DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 1, 16 and 19 are amended.
No new claim is added.
Claims 1-20 are pending.
Response to arguments
With respect to Applicant’s remarks filed on 12/19/2025; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented. The Office has supplied new grounds for rejection attached below in the FINAL office action and therefore the prior arguments are considered moot. However, even though applicant has amended the scope of the claims and the Office has provided new mapping of cited prior art below, the Office is still using most of the same cited prior art, thus the Office will attempt to address all remarks that remain relevant.
Applicant remarks:
Goossen does not teach amended claim 1, - “wherein the evidence grid is established in the map frame such that the accumulated SCH data (1) is retained in the evidence grid independent of the reference path and (2) remains when the reference path changes or resets.”
Office Response:
Please see new mapping above, specifically the mapping for the claims, specifically regarding “evidence grid”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-14, 16-20 are rejected under 35 U.S.C. 103 as being unpatented over US20210031760A1 to Ostafew et al; (herein after “Ostafew”) in view of CN 114894206 A to Pan et al. (herein after “Pan”).
Regarding claim 1, Ostafew teaches A method, obtaining a reference path associated with the autonomous vehicle, wherein the reference path is based on a drive plan and driveline data (see Ostafew para[0105] The driveline can be thought of as the line in the road over which the longitudinal axis of the AV coincides as the AV moves along the road. The driveline data can be used (e.g., by the reference-trajectory generation layer 504) to determine (i.e., generate, calculate, select, or otherwise determine) a coarse driveline from a first location to a second location.);
accumulating slow crossing hazard (SCH) data ( see Ostafew the trajectory planner process constraints related to static and dynamic objects,para[0090] the tracking component of the AV 302 can detect a parked vehicle 342 (i.e., a static object) and a bicycle 344 that is moving (i.e., a dynamic object that is a longitudinal constraint), in the evidence grid by performing at least one of a boundary intrusion detection procedure, an object intrusion detection procedure (See Ostafew para[0149], figure 10-12) , or a lead vehicle hazard detection procedure (see Ostafew para[0293] For example, the drivable area may be restricted more or less over time based on the detected intrusion); and
providing the SCH data to a process within the autonomous vehicle for further decision-making or risk mitigation(See Ostafew para[0004] controlling the AV according to the contingency trajectory; and, in response to the hazard object intruding into the path of the AV, controlling the AV to perform a maneuver to avoid the hazard object).
However, Ostafew does not expressly disclose or otherwise teach obtaining a risk field comprising an evidence grid established in a map frame associated with an autonomous vehicle. Nevertheless, Pan same field of endeavor teaches obtaining a risk field comprising an evidence grid established in a map frame associated with an autonomous vehicle (See Pan para[0007] Acquire a high-precision map and an obstacle risk field (SL), wherein the obstacle risk field is pre-constructed based on multi-frame perception information of the obstacles)
wherein the evidence grid is established in the map frame such that the accumulated SCH data (1) is retained in the evidence grid independent of the reference path and (2) remains when the reference path changes or resets. (See Pan at least para[0131] Step S42: Project each grid point in the probability grid point set as an independent obstacle onto the reference line SL coordinate system, and determine the projected coordinates of each grid point in the reference line SL coordinate system ).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid which can be update and populated with information obtained from the onboard sensors and independent to reference line and SCH data in order to allow to enable motor vehicles to achieve autonomous driving without the need for active human operation (see para[0002]) and to obtain the force situation of the vehicle at any position in the potential field (See para[0003]).
Regarding claim 2, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein performing the boundary intrusion detection procedure comprises accumulating non-drivable points wherein each non-drivable point is based on an output of a sensor associated with the autonomous vehicle (See Ostafew para[0170] Boundary points, such as a boundary point 1008, originate from static objects. For example, the boundary points can be derived from data from a LiDAR sensor) in the evidence grid (See Pan para[0063] S12. Determine the area occupied by the obstacle in the current frame of the obstacle grid map based on the obstacle's current frame perception data, para[0059] The perception data is output by a known upstream perception module, and a current frame obstacle raster map is established in the Frenet coordinate system with the vehicle's current position)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid which can be update and populated with information obtained from the onboard sensors and independent to reference line and SCH data in order to allow to enable motor vehicles to achieve autonomous driving without the need for active human operation (see para[0002]) and to obtain the force situation of the vehicle at any position in the potential field (See para[0003]).
Regarding claim 3, Ostafew and Pan remain applied as claim 1. Ostafew teaches performing the boundary intrusion detection procedure further comprises: determining a driveline that accounts for the non-drivable points while following the reference path (See Ostafew para[0131] A view 630 illustrates using teleoperation data, such as described with respect to the teleoperation map data 512 of FIG. 5, in order to determine a driveline.); and
classifying (See Ostafew para[0035] An external object can be a dynamic (i.e., moving) object, such as a pedestrian, a remote vehicle, a motorcycle, a bicycle, etc. The dynamic object can be oncoming (toward the vehicle) or can be moving in the same direction as the vehicle. The dynamic object can be moving longitudinally or laterally with respect to the vehicle. A static object can become a dynamic object, and vice versa. ) the at least one of the static hazards or the slow crossing hazards as static risk field boundaries (See Ostafew para[0166] The examples 1000, 1100, and 1200 illustrate determining static boundaries for a drivable area. That is, FIGS. 10-12 are examples of adjusting a drivable area for static objects as described with respect to the operation 830 of FIG. 8. That is, the examples 1000, 1100, and 1200 illustrate determining, along the coarse driveline of an AV, where the AV can be driven with reference to static objects.).
However, Ostafew does not expressly disclose or otherwise teach parsing the evidence grid for evidence of at least one of static hazards or slow crossing hazards (with Ostafew’s static and slower vehicle). Nevertheless, Pan same field of endeavor teaches parsing the evidence grid for evidence of at least one of static hazards or slow crossing hazards ( see Pan para[0059] In some implementations, obstacles may include static and dynamic obstacles, FIG. 6 is a schematic diagram of a method for determining a current occupied area of a static obstacle in a current frame obstacle grid graph according to an embodiment of the present invention; para[0054] In step S100, the pre-built obstacle risk field SL can be obtained through a front-end module or a memory.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid which can be update and populated with information obtained from the onboard sensors and independent to reference line and SCH data in order to allow to enable motor vehicles to achieve autonomous driving without the need for active human operation (see para[0002]) and to obtain the force situation of the vehicle at any position in the potential field (See para[0003]).
Regarding claim 4, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein classifying the at least one of the static hazards or the slow crossing hazards comprises classifying the at least one of the static hazards or the slow crossing hazards based on a visibility of a local region. (see Ostafew at least para[0005], [0158],[0319]-[0321]).
Regarding claim 5, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein the visibility of the local region is based on at least one of a line-of-sight determination or a visible duration determination. (See Ostafew para[0005] identifying a farthest visible location along the trajectory, wherein the farthest visible location is a location along the trajectory such that the AV would be capable of detecting the object at said location;).
Regarding claim 6, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein classifying the at least one of the static hazards or the slow crossing hazards comprises classifying (See Ostafew para[0035]) the at least one of the static hazards or the slow crossing hazards based on a position (See Ostafew at least para[0299] while the AV is traveling in a lane, a road user (e.g., an object) is identified based on sensor data. One or more road users can be identified. Position and/or velocity information can be identified with/for the object based on a state maintained for the object ) of the at least one of the static hazards or the slow crossing hazards relative to the reference path (see Ostafew para[0208] a longitudinal constraint object can be a vehicle that is in front of the AV and that is moving slower than the AV. As such, the AV is to be slowed down to avoid rear-ending the dynamic object.).
Regarding claim 7, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein classifying (See Ostafew para[0035] the at least one of the static hazards or the slow crossing hazards comprises: filtering raw boundary data through a noise filter; and detecting an intrusion beyond a sensor noise level (See Ostafew para[0299] the uncertainty can be due to sensor noise, intention uncertainty, or other uncertainty. An uncertainty zone (for example, as described with respect to the bounding box 1906 of FIG. 19) can be associated with the object. A contingency plan (trajectory), which can include at least one of a lateral contingency or a longitudinal contingency is calculated for the AV; para [0041] As such, the planned trajectory may be based on false positives, noise in the sensor data, and other uncertainties.).
Regarding claim 8, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein performing the object intrusion detection procedure comprises: accumulating objects classified as stationary (See Ostafew para[0035]),
tracking a boundary of the at least one object over time; and classifying the at least one object as a slow crossing hazard (See Ostafew para[0182] the boundary points within a bin can be tracked over time. If it is determined that the group of boundary points within a bin is moving slightly (i.e., less than a threshold level of movement)).
However, Ostafew does not expressly disclose or otherwise teach using the evidence grid to evidence of at least one of static hazards or slow crossing hazards is adjacent to the reference path(with Ostafew’s static and slower vehicle detection in the path). Nevertheless, Pan same field of endeavor teaches accumulating objects classified as stationary in the evidence grid (See Pan para[0063] S12. Determine the area occupied by the obstacle in the current frame of the obstacle grid map based on the obstacle's current frame perception data, para[0059] The perception data is output by a known upstream perception module, and a current frame obstacle raster map is established in the Frenet coordinate system with the vehicle's current position) ; parsing the evidence grid to identify at least one object that is adjacent to the reference path( see Pan para[0059] In some implementations, obstacles may include static and dynamic obstacles, FIG. 6 is a schematic diagram of a method for determining a current occupied area of a static obstacle in a current frame obstacle grid graph according to an embodiment of the present invention; para[0054] In step S100, the pre-built obstacle risk field SL can be obtained through a front-end module or a memory.);
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid which can be update and populated with information obtained from the onboard sensors and independent to reference line and SCH data in order to allow to enable motor vehicles to achieve autonomous driving without the need for active human operation (see para[0002]) and to obtain the force situation of the vehicle at any position in the potential field (See para[0003]).
Regarding claim 9, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein classifying (See Ostafew para[0035] the at least one object as the slow crossing hazard comprises classifying the at least one object as the slow crossing hazard based on a sensor noise level. (See Ostafew para[0299] the uncertainty can be due to sensor noise, intention uncertainty, or other uncertainty).
Regarding claim 10, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein performing the lead vehicle hazard (See para[0164,[0208] and [0211] longitudinal constrain object can be a vehicle that is in front of the AV and that is moving slower than the AV)detection procedure comprises: monitoring a lead vehicle for irregular orientations (See Ostafew para[0258] uncertainty orientation) ; and classifying the lead vehicle as a slow crossing hazard (See para[0242] dynamic object classification (e.g., classifying an object as an oncoming vehicle, a lead vehicle, or a lateral constraint, para[0211] an oncoming dynamic object that blocks the trajectory of the AV can be treated as an in-lane (i.e., in the same lane as the AV) static object. For example, a lead dynamic object (i.e., a dynamic object that is in front of and moving in the same direction as the AV) can be treated as a longitudinal constraint in the discrete-time speed plan. For example, a dynamic object that is near the planned driveline of the AV can be treated as a lateral constraint).
Regarding claim 11, Ostafew and Pan remain applied as claim 1. Ostafew teaches performing an assessment to verify that there are no other possible explanations for the candidate SCHs. (see Ostafew for the boundary movement para[0183], vehicle motion para[0138], vehicle orientation para[0060]).
Nevertheless, Pan teaches wherein accumulating slow crossing hazard (SCH) data in the evidence grid comprises: accumulating candidate SCHs in the evidence grid (See Pan para[0063] S12. Determine the area occupied by the obstacle in the current frame of the obstacle grid map based on the obstacle's current frame perception data, para[0059] The perception data is output by a known upstream perception module, and a current frame obstacle raster map is established in the Frenet coordinate system with the vehicle's current position).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid which can be update and populated with information obtained from the onboard sensors and independent to reference line and SCH data in order to allow to enable motor vehicles to achieve autonomous driving without the need for active human operation (see para[0002]) and to obtain the force situation of the vehicle at any position in the potential field (See para[0003]).
Regarding claim 12, Ostafew and Pan remain applied as claim 1. Ostafew teaches further comprising: determining a speed constraint based on an SCH probability (See Ostafew para[0143] A speed profile can be generated by solving for the fastest speed profile along the coarse driveline that satisfies the constraints on speed); and determining an SCH constraint position relative to the reference path (See Ostafew para[0105] The driveline data can be used (e.g., by the reference-trajectory generation layer 504) to determine (i.e., generate, calculate, select, or otherwise determine) a coarse driveline from a first location to a second location. The driveline can be thought of as the line in the road over which the longitudinal axis of the AV coincides as the AV moves along the road).
Regarding claim 13, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein determining the speed constraint comprises determining a 0-speed constraint based on an SCH intensity (see Ostafew paras[0088]-[0090] based on the speed of the object, parked vehicle is considered as static object(0-speed constraint), para[0182])
a lateral distance to the reference path (see Ostafew para[0281] As the first hazard 2106 is very close to the AV 2102, the first hazard 2106 can incur by only a small lateral distance (as indicated by a maximum lateral incursion 2112) onto the lane 2104. , lateral constraint).
Regarding claim 14, Ostafew and Pan remain applied as claim 1. Ostafew teaches further comprising stopping the autonomous vehicle at a stopping position before reaching the SCH constraint position (Similar as static blockage, see Ostafew para[0154] when the adjusted drivable area, accounting for static objects, contains a static blockage, the process 800 adjusts the discrete-time speed plan such that the AV comes to a stop a prescribed distance before the static blockage.).
Regarding claim 16, Ostafew teaches A vehicle, comprising: one or more sensors; a memory; and a processor configured to execute instructions stored in the memory to(See Ostafew para[0006] The system includes a memory and a processor ) : obtain a reference path associated with the autonomous vehicle, wherein the reference path is based on a drive plan and driveline data(see Ostafew para[0105] The driveline can be thought of as the line in the road over which the longitudinal axis of the AV coincides as the AV moves along the road. The driveline data can be used (e.g., by the reference-trajectory generation layer 504) to determine (i.e., generate, calculate, select, or otherwise determine) a coarse driveline from a first location to a second location.);
accumulate slow crossing hazard (SCH) data( see Ostafew the trajectory planner process constraints related to static and dynamic objects,para[0090] the tracking component of the AV 302 can detect a parked vehicle 342 (i.e., a static object) and a bicycle 344 that is moving (i.e., a dynamic object that is a longitudinal constraint), by performing at least one of a boundary intrusion detection procedure, an object intrusion detection procedure(See Ostafew para[0149], figure 10-12), or a lead vehicle hazard detection procedure(see Ostafew para[0293] For example, the drivable area may be restricted more or less over time based on the detected intrusion); and
provide the SCH data to a process within the autonomous vehicle for further decision-making or risk mitigation (See Ostafew para[0004] controlling the AV according to the contingency trajectory; and, in response to the hazard object intruding into the path of the AV, controlling the AV to perform a maneuver to avoid the hazard object).
However, Ostafew does not expressly disclose or otherwise teach obtaining a risk field comprising an evidence grid established in a map frame associated with an autonomous vehicle. Nevertheless, Pan same field of endeavor teaches obtain a risk field comprising an evidence grid established in a map frame associated with an autonomous vehicle (See Pan para[0007] Acquire a high-precision map and an obstacle risk field (SL), wherein the obstacle risk field is pre-constructed based on multi-frame perception information of the obstacles) ;
accumulate slow crossing hazard (SCH) data in the evidence grid (See Pan para[0063] S12. Determine the area occupied by the obstacle in the current frame of the obstacle grid map based on the obstacle's current frame perception data, para[0059] The perception data is output by a known upstream perception module, and a current frame obstacle raster map is established in the Frenet coordinate system with the vehicle's current position)
wherein the evidence grid is established in the map frame such that the accumulated SCH data (1) is retained in the evidence grid independent of the reference path and (2) remains when the reference path changes or resets. (See Pan at least para[0131] Step S42: Project each grid point in the probability grid point set as an independent obstacle onto the reference line SL coordinate system, and determine the projected coordinates of each grid point in the reference line SL coordinate system )
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid which can be update and populated with information obtained from the onboard sensors and independent to reference line and SCH data in order to allow to enable motor vehicles to achieve autonomous driving without the need for active human operation (see para[0002]) and to obtain the force situation of the vehicle at any position in the potential field (See para[0003]).
Regarding claim 17, Ostafew and Pan remain applied as claim 1. Ostafew teaches performing an assessment to verify that there are no other possible explanations for the candidate SCHs. (see Ostafew for the boundary movement para[0183], vehicle
motion para[0138], vehicle orientation para[0060]).
Nevertheless, Pan teaches accumulate candidate SCHs in the evidence grid (See Pan para[0063] S12. Determine the area occupied by the obstacle in the current frame of the obstacle grid map based on the obstacle's current frame perception data, para[0059] The perception data is output by a known upstream perception module, and a current frame obstacle raster map is established in the Frenet coordinate system with the vehicle's current position).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid which can be update and populated with information obtained from the onboard sensors and independent to reference line and SCH data in order to allow to enable motor vehicles to achieve autonomous driving without the need for active human operation (see para[0002]) and to obtain the force situation of the vehicle at any position in the potential field (See para[0003]).
Regarding claim 18, Ostafew and Pan remain applied as claim 1. Ostafew teaches wherein the processor is further configured to: determine a speed constraint based on an SCH probability(See Ostafew para[0143] A speed profile can be generated by solving for the fastest speed profile along the coarse driveline that satisfies the constraints on speed);; and determine an SCH constraint position relative to the reference path (See Ostafew para[0105] The driveline data can be used (e.g., by the reference-trajectory generation layer 504) to determine (i.e., generate, calculate, select, or otherwise determine) a coarse driveline from a first location to a second location. The driveline can be thought of as the line in the road over which the longitudinal axis of the AV coincides as the AV moves along the road).
Regarding claim 19, Ostafew and Pan remain applied as claim 1. Ostafew teaches A non-transitory computer-readable medium storing instructions operable (See para[0057]) to cause one or more processors to perform operations comprising:
obtaining a reference path associated with the autonomous vehicle, wherein the reference path is based on a drive plan and driveline data (see Ostafew para[0105] The driveline can be thought of as the line in the road over which the longitudinal axis of the AV coincides as the AV moves along the road. The driveline data can be used (e.g., by the reference-trajectory generation layer 504) to determine (i.e., generate, calculate, select, or otherwise determine) a coarse driveline from a first location to a second location.);
accumulating slow crossing hazard (SCH) data( see Ostafew the trajectory planner process constraints related to static and dynamic objects,para[0090] the tracking component of the AV 302 can detect a parked vehicle 342 (i.e., a static object) and a bicycle 344 that is moving (i.e., a dynamic object that is a longitudinal constraint) in the evidence grid by performing at least one of a boundary intrusion detection procedure(see para[0149] boundary points), an object intrusion detection procedure(See Ostafew para[0149], figure 10-12) , or a lead vehicle hazard detection procedure (see Ostafew para[0293] For example, the drivable area may be restricted more or less over time based on the detected intrusion); and providing the SCH data to a process within the autonomous vehicle for further decision-making or risk mitigation(See Ostafew para[0004] controlling the AV according to the contingency trajectory; and, in response to the hazard object intruding into the path of the AV, controlling the AV to perform a maneuver to avoid the hazard object).
However, Ostafew does not expressly disclose or otherwise teach obtaining a risk field comprising an evidence grid established in a map frame associated with an autonomous vehicle. Nevertheless, Pan same field of endeavor teaches obtaining a risk field comprising an evidence grid established in a map frame associated with an autonomous vehicle See Pan para[0007] Acquire a high-precision map and an obstacle risk field (SL), wherein the obstacle risk field is pre-constructed based on multi-frame perception information of the obstacles),
accumulating slow crossing hazard (SCH) data in the evidence grid (See Pan para[0063] S12. Determine the area occupied by the obstacle in the current frame of the obstacle grid map based on the obstacle's current frame perception data, para[0059] The perception data is output by a known upstream perception module, and a current frame obstacle raster map is established in the Frenet coordinate system with the vehicle's current position)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid which can be update and populated with information obtained from the onboard sensors and independent to reference line and SCH data in order to allow to enable motor vehicles to achieve autonomous driving without the need for active human operation (see para[0002]) and to obtain the force situation of the vehicle at any position in the potential field (See para[0003]).
Regarding claim 20, Ostafew and Pan remain applied as claim 1. Ostafew teaches The non-transitory computer-readable medium of claim 19, the operations further comprising: determining a speed constraint based on an SCH probability (See Ostafew para[0143] A speed profile can be generated by solving for the fastest speed profile along the coarse driveline that satisfies the constraints on speed); and determining an SCH constraint position relative to the reference path. (See Ostafew para[0105] The driveline data can be used (e.g., by the reference-trajectory generation layer 504) to determine (i.e., generate, calculate, select, or otherwise determine) a coarse driveline from a first location to a second location. The driveline can be thought of as the line in the road over which the longitudinal axis of the AV coincides as the AV moves along the road).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatented over US20210031760A1 to Ostafew et al; (herein after “Ostafew”) in view of CN 114894206 A to Pan et al. (herein after “Pan”) and US2004119610A1 to Maemura et al. (herein after “Maemura”).
Regarding claim 15, Ostafew and Pan remain applied as claim 12. However, Ostafew and Pan do not teach wherein stopping the autonomous vehicle comprises stopping the autonomous vehicle at least a full car’s length from the SCH constraint position. Nevertheless, Maemura same field of endeavor teaches wherein stopping the autonomous vehicle comprises stopping the autonomous vehicle at least a full car’s length from the SCH constraint position (see Maemura para [0104] If only the parked vehicle 21 on the near side is present, the vehicle stops at a position ahead of the full length of the vehicle 1 relative to the parked vehicle 21).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Ostafew’s method for contingency planning for an autonomous vehicle with Pan’s evidence grid and Maemura’s stopping at a full length of a car in order to allow to guide to perform parking operation (consider as stop of a vehicle) by stopping at a prescribed distance (see abstract and para[0007]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NAZIA AFRIN/Examiner, Art Unit 3666
/JESS WHITTINGTON/Primary Examiner, Art Unit 3666c
