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 21-23, 27, 30-32, 36, and 39-40 have been amended.
Claims 1-20, 37-38, and 41-42 have been cancelled.
Claims 43-44 have been newly added.
Claims 21-36, 39-40, and 43-44 are currently pending and addressed below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-22, 28, 30-31, 39, and 43-44 are rejected under 35 U.S.C. 103 as being unpatentable over Dean et al. (US 2020/0019175), hereinafter referred to as Dean, in view of Park et al. (US 2024/0078774), hereinafter referred to as Park. Dean and Park are considered analogous to the claimed invention because they are in the same field of path planning and determining obstacle data. The examiner notes that the reference by Park was effectively filed on 12 November 2020, in view of its Foreign Application Priority Data.
Regarding claim 21, Dean teaches:
A method comprising:… determining risks of encountering obstacles on the plurality of available path segments ("The risk factors represent different types of undesirable events that may occur on the path segment... Other examples of risk factors include a bad experience risk, which includes hard stops, jerking motions, and other events that impact a passenger experience, and a harmful event risk, including a collision involving the vehicle." – see at least Dean: paragraph 0063) (The examiner notes that the bad experience risks including a collision involving the vehicle as taught by Dean corresponds to the claimed risks of encountering obstacles)
based on the historical navigation data ("Such training can correspond to supervised or unsupervised machine learning methods to accurately quantify fractional risk for traversing any given path segment based on historical harmful event data and/or close call data from AV driving logs and other sensor systems (e.g., driver computing devices)." – see at least Dean: paragraph 0019);
assigning costs to the plurality of available path segments based on the determined risks ("Among other benefits, the examples described herein achieve a technical effect of safely expanding autonomous vehicle operations through dynamic risk analysis of driving log data and the creation of navigation maps annotated with risk factor cost data. These risk factor cost data provide for an autonomous vehicle navigation system that increases safety and reduces the likelihood of bad experiences while riding in the vehicle." – see at least Dean: paragraph 0020);
and determining a path from a starting point to an ending point based on the assigned costs ("In some aspects, the global route planner 272 combines path segments in the geographic region to generate a travel route from a first location (e.g., a pick-up location for a passenger) to a second location (e.g., a travel destination) based on the path segment values in the annotated maps. For example, the global route planner 272 can determine the travel route with the lowest total cost, or at least a travel route that avoids higher cost alternatives." – see at least Dean: paragraph 0050),
wherein the determined path is used by a drone for autonomous navigation ("The vehicle 200 incorporating the vehicle control system 220 can be a ground-based vehicle (e.g., car, truck, bus), an air-based vehicle (e.g., airplane, drone, helicopter, or other aircraft), or other type of vehicle (e.g., watercraft)... In other implementations, the vehicle 200 is an autonomous vehicle (AV) configured to drive, navigate, operate, etc. with minimal and/or no interaction from a human driver. For example, when the vehicle 200 is an autonomous vehicle, the vehicle 200 can be configured to operate in one or more modes such as, for example, a fully autonomous operational mode and/or a semi-autonomous operational mode." – see at least Dean: paragraph 0036).
Dean does not explicitly disclose, but Park teaches:
counting, from historical navigation data of a plurality of available path segments and for each of the plurality of available path segments, a number of times an obstacle was encountered on the available path segment (“The tracking management unit 330 manages the tracking information of the cell used to detect the dynamic obstacle for every cell of a map stored in the storage unit 310, based on sensor data acquired from the sensor mounted in the robot. Here, the tracking information includes first tracking information representing how many times the laser is focused on the cell and second tracking information representing how many times the laser passes through the cell.” – see at least Park: paragraphs 0184-0185) (The examiner notes that the first tracking information as taught by Park indicates a counted number of times an obstacle was detected in a particular cell, and the second tracking information as taught by Park indicates a counted number of times an obstacle was not detected in a particular cell. While the examiner recognizes that Park alone does not teach encountering obstacles along a plurality of available path segments, the combination of Dean and Park is considered to fully teach the claimed limitations, as set forth in further detail below)
and a number of times the available path segment was navigated (“Here, the obstacle presence probability is obtained by dividing the first tracking information by a sum of the first tracking information and the second tracking information.” – see at least Park: paragraph 0018) (The examiner notes that the sum of the first tracking information and the second tracking information as taught by Park corresponds to a total number of times a cell was scanned for obstacles);
wherein determining the risks is based on the counted numbers (“Here, the obstacle presence probability is acquired based on the first tracking information and the second tracking information. For example, the obstacle presence probability (p) may be obtained by dividing the first tracking information (hit) by a sum of the first tracking information (hit) and the second tracking information (miss)” – see at least Park: paragraph 0203).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Dean with these above aforementioned teachings from Park to include counting, from historical navigation data of a plurality of available path segments and for each of the plurality of available path segments, a number of times an obstacle was encountered on the available path segment and a number of times the available path segment was navigated, wherein determining the risks is based on the counted numbers. At the time of the effective filing date of the claimed invention, one of ordinary skill in the art would have been motivated to incorporate Park’s use of first tracking information and second tracking information with Dean’s system for autonomous vehicle routing in order to use the tracking information to calculate the probability that a dynamic obstacle is present (“Here, the dynamic obstacle detection unit detects whether a cell whose accumulated sensing time information is larger than a predetermined reference time is a dynamic obstacle based on an obstacle presence probability and first tracking information and the obstacle presence probability is acquired based on the first tracking information and the second tracking information.” – see at least Park: paragraph 0016). Doing so would provide the benefit of improved path planning ability based on the tracking data for dynamic obstacles (“When the dynamic obstacle is not removed, the robot may unnecessarily plan a detour path or have a difficulty to localize... Therefore, development of a technique which detects and removes the dynamic obstacle while perform the simultaneous localization and mapping (SLAM) is necessary.” – see at least Park: paragraph 0007).
The examiner notes that, as set forth in further detail above, the primary reference by Dean already teaches collection of historical driving data via driving logs, wherein the driving log data includes data indicative of encounters with obstacles (see at least Dean: paragraph 0031) and data collected from vehicles traversing available path segments (see at least Dean: paragraph 0012), and wherein the driving log data is used to calculate a fractional risk quantity for a vehicle traveling along a given path segment (see at least Dean: paragraph 0012). Given a set of organized data such as the driving log data described by Dean (i.e., wherein the driving log data includes data from a plurality of traversals over a plurality of path segments, and an event parser which selectively identifies events of interest in which an obstacle was encountered), it is considered well within the ability of one or ordinary skill in the art to count the number of instances of events of interest and to count a total number of recorded traversals. As such, Park is merely applied to teach additional details regarding a particular example embodiment which explicitly includes counting a number of times an obstacle was detected and a number of times an obstacle was not detected, and using these counted numbers to calculate an obstacle presence probability, wherein the embodiment taught by Park includes conventional methods which are well known by one of ordinary skill in the art (e.g., simple counting and arithmetic operations). One of ordinary skill in the art would therefore be readily capable of modifying Dean to reach the claimed invention, based at least on the teachings and motivations provided by Park as set forth above.
Regarding claim 22, Dean in view of Park teaches all of the elements of the current invention as stated above. Dean further teaches:
further comprising modifying at least one cost corresponding to at least one path segment of the plurality of available path segments based on data indicating a change in risk of encountering an obstacle on the at least one path segment ("In some aspects, the navigation route cost system periodically updates values for each of the path segments (650). For example, conditions such as traffic, weather, or lighting may change over the course of a single trip. These changing conditions can affect the autonomous performance of the SDAVs and FAVs such that current risks and travel times for the remainder of the trip or for certain path segments may increase or decrease. As a result of these changes, the route planning system can update the travel route in order to select a set of lower cost path segments to reach the destination (660)." – see at least Dean: paragraph 0077).
Regarding claim 28, Dean in view of Park teaches all of the elements of the current invention as stated above. Dean further teaches:
further comprising: navigating the drone along the path to a destination ("The local route planner 274 can provide the selected motion plan to a vehicle controller 280 that controls one or more vehicle controls 290 (e.g., actuators or other devices that control throttle, steering, braking, etc.) to execute the selected motion plan." – see at least Dean: paragraph 0053);
identifying an obstacle along the path ("In some implementations, the perception system can determine state data for each object over a number of iterations. In particular, the perception system can update the state data for each object at each iteration. Thus, the perception system can detect and track objects that may impact risk factors or travel time along a given path segment (720)." – see at least Dean: paragraph 0079);
accessing a path planning graph ("In addition to using the sensor data, the perception system 230 can retrieve map data (e.g., localization maps) that provide detailed information about the surrounding environment of the vehicle. The map data can provide information regarding the identity and location of different paths (e.g., roads, road segments, lanes, lane segments, parking lanes, turning lanes, bicycle lanes, or other portions of a particular path). In some examples, path segments within the map data can include one or more descriptors including, for example, a path segment identifier, a start point for the path segment, an end point for the path segment, a directionality (e.g., direction of traffic flow), and/or connectivity identifiers for other path segments that are predecessors and/or successors to a given path segment." – see at least Dean: paragraph 0041) (The examiner notes that the map data including information regarding different path segment as taught by Dean corresponds to the claimed path planning graph);
generating a modified path planning graph based on the identified obstacle and the accessed path planning graph ("Sensor data from the object detection sensors 214 can include information that describes the location (e.g., in three-dimensional space relative to the vehicle 200) of points that correspond to objects within the surrounding environment of the vehicle 200 (e.g., at one or more times). In some implementations, the sensors 210 can be used to facilitate navigation of vehicle 200 when the vehicle 200 is operating in an autonomous mode. In addition, the sensors 210 gather and log data as the vehicle 200 traverses routes within a given geographic region." – see at least Dean: paragraphs 0038-0039);
and determining an alternate path to the destination based on the modified path planning graph ("For example, if the local route planner 274 determines that the vehicle 200 should deviate from the travel route (e.g., to avoid an object in the road or pass a slow driver), the local route planner 274 can select an alternate path segment based on which alternate path segment has lower travel time or risk factor costs." – see at least Dean: paragraph 0052).
Regarding claim 30, this claim is substantially similar to claim 21 and is, therefore, rejected in the same manner as claim 21 as has been set forth above.
Regarding claim 31, this claim is substantially similar to claim 22 and is, therefore, rejected in the same manner as claim 22 as has been set forth above.
Regarding claim 39, this claim is substantially similar to claim 21 and is, therefore, rejected in the same manner as claim 21 as has been set forth above.
Regarding claim 43, Dean in view of Park teaches all of the elements of the current invention as stated above. Dean does not explicitly disclose, but Park teaches:
wherein determining the risks comprises, for each available path segment of the plurality of available path segments, dividing the number of times an obstacle was encountered on the available path segment by the number of times the available path segment was navigated (“Here, the obstacle presence probability is acquired based on the first tracking information and the second tracking information. For example, the obstacle presence probability (p) may be obtained by dividing the first tracking information (hit) by a sum of the first tracking information (hit) and the second tracking information (miss)” – see at least Park: paragraph 0203).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Dean with these above aforementioned teachings from Park such that determining the risks comprises, for each available path segment of the plurality of available path segments, dividing the number of times an obstacle was encountered on the available path segment by the number of times the available path segment was navigated. At the time of the effective filing date of the claimed invention, one of ordinary skill in the art would have been motivated to incorporate Park’s use of first tracking information and second tracking information with Dean’s system for autonomous vehicle routing in order to use the tracking information to calculate the probability that a dynamic obstacle is present (“Here, the dynamic obstacle detection unit detects whether a cell whose accumulated sensing time information is larger than a predetermined reference time is a dynamic obstacle based on an obstacle presence probability and first tracking information and the obstacle presence probability is acquired based on the first tracking information and the second tracking information.” – see at least Park: paragraph 0016). Doing so would provide the benefit of improved path planning ability based on the tracking data for dynamic obstacles (“When the dynamic obstacle is not removed, the robot may unnecessarily plan a detour path or have a difficulty to localize... Therefore, development of a technique which detects and removes the dynamic obstacle while perform the simultaneous localization and mapping (SLAM) is necessary.” – see at least Park: paragraph 0007).
The examiner notes that, as set forth in further detail above, the primary reference by Dean already teaches collection of historical driving data via driving logs, wherein the driving log data includes data indicative of encounters with obstacles (see at least Dean: paragraph 0031) and data collected from vehicles traversing available path segments (see at least Dean: paragraph 0012), and wherein the driving log data is used to calculate a fractional risk quantity for a vehicle traveling along a given path segment (see at least Dean: paragraph 0012). Given a set of organized data such as the driving log data described by Dean (i.e., wherein the driving log data includes data from a plurality of traversals over a plurality of path segments, and an event parser which selectively identifies events of interest in which an obstacle was encountered), it is considered well within the ability of one or ordinary skill in the art to count the number of instances of events of interest and to count a total number of recorded traversals. As such, Park is merely applied to teach additional details regarding a particular example embodiment which explicitly includes counting a number of times an obstacle was detected and a number of times an obstacle was not detected, and using these counted numbers to calculate an obstacle presence probability, wherein the embodiment taught by Park includes conventional methods which are well known by one of ordinary skill in the art (e.g., simple counting and arithmetic operations). One of ordinary skill in the art would therefore be readily capable of modifying Dean to reach the claimed invention, based at least on the teachings and motivations provided by Park as set forth above.
Regarding claim 44, Dean in view of Park teaches all of the elements of the current invention as stated above. Dean further teaches:
wherein the obstacles are objects that are not known or whose locations are not known prior to navigation (“Furthermore, vehicle computing devices 310 and server computing devices 350 can dynamically determine values for the path segments and adjust a travel route accordingly during operation of the vehicle 300. Examples described herein recognize that conditions, such as traffic, weather, or lighting may change over the course of a single trip. These changing conditions can affect the autonomous performance of the SDAVs and FAVs such that current risks for the remainder of the trip or for certain path segments may increase to unacceptable levels” – see at least Dean: paragraphs 0059-0060) (The examiner notes that the dynamically determined data and changing conditions as taught by Dean are indicative of obstacle data that is not known prior to navigation (e.g., real-time dynamic positions of other vehicles and/or pedestrians)).
Claims 23-24, 32-33, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Dean in view of Park, further in view of Whittaker et al. (US 2008/0059015), hereinafter referred to as Whittaker. Whittaker is considered analogous to the claimed invention because they are in the same field of path planning.
Regarding claim 23, Dean in view of Park teaches all of the elements of the current invention as stated above. Dean further teaches:
wherein modifying the at least one cost corresponding to the at least one path segment comprises: decreasing the cost of a first path segment based on historical data indicating a low risk of encountering an obstacle on the first path segment,… or increasing the cost of a second path segment based on historical data indicating a high risk of encountering an obstacle on the second path segment ("As one example, the local route planner 274 can determine a cost function for each of one or more candidate motion plans for the vehicle 200 based at least in part on the current locations and/or predicted future locations of any detected objects. For example, the cost function can describe a cost (e.g., over time) of adhering to a particular candidate motion plan. For example, the cost described by a cost function can increase when the vehicle expects to impact another object and/or deviate from the travel route." – see at least Dean: paragraph 0052),
Dean does not explicitly disclose, but Whittaker teaches:
wherein an increased cost corresponds to a decreased path speed for the second path segment ("The speed ranges for each risk level are assigned to help maintain this assumption. Given these two assumptions, the algorithm to increase speed iteratively adjusts a speed scale factor which is applied to the speed for every point in the path. The speed at each waypoint is limited to the lower of the maximum permissible speed or the upper speed bound for the assigned risk level at the point." – see at least Whittaker: paragraph 0032).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Dean with these above aforementioned teachings from Whittaker such that an increased cost corresponds to a decreased path speed for the second path segment. At the time of the effective filing date of the claimed invention, one of ordinary skill in the art would have been motivated to incorporate Whittaker’s speed-setting process for an autonomous vehicle with Dean’s system for autonomous vehicle routing in order to automatically adjust the speed of a vehicle along a path based on an assessed risk (“During pre-planning, a speed setting process specifies the target speeds for an autonomous vehicle given a target elapsed time to complete a pre-planned path. Speed setting is performed by assessing the risk for a given robot to traverse a section of terrain based on available information. An automated process then preferably uses a speed policy generated by combining the risk assessment with any speed limits imposed on the course to assign planned speeds to each waypoint in the path.” – see at least Whittaker: paragraph 0028). Doing so would provide the benefit of maximizing a vehicle speed along a route with consideration of the determined risks along the route (“The speed at each waypoint is limited to the lower of the maximum permissible speed or the upper speed bound for the assigned risk level at the point. The iteration process is either terminated by achieving the desired elapsed time or by maximizing the possible speed at all points along the route.” – see at least Whittaker: paragraph 0032).
The examiner notes that while Whittaker is primarily directed towards ground vehicles, one of ordinary skill in the art would recognize that the same benefits as taught by Whittaker regarding autonomous vehicle navigation would apply regardless of the particular type of vehicle, because it is a common and well-known practice to reduce vehicle speed while the vehicle is traveling in areas with a high risk of collision (“The speed planner 730 operates on the output of the conformal planner 726 and preemptively slows the robot for any sharp turns that may result when the conformal planner 726 generates a plan to avoid obstacles. Additionally, the speed planner 730 may take into account information from the route that is beyond the sensor field of view, such as speed limits and upcoming turns, to ensure that speeds are safe entering turns and dangerous areas.” – see at least Whittaker: paragraph 0070).
Regarding claim 24, Dean in view of Park and Whittaker teaches all of the elements of the current invention as stated above. Dean further teaches:
estimating a travel time for each of the plurality of available path segments based on the determined path speeds ("As described herein, travel times and risk factors associated with operating an AV along a given path segment can be determined and refined computationally through risk regression and machine learning techniques." – see at least Dean: paragraph 0011),
wherein the assigned costs correspond to the estimated travel times of the plurality of available path segments ("In addition to the risk factor cost layers, the event parser 120 can extract features such as road distances, speed limits, traffic signals, school zones, etc. from the localization maps or other mapping data in the data store 140 in order to calculate a score for a travel time cost layer." – see at least Dean: paragraph 0032).
Dean does not explicitly disclose, but Whittaker teaches:
wherein assigning the costs to the plurality of available path segments comprises: determining a path speed for each of the plurality of available path segments based on the determined risk of encountering an obstacle along the available path segment ("The speed ranges for each risk level are assigned to help maintain this assumption. Given these two assumptions, the algorithm to increase speed iteratively adjusts a speed scale factor which is applied to the speed for every point in the path. The speed at each waypoint is limited to the lower of the maximum permissible speed or the upper speed bound for the assigned risk level at the point. The iteration process is either terminated by achieving the desired elapsed time or by maximizing the possible speed at all points along the route." – see at least Whittaker: paragraph 0032);
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Dean with these above aforementioned teachings from Whittaker such that assigning the costs to the plurality of available path segments comprises: determining a path speed for each of the plurality of available path segments based on the determined risk of encountering an obstacle along the available path segment. At the time of the effective filing date of the claimed invention, one of ordinary skill in the art would have been motivated to incorporate Whittaker’s speed-setting process for an autonomous vehicle with Dean’s system for autonomous vehicle routing in order to automatically adjust the speed of a vehicle along a path based on an assessed risk (“During pre-planning, a speed setting process specifies the target speeds for an autonomous vehicle given a target elapsed time to complete a pre-planned path. Speed setting is performed by assessing the risk for a given robot to traverse a section of terrain based on available information. An automated process then preferably uses a speed policy generated by combining the risk assessment with any speed limits imposed on the course to assign planned speeds to each waypoint in the path.” – see at least Whittaker: paragraph 0028). Doing so would provide the benefit of maximizing a vehicle speed along a route with consideration of the determined risks along the route (“The speed at each waypoint is limited to the lower of the maximum permissible speed or the upper speed bound for the assigned risk level at the point. The iteration process is either terminated by achieving the desired elapsed time or by maximizing the possible speed at all points along the route.” – see at least Whittaker: paragraph 0032).
Regarding claim 32, this claim is substantially similar to claim 23 and is, therefore, rejected in the same manner as claim 23 as has been set forth above.
Regarding claim 33, this claim is substantially similar to claim 24 and is, therefore, rejected in the same manner as claim 24 as has been set forth above.
Regarding claim 40, this claim is substantially similar to claim 23 and is, therefore, rejected in the same manner as claim 23 as has been set forth above.
Claims 25 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Dean in view of Park and Whittaker, further in view of Gariepy et al. (US 2017/0197643), hereinafter referred to as Gariepy. Gariepy is considered analogous to the claimed invention because they are in the same field of path planning.
Regarding claim 25, Dean in view of Park and Whittaker teaches all of the elements of the current invention as stated above. Dean does not explicitly disclose, but Gariepy teaches:
wherein: the determined path speeds comprise a low path speed and a high path speed ("Speed of travel can be set in a variety of ways. For example, vehicle 104 may simply select the maximum speed of which vehicle 104 is capable. In other examples, vehicle 104 can apply restrictions (such as speed limits) stored in memory 254 to the speed selected. In further examples, vehicle 104 can vary the speed based on operational parameters that are required to conform with the path. For example, when the path requires vehicle 104 to make a turn, vehicle 104 may be configured to select a lower speed for the duration of the turn." – see at least Gariepy: paragraph 0042);
the low path speed corresponds to a speed at which the drone is expected to be able to stop in time when a sensor of the drone detects an obstacle ("Specifically, at block 450, processor 250 is configured to set a speed that reduces the size of second sensor region 520 sufficiently for the obstacle detected at block 430 (obstacle 600, in the present example) to no longer fall within second sensor region 520." – see at least Gariepy: paragraph 0063) (The examiner notes that the size of a second sensor region corresponds to a sensing range which ensures that the vehicle can safely stop before colliding with an obstacle ("For example, the second sensor region can be set as a predetermined multiple of the stopping distance of vehicle 104 (e.g. twice the stopping distance), or as the stopping distance supplemented with a distance that vehicle 104 will travel in a predetermined amount of time at its current speed." – see at least Gariepy: paragraph 0049). Further, Gariepy teaches the embodiment in which the vehicle is an aerial vehicle (e.g., a drone));
and the high path speed corresponds to a speed that exceeds an expected stopping ability of the drone when the sensor of the drone detects an obstacle ("when the new path is available (or the obstacle detected at block 430 moves away), path execution at block 410 can resume, generally leading to an increase in vehicle speed." – see at least Gariepy: paragraph 0066).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Dean with these above aforementioned teachings from Gariepy such that the determined path speeds comprise a low path speed and a high path speed; the low path speed corresponds to a speed at which the drone is expected to be able to stop in time when a sensor of the drone detects an obstacle, and the high path speed corresponds to a speed that exceeds an expected stopping ability of the drone when the sensor of the drone detects an obstacle. At the time of the effective filing date of the claimed invention, one of ordinary skill in the art would have been motivated to incorporate Gariepy’s method of adjusting a vehicle speed with Dean’s system for autonomous vehicle routing in order to adjust vehicle operation based on a risk of encountering an obstacle (“The presence of obstacle 600 in second sensor region 520 indicates that there is a risk that obstacle 600 could enter first sensor region 516 and trigger an emergency stop in vehicle 104. Returning to FIG. 4, at block 435 vehicle 104 is configured to take various actions to reduce or eliminate that risk. These actions, in general, correspond to the second fail-safe routine mentioned earlier.” – see at least Gariepy: paragraph 0058). Doing so would provide the benefit of reducing the risk of colliding with an obstacle or needing to interrupt operation of the vehicle (“In order to reduce interruptions to the operation of vehicle 104 (that is, in completing the task of travelling to target location 512), while also reducing or eliminating the risk of colliding with obstacle 600 (which, as is evident from FIG. 6, would occur if vehicle 104 continued along path 500), vehicle 104 is therefore configured to perform additional actions as part of the second fail-safe routine.” – see at least Gariepy: paragraph 0060).
Regarding claim 34, this claim is substantially similar to claim 25 and is, therefore, rejected in the same manner as claim 25 as has been set forth above.
Claims 26, 29, and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Dean in view of Park and Gariepy.
Regarding claim 26, Dean in view of Park and Gariepy teaches all of the elements of the current invention as stated above. Dean does not explicitly disclose, but Gariepy teaches:
further comprising: determining a low path speed for a first available path segment of the plurality of available path segments, wherein the low path speed corresponds to a speed at which the drone is expected to be able to stop in time when a sensor of the drone detects an obstacle ("Specifically, at block 450, processor 250 is configured to set a speed that reduces the size of second sensor region 520 sufficiently for the obstacle detected at block 430 (obstacle 600, in the present example) to no longer fall within second sensor region 520." – see at least Gariepy: paragraph 0063);
and in response to the drone traveling the first available path segment without encountering an obstacle, changing the low path speed for the first available path segment to a high path speed that exceeds an expected stopping ability of the drone when the sensor of the drone detects an obstacle ("when the new path is available (or the obstacle detected at block 430 moves away), path execution at block 410 can resume, generally leading to an increase in vehicle speed." – see at least Gariepy: paragraph 0066).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Dean with these above aforementioned teachings from Gariepy to include determining a low path speed for a first available path segment of the plurality of available path segments, wherein the low path speed corresponds to a speed at which a drone is expected to be able to stop in time when a sensor of the drone detects an obstacle, and in response to the drone traveling the first available path segment without encountering an obstacle, changing the path speed for the first available path segment to a high path speed that exceeds an expected stopping ability of the drone when the sensor of the drone detects an obstacle. At the time of the effective filing date of the claimed invention, one of ordinary skill in the art would have been motivated to incorporate Gariepy’s method of adjusting a vehicle speed with Dean’s system for autonomous vehicle routing in order to adjust vehicle operation based on a risk of encountering an obstacle (“The presence of obstacle 600 in second sensor region 520 indicates that there is a risk that obstacle 600 could enter first sensor region 516 and trigger an emergency stop in vehicle 104. Returning to FIG. 4, at block 435 vehicle 104 is configured to take various actions to reduce or eliminate that risk. These actions, in general, correspond to the second fail-safe routine mentioned earlier.” – see at least Gariepy: paragraph 0058). Doing so would provide the benefit of reducing the risk of colliding with an obstacle or needing to interrupt operation of the vehicle (“In order to reduce interruptions to the operation of vehicle 104 (that is, in completing the task of travelling to target location 512), while also reducing or eliminating the risk of colliding with obstacle 600 (which, as is evident from FIG. 6, would occur if vehicle 104 continued along path 500), vehicle 104 is therefore configured to perform additional actions as part of the second fail-safe routine.” – see at least Gariepy: paragraph 0060).
Regarding claim 29, Dean in view of Park and Gariepy teaches all of the elements of the current invention as stated above. Dean does not explicitly disclose, but Gariepy teaches:
further comprising: determining whether the identified obstacle expired, wherein determining whether the identified obstacle expired comprises one of (1) setting a timer and determining whether the timer expired or (2) receiving a report from another drone indicating that it did not encounter the obstacle ("Reducing vehicle speed to zero presents an interruption in the operation of vehicle 104, but because the first fail-safe routine (i.e. emergency stop) has not occurred, resuming operation is still relatively straightforward, in that when the new path is available (or the obstacle detected at block 430 moves away), path execution at block 410 can resume, generally leading to an increase in vehicle speed." – see at least Gariepy: paragraph 0066);
and in response to determining that the identified obstacle expired, restoring the path planning graph ("In addition, it is possible for some obstacles, such as other vehicles, human operators and the like, to move, thus rendering their indicated positions in the map (if they were represented in the map) inaccurate. As will be discussed in greater detail below, vehicle 104 is configured to perform various actions to reduce or eliminate the likelihood of collisions with objects not accounted for in the path, while also reducing or eliminating interruptions in the completion of vehicle 104's tasks." – see at least Gariepy: paragraph 0021).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Dean with these above aforementioned teachings from Gariepy to include determining whether the identified obstacle expired, wherein determining whether the identified obstacle expired comprises one of (1) setting a timer and determining whether the timer expired or (2) receiving a report from another drone indicating that it did not encounter the obstacle, and in response to determining that the identified obstacle expired, restoring the path planning graph. At the time of the effective filing date of the claimed invention, one of ordinary skill in the art would have been motivated to incorporate Gariepy’s method of updating an obstacle map with Dean’s system for autonomous vehicle routing in order to obtain up-to-date information on obstacle locations (“Vehicle 104 also includes at least one machine vision sensor 216 for detecting objects in the surroundings of vehicle 104. For example, vehicle 104 can include any suitable one of, or any suitable combination of, laser-based sensing devices (e.g. a LIDAR sensor), cameras and the like.” – see at least Gariepy: paragraph 0027). Doing so would provide the benefit of accounting for movement of obstacles within an environment (“Such predictions can also take into account the movement of obstacles, therefore generating a prediction not only of the future position of vehicle 104, but also a future position of an obstacle based on the current observed (e.g. via sensor 216) motion of the obstacle.” – see at least Gariepy: paragraph 0072).
Regarding claim 35, this claim is substantially similar to claim 26 and is, therefore, rejected in the same manner as claim 26 as has been set forth above.
Claims 27 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Dean in view of Dubner (US 2022/0180738), hereinafter referred to as Dubner. Dubner is considered analogous to the claimed invention because they are in the same field of path planning.
Regarding claim 27, Dean in view of Park teaches all of the elements of the current invention as stated above. Dean does not explicitly disclose, but Dubner teaches:
wherein the plurality of available path segments are available path segments in a warehouse ("Vehicle pathway 104 may be a road, highway, a warehouse aisle, factory floor, or a pathway not connected to the earth's surface." – see at least Dubner: paragraph 0019),
the method further comprising: resetting one or more of the assigned costs at least once a day ("A temporary change to pathway infrastructure may include a variety of lengths of time, including a short period, such as hours, or a longer period, such as a year." – see at least Dubner: paragraph 0020).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have modified Dean with these above aforementioned teachings from Dubner such that the available path segments are available path segments in a warehouse, and further comprising: resetting one or more of the assigned costs at least once a day. At the time of the effective filing date of the claimed invention, one of ordinary skill in the art would have been motivated to incorporate Dubner’s method of assessing risk during vehicle travel with Dean’s system for autonomous vehicle routing in order to guide vehicle movement in an environment where pathway infrastructure may change over time, such as in a warehouse (“Vehicle pathway 104 may include a temporary zone, such as TTC zone 102, on vehicle pathway 104. TTC zone 102 may represent a section of vehicle pathway 104 that includes temporary changes to pathway infrastructure, such as through the use of traffic control devices.” – see at least Dubner: paragraph 0020). Doing so would provide the benefit of allowing a vehicle to adapt to temporary changes in an environment (“For example, TTC zone 102 may have navigational characteristics such as a traffic pattern change, worker presence, lane modifications, road surface quality, construction standards changes, or other conditions that are not normally present on or near vehicle pathway 104. Due to these temporary changes to pathway infrastructure in pathway 104, TTC zone 102 may have a different risk of an adverse event occurring in TTC zone 102 compared to ordinary conditions of pathway 104.” – see at least Dubner: paragraph 0022).
Regarding claim 36, this claim is substantially similar to claim 27 and is, therefore, rejected in the same manner as claim 27 as has been set forth above.
Response to Arguments
Applicant’s arguments filed 31 August 2026 with respect to claims 21-36, 39-40, and 43-44 have been considered but are moot in view of the new grounds of rejection based on the teachings of the newly relied upon reference by Park, which has been introduced to address the amended claims.
In particular, the amended claims add limitations which recite counting, from historical navigation data of a plurality of available path segments and for each of the plurality of available path segments, a number of times an obstacle was encountered on the available path segment and a number of times the available path segment was navigated, and wherein determining the risks is based on the counted numbers. The examiner acknowledges that while the primary reference by Dean teaches relevant aspects of the claimed invention, as set forth in further detail above and in the Final Rejection filed 29 May 2026, Dean does not explicitly teach counting instances of encountering obstacles and navigating path segments.
To address the Applicant’s Response, the examiner has introduced the reference by Park which explicitly teaches tracking (i.e., counting) a number of times an obstacle was detected at a given location on a map and a total number of times the given location on the map was scanned for obstacles (see at least Park: paragraphs 0184-185), and determining an obstacle presence probability by dividing the number of times an obstacle was detected at the given location on a map by the total number of times the given location on the map was scanned for obstacles (see at least Park: paragraph 0018). As such, Park is considered to cure the aforementioned deficiencies of Dean, as set forth in further detail above in at least the rejection of claim 21 under 35 U.S.C. 103.
The examiner notes that since the primary reference by Dean already relevant aspects of the claimed invention, including collection of driving log data from vehicles traversing available path segments (see at least Dean: paragraph 0012), parsing the driving log data to identify events of interest (e.g., encounters with obstacles) (see at least Dean: paragraph 0031), and using the driving log data to calculate a fractional risk quantity for a vehicle traveling along a given path segment (see at least Dean: paragraph 0012), it is considered well within the ability of one or ordinary skill in the art to count the number of instances of events of interest and to count a total number of recorded traversals.
As such, Park is merely applied to teach additional details regarding a particular example embodiment which explicitly includes counting a number of times an obstacle was detected and a number of times an obstacle was not detected, and using these counted numbers to calculate an obstacle presence probability, wherein the embodiment taught by Park includes conventional methods which are well known by one of ordinary skill in the art (e.g., simple counting and arithmetic operations). Given that the distinctions between the instant claims and the reference by Dean amount to merely well-known arithmetic operations (e.g., the counting and division operations taught by Park to calculate an obstacle presence probability), one of ordinary skill in the art would therefore be readily capable of modifying Dean to reach the claimed invention, based at least on the teachings and motivations provided by Park as set forth above, and based on the rationale of combining prior art elements according to known methods to yield predictable results (see at least MPEP 2143(I)(A)).
As per newly added claim 43, Park is considered to teach the claimed limitations by explicitly teaching a division operation between two counted values to calculate an obstacle presence probability (see at least Park: paragraph 0203), as described in further detail above.
As per newly added claim 44, Dean is considered to teach the claimed limitations by explicitly teaching the use of dynamic and changing conditions, such as real-time positions of other vehicles and pedestrians (see at least Dean: paragraphs 0042 and 0059-0060), which are obstacles whose locations are not known prior to navigation.
As such, the pending claims are rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kwon et al. (US 2023/0058999) teaches a method for controlling a robot, including obtaining an occupancy frequency for each of a plurality of areas based on a counted number of times that each of the plurality of areas is occupied by one or more obstacles.
Xia et al. (US 2018/0307243) teaches a method and device for drawing a room layout, including a sweeping robot provided with a counter corresponding to the reference obstacle, wherein each time the reference obstacle is encountered, the sweeping robot instructs the counter to add 1.
Zhou et al. (US 2021/0080272) teaches a path planning system and method for a robot, including determining an obstacle influence factor which includes an obstacle avoiding difficulty coefficient and a probability of appearance of an obstacle on each of a plurality of path segments.
Zhang et al. (US 2021/0089793) teaches a method and apparatus for processing information, including counting a number of obstacles that have appeared in a grid region, determining a sum of appearance rates of the obstacles having appeared in the grid region, and computing a ratio of the sum of said sum to said number to use as a confidence degree of the grid region.
Okada et al. (US 2023/0244235) teaches a route planning system for a mobile robot, including calculating a degree of certainty that an obstacle is present at a location, and planning a movement route based on the calculated degree of certainty.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOMINICK ANTHONY MULDER whose telephone number is (571)272-3610. The examiner can normally be reached Monday - Friday 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RAMYA P BURGESS can be reached at (571)272-6011. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.M./Examiner, Art Unit 3661
/TUAN C TO/Primary Examiner, Art Unit 3661
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