DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding Claim Rejections - 35 USC § 112:
Applicant's arguments filed 9/15/2025 have been fully considered and are persuasive. The
rejection of claims 1, 3-9, 11 and 13-19 and 21-24 for failing to comply with the written description requirement is withdrawn.
Regarding Claim Rejections - 35 USC § 101:
Applicant's arguments filed 9/15/2025 have been fully considered but they are not persuasive. The amendment to independent claims 1, 11, 23 and 14 does not overcome the rejection. The claimed invention describes an intended use of running a simulation of the vehicle using the log data segment and the appended trajectory to evaluate performance of software for controlling an autonomous vehicle; but does not actually claim or recite an actual practical application. Merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a practical application. There is no indication that the claims as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular vehicle navigation or control problem, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claims, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole are not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Thus, the rejection is maintained.
Regarding Claim Rejections - 35 USC § 103:
Applicant’s arguments with respect to claims 1, 3, 8-9, 13 and 18-19 have been considered but are moot. Amendment to independent claims 1, 11, 23 and 24 necessitates new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-9, 11, 13-19 and 21-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is directed to method of evaluating performance of to test software for controlling the an autonomous vehicle in the autonomous driving mode (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed
to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method comprising:
identifying, by one or more processors that was captured by a perception system of a vehicle having one or more sensors;
identifying, by the one or more processors, an observation of a road user object in the log data segment, wherein: the observation of the road user object occurs after the start of the log data segment, the observation includes i) a point in time when the perception system initially observes the road user object and ii) an observed location of the road user object at the point in time, and; the log data segment does not include another observation of the road user object before the point in time;
estimating, by the one or more processors, a speed of the road user object at the point in time;
estimating, by the one or more processors and based on the estimated speed of the road user object, a distance traveled by the road user object from the start of the log data segment to the point in time;
determining, by the one or more processors and based on the estimated distance traveled by the road user object, a location of the road user object;
determining, by the one or more processors, a trajectory of the road user object between the determined location of the road user object and the observed location of the road user object;
appending, by the one or more processors, the trajectory of the road user object to the log data segment to account for movement of the road user object before the point in time; and
running, by the one or more processors, a simulation of a virtual autonomous vehicle using the log data segment having the trajectory of the road user object appended thereto, wherein running the simulation includes: controlling the virtual autonomous vehicle in an autonomous driving mode to interact with the road user object according to the trajectory; and
evaluating performance of to test software for controlling the actual an autonomous vehicle in the autonomous driving mode.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind, but for the recitation of “by one or more processors”. That is, other than reciting “by one or more processors” nothing in the claim elements precludes the step from practically being performed in the mind. For example, “identifying…”, “estimating…” and “determining…” in the context of this claim encompasses a person observing a road user object and forming a simple judgement based on the observed tracking of the road user object. The mere nominal recitation of “by one or more processors” does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process. Accordingly, the claim recites at least one abstract idea.
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as
follows (where the underlined portions are the “additional limitations”), while the bolded portions
continue to represent the “abstract idea”:
A method comprising:
identifying, by one or more processors that was captured by a perception system of a vehicle having one or more sensors;
identifying, by the one or more processors, an observation of a road user object in the log data segment, wherein: the observation of the road user object occurs after the start of the log data segment, the observation includes i) a point in time when the perception system initially observes the road user object and ii) an observed location of the road user object at the point in time, and; the log data segment does not include another observation of the road user object before the point in time;
estimating, by the one or more processors, a speed of the road user object at the point in time;
estimating, by the one or more processors and based on the estimated speed of the road user object, a distance traveled by the road user object from the start of the log data segment to the point in time;
determining, by the one or more processors and based on the estimated distance traveled by the road user object, a location of the road user object;
determining, by the one or more processors, a trajectory of the road user object between the determined location of the road user object and the observed location of the road user object;
appending, by the one or more processors, the trajectory of the road user object to the log data segment to account for movement of the road user object before the point in time; and
running, by the one or more processors, a simulation of a virtual autonomous vehicle using the log data segment having the trajectory of the road user object appended thereto, wherein running the simulation includes: controlling the virtual autonomous vehicle in an autonomous driving mode to interact with the road user object according to the trajectory; and
evaluating performance of to test software for controlling the actual an autonomous vehicle in the autonomous driving mode.
For the following reason(s), the examiner submits that the above identified additional
limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “appending…” and “running…” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, “appending…the determined trajectory to the log data segment” is considered insignificant extra-solution activity and is not too complex to be performed in the human mind with the aid of a pen and paper. The limitations “running….a simulation of a virtual autonomous vehicle using the log data….” and “evaluating performance of software for controlling the actual an autonomous vehicle” are both forms of data processing/output and considered insignificant extra-solution activity; merely indicating a field of use or technological environment in which to apply a judicial exception. It describes an intended use of running a simulation of the vehicle using the log data segment and the appended trajectory for evaluating performance of software for controlling the actual an autonomous vehicle in the autonomous driving mode; but does not actually claim the action of controlling. The “running” step not a control step, it does not add practical application to the abstract idea, it merely indicates a field of use or technological environment in which to apply a judicial exception.
Additionally, the claim recites the additional limitation of “a log data segment of sensor data captured by a perception system of a vehicle having one or more sensors”. The sensors are recited at a high level of generality (i.e. as a general means of gathering data for use in the identifying step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. There is no indication that the sensors are anything other than a generic, off the-shelf computer components.
Lastly, the “processor” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. The autonomous system is recited at a high level of generality and merely automates the identifying, estimating and determining steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular vehicle navigation or control problem, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include
additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the “identifying…”, “estimating…” and “determining…”… amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “appending…”,“running…” and “evaluating”; the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in
Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of “appending…”,“running…” and “evaluating” are well-understood, routine, and conventional activities, and the specification does not provide any indication that the processor is anything other than a conventional computer network component. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Same analysis applied to independent claims 11, 23 and 24.
Dependent claims 3-9, 13-19 and 21-22 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 3-9, 13-19 and 21-22 are not patent eligible under the same rationale as provided for in the rejection of Claim 1. Therefore, claims 1, 3-9, 11, 13-19 and 21-24 are ineligible under 35 USC §101.
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, 3, 8-9, 11, 13, 18-19, and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over O’Malley et al. (US 20200410062 A1; hereafter O’Malley) in view of Shokonji et al. (US 20230230368 A1; hereafter Shokonji).
Regarding claim 1, O’Malley teaches a method comprising: identifying, by one or more processors (see at least, [0016] the scenario generator can identify objects represented in the log data), a start of a log data segment of sensor data (see at least, [0048] “the log data…can represent a state…of the environment…at a time t.sub.0…can be an initial state of the environment…that is associated with a beginning…portion of the log data) that was captured by a perception system of a vehicle having one or more sensors (see at least, [0048] The log data…can represent various states of the environment… along a timeline…an environment as represented by captured sensor data, data based on captured
sensor data); identifying, by the one or more processors, an observation of a road user object in the log data segment (see at least, [0018] the log data can represent a period of time that runs from time t.sub.0 to time t.sub.5. The scenario generator can identify a pedestrian that is detected by the vehicle at a time t.sub.1), wherein: the observation of the road user object occurs after the start of the log data segment (see at least, [0018] The scenario generator can identify a pedestrian that is detected by the vehicle at a time t.sub.1), the observation includes i) a point in time when the perception system initially observes the road user object and ii) an observed location of the road user object at the point in time (see at least, [0055] Using the perception data…the scenario generator can determine a log data attributes table 126 that identifies attributes associated with one or more objects of the environment 102 at various times of the log data 108…the attributes can indicate…a pose of the
object…a trajectory of the object, an instantiation attribute of the object), and; the log data
segment does not include another observation of the road user object before the point in time (see at least, [0090] the placement of the occluding object 514 occludes the portion of the environment such that the vehicle 510 is unable to detect the object 516 the time t.sub.0 506); estimating, by the one or more processors, a speed of the road user object at the point in time (see at least, (Fig 1, [0058] the log data attributes table 126 can indicate that object 1 traveled at a velocity of 5 meters/second (m/s) at a time t.sub.0, traveled at a velocity of 4.8 m/s at a time t.sub.1…the scenario generator can determine…estimated attributes associated with an object); determining, by the one or more processors, a trajectory of the road user object between the determined location of the road user object and the observed location of the road user object (see at least, [0019] the vehicle can predict a trajectory and the trajectory can include a predicted trajectory associated with the object. The object waypoint can indicate a position in the environment that indicates a route between two locations…an object can travel from a location A to a location C through a waypoint B); appending, by the one or more processors, the trajectory of the road user object to the log data segment to account for movement of the road user object before the point in time (see at least, [0090] “the vehicle…is unable to detect the object…the time t.sub.0…At a time t.sub.1…the vehicle…can update its trajectory to updated vehicle trajectory….The log data can indicate that as the object…became visible (unoccluded)
….perception engine could detect a portion of the object's… trajectory depicted as the observed
trajectory…also referred to as a logged trajectory); and running, by the one or more processors, a simulation of a virtual autonomous vehicle using the log data segment having the trajectory of the road user object appended thereto (see at least, [0092] the scenario generator…can generate simulation
data that represents states 526 and 528 of a simulated environment that are associated with times t.sub.x 530 and t.sub.y 532…executing an updated simulated vehicle trajectory 544 based on detecting the simulated object 540 crossing in front of the simulated vehicle 534), wherein running the
simulation includes: controlling the virtual autonomous vehicle in an autonomous driving mode to interact with the road user object according to the trajectory (see at least, [0007] FIG. 5A (shown below) depicts an example environment that includes a vehicle and an object that becomes visible and an observed trajectory associated with the object and an example simulated environment that includes a simulated vehicle and a simulated object that becomes visible with an observed trajectory and an inferred trajectory); and
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evaluating performance of to test software for controlling the actual an autonomous vehicle in the autonomous driving mode (see at least, [0011] simulations can be used to test a controller of an autonomous vehicle…can be used to validate software (e.g., an autonomous controller) executed on autonomous vehicles to ensure that the software is able to safely control such autonomous vehicles).
O’Malley does not explicitly teach estimating, by the one or more processors and based on the estimated speed of the road user object, a distance traveled by the road user object from the start of the log data segment to the point in time; determining, by the one or more processors and based on the estimated distance traveled by the road user object, a location of the road user object. However, Shokonji teaches these limitations.
Shokonji teaches estimating, by the one or more processors and based on the estimated speed of the road user object, a distance traveled by the road user object (see at least, [0140] The distance threshold is set to a larger value as the moving speed of the object that is the evaluation target is higher… acquired from the object information included in the recognition result output by the recognition system 320) from the start of the log data segment to the point in time (see at least, [0195] the point cloud data 531(t), 531(t-1), and 531(t-2), the point cloud data corresponding to the object region of the imaged image 520(t) is set as the extraction target); determining, by the one or more processors and based on the estimated distance traveled by the road user object, a location of the road user object (see at least, [0195] the distance to the recognized object becomes closer by the time of the elapsed frame. Therefore, in the point cloud data 531(t-1) and 531(t-2), the distance information of the point cloud data corresponding to the object region is different from that of the point cloud data 531(t). Therefore, the distance information of the point cloud data 531(t-1) and 531(t-2) is corrected on the basis of the distance traveled by the subject vehicle at the time of the elapsed frame).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Malley to include estimating, by the one or more processors and based on the estimated speed of the road user object, a distance traveled by the road user object from the start of the log data segment to the point in time; determining, by the one or more processors and based on the estimated distance traveled by the road user object, a location of the road user object as taught by Shokonji in order to increase point cloud data extracted corresponding to the object region and to avoid a decrease in the reliability of the point cloud data (Shokonji, [0213]).
Regarding Claim 3, the combination of O’Malley and Shokonji teaches the method of claim 1. Shokonji further teaches wherein estimating the distance traveled by the road user object is further based on a difference between the point in time and the start of the log data segment (see at least, [0195] the distance to the recognized object becomes closer by the time of the elapsed frame. Therefore, in the point cloud data 531(t-1) and 531(t-2), the distance information of the point cloud data corresponding to the object region is different from that of the point cloud data 531(t). Therefore, the distance information of the point cloud data 531(t-1) and 531(t-2) is corrected on the basis of the distance traveled by the subject vehicle at the time of the elapsed frame).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Malley to include estimating the distance traveled by the road user object is further based on a difference between the point in time and the start of the log data segment as taught by Shokonji in order to increase point cloud data extracted corresponding to the object region and to avoid a decrease in the reliability of the point cloud data (Shokonji, [0213]).
Regarding Claim 8, the combination of O’Malley and Shokonji teaches the method of claim 1. O’Malley further teaches wherein determining the trajectory of the road user object includes determining a plurality of waypoints between the determined location of the road user object and the and observed location of the road user object (see at least, [0093], attributes component...can determine
waypoint(s) and/or paths associated with the simulated object…such waypoints or paths can be
based at least in part on the log data representing the object corresponding to the simulated object)and determining a plurality of timestamps corresponding to the plurality of waypoints (see at least, [0062] The log data…can include time data that associates actions of the vehicle(s)…and observations (e.g., sensor data) with a set of timestamps).
Regarding Claim 9, the combination of O’Malley and Shokonji teaches the method of claim 8. O’Malley further teaches wherein determining the plurality of waypoints (see at least, [0093], attributes component...can determine waypoint(s) and/or paths associated with the simulated object…such
waypoints or paths can be based at least in part on the log data representing the object corresponding to the simulated object) and determining the plurality of timestamps is based on a frame rate of the log data segment (see at least, [0062] The log data…can include time data that associates actions of the vehicle(s)…and observations (e.g., sensor data) with a set of timestamps).
Regarding Claim 11, O’Malley teaches a method of comprising: identifying, by one or more processors (see at least, [0016] the scenario generator can identify objects represented in the log data), an end of a log data segment of sensor data captured by a perception system of a vehicle (see at least, Fig 2, [0048] the log data…can represent a state…of the environment….at a later time …t.sub.2…at 15 seconds) having one or more sensors (see at least, [0048] The log data…can represent various states of the environment…along a timeline…an environment as represented by captured sensor data, data based on captured sensor data); and identifying, by the one or more processors, an observation of a road user object in the log data segment, wherein: the observation of the road user object occurs before the end of the log data segment (see at least, ([0018] the log data can represent a period of time that runs from time t.sub.0 to time t.sub.5), including of the road user object includes i) a point in time after which the perception system no longer observes the road user object and ii) an observed location of the road user object at the point in time, and sensor data (see at least, [0018] The scenario generator can identify a pedestrian that is detected by the vehicle at a time t.sub.1 and becomes occluded by a tree at time t.sub.3. The scenario generator can associate object attributes with the pedestrian that correspond to, for example, each time that the vehicle can detect the pedestrian…e.g., t.sub.1, t.sub.2, and t.sub.3); the log data segment does not include another observation of the road user object after the point in time (see at least, [0057] the termination attribute can indicate a manner in which the vehicle(s) 104 can no longer detect the object…the object 106(1) becoming occluded (e.g., the object 106(1) can move to a position behind an occluding object such as a building); estimating, by the one or more processors, a speed of the road user object at the point in time ([0029] After the pedestrian crosses the crosswalk, the pedestrian can become occluded by an object such as a tree…The scenario generator can determine an estimated trajectory associated with the pedestrian…use the velocity attribute to determine that the simulated pedestrian can maintain a velocity and continue walking in the direction of travel indicated by the observed trajectory); determining, by the one or more processors, a trajectory for of the road user object between the observed location of the road user object and the location of the road user object (see at least, [0029] After the pedestrian crosses the crosswalk, the pedestrian can become occluded by an object such as a tree…The scenario generator can determine an estimated trajectory associated with the pedestrian); appending, by the one or more processors, the trajectory of the road user object to the log data segment to account for movement of the road user object after the point in time (see at least, [0090] the vehicle…is unable to detect the object…the time t.sub.0…At a time t.sub.1…the vehicle…can update its trajectory to updated vehicle trajectory….The log data can indicate that as the object…became visible (unoccluded)
….perception engine could detect a portion of the object's… trajectory depicted as the observed
trajectory…also referred to as a logged trajectory); and running, by the one or more processors, a simulation of a virtual autonomous vehicle using the log data segment having the trajectory appended thereto (see at least, [0092] the scenario generator…can generate simulation data that represents
states 526 and 528 of a simulated environment that are associated with times t.sub.x 530 and t.sub.y 532…executing an updated simulated vehicle trajectory 544 based on detecting the
simulated object 540 crossing in front of the simulated vehicle 534), wherein running the simulation includes: controlling the virtual autonomous vehicle in an autonomous driving mode to interact with the road user object according to the trajectory (see at least, [0007] FIG. 5A (shown below) depicts an example environment that includes a vehicle and an object that becomes visible and an observed trajectory associated with the object and an example simulated environment that includes a simulated vehicle and a simulated object that becomes visible with an observed trajectory and an inferred trajectory); and
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evaluating performance of software for controlling an autonomous vehicle in the autonomous driving mode (see at least, [0011] simulations can be used to test a controller of an autonomous vehicle…can be used to validate software (e.g., an autonomous controller) executed on autonomous vehicles to ensure that the software is able to safely control such autonomous vehicles).
O’Malley does not explicitly teach estimating, by the one or more processors and based on the estimated speed of the road user object, a distance traveled by the road user object from the point in time to the end of the log data segment; determining, by the one or more processors and based on the estimated distance traveled by the road user object, a location of the road user object. However, Shokonji teaches these limitations.
Shokonji teaches estimating, by the one or more processors and based on the estimated speed of the road user object (see at least, [0140] The distance threshold is set to a larger value as the moving
speed of the object that is the evaluation target is higher… acquired from the object information included in the recognition result output by the recognition system 320), a distance traveled by the road user object from the point in time to the end of the log data segment (see at least, [0195] FIG. 18, point cloud data 531(t) obtained at time…image 520(t) at current time t…the point cloud data corresponding to the object region of the imaged image 520(t) is set as the extraction target…the
distance to the recognized object becomes closer by the time of the elapsed frame); determining, by the one or more processors and based on the estimated distance traveled by the road user object, a location of the road user object (see at least, [0195] the distance to the recognized object becomes closer by the time of the elapsed frame. Therefore, in the point cloud data 531(t-1) and 531(t2),
the distance information of the point cloud data corresponding to the object region is different from that of the point cloud data 531(t). Therefore, the distance information of the point cloud data 531(t-1) and 531(t-2) is corrected on the basis of the distance traveled by the subject vehicle at the time of the elapsed frame).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Malley to include estimating, by the one or more processors and based on the estimated speed of the road user object, a distance traveled by the road user object from the point in time to the end of the log data segment; determining, by the one or more processors and based on the estimated distance traveled by the road user object, a location of the road user object as taught by Shokonji in order to increase point cloud data extracted corresponding to the object region and to avoid a decrease in the reliability of the point cloud data (Shokonji, [0213]).
Regarding Claim 13, the combination of O’Malley and Shokonji teaches the method of claim 11. The combination does not explicitly teach wherein estimating the distance traveled by the road user object is further based on a difference between the point in time and the start of the log data segment. However, Shokonji teaches this limitation (see at least, [0195] the distance to the recognized
object becomes closer by the time of the elapsed frame. Therefore, in the point cloud data 531(t-1) and 531(t-2), the distance information of the point cloud data corresponding to the object region is different from that of the point cloud data 531(t). Therefore, the distance information of the point
cloud data 531(t-1) and 531(t-2) is corrected on the basis of the distance traveled by the subject
vehicle at the time of the elapsed frame).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of O’Malley and Shokonji to include estimating the distance traveled by the road user object is further based on a difference between the point in time and the start of the log data segment as taught by Shokonji in order to increase point cloud data extracted corresponding to the object region and to avoid a decrease in the reliability of the point cloud data (Shokonji, [0213]).
Regarding Claim 18, the combination of O’Malley and Shokonji teaches the method of claim 11. O’Malley further teaches wherein determining the trajectory of the road user object includes: determining a plurality of waypoints between the determined location of the road user object and the observed location of the road user object (see at least, [0093], attributes component...can determine waypoint(s) and/or paths associated with the simulated object…such waypoints or paths can be based at least in part on the log data representing the object corresponding to the simulated object)and determining a plurality of timestamps corresponding to the plurality of waypoints (see at least, [0062] The log data…can include time data that associates actions of the vehicle(s)…and observations (e.g., sensor data) with a set of timestamps).
Regarding Claim 19, the combination of O’Malley and Shokonji teaches the method of claim 18. O’Malley further teaches wherein determining the plurality of waypoints (see at least, [0093] attributes component...can determine waypoint(s) and/or paths associated with the simulated object…such waypoints or paths can be based at least in part on the log data representing the object corresponding to the simulated object) and determining the corresponding plurality of timestamps is based on a frame rate of the log data segment (see at least, [0062] the log data…can include time data that associates actions of the vehicle(s)…and observations (e.g., sensor data) with a set of
timestamps).
Regarding Claim 21, the combination of O’Malley and Shokonji teaches the method of claim 1. O’Malley further teaches wherein running simulation further includes a perception system of the virtual autonomous vehicle detecting the road user object after the point in time thereby improving sensor recall in the simulation (see at least, [0018] objects traversing the environment according to temporal triggers may not interact with the simulated autonomous vehicle as intended to test such updated planning algorithms. To rectify such deficiencies…the scenario generator can determine object attributes associated with the objects…can identify a pedestrian that is detected by the vehicle at a time t.sub.1 and becomes occluded by a tree at time t.sub.3).
Regarding Claim 22, the combination of O’Malley and Shokonji teaches the method of claim 11. O’Malley further teaches wherein running the simulation includes a perception system of the virtual autonomous vehicle detecting the road user object after the point in time thereby improving sensor recall in the simulation (see at least, [0018] objects traversing the environment according to temporal triggers may not interact with the simulated autonomous vehicle as intended to test such updated planning algorithms. To rectify such deficiencies…the scenario generator can determine object attributes associated with the objects…can identify a pedestrian that is detected by the vehicle at a time t.sub.1 and becomes occluded by a tree at time t.sub.3).
Regarding claim 23, O’Malley teaches a system comprising one or more processors configured to: identify a start of a log data segment of sensor data (see at least, [0048] “the log data…can represent a state…of the environment…at a time t.sub.0…can be an initial state of the environment…that is associated with a beginning…portion of the log data) captured by a perception system of a vehicle having one or more sensors (see at least, [0048] The log data…can represent various states of the environment… along a timeline…an environment as represented by captured sensor data, data based on captured sensor data), identify an observation of a road user object in the log data segment (see at least, [0018] the log data can represent a period of time that runs from time t.sub.0 to time t.sub.5. The scenario generator can identify a pedestrian that is detected by the vehicle at a time t.sub.1), wherein: the observation of the road user object occurs after the start of the log data segment (see at least, [0018] The scenario generator can identify a pedestrian that is detected by the vehicle at a time t.sub.1), the observation of the road user object includes i) a point in time when the perception system initially observes the road user object and ii an observed location of the road user object at the point in time (see at least, [0055] Using the perception data…the scenario generator can determine a log data attributes table 126 that identifies attributes associated with one or more objects of the environment 102 at various times of the log data 108…the attributes can indicate…a pose of the
object…a trajectory of the object, an instantiation attribute of the object), and; the log data segment does not include another observation of the road user object before the point in time (see at least, [0090] the placement of the occluding object 514 occludes the portion of the environment such that the vehicle 510 is unable to detect the object 516 the time t.sub.0 506); estimate a speed of the road user object at the point in time when the road user object was initially observed (see at least, (Fig 1, [0058] the log data attributes table 126 can indicate that object 1 traveled at a velocity of 5 meters/
second (m/s) at a time t.sub.0, traveled at a velocity of 4.8 m/s at a time t.sub.1…the scenario generator can determine…estimated attributes associated with an object); determine a trajectory of the road user object between the determined location of the road user object and the observed location of the road user object (see at least, [0019] the vehicle can predict a trajectory and the trajectory can include a predicted trajectory associated with the object. The object waypoint can indicate a position in the environment that indicates a route between two locations…an object can travel from a location A to a location C through a waypoint B); append the trajectory of the road user object to the log data segment to account for movement of the road user object before the point in time (see at least, [0090] “the vehicle…is unable to detect the object…the time t.sub.0…At a time t.sub.1…the vehicle…can update its trajectory to updated vehicle trajectory….The log data can indicate that as the object…
became visible (unoccluded)….perception engine could detect a portion of the object's… trajectory
depicted as the observed trajectory…also referred to as a logged trajectory); and run a simulation of a virtual autonomous vehicle, using the log data segment having the trajectory of the road user object appended thereto (see at least, [0090] “the vehicle…is unable to detect the object…the time t.sub.0
…At a time t.sub.1…the vehicle…can update its trajectory to updated vehicle trajectory….The log data can indicate that as the object…became visible (unoccluded)….perception engine could detect a portion of the object's… trajectory depicted as the observed trajectory…also referred to as a logged
trajectory), by being configured to: control the virtual autonomous vehicle in an autonomous driving mode to interact with the road user object according to the trajectory (see at least, [0007] FIG. 5A
(shown below) depicts an example environment that includes a vehicle and an object that becomes visible and an observed trajectory associated with the object and an example simulated environment that includes a simulated vehicle and a simulated object that becomes visible with an observed trajectory and an inferred trajectory); and
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evaluate performance of software for controlling an autonomous vehicle in the autonomous driving mode see at least, [0011] simulations can be used to test a controller of an autonomous vehicle…can be used to validate software (e.g., an autonomous controller) executed on autonomous vehicles to ensure that the software is able to safely control such autonomous vehicles).
O’Malley does not explicitly teach estimate, based on the estimated speed of the road user object, a distance traveled by the road user object from the start of the log data segment to the point in time; determine, based on the estimated distance traveled by the road user object, a location of the road user object. However, Shokonji teaches these limitations.
Shokonji teaches estimate, based on the estimated speed of the road user object, a distance traveled by the road user object (see at least, [0140] The distance threshold is set to a larger value as the moving
speed of the object that is the evaluation target is higher… acquired from the object information
included in the recognition result output by the recognition system 320) from the start of the log data segment to the point in time (see at least, [0195] the point cloud data 531(t), 531(t-1), and 531(t-2),
the point cloud data corresponding to the object region of the imaged image 520(t) is set as the
extraction target); determine, based on the estimated distance traveled by the road user object, a location of the road user object (see at least, [0195] the distance to the recognized object becomes
closer by the time of the elapsed frame. Therefore, in the point cloud data 531(t-1) and 531(t-2), the distance information of the point cloud data corresponding to the object region is different from that of the point cloud data 531(t). Therefore, the distance information of the point cloud data 531(t-1) and 531(t-2) is corrected on the basis of the distance traveled by the subject vehicle at the time of the elapsed frame).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Malley to include estimate, based on the estimated speed of the road user object, a distance traveled by the road user object from the start of the log data segment to the point in time; determine, based on the estimated distance traveled by the road user object, a location of the road user object as taught by Shokonji in order to increase point cloud data extracted corresponding to the object region and to avoid a decrease in the reliability of the point cloud data (Shokonji, [0213]).
Regarding Claim 24, O’Malley teaches a system comprising one or more processors (see at least, [0016] the scenario generator can identify objects represented in the log data) configured to: identify an end of a log data segment of sensor data that was captured by a perception system of a vehicle (see at least, Fig 2, [0048] the log data…can represent a state…of the environment….at a later time …t.sub.2…at 15 seconds) having one or more sensors (see at least, [0048] The log data…can represent various states of the environment…along a timeline…an environment as represented by captured sensor data, data based on captured sensor data); identify an observation of a road user object in the log data segment, wherein: the observation of the road user object occurs before the end of the log data segment (see at least, ([0018] the log data can represent a period of time that runs from time t.sub.0 to time t.sub.5), the observation of the road user object includes i)a point in time after which the perception system no longer observes the road user object and ii) an observed location of the road user object at the point in time (see at least, [0018] The scenario generator can identify a pedestrian that is detected by the vehicle at a time t.sub.1 and becomes occluded by a tree at time t.sub.3. The scenario generator can associate object attributes with the pedestrian that correspond to, for example, each time that the vehicle can detect the pedestrian…e.g., t.sub.1, t.sub.2, and t.sub.3); the log data segment does not include another observation of the road user object after the point in time (see at least, [0057] the termination attribute can indicate a manner in which the vehicle(s) 104 can no longer detect the object…the object 106(1) becoming occluded (e.g., the object 106(1) can move to a position behind an occluding object such as a building); estimate a speed of the road user object at the point in time ([0029] After the pedestrian crosses the crosswalk, the pedestrian can become occluded by an object such as a tree…The scenario generator can determine an estimated trajectory associated with the pedestrian…use the velocity attribute to determine that the simulated pedestrian can maintain a velocity and continue walking in the direction of travel indicated by the observed trajectory); determine a trajectory of the road user object between the observed location of the road user object and the determined location of the road user object (see at least, [0029] After the pedestrian crosses the crosswalk, the pedestrian can become occluded by an object such as a tree…The scenario generator can determine an estimated trajectory associated with the pedestrian; append the trajectory of the road user object to the log data segment to account for movement of the road user object after the point in time (see at least, [0090] the vehicle…is unable to detect the object…the time t.sub.0…At a time t.sub.1…the vehicle…can update its trajectory to updated vehicle trajectory….The log data can indicate that as the object…became visible (unoccluded) ….perception engine could detect a portion of the object's… trajectory depicted as the observed trajectory…also referred to as a logged trajectory); and run a simulation of a virtual autonomous vehicle, using the log data segment having the trajectory of the road user object appended thereto (see at least, [0092] the scenario generator…can generate simulation data that represents states 526 and 528 of a simulated
environment that are associated with times t.sub.x 530 and t.sub.y 532…executing an updated
simulated vehicle trajectory 544 based on detecting the simulated object 540 crossing in front of the simulated vehicle 534), by being configured to: control the virtual autonomous vehicle in an autonomous driving mode to interact with the road user object according to the trajectory (see at least, [0007] FIG. 5A (shown below) depicts an example environment that includes a vehicle and an object that becomes visible and an observed trajectory associated with the object and an example simulated environment that includes a simulated vehicle and a simulated object that becomes visible with an observed trajectory and an inferred trajectory); and
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evaluate performance of software for controlling an autonomous vehicle in the autonomous driving mode (see at least, [0011] simulations can be used to test a controller of an autonomous vehicle…can be used to validate software (e.g., an autonomous controller) executed on autonomous vehicles to ensure that the software is able to safely control such autonomous vehicles).
O’Malley does not explicitly teach estimate, based on the estimated speed of the road user object, a distance traveled by the road user object from the point in time to the end of the log data segment; determine, based on the estimated distance traveled by the road user object, a location of the road user object. However, Shokonji teaches these limitations.
Shokonji teaches estimate, based on the estimated speed of the road user object (see at least, [0140] The distance threshold is set to a larger value as the moving speed of the object that is the evaluation target is higher… acquired from the object information included in the recognition result output by the recognition system 320), a distance traveled by the road user object from the point in time to the end of the log data segment (see at least, [0195] FIG. 18, point cloud data 531(t) obtained at time…image
520(t) at current time t…the point cloud data corresponding to the object region of the imaged image 520(t) is set as the extraction target…the distance to the recognized object becomes closer by the time of the elapsed frame); determine, based on the estimated distance traveled by the road user object, a location of the road user object (see at least, [0195] the distance to the recognized
object becomes closer by the time of the elapsed frame. Therefore, in the point cloud data 531(t-1) and 531(t2),
the distance information of the point cloud data corresponding to the object region is different from that of the point cloud data 531(t). Therefore, the distance information of the point cloud data 531(t-1) and 531(t-2) is corrected on the basis of the distance traveled by the subject vehicle at the time of the elapsed frame).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified O’Malley to include estimate, based on the estimated speed of the road user object, a distance traveled by the road user object from the point in time to the end of the log data segment; determine, based on the estimated distance traveled by the road user object, a location of the road user object as taught by Shokonji in order to increase point cloud data extracted corresponding to the object region and to avoid a decrease in the reliability of the point cloud data (Shokonji, [0213]).
Claims 4-7 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over O’Malley et al. (US 20200410062 A1; hereafter O’Malley) in view of Shokonji et al. (US 20230230368 A1; hereafter Shokonji in further view of and Zhu et al. (US 20210380141 A1; hereafter Zhu).
Regarding Claim 4, the combination of O’Malley and Shokonji teaches the method of claim 3. The combination does not explicitly teach wherein determining the location of the road user object includes: identifying a lane for the road user object; and traversing the lane backwards from the observed location of the road user object using the distance traveled by the road user object. However, Zhu teaches these limitations.
Zhu teaches determining the location of the road user object includes: identifying a lane for the road user object (see at least, [0040] The nearest lane can be a road path, a vehicle lane, a pedestrian sidewalk, a bike lane, a road boundary, or a road shoulder. Projection/lane direction determiner…can project the pedestrian onto a point (such as a midpoint of the lane) at the nearest lane and determine a direction of the lane at that point...Nearest lane determiner…can map the pedestrian onto a map data and determine a nearest lane for the pedestrian); and traversing the lane backwards from the observed location of the road user object using the distance traveled by the road user object (see at least, [0045] if the pedestrian is detected to be back….facing, ADV…can plan a trajectory based on a locked pedestrian path prediction….can determine that the facing direction of pedestrian…is back facing…a point of the nearest lane…such as a mid-point of lane).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of O’Malley and Shokonji to include determining the location of the road user object includes: identifying a lane for the road user object; and traversing the lane backwards from the observed location of the road user object using the distance traveled by the road user object as taught by Zhu so that the vehicle can avoid collision by determining a route without any interference from objects (Zhu, [0034]).
Regarding Claim 5, the combination of O’Malley, Shokonji and Zhu teaches the method of claim 4. O’Malley further teaches wherein the observation further includes a heading of the road user object (see at least, [0019] object parameters can be determined and instantiated as determined herein…The object pose can indicate an object's x-y-z coordinates (e.g., a position or position data) in the environment and/or can include a pitch, a roll, and/or a yaw associated with the object).
Zhu further teaches identifying the lane for the road user object is based on the heading of the road user object and a heading of the lane (see at least, [0040] Projection/lane direction determiner…can project the pedestrian onto a point (such as a midpoint of the lane) at the nearest lane and determine a direction of the lane at that point).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of O’Malley and Shokonji to include identifying the lane for the road user object is based on the heading of the road user object and a heading of the lane as taught by Zhu so that the vehicle can avoid collision by determining a route without any interference from objects (Zhu, [0034]).
Regarding Claim 6, the combination of O’Malley, Shokonji and Zhu teaches the method of claim 4. O’Malley further teaches wherein the observation further includes a heading of the road user object (see at least, [0019] object parameters can be determined and instantiated as determined herein…The object pose can indicate an object's x-y-z coordinates (e.g., a position or position data) in the environment and/or can include a pitch, a roll, and/or a yaw associated with the object).
Zhu further teaches identifying the lane for the road user object includes identifying, based on pre- stored map information, a lane that is closest to the observed location of the road user object and has a heading that is consistent with the heading of the road user object (see at least, [0040] The nearest lane can be a road path, a vehicle lane, a pedestrian sidewalk, a bike lane, a road boundary, or a road shoulder. Projection/lane direction determiner…can project the pedestrian onto a point (such as a midpoint of the lane) at the nearest lane and determine a direction of the lane at that point...Nearest lane determiner…can map the pedestrian onto a map data and determine a nearest lane for the pedestrian).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of O’Malley and Shokonji to include identifying the lane for the road user object includes identifying, based on pre- stored map information, a lane that is closest to the observed location of the road user object and has a heading that is consistent with the heading of the road user object as taught by Zhu so that the vehicle can avoid collision by determining a route without any interference from objects (Zhu, [0034]).
Regarding Claim 7, the combination of O’Malley, Shokonji and Zhu teaches the method of claim 4. Zhu further teaches wherein the determined location of the road user object is associated with at a center of the lane (see at least, [0040] Projection/lane direction determiner…can project the pedestrian onto a point (such as a midpoint of the lane) at the nearest lane).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of O’Malley and Shokonji to include the determined location of the road user object is associated with at a center of the lane as taught by Zhu so that for a given object, decision module…may decide to pass the object, while planning module…may determine whether to pass on the left side or right side of the object in order to avoid collision(Zhu, [0035]).
Regarding Claim 14, the combination of O’Malley and Shokonji teaches the method of claim 11. The combination does not explicitly teach wherein determining the location of the road user object includes: identifying a lane for the road user object; and traversing the lane forward from the final observed location of the road user object using the distance traveled by the road user object.
However, Zhu teaches these limitations.
Zhu teaches determining the location of the road user object includes: identifying a lane for the road user object (see at least, [0040] The nearest lane can be a road path, a vehicle lane, a pedestrian sidewalk, a bike lane, a road boundary, or a road shoulder. Projection/lane direction determiner…can project the pedestrian onto a point (such as a midpoint of the lane) at the nearest lane and determine a direction of the lane at that point...Nearest lane determiner…can map the pedestrian onto a map data and determine a nearest lane for the pedestrian); and traversing the lane forward from the final observed location of the road user object using the distance traveled by the road user object (see at least, [0045] if the pedestrian is detected to be back….facing, ADV…can plan a trajectory based on a locked pedestrian path prediction….can determine that the facing direction of pedestrian…is back facing…a point of the nearest lane…such as a mid-point of lane).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of O’Malley and Shokonji to include determining the location of the road user object includes: identifying a lane for the road user object; and traversing the lane forward from the final observed location of the road user object using the distance traveled by the road user object as taught by Zhu so that the vehicle can avoid collision by determining a route without any interference from objects (Zhu, [0034]).
Regarding Claim 15, the combination of O’Malley and Shokonji and Zhu teaches the method of claim 14. O’Malley further teaches the observation further includes a heading of the road user object (see at least, [0124] the objects component 640 can determine objects that interact with the vehicle associated with the log data…can determine that an object traversed along a trajectory that began 100 meters from the vehicle and continued to traverse the environment in a direction that is away from the vehicle).
Zhu further teaches wherein identifying the lane for the road user object is based on the heading for the road user object and a heading of the lane (see at least, [0040] Projection/lane direction determiner…
can project the pedestrian onto a point (such as a midpoint of the lane) at the nearest lane and determine a direction of the lane at that point).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of O’Malley and Shokonji to include identifying the lane for the road user object is based on the heading for the road user object and a heading of the lane as taught by Zhu so that the vehicle can avoid collision by determining a route without any interference from objects (Zhu, [0034]).
Regarding Claim 16, the combination of O’Malley, Shokonji and Zhu teaches the method of claim 14. O’Malley further teaches wherein the observation further includes a heading of the road user object (see at least, [0124] the objects component 640 can determine objects that interact with the vehicle associated with the log data…can determine that an object traversed along a trajectory that began 100 meters from the vehicle and continued to traverse the environment in a direction that is away from the vehicle).
Zhu further teaches identifying the lane for the road user object includes identifying, based on pre-stored map information, lane that is closest to the observed location of the road user object and has a heading that is consistent with the heading of the road user object (see at least, [0040] The nearest lane can be a road path, a vehicle lane, a pedestrian sidewalk, a bike lane, a road boundary, or a road shoulder. Projection/lane direction determiner…can project the pedestrian onto a point (such as a midpoint of the lane) at the nearest lane and determine a direction of the lane at that point...Nearest lane determiner…can map the pedestrian onto a map data and determine a nearest lane for the pedestrian, [0143] The linked list may include linked objects with properties including…direction
…projected location…when the segment ends).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of O’Malley and Shokonji to include identifying the lane for the road user object includes identifying, based on pre-stored map information, lane that is closest to the observed location of the road user object and has a heading that is consistent with the heading of the road user object as taught by Zhu so that the vehicle can avoid collision by determining a route without any interference from objects (Zhu, [0034]).
Regarding Claim 17, the combination of O’Malley, Shokonji and Zhu teaches the method of claim 14. Zhu further teaches wherein the determined location of the road user object is at-associated with a center of the lane (see at least, [0040] Projection/lane direction determiner…can project the pedestrian onto a point (such as a midpoint of the lane) at the nearest lane).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of O’Malley and Shokonji to include the determined location of the road user object is at-associated with a center of the lane as taught by Zhu so that for a given object, decision module…may decide to pass the object, while planning module…may determine whether to pass on the left side or right side of the object in order to avoid collision (Zhu, [0035]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mohan et al. (US 20210165932 A1) discloses a method for improving realism in simulations for testing software for operating a vehicle in an autonomous driving mode (Mohan, [0056] Each simulated behavior may be implemented via software, which may or may not be provisioned to autonomous vehicles…in the real world. For example, the simulation subsystem…can test software for a particular behavior by simulating the behavior to determine performance, safety, and other characteristics of the behavior, before the software is provisioned for use by one or more autonomous vehicles…in the real world, [0075] “use the computing device…to cause a simulation to run to test a particular set of software and can then determine whether/how to modify and/or provision the software based on results of the simulation”).
Lu et al. (US 20210173408 A1) discloses identifying the lane for the road user object includes using pre- stored map information to identify a closest lane to the initial location of the road user object having a heading that is consistent with the heading for the road user object (Lu, [0036] “ the ADV may recognize the obstacle as a vehicle (e.g., an emergency vehicle) that is sensed to be traveling in a driving lane based on the obstacle's heading and location that is referenced to map data that contains lane location and orientation that corroborates that the obstacle, based on the heading and location of the obstacle, appears to be driving in the driving lane”).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOYA PETTIEGREW whose telephone number is (313)446-6636. The examiner can normally be reached 8:30pm - 5:00pm M-F.
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/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662
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