination of Krivokon, Ding, and Thibaux further does not teach or suggest "determining, based at least on the data, stopping locations of the one or more first machines within the environment during the plurality of drives;
determining that a threshold number of the stopping locations is associated with a candidate line of the plurality of candidate lines; [and] determining, based at least on the threshold number of the stopping locations being associated with the candidate line of the plurality of candidate lines, that the candidate line includes the wait line for the traffic feature," as amended claim 1 recites.
Qibin does not remedy the deficiencies of Krivokon, Ding, and Thibaux. In the rejection of previously presented independent claim 1, the Office cites Qibin as allegedly teaching, "determining, based at least on map data, a plurality of candidate lines that potentially correspond to a wait line for a traffic feature located within the environment, the plurality of candidate lines at least partially crossing a lane" and "determining, based at least on the one or more locations being associated with a candidate line of the plurality of candidate lines, that the candidate line includes the wait line for the traffic feature." Id., pp. 4 and 5. However, Qibin describes determining a road stop line from at least one candidate by identifying a line based on map data and lane line data identified during a drive of a vehicle, acquiring an intersection area of the next intersection in the vehicle driving direction based on the map data, determining the identification lines to be selected in the intersection area, and determining one of the identified lines to be the road stop line based on the lane line data. In other words, Qibin describes selecting the road stop line from the candidate lines based on the map data and the lane line data.
However, Qibin merely describes that the lane line data includes a driving direction of the vehicle on the driving road, such as a lane line perpendicular to the actual road stop line. Additionally, Qibin describes that the road stop line is determined based on the distance between each of the identification lines to be selected for the intersection area and the lane line indicated by the lane line data. However, Qibin does not teach or suggest that the road stop line is determined based on a "stopping location" of the vehicle when approaching the identification lines. ForPage 16 of
instance, Qibin does not teach or suggest that the lane line data includes a "stopping location" of the vehicle when navigating. Rather, in Qibin, the process is performed to identify the road stop line before the vehicle arrives at the road stop line and, as such, cannot be determined based on a "stopping location" of the vehicle.” – As to point A the examiner respectfully disagrees. Applicant asserts that Qibin does not teach “determining, based at least on map data, a plurality of candidate lines that potentially correspond to a wait line for a traffic feature located within the environment, the plurality of candidate lines at least partially crossing a lane”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is using map information to understand the environment in which a traffic sign is located within wherein a stop line is to be determined based on the data provided wherein the potential stop line is crossing lanes within the road . Qibin teaches a determination of a stop line using map information and lane information wherein the lanes are determined to run perpendicular from the stop line and thus the stop line crosses the lane line (as mapped above in claim 1). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Qibin teaches “determining, based at least on map data, a plurality of candidate lines that potentially correspond to a wait line for a traffic feature located within the environment, the plurality of candidate lines at least partially crossing a lane”. Qibin teaches determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane; (Pg. 3 – “In one embodiment, a road stop line is determined from at least one candidate recognition line based on map data and lane line data recognized during driving of the vehicle” & See Also Pg. 1 – “and determining a road stop line from the at least one candidate recognition line based on map data and lane line data recognized during the driving of the vehicle, wherein the lane line data comprises lane lines indicating the driving direction of the vehicle on a driving road, and the lane line data does not comprise the candidate recognition line.” & See Also Fig. 3 & See Also Pg. 2 – “According to the stop line identifying method provided in the first aspect of the embodiment of the present application, the determining a road stop line from the at least one candidate identifying line based on the map data and the lane line data identified during the driving of the vehicle (equates to determining, based at least on map data, plurality of candidate lines potentially correspond to a wait line for a traffic feature; located within the environment, the plurality of candidate lines at least partially crossing a lane as the quote shows the determination of a stop line based on at least one candidate line which can be more than one and thus a plurality of candidate recognition lines may be determined wherein the quote specifically shows the candidate recognition line not including lane lines and thus are crossing a lane. ))
b. Applicant argues on page 4, “Additionally, Qibin further does not teach or suggest that the road stop line is determined based on multiple "drives" of one or more vehicles. Therefore, Qibin further does not teach or suggest that the road stop line is determined based on a "threshold number of the stopping locations" being associated with an identification line. Consequently, the combination of Krivokon, Ding, Thibaux, and Qibin does not teach or suggest "determining, based at least on the data, stopping locations of the one or more first machines within the environment during the plurality of drives; determining that a threshold number of the stopping locations is associated with a candidate line of the plurality of candidate lines; [and] determining, based at least on the threshold number of the stopping locations being associated with the candidate line of the plurality of candidate lines, that the candidate line includes the wait line for the traffic feature," as amended claim 1 recites.” - Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
c. Applicant argues on page 4-8 - “Applicant respectfully submits that Krivokon, Ding, Thibaux, and Qibin, whether take alone or in combination, do not teach or suggest, at least, "determin[ing], based at least on the drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; [and] determin[ing], based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that is associated with the traffic feature," as amended claim 10 recites.
In the rejection of previously presented dependent claim 21, which included features similar to amended independent claim 10, the Office acknowledges that the combination of Krivokon and Ding does not teach or suggest, "wherein the determination of the wait line that is associated with the traffic feature comprises: determining, based at least on the map data, that the environment refrains from having a line marking associated with the traffic feature; and determining, based at least on the environment refraining from having the line marking, the wait line associated with the traffic feature by at least projecting the wait line at line at a second location within the environment that is based at least on the one or more locations within the environment that the one or more first machines stopped when approaching the traffic feature." Office Action, p. 16. Consequently, the combination of Krivokon and Ding further does not teach or suggest "determin[ing], based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; [and] determin[ing], based at least on the environment not including the line marking and the stopping locations of the one orPage 18 of 28
more first machines when approaches the traffic feature, a wait line located within the environment that is associated with the traffic feature," as amended claim 10 recites.
Thibaux does not remedy the deficiencies of Krivokon and Ding. In the rejection of previously presented dependent claim 21, the Office cites Thibaux as allegedly teaching, "wherein the determination of the wait line that is associated with the traffic feature comprises: determining, based at least on the map data, that the environment refrains from having a line marking associated with the traffic feature; and determining, based at least on the environment refraining from having the line marking, the wait line associated with the traffic feature by at least projecting the wait line at line at a second location within the environment that is based at least on the one or more locations within the environment that the one or more first machines stopped when approaching the traffic feature." Id., pp. 16 and 17. However, the initial cited portion of Thibaux states:
According to another aspect, a method of controlling a vehicle operating in an autonomous driving mode is provided. The method comprises receiving, by one or more sensors of a perception system of the vehicle, sensor data associated with objects in an external environment of the vehicle; determining, by one or more processors of the vehicle, whether the vehicle is within a threshold distance of a lane endpoint along a portion of a roadway; performing, by the one or more processors upon determining that the vehicle is within the threshold distance of the lane endpoint, stop location detection. Performing the stop location detection includes: predicting a set of stop line points closest to an expected stop line; discarding any of the set of stop line points not located within a region of interest associated with the lane endpoint; and projecting any remaining stop line points of the set onto a lane tangent to identify a predicted stopping location. The method further includes causing, by the one or more processors, the vehicle to stop along
the roadway according to the predicted stopping location. Thibaux, col. 2, 11. 9-27.
As shown, Thibaux describes performing stop location detection by predicted a set of stop line points closest to an expected stop line, discarding any of the set of stop line points not located within a region of interest, and then projecting any remaining stop line points to identify a predicted
stopping location. Id. Thibaux then describes causing the vehicle to stop along the roadway
according to the predicted stopping location. Id. As such, this cited portion of Thibaux does not
teach or suggest using the stopping location of the vehicle to determine the predicted stopping
location. Rather, the predicted stopping location in Thibaux is determined before the vehicle is
caused to stop.
Additionally, another cited portion of Thibaux states:
In one example, the computing devices 202 may form an autonomous driving computing system incorporated into vehicle100. The autonomous driving computing system may be capable of communicating with various components of the vehicle. For example, the computing devices 202 may be in communication with various systems of the vehicle, including a driving system including a deceleration system 212 (for controlling braking of the vehicle), acceleration system 214 (for controlling acceleration of the vehicle), steering system 216 (for controlling the orientation of the wheels and direction of the vehicle), signaling system 218 (for controlling turn signals), navigation system 220 (for navigating the vehicle to a location or around objects) and a positioning system 222 (for determining the position of the vehicle, e.g., including the vehicle's pose, e.g., position and orientation along the roadway or pitch, yaw and roll of the vehicle chassis relative to a coordinate system). The autonomous driving computing system may employ a planner module 223, in accordance with the navigation system 220, the positioning system 222 and/or other components of the system, e.g., for determining a route from a starting point to a destination, for identifying a stop location at an intersection, or for making modifications to various driving aspects
in view of current or expected traction conditions.
Id., col. 7, 11. 23-46.
As shown, Thibaux describes that the autonomous driving computing system may
determine a route from a starting point to a destination, for identifying a stop location at an
intersection. Id. However, again, Thibaux merely describes determining the stop location, but
does not teach or suggest that the determined stop location is at a location for which the vehicle
actually stopped. More specifically, and similar to above, the stop location in Thibaux is identified
before the vehicle actually stops.
Rather, and as described throughout Thibaux such as starting at col. 12, 1. 36, Thibaux describes that a stop line location is determined based on processing sensor data from a sensor of the vehicle and/or using a map. However, Thibaux does not teach or suggest that a stop line location is determined based on an actual "stop location" of the vehicle when navigating through the environment. Therefore, Thibaux does not teach or suggest that a stop line location is determined based on multiple "stopping locations" of one or more vehicles. Consequently, Thibaux does not teach or suggest "determin[ing], based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; [and] determin[ing], based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that is associated with the traffic feature," as amended claim 10 recites.
Additionally, for at least the reasons above, Qibin does not remedy the deficiencies of Krivokon, Ding, and Thibaux. For instance, Qibin does not teach or suggest determining a "wait line" based on "stopping locations" of vehicles. Consequently, the combination of Krivokon, Ding, Thibaux, and Qibin does not teach or suggest "determin[ing], based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; [and] determin[ing], based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that is associated with the traffic feature," as amended claim 10 recites.” – As to point A the examiner respectfully disagrees. Applicant asserts that Thibaux does not teach “determining, based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; determining, based at least on the environment not including the line marking and the stopping locations of the one more first machines when approaches the traffic feature, a wait line located within the environment that is associated with the traffic feature”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is use a plurality of driving data to determine a stopping location when the vehicle is entering or getting near the traffic feature and to determine the location of the stop line when there is not a stop line present. Thibaux teaches a projection of a stop line being made after it is not currently physically being inserted or within the map data wherein as the vehicle is approaching the determination as to where the stop line would be is made. Krivoken teaches a plurality of driving data being used from a plurairty of first machines wherein the stopline data from drives is gathered. (as mapped above in claim 10). Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Krivoken-Thibaux teaches “determining, based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; determining, based at least on the environment not including the line marking and the stopping locations of the one more first machines when approaches the traffic feature, a wait line located within the environment that is associated with the traffic feature”. Krivoken teaches : determine, based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; (Pg. 20 – [0055] – “FIG. 6 is an example camera image 600 captured by a camera of perception system 172 of vehicle 100 as the vehicle approaches intersection 202 of the map information. In this example, traffic light 620 and part of an intersection 630 and a lane 640 are captured in camera image 600. This camera image may be processed and used to generate initial training data for the model.” & See Also Pg. 17 – [0035] – “Each feature may be stored as graph data and may be associated with information such as a geographic location and whether or not it is linked to other related features, for example, a stop sign may be linked to a road and an intersection,” (equates to determine, based at least on the plurality of drives, stopping locations of one or more first machines within the environment when approaching the traffic feature; as the quote shows the locations of the vehicles approaching the intersection containing stop line information being tracked and stored.) )
Thibaux teaches: comprising: one or more processors to: determine, based at least on map data, that an environment does not include a line marking for stopping a traffic feature; (Pg. 1 – Abstract – “While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes” (equates to comprising: one or more processors to: determine, based at least on map data, that an environment does not include a line marking for stopping a traffic feature; as the quote shows the stop line not being included in the map data compared to the real time painted line )) determine, based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that associated with the traffic feature (Pg. 1 – Abstract – “While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes” (equates to determine, based at least on the environment not including the line marking and the stopping locations of the one or more first machines when approaches the traffic feature, a wait line located within the environment that associated with the traffic feature as the quote shows the absence of a stop line being detected and the neural network in conjunction with the taken in data from the vehicle being used to determine the location of the stop line. )) updating the map data to indicate that the candidate line includes the wait line for the traffic feature. (Pg. 15 – [0005] – “Regardless of whether it is a stop line or a no stop line situation, once the stop location is determined it is added to 15 the map (e.g., as a layer in a road graph).)
d. Applicant argues on page 9-11, “Additionally, Applicant respectfully submits that a person of ordinary skill in the art would not have been motivated to combine Krivokon, Ding, Thibaux, and Qibin in the manner proposed
by the Office. Although each reference generally relates to vehicle operation in proximity to traffic control features, the references are directed to fundamentally different technical problems and employ materially different solutions.
For instance, Krivokon is directed to determining lane states associated with traffic lights so that an autonomous vehicle may determine whether a particular lane is governed by a traffic signal and may operate accordingly. Krivokon focuses on traffic-light recognition, lane-state classification, and autonomous vehicle control decisions. Krivokon does not seek to determine where a vehicle should stop, does not determine wait lines associated with traffic features, does not analyze historical stopping behavior of multiple vehicles, and does not update map data based on observed vehicle stopping locations. Thus, Krivokon is concerned with determining whether a lane is permitted to proceed, rather than determining the physical location of a wait line associated with a traffic feature.
Ding addresses a different problem altogether. For instance, Ding evaluates a driver's deceleration behavior relative to a previously known stop line in order to assess driver performance and safety. The stop line is already known and serves merely as a reference point for evaluating driver behavior. Ding therefore assumes the existence and location of the stop line and does not attempt to discover, infer, generate, or validate a wait line from vehicle trajectories. As a result, Ding teaches away from Applicant's approach because Ding relies upon preexisting stop-line information rather than deriving such information from observed driving behavior.
Thibaux likewise addresses a different problem. For instance, Thibaux is directed to identifying stopping locations using sensor observations, localization information, and machine- learning-based stop-line detection techniques. The objective of Thibaux is to determine where a vehicle should stop based on environmental observations and detected roadway features. Thibaux
therefore relies upon perception data and predictive models to identify stopping locations. In contrast, the present application determines wait lines using collective historical driving behavior associated with multiple drives and uses the resulting information to update map data. Incorporating Thibaux into Krivokon would merely add sensor-based stop-line detection capabilities and would not lead a skilled artisan toward determining a wait line from aggregated stopping locations of multiple drives.
Qibin is similarly directed to identifying existing stop lines from candidate line markings detected within perception data. Qibin evaluates candidate line markings based on lane-line information and map information in order to determine which candidate marking corresponds to an existing stop line. Qibin therefore assumes the presence of candidate roadway markings and seeks to identify which marking represents the stop line. In contrast, Applicant's invention determines a wait line based on where vehicles actually stop when approaching a traffic feature and, in some embodiments, projects a wait line even when no corresponding roadway marking exists. Consequently, Qibin's reliance upon detected roadway markings is fundamentally inconsistent with Applicant's behavioral approach.
Moreover, even if a skilled artisan were to combine the teachings of Krivokon, Ding, Thibaux, and Qibin, which Applicant does not concede, the resulting system would still not arrive at the claimed invention. The proposed combination would at most yield a system that recognizes traffic-light states, evaluates driver behavior relative to known stop lines, detects stop lines from sensor information, and identifies candidate roadway markings. The combination would not teach or suggest determining a plurality of candidate lines that potentially correspond to a wait line, determining stopping locations associated with multiple drives, selecting a candidate line based on the stopping locations, determining that the selected candidate line constitutes a wait line for a
traffic feature, and updating map data to indicate the wait line. Achieving Applicant's claimed invention would therefore require substantial redesign and reconstruction of the references using Applicant's disclosure as a blueprint, which is improper hindsight reconstruction under 35 U.S.C. § 103.
Accordingly, Applicant respectfully submits that the Office has not established a sufficient rationale that would have motivated a person of ordinary skill in the art to combine the references in the manner required to arrive at the claimed invention, and the rejection should therefore be withdrawn.” – As to point D the examiner respectfully disagrees. Applicant asserts that the combination of Krivokon, Ding, Thibaux, and Qibin leads to the use improper hindsight bias and reconstruction to formulate a rejection against the current application. However through the utilization of Krivoken’s method including plurality of drive data for detecting stop lines, wherein with combination of Ding’s scoring system allows for a easily calculable way of ranking driving data . Thibaux simply adds a means of stop line projection to be inserted if the stop line itself is not already included in the historical map data, and again Qibin ensures lane lines are used within the stop line determination as well as s detection of athe plurality of candidae lines for a variety of choice before the right one is selected. During Patent examination, It is prima facie obvious to combine two compositions each of which is taught by the prior art to be useful for the same purpose, in order to form a third composition to be used for the very same purpose (See MPEP 2144.06).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US20200393265 According to an aspect of an embodiment, operations may comprise receiving a set of one or more lane line points each representing a location on a lane line in an HD map, determining an approximate lane line point representing an approximate location on the lane line in the HD map, identifying a region of the HD map surrounding the approximate lane line point, automatically calculating a predicted lane line point in the region representing a predicted location of the lane line in the HD map, displaying, on a user interface, the predicted lane line point on the lane line in the HD map, receiving, from a user through the user interface, confirmation that the predicted lane line point is an actual lane line point, and adding the actual lane line point to the set of one or more lane line points representing locations on the lane line in the HD map.
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/R.A.W./Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
9/28/26
