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 reply to the Application Number 18/576,686 filed on 09/16/2025
Claims 1, 4, 5 and 7 are currently pending and have been examined. Claims 2, 3, 6 and 8 – 12 have been cancelled.
This action is made FINAL.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Roufas et al. (US 20200070923 A1) further in view of Qian et al. (Optimal Path Planning), Wang et al. (US 20040098185 A1) and Tran et al. (US 10992755 B1).
Regarding claim 1, Roufas teaches a two-wheeled, self-balancing robot comprising: a pair of drive wheels; a support structure operably coupled with the pair of drive wheels; a self-balancing and drive system operably coupled with the support structure and the pair of drive wheels to output a drive power to the pair of drive wheels to maintain balance of the support structure in response to data; at least one sensor collecting and outputting the data to the self-balancing and drive system, (Roufas: Paragraph 0041: “The base 102 may further include various components (not shown) for providing movement and self-balancing functions, including two electric motors (one motor for each base wheel 255), one or more batteries, various sensors, and computer hardware and software. These components may be mounted or included anywhere on the base 102, such as within the platform 250, the base wheels 255, and/or the handle 260. The sensors detect various parameters such as changes in direction, speed, and tilt/pitch. For example, the sensors may include tilt sensors, accelerometers, gyroscopes, and the like. Each motor may separately provide power to each base wheel 255 for moving the base 102 forward and backward and for turning the base 102 left and right. The computer hardware may include memory and processors for storing and executing the computer software that provide movement and self-balancing functions of the base 102. For example the handle may receive inputs from the user to turn the base left or right. In response, the computer hardware and software may individually adjust the power to each motor which causes the left and right wheels to rotate at different rates to effectuate the left or right turn.”,
Supplemental Note: Figure 2 and the citation above describes a self-balancing robot with a pair of electric motorized wheels with sensors and computer hardware for controlling its functions. The electric motors for driving the wheels are interpreted as part of the drive system and the actuators as they take electrical energy for motorized movement, thus converting one energy source to a mechanical movement)
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wherein the at least one sensor comprises: a LiDAR system coupled to the support structure and outputting data to the self-balancing and drive system; (Roufas: Paragraph 0080: “FIG. 16 illustrates an example carriage 104 having a set of sensors that can be implemented in the transportation robot 100 of FIG. 3, according to various embodiments of the present invention. As shown, a set of one or more sensors 105 may be mounted on the carriage 104. The sensors 105 include different types of sensors for performing various functions, such as object detection, localization, detection of a user/rider onboard the base 102, and detection of a payload on the carriage 104. For example, sensors 105 include ultrasonic distance measurement sensors oriented in various directions, multiple and various cameras for monocular/stereo vision and depth perception, lidar, laser scanners, three-dimensional depth sensing stereo cameras, radars, GNSS antennas, visible and non-visible light emitters/detectors, and the like. Further, sensors 105 may include global positioning systems, accelerometers, gyroscopes, and the like. The sensors 105 may comprise additional sensors that are not typically included in the base unit 102 for enabling self-balancing functions of the base unit 102. In these embodiments, the sensors 105 may be mounted to or included within the carriage 104 for the purpose of autonomous driving.”)
a monocular network camera coupled to the support structure and outputting data to the self-balancing and drive system; at least one actuator operably coupled to the self-balancing and drive system, (Roufas: Paragraph 0041: “The base 102 may further include various components (not shown) for providing movement and self-balancing functions, including two electric motors (one motor for each base wheel 255), one or more batteries, various sensors, and computer hardware and software. These components may be mounted or included anywhere on the base 102, such as within the platform 250, the base wheels 255, and/or the handle 260.”; Paragraph 0080: “FIG. 16 illustrates an example carriage 104 having a set of sensors that can be implemented in the transportation robot 100 of FIG. 3, according to various embodiments of the present invention. As shown, a set of one or more sensors 105 may be mounted on the carriage 104. The sensors 105 include different types of sensors for performing various functions, such as object detection, localization, detection of a user/rider onboard the base 102, and detection of a payload on the carriage 104. For example, sensors 105 include ultrasonic distance measurement sensors oriented in various directions, multiple and various cameras for monocular/stereo vision and depth perception, lidar, laser scanners, three-dimensional depth sensing stereo cameras, radars, GNSS antennas, visible and non-visible light emitters/detectors, and the like. Further, sensors 105 may include global positioning systems, accelerometers, gyroscopes, and the like. The sensors 105 may comprise additional sensors that are not typically included in the base unit 102 for enabling self-balancing functions of the base unit 102. In these embodiments, the sensors 105 may be mounted to or included within the carriage 104 for the purpose of autonomous driving.”)
… at least one processor configured to output a control signal to the self-balancing and drive system, (Roufas: Paragraph 0041: “The computer hardware may include memory and processors for storing and executing the computer software that provide movement and self-balancing functions of the base 102.”)
In sum Roufas teaches a two-wheeled, self-balancing robot comprising: a pair of drive wheels; a support structure operably coupled with the pair of drive wheels; a self-balancing and drive system operably coupled with the support structure and the pair of drive wheels to output a drive power to the pair of drive wheels to maintain balance of the support structure in response to data; at least one sensor collecting and outputting the data to the self-balancing and drive system, wherein the at least one sensor comprises: a LiDAR system coupled to the support structure and outputting data to the self-balancing and drive system; a monocular network camera coupled to the support structure and outputting data to the self-balancing and drive system; at least one actuator operably coupled to the self-balancing and drive system and at least one processor configured to output a control signal to the self-balancing and drive system. Roufas however does not teach wherein the at least one actuator comprises two digital servos connected with a pendulum and steer rod; whereas Qian does.
Qian teaches wherein the at least one actuator comprises two digital servos connected with a pendulum and steer rod; and (Qian: Page 394: Section 2.1: Paragraph 1: “The two-wheel self-balancing vehicle model is shown in Fig. 1 as the following. In the figure, R stands for the radius of the left and right wheels; mR=mRL=mRR stands for the mass of the wheel; mP stands for the mass of the vehicle body;”; Page 395: Section 2.1: Body: Paragraph 1: “The inverted pendulum-like body is unique to the self-balancing vehicle. Because the body is heavy, located on the top of the Quantun, the self-balancing vehicle belongs to the intrinsically unstable system. The principle of dynamic balance is adopted in body stability control during vehicle movement. According to the function and the use of different occasions, the car body has a lot of room for transformation. The car body can be W-manned, as a mobile vehicle, such as segway and ninebot, or carry analytical instruments to detect unknown environments, especially unstructured environments that people cannot reach; or W-manned mobile manipulator can complete welding and explosion removal tasks under dangerous and harsh conditions, such as welding robots.”; Page 395: Section 2.1: Driving Load: Paragraph 1: “The driving force of the self-balancing vehicle comes from two DC motors which are tightly connected with the left and right wheels. When the motor rotates, it directly drives the wheels, and the wheels rotate synchronously with the motor without any speed change or sunning links.”,
Supplemental Note: Fig. 1 shows the self-balancing robot where the wheels are connected to motors to drive the vehicle and provide self-balancing by adjusting the mass at the top of the vehicle connected to a steer rod. The mass is interpreted as the pendulum)
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Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Roufas with the teachings of Qian with a reasonable expectation of success. Both Roufas and Qian teach self-balancing vehicles with two actuated wheels and a handle or rod vertically attached to the wheel system. Qian further teaches the ability of self-balancing by utilizing a mass of at the top of the vertical rod that adjusts the center of gravity of the system to provide balance as it is traveling. Roufas teaches it’s self-balancing technique by the use of sensors and wheel actuators to keep the pitch of the vehicle up-right (Roufas: Paragraph 0041). One with knowledge in the art would find the addition of Qian’s technique with the vehicle of Roufas as a use of a known technique to improve similar devices in the same way. For example, both teach the ability to actuate the wheels to keep the balance of the vehicle, Qian further teaching an inverted pendulum system with a mass on top to also provide the balance. The addition of Qian’s self-balancing system can improve the vehicle of Roufas of just currently using the wheels for the balancing. However neither Roufas or Qian teach wherein at least one of the at least one processor and the self-balancing and drive system is configured to provide the drive power to the pair of drive wheels to provide real time learning for obstacle avoidance control whereas Wang does.
Wang teaches wherein at least one of the at least one processor and the self-balancing and drive system is configured to provide the drive power to the pair of drive wheels to provide real time learning for obstacle avoidance control, (Wang: Paragraph 0004: “Several types of light vehicles have been proposed to address the above issues through either inline two wheels (i.e. motorcycle) with two supporting wheels or three wheels (tricycle). The first type still requires rider to keep balance in normal riding condition when the supporting wheels are retracted. Therefore, in general, it still requires complex skill to ride it”; Paragraph 0073: “An embodiment of the computer control may improve brake effectiveness through integrated control and providing automatic braking capability. The brake subsystem may prevent wheel skidding, and keep the vehicle balanced during hard braking, avoiding vehicle flip-over and fall. The subsystem also detects obstacles through sensors, then activates brake automatically to avoid collision.”; Paragraph 0108: “Other control methods, such as an artificial neural network (ANN), Cerebella Model Articulated Control (CMAC) in particular, and fuzzy control, can also be used to replace optimal control method to achieve similar objectives. The effectiveness of an ANN controller does not depend on detailed and accurate mechanic model of the vehicle. This can be a major benefit when the system is too complex to build an accurate model. An additional benefit of ANN is its adaptivity. The control behavior can be easily and quickly tuned to give optimal response under changing conditions. One example of it is weight shift of the vehicle. Suppose a load is shifted from one portion of the vehicle to other, the center of gravity changes. That impacts the vehicle dynamic behavior. By being able to adapt the change, the vehicle can quickly tune itself to the optimal stable dynamic states. The vehicle's dynamic behavior may depend upon training data.”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Roufas with the teachings of Wang with a reasonable expectation of success. Roufas teaches the ability of the self-balancing vehicle to perform navigation however Roufas does not mention obstacle avoidance measures. Wang further teaches the use of a neural network that is able to learn from its environment and if an obstacle is detected, it is able to stop the vehicle to avoid collision. One with knowledge in the art would find it obvious to try implement this function with Roufas. Roufas is merely utilizing the wheel actuators to help keep its vehicle in balance, thus any obstacle will affect the self-balancing function as it interacts with the wheels. For example, if a self-balancing vehicles hits a wall on a flat surface, it can fall down whereas a 3-wheeled or 4-wheeled vehicle is prone to stand upright as it has more supports and does not require the constant need of the wheels actuating to keep it balanced. The obstacle avoidance measures of Wang mitigate situations where obstacles can knock the vehicle off its balance, causing additional damage to the vehicle while also toppling the vehicle over in which additional assistance is required to get it back up. However neither Roufas or Wang teach wherein the obstacle avoidance control is provided based on a map model based on a neural network operating over a programmable platform whereas Tran does.
Tran teaches wherein the obstacle avoidance control is provided based on a map model based on a neural network operating over a programmable platform. (Tran: Col. 1, lines 52 – 56: “Smart car operations are detailed including capturing a point cloud from a vehicle street view and converting the point cloud to a 3D model; applying a trained neural network to detect street signs, cross walks, obstacles, or bike lanes; and updating a high definition (HD) map with the neural network output.”; Col. 2, lines 36 – 48: “The method includes determining an obstacle in the lane and changing the vehicle's path to avoid the obstacle, wherein the obstacle comprises rocks, a lane closure, an inoperative vehicle, or a vehicle in an accident. The path can be a road, a freeway or a highway. Map data may include obstacles, pedestrian crossway, bike lanes, and traffic signs. The bike lane can be detected by neural network by matching a portion of the roadway that has been designated by striping, signage, and pavement markings for the preferential or exclusive use of bicyclists. The method includes detecting a physical barrier (bollards, medians, raised curbs, etc.) that restricts the encroachment of motorized traffic.”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Roufas with the teachings of Tran with a reasonable expectation of success. As stated above, obstacle avoidance is not taught by Roufas and would be obvious to try to implement to one with knowledge in the art as it mitigates any opportunities of an obstacle knocking the vehicle off its balance, further damaging the vehicle and requiring additional assistance to set it back up. Tran teaches a system of obstacle avoidance by the use of a neural network that creates a 3D point cloud model which maps out the vehicle’s environment and able to distinguish the various obstacles. One with knowledge in the art would this system of Tran to be obvious to try to implement with the self-balancing vehicle of Roufas. This further allows the vehicle of Roufas to recognize what the obstacles in its environment are to better navigate through it. For example, a raised curb can be identified by the vehicle based on the neural engine as a restricted obstacle to not go over. This further includes any traffic signs that the vehicle can now identify and obey. The system can even identify rocks which can cause balance issues as they interact with the wheels of the vehicles. All of these functions improve the way the self-balancing vehicle of Roufas operates by allowing it process it’s environment and identify any hazardous objects.
Regarding claim 4, Roufas, as modified, teaches wherein the at least one sensor comprises an inertial measurement unit coupled to the support structure and outputting data to the self-balancing and drive system. (Roufas: Paragraph 0041: “The base 102 may further include various components (not shown) for providing movement and self-balancing functions, including two electric motors (one motor for each base wheel 255), one or more batteries, various sensors, and computer hardware and software. These components may be mounted or included anywhere on the base 102, such as within the platform 250, the base wheels 255, and/or the handle 260.”; Paragraph 0080: “FIG. 16 illustrates an example carriage 104 having a set of sensors that can be implemented in the transportation robot 100 of FIG. 3, according to various embodiments of the present invention. As shown, a set of one or more sensors 105 may be mounted on the carriage 104. The sensors 105 include different types of sensors for performing various functions, such as object detection, localization, detection of a user/rider onboard the base 102, and detection of a payload on the carriage 104. For example, sensors 105 include ultrasonic distance measurement sensors oriented in various directions, multiple and various cameras for monocular/stereo vision and depth perception, lidar, laser scanners, three-dimensional depth sensing stereo cameras, radars, GNSS antennas, visible and non-visible light emitters/detectors, and the like. Further, sensors 105 may include global positioning systems, accelerometers, gyroscopes, and the like. The sensors 105 may comprise additional sensors that are not typically included in the base unit 102 for enabling self-balancing functions of the base unit 102. In these embodiments, the sensors 105 may be mounted to or included within the carriage 104 for the purpose of autonomous driving.”,
Supplemental Note: as stated in specification paragraph 0203, the inertial measurement unit is referred to as a gyroscope)
Regarding claim 7, Roufas, as modified, teaches wherein the at least one processor comprises at least one microcontroller and a central processing unit (Roufas: Paragraph 0041: “The computer hardware may include memory and processors for storing and executing the computer software that provide movement and self-balancing functions of the base 102”; Paragraph 0037: “The processing hardware 120 is configured to retrieve and execute programming instructions stored within a memory (not shown) of the robot 100 or transmitted to the robot 100 via the communication engine 110… The processing hardware 120 may comprise additional computer hardware and software that is not typically included in the base unit 102 for enabling self-balancing functions of the base unit 102. In these embodiments, the processing hardware 120 may be mounted on or included within the carriage 104 for providing additional functionality.”)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Roufas et al. (US 20200070923 A1) in view of Qian et al. (Optimal Path Planning), Wang et al. (US 20040098185 A1) and Tran et al. (US 10992755 B1) as applied to claim 1 above, and further in view of Ebrahimi et al. (US 11037320 B1).
Regarding claim 5, Roufas, as modified, teaches self-balancing and drive system (Roufas: Paragraph 0003: “A currently popular robotic device is a self-balancing vehicle (“base”) used for personal transportation. A self-balancing base typically includes two wheels, a platform, an optional handle, two electric motors, a battery, sensors, and computer hardware and software.”)
In sum, Roufas teaches self-balancing and drive system. Roufas however does not teach at least one sensor comprises two-wheel encoders coupled to the support structure and outputting data whereas Ebrahimi does.
Ebrahimi teaches wherein the at least one sensor comprises two-wheel encoders coupled to the support structure and outputting data to the (Ebrahimi: Col. 22, lines 3 – 7: “For example, data from a digital camera (i.e., passive sensor) is used in a primary method for mapping and localization and data from a wheel encoder (i.e., active sensor) is used in a secondary method for mapping and localization.”; Abstract: “A method including detecting an object in a line of sight of at least one sensor; adjusting a current path of the robot to include a detour path around the object, instructing the robot to resume along the current path after avoiding the object,”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Roufas with the teachings of Ebrahimi with a reasonable expectation of success. Both Roufas and Ebrahimi teach an autonomous robot able to sense and travel around its environment. Ebrahimi further teaches an addition of a wheel encoder which is used for mapping and localization. One with knowledge in the art would find it obvious to try to implement these wheel encoders with the wheels of the autonomous robot taught by Roufas. As taught by Ebrahimi, this can be used as a secondary method for mapping the environment and able to localize the autonomous robot, this increases the accuracy of the robot’s position as it has another data set to compare with as it autonomously travels through an environment with objects thus mitigating any incorrect movements.
Response to Arguments
Applicant’s arguments, see section REJECTION UNDER 35 USC 102 AND 103 of the REMARKS with respect to claim(s) 1 – 12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIVAM SHARMA whose telephone number is (703)756-1726. The examiner can normally be reached Monday-Friday 8:00-5:00.
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/SHIVAM SHARMA/ Examiner, Art Unit 3665
/Erin D Bishop/ Supervisory Patent Examiner, Art Unit 3665