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
• This action is in reply to the Application Number 19/251,269 filed on 06/26/2025.
• Claims 15-28 are currently pending and have been examined.
• This action is made NON-FINAL.
• The examiner would like to note that this application is now being handled by examiner Kai Wang.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The certified copy has been filed in Application No. 19/251,269 filed on 06/26/2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/26/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 27 is objected to because of the following: Claim 27 is directed towards a vehicle but it is dependent on claim 26 which is directed towards a vehicle control system. Therefore, claim 27 appears to be directed towards two separate (but not distinct) inventions. It is recommended that the claim 27 should be re-write so that it is in independent form and includes all the limitations from claim 26.
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 15, 26 and 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The complete step-by-step analysis under 35 U.S.C. 101 is provided below:
STEP One: Do Claims 15, 26 and 28 Fall Within One of The Statutory Categories?
Yes, claim 15 is directed towards a method (process), claim 26 and 28 are directed towards a machine.
STEP Two A , Prong One: Is a Judicial Exception Recited?
Yes, claims 15, 26 and 28 recite “determining values for a set of control parameters for controlling a trajectory of the vehicle using a first constraint regime; and in response to detecting a change in an environment complexity, determining values for the set of control parameters using a second constraint regime”. This limitation, as drafted, is a simple process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind. For example, the claim encompasses a person determining values for a set of control parameters for controlling a trajectory of the vehicle using a first constraint regime during a first period, and determining values for the set of control parameters using a second constraint regime during a second period in response to detecting a change in an environment. The mere nominal recitation of application of the elastic tube model predictive control, homothetic tube model predictive control, or rigid tube model predictive control to determine the control parameters does not take the claim limitations out of the mental process grouping. Thus, the claims 15, 26 and 28 recite a mental process.
STEP Two A , Prong Two: Is the Abstract Idea integrated into a Practical Application?
No. The claim 15 recites additional elements of “ computer-implemented”. The “implemented by a computer” describes a generic work vehicle and a generic processor or controller that automatically performs the otherwise mental update process and merely describes how to generally “apply” the otherwise mental judgements in a generic or general-purpose computer environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
STEP Two B: Does the Claim as a whole amount to significantly more than the Judicial Exception?
No. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra-solution activity. The claim is ineligible.
Dependent claims 16-25 and 27 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of the dependent claims 16-25 and 27 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 are not patent eligible under the same rational as provided for the rejection of claims 15 and 26.
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.
Claim(s) 15, 20-28 are rejected under 35 U.S.C. 103 as being unpatentable over Shi, Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives, Annual Reviews in Control, Volume 52, 2021, Pages 170-196 in view of Yashiro (US20200307583A1).
Regarding claims 15, 25-26 and 28:
Shi teaches:
A computer-implemented method for controlling a vehicle in an environment comprising ( Shi, abstract, “the development and application of model predictive control (MPC) for autonomous intelligent mechatronic systems (AIMS)”, and page 183, “design and introduce a safety constraint in MPC to achieve collision free real-time motion planning in complex environments”, and page 171, “computer control system…autonomous vehicles”)
wherein: the first constraint regime is one of elastic tube model predictive control, homothetic tube model predictive control, or rigid tube model predictive control;(Shi teaches the constraint regimes of the elastic tube model predictive control, homothetic tube model predictive control, or rigid tube model predictive control in Table 1 as “tube-based MPC has a similar order of computational complexity to standard MPC, while providing less conservative closed loop performance”, section 2.2.1, page 172 )
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and the second constraint regime is a different one of elastic tube model predictive control, homothetic tube model predictive control, or rigid tube model predictive control. (Shi teaches the constraint regimes of the elastic tube model predictive control, homothetic tube model predictive control, or rigid tube model predictive control as “tube-based MPC has a similar order of computational complexity to standard MPC, while providing less conservative closed loop performance”, section 2.2.1, page 172 ) Examiner note: Shi disclosed constraint regimes for elastic tube model predictive control, homothetic tube model predictive control, or rigid tube model predictive control. However, Shi does not expressly disclose that a second constraint regime is a different one of the elastic tube MPC, homothetic tube MPC, or rigid tube MPC regimes than the first constraint regime. Nevertheless, it would have been an obvious design choice for one of ordinary skill in the art, at the time of the invention, to select two different tube-based MPC constraint regimes from the regimes disclosed by Shi for use as the first and second constraint regimes. Such a selection would have constituted a predictable design choice among known tube-based MPC techniques, each of which Shi identified as providing a comparable order of computational complexity to standard MPC while offering less conservation closed-loop performance. The claimed use of different tube-based MPC constraint regimes therefore would have been an obvious matter of design choice, since the invention failed to provide novel or unexpected results from the usage of said claimed second constraint regime as a different tube-based MPC than the first constraint regime.
Shi does not explicitly teach, but Yashiro teaches:
during a first period, (Yashiro, para [100], “the predetermined time period (two seconds ”)
determining values for a set of control parameters for controlling a trajectory of the vehicle using a first constraint regime; (Yashiro, para [51], “generates a target track which the host vehicle is going to run autonomously”, para [08], “target vehicle-to-vehicle distance”, and para [52], “ a target speed and a target acceleration”, claim 10, “in the first control condition”)
and in response to detecting a change in an environment complexity, (Yashiro, para [05], “the control condition is shifted even if the vehicle-to-vehicle distance is unstable”)
during a second period, (Yashiro, para [113],” the time period for which the second control condition continues can be made longer” )
determining values for the set of control parameters using a second constraint regime, (Yashiro, para [51], “generates a target track which the host vehicle is going to run autonomously”, para [08], “target vehicle-to-vehicle distance”, and para [52], “ a target speed and a target acceleration”, and para [98], “the control condition is switched from the first control condition (T1) to the second control condition (T2)”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify apply Yashiro’s environment-triggered mode-switching mechanism to the tube-MPC based vehicle controller described by Shi, using Shi’s tube-MPC regimes as the different control conditions in order to include during a first period, determining values for a set of control parameters for controlling a trajectory of the vehicle using a first constraint regime; and
in response to detecting a change in an environment complexity, during a second period, determining values for the set of control parameters using a second constraint regime. One of ordinary skill in the art would have been motivated to make this modification so “ the stability of the control can be enhanced.”(Yashiro, description)
Regarding claim 20,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 15. Shi does not explicitly teach, but Yashiro teaches:
The computer-implemented method of claim 15, wherein detecting a change in environment complexity comprises determining a change in a number and/or class of objects detected in the environment.( Yashiro, para [82], “If the result of step S121 is that the count C is greater than the predetermined value (if No in step S121), the switch controller 143 switches the control condition from the first control condition to the second control condition”, and para [28], “detects at least a position (distance and bearing) of an object”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify apply Yashiro’s environment-triggered mode-switching mechanism to the tube-MPC based vehicle controller described by Shi, using Shi’s tube-MPC regimes as the different control conditions in order to include wherein detecting a change in environment complexity comprises determining a change in a number and/or class of objects detected in the environment. One of ordinary skill in the art would have been motivated to make this modification so “ the stability of the control can be enhanced.”(Yashiro, description)
Regarding claim 21,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 20. Shi does not explicitly teach, but Yashiro teaches:
The computer-implemented method of claim 20, wherein the at least one object comprises one of a pedestrian, a bicycle, or a vehicle. (Yashiro, para [70], “the preceding vehicle is recognized based on the result of the detection by the radar unit”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify apply Yashiro’s environment-triggered mode-switching mechanism to the tube-MPC based vehicle controller described by Shi, using Shi’s tube-MPC regimes as the different control conditions in order to include wherein the at least one object comprises one of a pedestrian, a bicycle, or a vehicle. One of ordinary skill in the art would have been motivated to make this modification so “ the stability of the control can be enhanced.”(Yashiro, description)
Regarding claim 22,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 15. Shi does not explicitly teach, but Yashiro teaches:
The computer-implemented method of claim 15, wherein the set of control parameters comprises at least one of: a brake control, an accelerator control, a gearbox control, or a steering input.( Yashiro, Para [55], “The speed controller 164 controls the running drive force output unit 200 (see FIG. 1) or the brake unit 210”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify apply Yashiro’s environment-triggered mode-switching mechanism to the tube-MPC based vehicle controller described by Shi, using Shi’s tube-MPC regimes as the different control conditions in order to include wherein the set of control parameters comprises at least one of: a brake control, an accelerator control, a gearbox control, or a steering input. One of ordinary skill in the art would have been motivated to make this modification so “ the stability of the control can be enhanced.”(Yashiro, description)
Regarding claim 23,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 15. Shi does not explicitly teach, but Yashiro teaches:
The computer-implemented method of claim 15, wherein detecting a change in environment complexity comprises performing semantic segmentation on sensor data obtained from a sensor system mounted to the vehicle. ( Yashiro, Para [30], “The object recognizer 16 recognizes the position, kind, velocity and so on of the object by performing sensor fusion on results of the detections by some or all of the camera 10, the radar unit 12”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify apply Yashiro’s environment-triggered mode-switching mechanism to the tube-MPC based vehicle controller described by Shi, using Shi’s tube-MPC regimes as the different control conditions in order to include wherein detecting a change in environment complexity comprises performing semantic segmentation on sensor data obtained from a sensor system mounted to the vehicle. One of ordinary skill in the art would have been motivated to make this modification so “ the stability of the control can be enhanced.”(Yashiro, description)
Regarding claim 24,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 23. Shi does not explicitly teach, but Yashiro teaches:
The computer-implemented method of claim 23, wherein the sensor system comprises at least one of: radar, camera, lidar, inertial sensors, magnetometer, control position sensors, or drive train telemetry sensors. ( Yashiro, Para [30], “The object recognizer 16 recognizes the position, kind, velocity and so on of the object by performing sensor fusion on results of the detections by some or all of the camera 10, the radar unit 12”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify apply Yashiro’s environment-triggered mode-switching mechanism to the tube-MPC based vehicle controller described by Shi, using Shi’s tube-MPC regimes as the different control conditions in order to include wherein the sensor system comprises at least one of: radar, camera, lidar, inertial sensors, magnetometer, control position sensors, or drive train telemetry sensors. One of ordinary skill in the art would have been motivated to make this modification so “ the stability of the control can be enhanced.”(Yashiro, description)
Regarding claim 27,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 15. Shi does not explicitly teach, but Yashiro teaches:
A vehicle comprising an imaging system and a system according to claim 26, wherein: the imaging system is configured to image the environment proximal to the vehicle and determine the environment complexity; and the system is further configured to receive the environment complexity from the imaging system. ( Yashiro, Para [30], “The object recognizer 16 recognizes the position, kind, velocity and so on of the object by performing sensor fusion on results of the detections by some or all of the camera 10, the radar unit 12”, para [05], “the control condition is shifted even if the vehicle-to-vehicle distance is unstable”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify apply Yashiro’s environment-triggered mode-switching mechanism to the tube-MPC based vehicle controller described by Shi, using Shi’s tube-MPC regimes as the different control conditions in order to include wherein: the imaging system is configured to image the environment proximal to the vehicle and determine the environment complexity; and the system is further configured to receive the environment complexity from the imaging system. One of ordinary skill in the art would have been motivated to make this modification so “ the stability of the control can be enhanced.”(Yashiro, description)
Claim(s) 16, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Shi, “Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives”, Annual Reviews in Control, Volume 52, 2021, Pages 170-196 in view of Yashiro (US20200307583A1), further in view of Raković, "Elastic tube model predictive control", 2016 American Control Conference (ACC), Boston, MA, USA, 2016, pp. 3594-3599.
Regarding claim 16,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 15. Shi teaches:
The computer-implemented method of claim 15, wherein elastic tube model predictive control comprises: computing a tube for restricting the trajectory of the vehicle to an interior of the tube; (Shi, Table 1 describe the elastic tube model predictive control, and page 172, “generates a tube of trajectories, where each trajectory is related to one possible realization of the uncertainty. The essential idea of tube-based MPC is to keep all possible trajectories in the tube”)
and determining the set of control parameters that guarantee the trajectory of the vehicle is within the interior of the tube; (Shi, page 173, “The essential idea of tube-based MPC is to tighten the state constraint based on a local feedback control law such that the constraint satisfaction is guaranteed for all possible realizations of uncertainties.”)
Shi does not explicitly teach, but Raković teaches:
wherein a cross-section of the tube is a convex polytope defined by an intersection of a plurality of half-spaces and each of the plurality of half-spaces is independently adjustable.( Raković, page 3594, “A set X⊂Rn is [a]C-set if it is compact, convex, and contains the origin. A set X⊂Rn is a proper C-set (PC- set) if it is a C-set and contains the origin in its (non-empty) interior. A polyhedron is the intersection of a finite number of open and/or closed half-spaces and a polytope is a closed and bounded polyhedron.”) Examiner note: Raković teaches the elastic tube model predictive control which the cross-sections Xk and Uk of the state and control tubes are parameterized in terms of the centers zk and vk and vector-valued elasticity parameters ak as Xk=zk⊕S(ak) and Uk=vk⊕K(ak)S(ak), and modify the vector value of elasticity parameters will independently adjust the half-spaces.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the elastic tube model predictive control from Shi to include these above teachings from Raković in order to include wherein a cross-section of the tube is a convex polytope defined by an intersection of a plurality of half-spaces and each of the plurality of half-spaces is independently adjustable. One of ordinary skill in the art would have been motivated to make this modification as “These novel features result in an improved tube MPC at the cost of a manageable increase in computational complexity”.( Raković, abstract)
Regarding claim 19,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 15. Shi does not explicitly teach, but Yashiro teaches:
The computer-implemented method of claim 15, wherein the change in the environment complexity comprises an increase in environment complexity (Yashiro, para [05], “the control condition is shifted even if the vehicle-to-vehicle distance is unstable”)
Shi does not explicitly teach, but Raković teaches:
and the second constraint regime has more adjustable parameters than the first constraint regime. Raković teaches the elastic tube model predictive control which has more adjustable parameters such as the vector value of elasticity parameters will independently adjust the half-spaces.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the elastic tube model predictive control from Shi to include these above teachings from Raković in order to include the second constraint regime has more adjustable parameters than the first constraint regime. One of ordinary skill in the art would have been motivated to make this modification as “These novel features result in an improved tube MPC at the cost of a manageable increase in computational complexity”.( Raković, abstract)
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Shi, “Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives”, Annual Reviews in Control, Volume 52, 2021, Pages 170-196 in view of Yashiro (US20200307583A1), further in view of Raković, “Homothetic tube model predictive control”, Automatica, Volume 48, Issue 8, 2012, Pages 1631-1638,
Regarding claim 17,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 15. Shi teaches:
The computer-implemented method of claim 15, wherein homothetic tube model predictive control comprises: computing a tube for restricting the trajectory of the vehicle to an interior of the tube; (Shi, Table 1 describe the homothetic tube model predictive control, and page 172, “generates a tube of trajectories, where each trajectory is related to one possible realization of the uncertainty. The essential idea of tube-based MPC is to keep all possible trajectories in the tube”)
and determining the set of control parameters that guarantee the trajectory of the vehicle is within the interior of the tube; (Shi, page 173, “The essential idea of tube-based MPC is to tighten the state constraint based on a local feedback control law such that the constraint satisfaction is guaranteed for all possible realizations of uncertainties.”)
Shi does not explicitly teach, but Raković teaches:
wherein a cross-section of the tube is a convex polytope and differs from a pre-determined convex polytope by an adjustable scale factor (Raković, page 1632, “A polyhedron is the intersection of a finite number of open and/or closed half-spaces and a polytope is a closed and bounded polyhedron. The interior of a set X is denoted by interior(X)”, and page 1632, “homothetic tube scalings as additional decision variables affords more flexibility in handling the predicted transient effects of the disturbance. The homothetic tube scalings and, hence, the ‘‘diameters’’ of the homothetic tubes are optimized on-line”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the elastic tube model predictive control from Shi to include these above teachings from Raković in order to include wherein a cross-section of the tube is a convex polytope and differs from a pre-determined convex polytope by an adjustable scale factor. One of ordinary skill in the art would have been motivated to make this modification as “computationally efficient and it induces strong system theoretic properties”.( Raković, abstract)
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Shi, “Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives”, Annual Reviews in Control, Volume 52, 2021, Pages 170-196 in view of Yashiro (US20200307583A1), further in view of Raković, "Invariant approximations of the minimal robust positively Invariant set," in IEEE Transactions on Automatic Control, vol. 50, no. 3, pp. 406-410, March 2005,
Regarding claim 18,
Shi in view of Yashiro, as shown in the rejection above, discloses the limitations of claim 15. Shi teaches:
The computer-implemented method of claim 15, wherein rigid tube model predictive control comprises: computing a tube for restricting the trajectory of the vehicle to an interior of the tube; (Shi, Table 1 describe the rigid tube model predictive control, and page 172, “generates a tube of trajectories, where each trajectory is related to one possible realization of the uncertainty. The essential idea of tube-based MPC is to keep all possible trajectories in the tube”)
and determining the set of control parameters that guarantee the trajectory of the vehicle is within the interior of the tube; (Shi, page 173, “The essential idea of tube-based MPC is to tighten the state constraint based on a local feedback control law such that the constraint satisfaction is guaranteed for all possible realizations of uncertainties.”)
Shi does not explicitly teach, but Raković teaches:
wherein a cross-section of the tube is a pre-determined convex polytope. (Raković, page 408, “W is a polytope (bounded and closed polyhedron)”
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the elastic tube model predictive control from Shi to include these above teachings from Raković in order to include wherein a cross-section of the tube is a pre-determined convex polytope. One of ordinary skill in the art would have been motivated to make this modification as “permit the efficient computation”.( Raković, Conclusion)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SUZUKI (US20230303163A1) teaches controlling a vehicle’s steering so it can follow a target path while still feeling smooth when the vehicle first starts moving. The system repeatedly builds a vehicle model using current vehicle data such as weight, center of gravity, speed, steering angle, road curvature, and deviation from the desired path. Using that model, it calculates an “optimal” steering angle by minimizing an evaluation function that balances path tracking and steering smoothness.
Hwang (US 20230131835 A1) teaches a control system for an independently driven electric vehicle, such as a vehicle with separate wheel motors. The system uses travel information and route/path information to help the vehicle stay on course during autonomous driving. One part of the system calculates a steering angle so the vehicle can follow a look-ahead point along the route. Another part uses torque vectoring, meaning it adjusts motor torque at different wheels to help the vehicle turn and track its path. The torque vectoring controller uses lateral error and angular error to generate a control moment. It then distributes motor torque among the rear and front wheel motors. If the rear wheel motor cannot supply enough torque, extra torque is assigned to the front wheel motor. The system may also interact with electronic stability control by comparing slip-angle values and deciding when ESC should take over. Overall, the invention combines steering control, wheel torque control, and stability management for autonomous driving.
Rien (US 20230050192 A1) teaches a traffic coordination system for connected and automated vehicles in busy road networks. The system looks at several vehicles at once, traveling through a road network with intersections and merge areas. It uses real-time vehicle and infrastructure data to decide when each vehicle should enter, exit, rather than planning for each vehicle independently. It uses real-time communication from roadside infrastructure and vehicles to estimate where each vehicle is, where it is going, and when it will slow down, wait, or proceed through shared conflict areas. The system is designed for mixed traffic, meaning automated vehicles and human-driven reach each road segment. The system then computes a schedule for vehicles to enter and exit intersections and merge areas safely. It also sets target average speeds and, for automated vehicles, vehicles are handled together. It can estimate the likely routes of human-driven vehicles and use that information in the planning process. The core output may include planned stops. The goal is to reduce crashes, delays, and energy use while keeping traffic moving smoothly. The approach is designed for mixed traffic, meaning automated vehicles and human is a coarse motion plan for each automated vehicle, including timing and average speed through each road segment. The plan may also-driven vehicles share the same roads. It can handle multiple conflict zones such as intersections and merging points. The application also contemplates implementation in edge computers or cloud systems so the include planned stops, such as for pickup or delivery tasks. The optimization is performed globally for multiple vehicles at once rather than vehicle computation can happen in real time. In shipping-yard settings, it can coordinate trucks, spotters, and yard-jockey-type vehicles as well.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAI NMN WANG whose telephone number is (571)270-5633. The examiner can normally be reached Mon-Fri 0800-1700.
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/KAI NMN WANG/Examiner, Art Unit 3664
/REDHWAN K MAWARI/Primary Examiner, Art Unit 3664
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