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 .
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 9 are rejected under 35 U.S.C. § 103 as being unpatentable over Alofs (U.S. Patent No. 5,916,285) in view of Jones (U.S. Patent Application Publication No. 2004/0049877 A1) and further in view of Ibrahim (U.S. Patent No. 6,269,306 B1)
Regarding Claim 1,
Disclosure by Alofs
Alofs teaches:
• A method
See at least:
“The functioning of a vehicle adapted with a track wheel caster assembly, as depicted in FIGS. 2(a&b), in accordance with a preferred embodiment of the present invention can be described as follows and best understood by referencing FIG. 3.” (Alofs, col. 4, ll. 41–46).
Rationale:
Alofs expressly teaches the operation of a vehicle adapted with a track-wheel caster assembly, thereby teaching a method.
• of correction of odometry errors
See at least:
“This invention relates to a vehicle navigation and guidance System comprising an apparatus for measuring and accounting for the lateral movement of the vehicle and a method of guiding a vehicle using the same.” (Alofs, col. 1, ll. 10–15).
Rationale:
Alofs teaches measuring and accounting for lateral vehicle movement. The correction of odometry errors is rendered obvious because Alofs improves position determination by accounting for crabbing, scrubbing, side slip, and lateral movement that conventional odometry/dead-reckoning systems fail to capture.
• during the autonomous drive
See at least:
“A preferred embodiment of the present invention is a driverleSS Vehicle comprising a navigation and guidance System having an angular motion Sensor and a track wheel caster assembly equipped with a caster pivot Sensor and a wheel rotation Sensor…” (Alofs, col. 3, ll. 17–22).
Rationale:
Alofs expressly teaches a driverless vehicle with a navigation and guidance system, corresponding to autonomous drive.
• of a wheel-equipped apparatus
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising a navigation and guidance System equipped with an angular motion Sensor 12, a track wheel caster assembly 40, at least one computer processor 16, and a front and a rear steering mechanism 18 and 20, respectively.” (Alofs, col. 3, ll. 28–34).
Rationale:
Alofs expressly teaches a driverless vehicle with steering and wheel/caster components, corresponding to a wheel-equipped apparatus.
• comprising drive means
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising … a front and a rear steering mechanism 18 and 20, respectively.” (Alofs, col. 3, ll. 28–34).
Rationale:
Alofs expressly teaches a dual-end steering driverless vehicle having front and rear steering mechanisms. Under the broadest reasonable interpretation, the claimed drive means is met or at least rendered obvious by Alofs’s vehicle steering/drive structure because a driverless vehicle having front and rear steering mechanisms would have included mechanical or electromechanical structure for causing and controlling vehicle movement. At minimum, it would have been obvious to one of ordinary skill in the art that Alofs’s driverless vehicle includes conventional motors, actuators, or equivalent drive/steering means operatively associated with vehicle wheels to propel and steer the vehicle.
• operatively connected to at least two drive wheels,
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising … a front and a rear steering mechanism 18 and 20, respectively.” (Alofs, col. 3, ll. 28–34).
Rationale:
Alofs expressly teaches a dual-end steering driverless vehicle having front and rear steering mechanisms. It would have been obvious to one of ordinary skill in the art that the steering/drive mechanisms of a driverless vehicle are operatively connected to vehicle wheels, including at least two drive wheels, because such a vehicle cannot be propelled, steered, or guided along a selected path unless the steering/drive mechanisms are mechanically or electromechanically connected to wheels that control vehicle movement.
• at least one pivoting wheel
See at least:
“The track wheel caster assembly 40 … comprises: a free wheeling contact wheel 42, a mounting plate 44, a freely pivoting castor Sub-assembly 46, a wheel rotation sensor 48, and a caster pivot sensor 50.” (Alofs, col. 3, ll. 45–50).
Rationale:
Alofs expressly teaches a free-wheeling contact wheel in a freely pivoting caster sub-assembly, corresponding to at least one pivoting wheel.
• that freely pivots
See at least:
“The freely pivoting castor sub-assembly 46 typically comprises a horizontal offset … between the caster stem 64 and the contact wheel axle 66.” (Alofs, col. 3, ll. 62–67).
Rationale:
Alofs expressly teaches that the caster sub-assembly freely pivots.
• about an axis of rotation
See at least:
“Preferably, the caster pivot sensor 50 is an absolute shaft encoder, as depicted in FIG. 4, positioned on the caster Stem 64. An absolute shaft encoder provides a Signal identifying the absolute position of the measured shaft.” (Alofs, col. 4, ll. 15–20).
Rationale:
Alofs expressly teaches a caster stem and absolute shaft encoder positioned on that caster stem. The caster stem defines the axis about which the caster pivots.
• and is operatively connected to a sensor,
See at least:
“The track wheel caster assembly 40 … comprises … a wheel rotation sensor 48, and a caster pivot sensor 50.” (Alofs, col. 3, ll. 45–50).
Rationale:
Alofs expressly teaches that the pivoting caster assembly includes a wheel rotation sensor and a caster pivot sensor, thereby teaching that the pivoting wheel/caster assembly is operatively connected to a sensor.
• and a controller
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising a navigation and guidance System equipped with … at least one computer processor 16…” (Alofs, col. 3, ll. 28–33).
Rationale:
Alofs expressly teaches a computer processor in the navigation and guidance system. The computer processor corresponds to the claimed controller.
• operatively connected to said drive means
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising … at least one computer processor 16, and a front and a rear steering mechanism 18 and 20, respectively.” (Alofs, col. 3, ll. 28–34).
“The vehicle’s navigation and guidance System on the vehicle operates in a conventional manner by Sampling data from various Sensors at Short time intervals and Steering the vehicle responsive to information received from these inputs.” (Alofs, col. 4, ll. 44–49).
Rationale:
Alofs expressly teaches a navigation and guidance computer processor and front/rear steering mechanisms. To the extent Alofs does not spell out the electrical or mechanical signal path between the processor and steering/drive mechanisms, it would have been obvious to one of ordinary skill in the art that the controller is operatively connected to said drive means because Alofs’s automated steering responsive to sensor inputs cannot be implemented unless the navigation/guidance processor provides control information to the steering/drive mechanisms. This is a §103 obviousness finding grounded in the disclosed automated steering function.
• and to said sensor,
See at least:
“As the vehicle moves, the wheel rotation sensor 48 and/or the caster pivot sensor 50 … will Sense the motion and transmit a corresponding Signal to the navigation and guidance System’s computer processor 16.” (Alofs, col. 5, ll. 10–15).
Rationale:
Alofs expressly teaches that the wheel rotation sensor and/or caster pivot sensor transmits a corresponding signal to the navigation and guidance system’s computer processor, thereby teaching operative connection to said sensor.
• said method comprising:
See at least:
“The functioning of a vehicle adapted with a track wheel caster assembly … can be described as follows…” (Alofs, col. 4, ll. 41–46).
Rationale:
Alofs expressly introduces the operative method used by the vehicle navigation and guidance system.
• a determining phase,
See at least:
“The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs expressly teaches calculating the vehicle’s current location, corresponding to a determining phase.
• wherein the controller determines
See at least:
“As the vehicle moves, the wheel rotation sensor 48 and/or the caster pivot sensor 50 … will Sense the motion and transmit a corresponding Signal to the navigation and guidance System’s computer processor 16.” (Alofs, col. 5, ll. 10–15).
Rationale:
Alofs teaches that sensor-derived motion signals are transmitted to the computer processor, and that the navigation and guidance system uses those values to calculate current location. Thus, the controller/computer processor performs the determination.
• a differential in location
See at least:
“Once the measurements and calculations of these variables are determined…” followed by the calculation of vehicle movement components, and “The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location So that the guidance System can determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 27–36).
Rationale:
A differential in location is inherently present because Alofs’s express disclosure of calculating current location and determining how to direct the vehicle toward a desired path/location necessarily and inevitably requires determining the difference between the current location and the desired/predefined location.
• between a current position of the wheeled-equipped apparatus
See at least:
“The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs expressly teaches calculating the vehicle’s current location, corresponding to the current position of the wheeled-equipped apparatus.
• and a predefined position for the wheeled-equipped apparatus,
See at least:
“…guide a point on the vehicle designated as the pivot point P along a Selected path or toward a designated location.” (Alofs, col. 4, ll. 49–53).
Rationale:
Alofs expressly teaches a selected path and designated location. A selected/designated location corresponds to a predefined position for the wheeled-equipped apparatus.
• the current position
See at least:
“The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs expressly teaches the vehicle’s current location, corresponding to the current position.
• being determined
See at least:
“The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs expressly teaches determining/calculating the current location.
• based at least in part
See at least:
“The motion of the vehicle Rm, as sensed by the wheel rotation sensor 48, is the product of the wheel rotation angle Wa, in radians, determined directly from information Supplied by the wheel rotation Sensor and the wheel radius Wr.” (Alofs, col. 5, ll. 11–17).
Rationale:
Alofs expressly teaches determining vehicle motion based at least in part on information supplied by a wheel rotation sensor.
• the predefined location
See at least:
“…toward a designated location.” (Alofs, col. 4, ll. 49–53).
Rationale:
Alofs expressly teaches a designated location, corresponding to the predefined location.
• being disposed
See at least:
“…guide a point on the vehicle designated as the pivot point P along a Selected path or toward a designated location.” (Alofs, col. 4, ll. 49–53).
Rationale:
Alofs teaches a selected path and designated location. A designated location associated with the selected path is a predefined location disposed relative to the path.
• along a predefined path
See at least:
“…guide a point on the vehicle designated as the pivot point P along a Selected path…” (Alofs, col. 4, ll. 49–52).
Rationale:
Alofs expressly teaches guiding the vehicle along a selected path, corresponding to a predefined path.
• for the wheeled-equipped apparatus;
See at least:
“…guide a point on the vehicle designated as the pivot point P along a Selected path…” (Alofs, col. 4, ll. 49–52).
Rationale:
Alofs teaches that the selected/predefined path is for the driverless vehicle.
• an acquisition phase,
See at least:
“The vehicle’s navigation and guidance System on the vehicle operates in a conventional manner by Sampling data from various Sensors at Short time intervals…” (Alofs, col. 4, ll. 44–49).
Rationale:
Alofs expressly teaches sampling sensor data at short time intervals, corresponding to an acquisition phase.
• wherein said sensor acquires
See at least:
“Additionally, the caster pivot sensor 50 may be any device that provides a signal responsive to the rotational movement of the caster Sub-assembly 46 with respect to the caster mounting plate 44.” (Alofs, col. 4, ll. 12–15).
Rationale:
Alofs expressly teaches that the caster pivot sensor acquires information responsive to rotational movement of the caster sub-assembly.
• an angle of rotation
See at least:
“The motion of the vehicle Pm, as sensed by the caster pivot sensor 50, is the product of the change in the pivot angle (final angle Sf minus initial angle Si)…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly teaches a caster pivot-angle change, corresponding to an angle of rotation.
• of said at least one pivoting wheel
See at least:
“…the change in the pivot angle … of the caster…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly teaches that the sensed angle is the pivot angle of the caster/pivoting wheel assembly.
• with respect to an axis of rotation
See at least:
“Preferably, the caster pivot sensor 50 is an absolute shaft encoder … positioned on the caster Stem 64. An absolute shaft encoder provides a Signal identifying the absolute position of the measured shaft.” (Alofs, col. 4, ll. 15–20).
Rationale:
Alofs teaches measuring the caster’s shaft position using an absolute shaft encoder. The shaft/caster stem defines the axis of rotation with respect to which the caster angle is measured.
• when the wheeled-equipped apparatus
See at least:
“As the vehicle moves, the wheel rotation sensor 48 and/or the caster pivot sensor 50 … will Sense the motion…” (Alofs, col. 5, ll. 10–13).
Rationale:
Alofs teaches sensor acquisition while the vehicle is moving.
• is at the current position,
See at least:
“The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs teaches that sensor-derived measurements are used to determine the current location. Thus, the acquired angle information corresponds to the current position at the measurement interval.
• wherein the axis of rotation
See at least:
“Preferably, the caster pivot sensor 50 is an absolute shaft encoder … positioned on the caster Stem 64.” (Alofs, col. 4, ll. 15–18).
Rationale:
Alofs teaches the caster stem/shaft structure that defines the caster pivot axis.
• is perpendicular
See at least:
“The freely pivoting castor sub-assembly 46 typically comprises a horizontal offset … between the caster stem 64 and the contact wheel axle 66.” (Alofs, col. 3, ll. 62–67).
Rationale:
Alofs teaches a conventional ground-travel caster assembly having a caster stem and contact-wheel axle. It would have been obvious to one of ordinary skill in the art that the caster stem/pivot axis in such a freely pivoting caster is vertical and perpendicular to the travel/rest surface because that geometry is the conventional structure enabling free caster pivoting while the contact wheel rests on the floor.
• to a rest surface
See at least:
“…the Surface upon which the vehicle is travelling.” (Alofs, col. 3, ll. 22–25).
Rationale:
Alofs expressly teaches the surface on which the vehicle travels. In the context of Alofs’s free-wheeling contact wheel 42 in the freely pivoting caster sub-assembly 46, the surface on which the vehicle travels is the same physical support surface on which the pivoting wheel rests. Thus, under the broadest reasonable interpretation, Alofs renders obvious the claimed rest surface of the pivoting wheel.
• of said at least one pivoting wheel
See at least:
“The track wheel caster assembly 40 … comprises: a free wheeling contact wheel 42 … a freely pivoting castor Sub-assembly 46…” (Alofs, col. 3, ll. 45–50).
Rationale:
Alofs expressly teaches that the contact wheel is part of the freely pivoting caster assembly.
• and the angle of rotation
See at least:
“…the change in the pivot angle (final angle Sf minus initial angle Si)…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly teaches the caster pivot-angle change.
• is an angle
See at least:
“The average caster angle SA between two measuring intervals is the initial angle Si plus half the change in the pivot angle over the measurement interval.” (Alofs, col. 5, ll. 24–27).
Rationale:
Alofs expressly teaches a caster angle.
• about the axis of rotation
See at least:
“Additionally, the caster pivot sensor 50 may be any device that provides a signal responsive to the rotational movement of the caster Sub-assembly 46 with respect to the caster mounting plate 44.” (Alofs, col. 4, ll. 12–15).
Rationale:
Alofs expressly teaches rotational movement of the caster sub-assembly about the caster pivot/stem axis.
• between a first predefined reference direction
See at least:
“The average caster angle SA between two measuring intervals is the initial angle Si plus half the change in the pivot angle over the measurement interval.” (Alofs, col. 5, ll. 24–27).
“The measured distance between the two points is recorded as Cy the distance in the Y-direction and Cx the distance in the X-direction both with respect to the vehicle’s frame of reference.” (Alofs, col. 5, ll. 1–5).
“Preferably, the caster pivot sensor 50 is an absolute shaft encoder … positioned on the caster Stem 64. An absolute shaft encoder provides a Signal identifying the absolute position of the measured shaft.” (Alofs, col. 4, ll. 15–20).
Rationale:
Alofs expressly teaches an initial angle Si used as the reference for determining the change in pivot angle and calculating the average caster angle SA. Under the broadest reasonable interpretation, the claimed “first predefined reference direction” does not require an immutable global compass direction or fixed factory-calibrated axis; it requires a reference direction defined before determining the relative pivot-wheel orientation. Alofs’s initial angle Si is established before the final angle Sf is used to determine the pivot-angle change for the measurement interval, and Alofs performs the calculations with respect to the vehicle’s frame of reference. The claim recites a first predefined reference direction “of the pivoting wheel,” which under the broadest reasonable interpretation identifies the pivoting wheel’s orientation reference rather than requiring a coordinate system intrinsic to the wheel itself. Alternatively, Alofs’s absolute shaft encoder identifies the absolute position of the measured caster shaft and therefore necessarily uses a physical zero-position or reference orientation from which shaft angle is encoded. That absolute encoder reference direction constitutes a predefined reference direction of the caster pivot shaft and thus of the pivoting wheel.
• of the pivoting wheel
See at least:
“…the change in the pivot angle … of the caster…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly teaches that the initial/final pivot-angle relationship is the pivot angle of the caster/pivoting wheel.
• and a second variable orientation direction
See at least:
“…the change in the pivot angle (final angle Sf minus initial angle Si)…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly teaches a final angle Sf relative to initial angle Si. Because Sf changes as the caster pivots during vehicle movement, Sf corresponds to the second variable orientation direction.
• of the pivoting wheel
See at least:
“…the change in the pivot angle … of the caster…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly teaches that the second variable orientation direction is the caster’s pivot orientation.
• when the wheeled-equipped apparatus
See at least:
“As the vehicle moves…” (Alofs, col. 5, ll. 10–13).
Rationale:
Alofs teaches sensing while the vehicle moves.
• is at the current position;
See at least:
“The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs teaches that the sensor-derived values are used to calculate the current location, so the acquired caster-angle information corresponds to the vehicle’s current position at the measurement interval.
• a generation phase,
See at least:
“…the guidance System can determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 34–36).
Rationale:
Alofs teaches a guidance/control phase following position calculation, corresponding to the generation phase.
• wherein said controller generates
See at least:
“The vehicle’s navigation and guidance System … operates … by Sampling data from various Sensors at Short time intervals and Steering the vehicle responsive to information received from these inputs.” (Alofs, col. 4, ll. 44–49).
Rationale:
Alofs teaches that the navigation and guidance system steers the driverless vehicle responsive to sensor inputs. It would have been obvious to one of ordinary skill in the art that the controller/computer processor generates the corresponding machine-readable control output because automated steering of a driverless vehicle cannot be physically implemented unless the processor’s navigation determination is communicated to the steering/drive mechanism.
• a corrective signal
See at least:
“The vehicle’s navigation and guidance System … operates … by Sampling data from various Sensors at Short time intervals and Steering the vehicle responsive to information received from these inputs.” (Alofs, col. 4, ll. 44–49).
Rationale:
Alofs teaches a navigation and guidance system that steers the driverless vehicle responsive to information received from sensor inputs. A corrective signal is inherent in this system because automated steering of a driverless vehicle in response to sensor-derived information necessarily and inevitably requires a processor-generated machine-readable control output directed to the steering/drive mechanism. Further and alternatively, even if the term “corrective signal” requires something beyond Alofs’s steering response, it would have been obvious to one of ordinary skill in the art that the navigation and guidance computer processor generates a machine-readable corrective signal to direct the steering/drive mechanisms because the automated steering function disclosed in Alofs cannot be physically implemented unless the processor’s navigation determination is communicated to the drive mechanism through a control signal.
• that controls said drive means
See at least:
“The vehicle’s navigation and guidance System … operates … by Sampling data from various Sensors at Short time intervals and Steering the vehicle responsive to information received from these inputs.” (Alofs, col. 4, ll. 44–49).
Rationale:
Alofs teaches steering the vehicle responsive to sensor inputs through the navigation and guidance system. Because the drive means is mapped to Alofs’s steering/drive mechanisms, it would have been obvious to one of ordinary skill in the art that the corrective signal controls said drive means to steer and guide the driverless vehicle.
• on the basis of the angle of rotation
See at least:
“The motion of the vehicle Pm, as sensed by the caster pivot sensor 50, is the product of the change in the pivot angle…” and “The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location So that the guidance System can determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 18–36).
Rationale:
Alofs expressly teaches that the caster pivot-angle change is used to calculate Pm, that Pm and SA are used in the Xm/Ym vehicle-motion calculations, and that Xm/Ym are used to calculate current location so the guidance system can determine how to direct the vehicle along a desired path or toward a desired location. Thus, the corrective signal is generated on the basis of the angle of rotation through the disclosed calculation chain: caster pivot angle → Pm/SA → Xm/Ym → current location → steering/guidance correction.
• of said at least one pivoting wheel
See at least:
“…the change in the pivot angle … of the caster…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly teaches that the relevant angle is the pivot angle of the caster/pivoting wheel assembly.
• so that the wheeled-equipped apparatus moves
See at least:
“…determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 34–36).
Rationale:
Alofs expressly teaches directing vehicle movement.
• so as to decrease the differential in location
See at least:
“…determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 34–36).
Rationale:
Decreasing the differential in location is inherent because Alofs’s express disclosure of directing the vehicle from its calculated current location toward a desired path/location necessarily and inevitably reduces the positional difference between the current and desired/predefined locations.
• between the current position
See at least:
“calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs expressly teaches the current position/current location.
• and the predefined location,
See at least:
“…toward a designated location.” (Alofs, col. 4, ll. 49–53).
Rationale:
Alofs expressly teaches the predefined/designated location.
• in accordance with:
See at least:
“Once the measurements and calculations of these variables are determined…” (Alofs, col. 5, ll. 27–31).
Rationale:
Alofs teaches that vehicle-motion/current-location determination is performed in accordance with calculated variables and equations.
• a predefined function
See at least:
“Ym=Rm Cos(SA)-Pm-Sin (SA)-Cx HC, and Xm=Rm'Sin (SA)+Pm Cos(SA)+Cy He…” (Alofs, col. 11, ll. 13–20).
Rationale:
Alofs expressly teaches predefined mathematical functions for calculating vehicle movement components. Correcting OCR artifacts from the reproduced text, Alofs teaches motion-component equations of the form Ym = Rm·Cos(SA) - Pm·Sin(SA) - Cx·Hc and Xm = Rm·Sin(SA) + Pm·Cos(SA) + Cy·Hc. The cited column and line location identifies Alofs’s disclosed Xm/Ym motion-component equations.
• having as arguments
See at least:
“Rm is a calculated distance based in part on information Supplied from Said wheel rotation Sensor, SA is a calculated average caster rotation angle based in part on information Supplied by Said caster pivot Sensor, Pm is a calculated vehicle motion based in part on information Supplied by Said angular motion Sensor and Said caster pivot Sensor…” (Alofs, col. 11, ll. 20–30).
Rationale:
Alofs expressly teaches functions using variables, including Rm, SA, Pm, Cx, Cy, and Hc, as arguments.
• at least said angle of rotation
See at least:
“SA is a calculated average caster rotation angle based in part on information Supplied by Said caster pivot Sensor…” (Alofs, col. 11, ll. 23–25).
Rationale:
Alofs expressly teaches using SA, a calculated average caster rotation angle, in the predefined function.
• of said at least one pivoting wheel,
See at least:
“SA is a calculated average caster rotation angle based in part on information Supplied by Said caster pivot Sensor…” (Alofs, col. 11, ll. 23–25).
Rationale:
Alofs teaches that the function uses a caster rotation angle based on information supplied by the caster pivot sensor, corresponding to the angle of rotation of the pivoting wheel.
Claim Limitations Not Explicitly Taught by Alofs
Alofs does not explicitly teach the following claim limitations:
• upon detected revolutions of the drive wheels,
• in such a way as to generate
• an angular velocity difference
• between said at least two drive wheels
• wherein said controller determines
• the angular velocity difference
• between said at least two drive wheels
• the function being based
• on one or more of the following algorithms:
• Kalman filter,
• Linear-Prediction,
• or Proportional-Integral Derivation control (PID);
• or
• an output signal
• of a previously trained neural network
• having as input signal
• at least said angle of rotation
• of said at least one pivoting wheel.
Disclosure by Jones
Jones teaches:
• upon detected revolutions of the drive wheels,
See at least:
“Each IR sensor/encoder combination 52WE is operative to measure the rotation of the associated wheel Subassembly 42A, 42B and transmit a signal corresponding thereto to the control module 60.” (Jones, 0040).
Rationale:
Jones expressly teaches measuring rotation of the associated right and left wheel subassemblies and transmitting a corresponding signal to the control module. In the Alofs/Jones combination, Alofs teaches determining current position based on sensed wheel-rotation-derived vehicle motion, and Jones supplies drive-wheel encoder measurements for the right and left drive-wheel subassemblies. It would have been obvious to one of ordinary skill in the art to use Jones’s detected drive-wheel rotations as odometry inputs because drive-wheel encoders provide direct propulsion-wheel displacement information in a differential-drive autonomous robot.
• in such a way as to generate
See at least:
“The motorS 42AM, 42BM of the main wheel Subassemblies 42A, 42B are operative to drive the main wheels … at different speeds … to effect turning patterns for the autonomous floor-cleaning robot 10…” (Jones, 0037).
Rationale:
Jones expressly teaches driving the main wheels at different speeds to generate turning patterns. The different-speed wheel control supplies the specific manner in which the drive means is controlled to generate the claimed wheel-speed difference.
• an angular velocity difference
See at least:
“The motorS 42AM, 42BM of the main wheel Subassemblies 42A, 42B are operative to drive the main wheels … at different speeds … to effect turning patterns…” (Jones, 0037).
Rationale:
Jones expressly teaches different-speed operation of the two main wheels. Different rotational speeds of the left and right drive wheels necessarily constitute an angular velocity difference between those wheels.
• between said at least two drive wheels
See at least:
“Such independent means includes right and left main wheel Subassemblies 42A, 42B, each Subassembly 42A, 42B having its own independently-operated motor 42AM, 42BM…” (Jones, 0034).
Rationale:
Jones expressly teaches two independently driven main wheel subassemblies between which the angular velocity difference is generated.
• wherein said controller determines
See at least:
“The control module 60 comprises the control circuitry … and microcontroller for the autonomous floor-cleaning robot 10 that controls the movement of the robot 10 during floor cleaning operations and in response to signals generated by the sensor Subsystem 50.” (Jones, 0043).
Rationale:
Jones expressly teaches a microcontroller controlling robot movement. Because Jones’s robot turns by different-speed operation of the right and left main wheels, it would have been obvious to one of ordinary skill in the art that the controller determines the relative wheel-speed commands needed to produce the desired movement. Differential-drive control, in which a controller determines distinct velocity commands for independently driven left and right wheels to effect steering, was a standard and well-understood robotics control technique. Implementing Jones’s independently operated motors through controller-determined angular velocity differences represents the application of a known technique to a known problem with a predictable result.
• the angular velocity difference
See at least:
“The motorS 42AM, 42BM of the main wheel Subassemblies 42A, 42B are operative to drive the main wheels … at different speeds … to effect turning patterns…” (Jones, 0037).
Rationale:
Jones teaches different-speed operation of the main wheels. The controller-determined difference between commanded wheel speeds is the angular velocity difference.
• between said at least two drive wheels
See at least:
“right and left main wheel Subassemblies 42A, 42B, each Subassembly 42A, 42B having its own independently-operated motor…” (Jones, 0034).
Rationale:
Jones expressly teaches the two drive-wheel subassemblies between which the controller-determined angular velocity difference is applied.
Motivation to Combine Alofs and Jones
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Alofs and Jones before them, to modify Alofs’s driverless-vehicle navigation and guidance method by implementing Jones’s drive-wheel encoder feedback and differential-drive wheel-speed control in which independently operated right and left main wheel subassemblies are driven at different speeds to effect turning patterns.
Alofs teaches the driverless vehicle, navigation/guidance system, computer processor, caster-angle sensing, current-location calculation, and steering correction context. Alofs also renders obvious drive means operatively connected to at least two drive wheels through its disclosure of a dual-end steering driverless vehicle having front and rear steering mechanisms. Jones is relied upon for the more specific limitation of detected revolutions of drive wheels and generating an angular velocity difference between at least two drive wheels because Jones expressly teaches wheel encoder measurement of right and left wheel subassemblies and independently operated right and left main wheel subassemblies driven at different speeds to effect turning.
A person of ordinary skill in the art would have looked to Jones specifically because Jones is in the same field of autonomous wheeled robotic control and addresses the same actuator-control problem of controlling a robot’s trajectory using independently driven wheels and measured wheel rotation. Jones supplies a known implementation for drive-wheel odometry and differential steering control that Alofs leaves open by broadly teaching conventional steering mechanisms and sensor-based navigation correction.
The combination uses known elements according to their established functions: Alofs’s navigation/guidance system provides the caster-angle-based location-correction guidance, and Jones’s drive-wheel encoders and differential-drive wheel-speed control provide known drive-wheel odometry and actuator-control implementation for carrying out vehicle steering corrections by commanding different left/right wheel speeds. MPEP §2143 recognizes combining prior art elements according to known methods to yield predictable results, simple substitution of one known element for another, and applying a known technique to a known device ready for improvement as proper rationales supporting obviousness.
A person of ordinary skill in the art would have had a reasonable expectation of success because Alofs’s navigation/guidance processor generates correction information from sensor-derived motion data, and Jones’s control module actuates independently controlled wheels in response to control signals and receives wheel-rotation information from wheel encoders. The proposed combination does not require overcoming any technical incompatibility; it applies known drive-wheel encoder feedback and known differential-drive steering implementation to Alofs’s known driverless-vehicle navigation correction system. Alofs col. 3–6 describes sensor-based navigation/guidance and steering, while Jones 0034, 0037, 0040, and 0043 describe a compatible motive subsystem, different-speed wheel operation, drive-wheel rotation measurement, and control-module-based movement control. These teachings are complementary rather than discouraging: Alofs does not criticize differential-drive steering or drive-wheel encoder odometry, and Jones does not criticize using sensor-derived navigation corrections or caster-angle sensing.
Claim Limitations Not Explicitly Taught by the Combination of Alofs and Jones
After combining the teachings of Alofs and Jones, the following claim limitations are not explicitly taught:
• the function being based
• on one or more of the following algorithms:
• Kalman filter,
• Linear-Prediction,
• or Proportional-Integral Derivation control (PID);
• or
• an output signal
• of a previously trained neural network
• having as input signal
• at least said angle of rotation
• of said at least one pivoting wheel.
Disclosure by Ibrahim
Ibrahim teaches:
• the function being based
See at least:
“Conventional Sensor error estimation is typically performed by developing Sensor error models, and then implementing the model parameter estimation as augmented equations in the Overall set of navigation equations, usually in a ‘Kalman’ filter.” (Ibrahim, col. 1, ll. 43–47).
Rationale:
Ibrahim expressly teaches navigation/sensor-error estimation functions based on sensor-error models and Kalman-filter-type estimation. This teaches basing the correction function on an algorithmic estimation technique.
• on one or more of the following algorithms:
See at least:
“Conventional Sensor error estimation is typically performed by developing Sensor error models, and then implementing the model parameter estimation as augmented equations in the Overall set of navigation equations, usually in a ‘Kalman’ filter.” (Ibrahim, col. 1, ll. 43–47).
Rationale:
Ibrahim teaches at least one of the claimed algorithmic alternatives, namely a Kalman filter. Because the claim recites “one or more,” the algorithmic group is satisfied by the Kalman/Kalman filter alternative. Linear-Prediction and PID are unrelied alternatives within the same “one or more” group and do not need to be separately established once the Kalman/Kalman filter alternative is taught.
• Kalman filter,
See at least:
“FIG. 9 is a graph illustrating the average bias estimates provided by a Kalman filter type System over a period of time. FIG. 10 is a graph illustrating the Scale factor estimates provided by a Kalman filter type System over a period of time.” (Ibrahim, col. 3, ll. 7–13).
Rationale:
As construed above, “Kalman filter” is treated as “Kalman filter.” Ibrahim expressly teaches a Kalman-filter-type system in the vehicle navigation sensor-error-estimation context.
• Linear-Prediction,
See at least:
“Conventional Sensor error estimation is typically performed by developing Sensor error models, and then implementing the model parameter estimation as augmented equations in the Overall set of navigation equations, usually in a ‘Kalman’ filter.” (Ibrahim, col. 1, ll. 45–52).
Rationale:
The rejection does not rely on Linear-Prediction as the selected algorithm. This limitation is accounted for as an unrelied alternative within the “one or more” algorithmic group. Because Ibrahim teaches the Kalman/Kalman-filter alternative, the “one or more” algorithmic requirement is met without separately establishing Linear-Prediction.
• or Proportional-Integral Derivation control (PID);
See at least:
“Conventional Sensor error estimation is typically performed by developing Sensor error models, and then implementing the model parameter estimation as augmented equations in the Overall set of navigation equations, usually in a ‘Kalman’ filter.” (Ibrahim, col. 1, ll. 45–52).
Rationale:
The rejection does not rely on PID as the selected algorithm. This limitation is accounted for as an unrelied alternative within the “one or more” algorithmic group. Because Ibrahim teaches the Kalman/Kalman-filter alternative, the “one or more” algorithmic requirement is met without separately establishing PID.
• or
See at least:
“Other attempts at estimating these types of Sensor errors have been made using neural networks.” (Ibrahim, col. 1, ll. 53–56).
“The System 10 utilizes linear neuron 14 to adaptively estimate Scale factor and the bias values…” (Ibrahim, col. 8, ll. 29–33).
Rationale:
Ibrahim expressly identifies neural networks as an alternative sensor-error-estimation approach and then teaches a linear-neuron estimator. These disclosures support the claim’s disjunctive “or” branch leading to the neural-estimator alternative.
• an output signal
See at least:
“The estimated Scale factor and bias is fed back into module 12 in order to predict the error of the INS heading angle for the next DGPS period.” (Ibrahim, col. 8, ll. 25–28).
Rationale:
Ibrahim teaches estimator output values fed back into the navigation module to predict heading-angle error. These values constitute an output signal of the estimator. In the Alofs/Jones/Ibrahim combination, the output signal of Ibrahim’s trained estimator corresponds to an estimated navigation correction value used in place of or in addition to Alofs’s direct caster-angle calculation to generate the corrective control signal.
• of a previously trained neural network
See at least:
“After obtaining the desired or ‘combined’ angle ψ in Eq. 17, the angle is inputted into the linear neuron 14, as shown in FIG. 1 and is used to train the linear neuron 14.” (Ibrahim, col. 6, ll. 56–60).
“The System 10 utilizes linear neuron 14 to adaptively estimate Scale factor and the bias values…” (Ibrahim, col. 8, ll. 29–33).
Rationale:
Primary construction: Under the broadest reasonable interpretation, “previously trained” encompasses any training that precedes the use of the trained output, including adaptive training occurring at each measurement cycle before the trained output is applied to navigation correction. Ibrahim teaches training the linear neuron using the combined angle and then using the trained/adapted neuron to estimate scale factor and bias values. Under the broadest reasonable interpretation, a “neural network” encompasses an adaptive neural computing element or assembly, including a single linear neuron, where the claim does not limit “neural network” to multi-layer, multi-node, or deep-learning architectures. Ibrahim expressly identifies neural networks as a known category of sensor-error estimation technique and then implements the adaptive estimation function with linear neuron 14.
Alternative construction: Even if “previously trained” is construed to require a temporally distinct training phase preceding use, Ibrahim teaches that the combined angle is used to train the linear neuron before the trained linear neuron is used to estimate scale factor and bias values for navigation correction. The training step at col. 6, ll. 56–60 temporally and functionally precedes the estimation-output step at col. 8, ll. 29–33.
Further alternative: To the extent “neural network” is construed to require more than a single linear neuron, it would have been obvious to one of ordinary skill in the art to implement Ibrahim’s neural estimator using a known multi-node neural-network architecture because Ibrahim expressly identifies neural networks as a known approach for estimating sensor errors and uses a linear neuron for the same adaptive estimation purpose. Scaling a known linear-neuron estimator to a more complex neural-network estimator would have been a routine implementation choice to improve estimation flexibility or accuracy.
• having as input signal
See at least:
“The neuron design is simply a simulation of Eq. 29, where the neuron weights are the estimated Scale factor δK and bias B, and the neuron inputs are the yaw rate Sum and time.” (Ibrahim, col. 8, ll. 17–22).
Rationale:
Ibrahim expressly teaches that the neural estimator has input signals. In the proposed combination, the specific input would be Alofs’s caster pivot angle because Alofs already uses that angle as the navigation-correction input.
• at least said angle of rotation
See at least:
“System module 12 is electrically, physically, and communicatively coupled to various Sensors … which provide input Signals to module 12…” and “module 12 receives … a yaw rate Signal ‘r’ from a conventional vehicle yaw rate Sensor…” (Ibrahim, col. 3, ll. 40–55).
“The neuron design is simply a simulation of Eq. 29 … and the neuron inputs are the yaw rate Sum and time.” (Ibrahim, col. 8, ll. 17–22).
“The average caster angle SA between two measuring intervals is the initial angle Si plus half the change in the pivot angle over the measurement interval.” (Alofs, col. 5, ll. 24–27).
Rationale:
Ibrahim does not expressly teach using a caster pivot angle as the neural-network input. However, it would have been obvious to one of ordinary skill in the art in view of the Alofs/Jones/Ibrahim combination to use Alofs’s caster pivot angle as at least one input signal to Ibrahim’s known navigation-error estimator. Ibrahim teaches that module 12 is coupled to various sensors that provide input signals, demonstrating that the estimator architecture is designed to receive vehicle navigation sensor signals generally. The specific use of yaw rate in Ibrahim is an implementation example of processing an angular vehicle-motion sensor signal, not a teaching that the estimator is limited exclusively to yaw-rate input.
Although Ibrahim’s yaw-rate input and Alofs’s caster pivot angle are not identical physical quantities, both are vehicle-motion-related angular sensor quantities used to estimate or correct navigation error. Ibrahim’s yaw-rate signal is an angular velocity quantity, while Alofs’s caster pivot angle is an angular displacement quantity derived from an absolute shaft encoder on the caster stem. A person of ordinary skill in autonomous vehicle navigation and control would have understood that angular displacement and angular velocity are related motion quantities and that converting, scaling, differentiating, integrating, or otherwise conditioning angular sensor signals for estimator input was routine signal processing within the ordinary level of skill. Thus, adapting Ibrahim’s sensor-error estimator to process Alofs’s caster pivot-angle input would have required only routine configuration of a known estimator architecture for a compatible class of angular motion sensor signals.
This adaptation represents a predictable use of Ibrahim’s known vehicle navigation sensor-error estimator with Alofs’s known caster-angle navigation-correction input. It does not rely on hindsight because Alofs itself identifies the caster pivot angle as a navigation-correction quantity, and Ibrahim independently teaches algorithmic estimation of vehicle navigation sensor error from angular sensor inputs.
• of said at least one pivoting wheel.
See at least:
“The motion of the vehicle Pm, as sensed by the caster pivot sensor 50, is the product of the change in the pivot angle…” (Alofs, col. 5, ll. 18–22).
“The neuron design is simply a simulation of Eq. 29 … and the neuron inputs are the yaw rate Sum and time.” (Ibrahim, col. 8, ll. 17–22).
Rationale:
Alofs teaches the angle of rotation of the pivoting caster wheel, and Ibrahim teaches using navigation sensor-derived angular input signals in a trained neural estimator. It would have been obvious to one of ordinary skill in the art to provide Alofs’s caster pivot-angle signal as an input to Ibrahim’s estimator to improve odometry/navigation correction in the combined autonomous wheeled apparatus.
Motivation to Combine Alofs, Jones, and Ibrahim
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Alofs, Jones, and Ibrahim before them, to further modify the Alofs/Jones autonomous-drive wheel-equipped apparatus and method by implementing the corrective computation using Ibrahim’s Kalman-filter-type estimation and/or trained-neural-estimator technique.
Alofs teaches using caster pivot angle and wheel-motion information to calculate current vehicle location and guide a driverless vehicle toward a selected path or desired location. Jones teaches a compatible differential-drive control implementation for independently controlled right and left drive wheels and corresponding wheel-rotation measurement. Ibrahim teaches vehicle navigation sensor-error estimation using known algorithmic approaches, including Kalman-filter-type estimation and trained linear-neuron estimation.
One of ordinary skill in the art would have been motivated to consult Ibrahim because Ibrahim addresses the same technical field of vehicle navigation accuracy and sensor-error correction, and because Alofs’s problem of correcting motion/position error is directly compatible with Ibrahim’s sensor-error estimation techniques. The selection of a Kalman filter, Linear-Prediction, PID, or neural estimator reflects a finite set of known, predictable navigation/control solutions for implementing a corrective function. MPEP §2143 recognizes applying known techniques to known devices ready for improvement and combining prior art elements according to known methods to yield predictable results as proper rationales for obviousness. MPEP §2145 further recognizes that an obvious-to-try rationale may support obviousness when one of ordinary skill chooses from a finite number of identified, predictable solutions with a reasonable expectation of success.
A person of ordinary skill in the art would have had a reasonable expectation of success because Alofs’s caster-angle-based navigation correction is designed to work within a driverless vehicle navigation/guidance system, Jones’s differential-drive control is a standard autonomous-vehicle propulsion/control implementation with independently controlled wheels and wheel-rotation measurement, and Ibrahim’s estimator is designed to process navigation sensor signals to improve vehicle position/heading reliability. Combining these teachings does not require overcoming any technical incompatibility because Alofs’s navigation processor generates correction information from sensor-derived motion data, Jones’s control module actuates independently controlled wheels in response to sensor/control signals while receiving wheel-rotation information, and Ibrahim’s estimator provides a known algorithmic technique for improving navigation sensor-error correction.
Further, the proposed use of Alofs’s caster pivot-angle signal in Ibrahim’s estimator does not change the principle of operation of either reference. Alofs still uses caster-angle-derived information to correct vehicle navigation, while Ibrahim’s estimator still processes vehicle-motion sensor information to estimate navigation error. The combination merely applies Ibrahim’s known estimator to Alofs’s known sensor-derived angular correction quantity. Alofs col. 3–6, Jones 0034, 0037, 0040, and 0043, and Ibrahim col. 1–8 contain complementary teachings directed to vehicle navigation, sensor-based correction, drive control, wheel-rotation measurement, and navigation-error estimation. These passages do not criticize, discredit, or discourage combining caster-angle-based navigation correction, differential-drive control, drive-wheel encoder odometry, and known navigation-error estimation algorithms.
Viewing Claim 1 as a whole, the claimed method is the predictable combination of: (1) Alofs’s known caster-angle-based driverless vehicle navigation method, which teaches the pivoting caster wheel, caster pivot sensor, computer processor, current-location determination, corrective steering, and predefined motion-calculation functions using caster angle as an argument; (2) Jones’s known drive-wheel encoder feedback and differential-drive wheel-speed control for autonomous robots, which supplies detected drive-wheel revolutions and the angular velocity difference between independently operated drive wheels; and (3) Ibrahim’s known vehicle navigation sensor-error estimation algorithms, which supply the Kalman-filter and neural-estimator alternatives. Each element performs the same function in the proposed combination that it performs in the reference from which it is drawn. The combination yields the predictable result of improved autonomous vehicle navigation correction and drive control, rather than a non-obvious or unexpected result.
Regarding Claim 9,
Disclosure by Alofs
Alofs discloses:
• An autonomous-drive wheel-equipped apparatus
See at least:
“A preferred embodiment of the present invention is a driverleSS Vehicle comprising a navigation and guidance System having an angular motion Sensor and a track wheel caster assembly equipped with a caster pivot Sensor and a wheel rotation Sensor…” (Alofs, col. 3, ll. 17–22).
Rationale:
Alofs expressly discloses a driverless vehicle having a navigation and guidance system and wheel/caster structures. A driverless vehicle corresponds to the claimed autonomous-drive wheel-equipped apparatus.
• comprising:
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising a navigation and guidance System equipped with an angular motion Sensor 12, a track wheel caster assembly 40, at least one computer processor 16, and a front and a rear steering mechanism 18 and 20, respectively.” (Alofs, col. 3, ll. 28–34).
Rationale:
Alofs expressly discloses a vehicle comprising multiple structural components, including the navigation and guidance system, computer processor, angular motion sensor, track wheel caster assembly, and steering mechanisms.
• drive means
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising a navigation and guidance System equipped with an angular motion Sensor 12, a track wheel caster assembly 40, at least one computer processor 16, and a front and a rear steering mechanism 18 and 20, respectively.” (Alofs, col. 3, ll. 28–34).
Rationale:
Alofs expressly discloses a dual-end steering driverless vehicle having front and rear steering mechanisms. Under the broadest reasonable interpretation, the claimed drive means is met or at least rendered obvious by Alofs’s vehicle steering/drive structure because a driverless vehicle having front and rear steering mechanisms would have included mechanical or electromechanical structure for causing and controlling vehicle movement. At minimum, it would have been obvious to one of ordinary skill in the art that Alofs’s driverless vehicle includes conventional motors, actuators, or equivalent drive/steering means operatively associated with vehicle wheels to propel and steer the vehicle.
• operatively connected to at least two drive wheels;
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising … a front and a rear steering mechanism 18 and 20, respectively.” (Alofs, col. 3, ll. 28–34).
Rationale:
Alofs expressly discloses a dual-end steering driverless vehicle having front and rear steering mechanisms. It would have been obvious to one of ordinary skill in the art that the steering/drive mechanisms of a driverless vehicle are operatively connected to vehicle wheels, including at least two drive wheels, because such a vehicle cannot be propelled, steered, or guided along a selected path unless the steering/drive mechanisms are mechanically or electromechanically connected to wheels that control vehicle movement.
• at least one pivoting wheel
See at least:
“The track wheel caster assembly 40 … comprises: a free wheeling contact wheel 42, a mounting plate 44, a freely pivoting castor Sub-assembly 46, a wheel rotation sensor 48, and a caster pivot sensor 50.” (Alofs, col. 3, ll. 45–50).
Rationale:
Alofs expressly discloses a free-wheeling contact wheel in a freely pivoting caster sub-assembly, corresponding to at least one pivoting wheel.
• operatively connected to a sensor,
See at least:
“The track wheel caster assembly 40 … comprises … a wheel rotation sensor 48, and a caster pivot sensor 50.” (Alofs, col. 3, ll. 45–50).
Rationale:
Alofs expressly discloses that the pivoting caster assembly includes a wheel rotation sensor and a caster pivot sensor, thereby disclosing that the pivoting wheel/caster assembly is operatively connected to a sensor.
• the pivoting wheel
See at least:
“The track wheel caster assembly 40 … comprises: a free wheeling contact wheel 42 … a freely pivoting castor Sub-assembly 46…” (Alofs, col. 3, ll. 45–50).
Rationale:
Alofs expressly discloses the pivoting wheel as the free-wheeling contact wheel associated with the freely pivoting caster sub-assembly.
• freely pivoting
See at least:
“The freely pivoting castor sub-assembly 46 typically comprises a horizontal offset … between the caster stem 64 and the contact wheel axle 66.” (Alofs, col. 3, ll. 62–67).
Rationale:
Alofs expressly discloses that the caster sub-assembly freely pivots.
• about an axis of rotation;
See at least:
“Preferably, the caster pivot sensor 50 is an absolute shaft encoder, as depicted in FIG. 4, positioned on the caster Stem 64. An absolute shaft encoder provides a Signal identifying the absolute position of the measured shaft.” (Alofs, col. 4, ll. 15–20).
Rationale:
Alofs expressly discloses a caster stem and absolute shaft encoder positioned on that caster stem. The caster stem defines the axis about which the caster pivots.
• and
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising a navigation and guidance System equipped with an angular motion Sensor 12, a track wheel caster assembly 40, at least one computer processor 16…” (Alofs, col. 3, ll. 28–33).
Rationale:
Alofs discloses the conjunctive combination of the pivoting caster structure with a navigation/guidance processor structure.
• a controller
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising a navigation and guidance System equipped with … at least one computer processor 16…” (Alofs, col. 3, ll. 28–33).
Rationale:
Alofs expressly discloses a computer processor in the navigation and guidance system. The computer processor corresponds to the claimed controller.
• operatively connected to said drive means
See at least:
“FIG. 1 depicts a dual-end steering driverless vehicle 10 comprising … at least one computer processor 16, and a front and a rear steering mechanism 18 and 20, respectively.” (Alofs, col. 3, ll. 28–34).
“The vehicle’s navigation and guidance System on the vehicle operates in a conventional manner by Sampling data from various Sensors at Short time intervals and Steering the vehicle responsive to information received from these inputs.” (Alofs, col. 4, ll. 44–49).
Rationale:
Alofs expressly discloses a navigation and guidance computer processor and front/rear steering mechanisms. To the extent Alofs does not spell out the electrical or mechanical signal path between the processor and steering/drive mechanisms, it would have been obvious to one of ordinary skill in the art that the controller is operatively connected to said drive means because Alofs’s automated steering responsive to sensor inputs cannot be implemented unless the navigation/guidance processor provides control information to the steering/drive mechanisms. This is a §103 obviousness finding grounded in the disclosed automated steering function.
• and to said sensor,
See at least:
“As the vehicle moves, the wheel rotation sensor 48 and/or the caster pivot sensor 50 … will Sense the motion and transmit a corresponding Signal to the navigation and guidance System’s computer processor 16.” (Alofs, col. 5, ll. 10–15).
Rationale:
Alofs expressly discloses that the wheel rotation sensor and/or caster pivot sensor transmits a corresponding signal to the navigation and guidance system’s computer processor, thereby disclosing operative connection to said sensor.
• wherein an angle of rotation
See at least:
“The motion of the vehicle Pm, as sensed by the caster pivot sensor 50, is the product of the change in the pivot angle (final angle Sf minus initial angle Si)…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly discloses a caster pivot-angle change, corresponding to an angle of rotation.
• of said at least one pivoting wheel
See at least:
“…the change in the pivot angle … of the caster…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly discloses that the angle is the pivot angle of the caster/pivoting wheel assembly.
• with respect to an axis of rotation,
See at least:
“Preferably, the caster pivot sensor 50 is an absolute shaft encoder … positioned on the caster Stem 64. An absolute shaft encoder provides a Signal identifying the absolute position of the measured shaft.” (Alofs, col. 4, ll. 15–20).
Rationale:
Alofs discloses measuring the caster’s shaft position using an absolute shaft encoder. The shaft/caster stem defines the axis of rotation with respect to which the caster angle is measured.
• the axis of rotation
See at least:
“Preferably, the caster pivot sensor 50 is an absolute shaft encoder … positioned on the caster Stem 64.” (Alofs, col. 4, ll. 15–18).
Rationale:
Alofs discloses the caster stem/shaft structure that defines the caster pivot axis.
• being perpendicular
See at least:
“The freely pivoting castor sub-assembly 46 typically comprises a horizontal offset … between the caster stem 64 and the contact wheel axle 66.” (Alofs, col. 3, ll. 62–67).
Rationale:
Alofs discloses a conventional ground-travel caster assembly having a caster stem and contact-wheel axle. It would have been obvious to one of ordinary skill in the art that the caster stem/pivot axis in such a freely pivoting caster is vertical and perpendicular to the travel/rest surface because that geometry is the conventional structure enabling free caster pivoting while the contact wheel rests on the floor.
• to a rest surface
See at least:
“…the Surface upon which the vehicle is travelling.” (Alofs, col. 3, ll. 22–25).
Rationale:
Alofs expressly discloses the surface on which the vehicle travels. In the context of Alofs’s free-wheeling contact wheel 42 in the freely pivoting caster sub-assembly 46, the surface on which the vehicle travels is the same physical support surface on which the pivoting wheel rests. Thus, under the broadest reasonable interpretation, Alofs renders obvious the claimed rest surface of the pivoting wheel.
• of said at least one pivoting wheel
See at least:
“The track wheel caster assembly 40 … comprises: a free wheeling contact wheel 42 … a freely pivoting castor Sub-assembly 46…” (Alofs, col. 3, ll. 45–50).
Rationale:
Alofs expressly discloses that the contact wheel is part of the freely pivoting caster assembly.
• and the angle of rotation
See at least:
“…the change in the pivot angle (final angle Sf minus initial angle Si)…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly discloses the caster pivot-angle change.
• being an angle
See at least:
“The average caster angle SA between two measuring intervals is the initial angle Si plus half the change in the pivot angle over the measurement interval.” (Alofs, col. 5, ll. 24–27).
Rationale:
Alofs expressly discloses a caster angle.
• about the axis of rotation
See at least:
“Additionally, the caster pivot sensor 50 may be any device that provides a signal responsive to the rotational movement of the caster Sub-assembly 46 with respect to the caster mounting plate 44.” (Alofs, col. 4, ll. 12–15).
Rationale:
Alofs expressly discloses rotational movement of the caster sub-assembly about the caster pivot/stem axis.
• between a first predefined reference direction
See at least:
“The average caster angle SA between two measuring intervals is the initial angle Si plus half the change in the pivot angle over the measurement interval.” (Alofs, col. 5, ll. 24–27).
“The measured distance between the two points is recorded as Cy the distance in the Y-direction and Cx the distance in the X-direction both with respect to the vehicle’s frame of reference.” (Alofs, col. 5, ll. 1–5).
“Preferably, the caster pivot sensor 50 is an absolute shaft encoder … positioned on the caster Stem 64. An absolute shaft encoder provides a Signal identifying the absolute position of the measured shaft.” (Alofs, col. 4, ll. 15–20).
Rationale:
Alofs expressly discloses an initial angle Si used as the reference for determining the change in pivot angle and calculating the average caster angle SA. Under the broadest reasonable interpretation, the claimed “first predefined reference direction” does not require an immutable global compass direction or fixed factory-calibrated axis; it requires a reference direction defined before determining the relative pivot-wheel orientation. Alofs’s initial angle Si is established before the final angle Sf is used to determine the pivot-angle change for the measurement interval, and Alofs performs the calculations with respect to the vehicle’s frame of reference. The claim recites a first predefined reference direction “of the pivoting wheel,” which under the broadest reasonable interpretation identifies the pivoting wheel’s orientation reference rather than requiring a coordinate system intrinsic to the wheel itself. Alternatively, Alofs’s absolute shaft encoder identifies the absolute position of the measured caster shaft and therefore necessarily uses a physical zero-position or reference orientation from which shaft angle is encoded. That absolute encoder reference direction constitutes a predefined reference direction of the caster pivot shaft and thus of the pivoting wheel.
• of the pivoting wheel
See at least:
“…the change in the pivot angle … of the caster…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly discloses that the initial/final pivot-angle relationship is the pivot angle of the caster/pivoting wheel.
• and a second variable orientation direction
See at least:
“…the change in the pivot angle (final angle Sf minus initial angle Si)…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly discloses a final angle Sf relative to initial angle Si. Because Sf changes as the caster pivots during vehicle movement, Sf corresponds to the second variable orientation direction.
• of the pivoting wheel,
See at least:
“…the change in the pivot angle … of the caster…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly discloses that the second variable orientation direction is the caster’s pivot orientation.
• and
See at least:
“The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location So that the guidance System can determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 31–36).
Rationale:
Alofs discloses the conjunctive relationship between determining navigation information and directing the vehicle based on that information.
• determines a differential in location
See at least:
“Once the measurements and calculations of these variables are determined…” followed by the calculation of vehicle movement components, and “The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location So that the guidance System can determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 27–36).
Rationale:
A differential in location is inherently present because Alofs’s express disclosure of calculating current location and determining how to direct the vehicle toward a desired path/location necessarily and inevitably requires determining the difference between the current location and the desired/predefined location.
• between a current position of the wheeled-equipped apparatus
See at least:
“The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs expressly discloses calculating the vehicle’s current location, corresponding to the current position of the wheeled-equipped apparatus.
• and a predefined position for the wheeled-equipped apparatus,
See at least:
“…guide a point on the vehicle designated as the pivot point P along a Selected path or toward a designated location.” (Alofs, col. 4, ll. 49–53).
Rationale:
Alofs expressly discloses a selected path and designated location. A selected/designated location corresponds to a predefined position for the wheeled-equipped apparatus.
• and
See at least:
“…calculate the vehicle’s current location So that the guidance System can determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 31–36).
Rationale:
Alofs discloses the conjunctive relationship between determining current location and generating guidance to direct the vehicle.
• generates a corrective signal
See at least:
“The vehicle’s navigation and guidance System … operates … by Sampling data from various Sensors at Short time intervals and Steering the vehicle responsive to information received from these inputs.” (Alofs, col. 4, ll. 44–49).
Rationale:
Alofs discloses a navigation and guidance system that steers the driverless vehicle responsive to information received from sensor inputs. A corrective signal is inherent in this system because automated steering of a driverless vehicle in response to sensor-derived information necessarily and inevitably requires a processor-generated machine-readable control output directed to the steering/drive mechanism. Further and alternatively, even if the term “corrective signal” requires something beyond Alofs’s steering response, it would have been obvious to one of ordinary skill in the art that the navigation and guidance computer processor generates a machine-readable corrective signal to direct the steering/drive mechanisms because the automated steering function disclosed in Alofs cannot be physically implemented unless the processor’s navigation determination is communicated to the drive mechanism through a control signal.
• that controls said drive means
See at least:
“The vehicle’s navigation and guidance System … operates … by Sampling data from various Sensors at Short time intervals and Steering the vehicle responsive to information received from these inputs.” (Alofs, col. 4, ll. 44–49).
Rationale:
Alofs discloses steering the vehicle responsive to sensor inputs through the navigation and guidance system. Because the drive means is mapped to Alofs’s steering/drive mechanisms, it would have been obvious to one of ordinary skill in the art that the corrective signal controls said drive means to steer and guide the driverless vehicle.
• on the basis of the angle of rotation
See at least:
“The motion of the vehicle Pm, as sensed by the caster pivot sensor 50, is the product of the change in the pivot angle…” and “The values of Xm and Ym are then used by the navigation and guidance System to calculate the vehicle’s current location So that the guidance System can determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 18–36).
Rationale:
Alofs expressly discloses that the caster pivot-angle change is used to calculate Pm, that Pm and SA are used in the Xm/Ym vehicle-motion calculations, and that Xm/Ym are used to calculate current location so the guidance system can determine how to direct the vehicle along a desired path or toward a desired location. Thus, the corrective signal is generated on the basis of the angle of rotation through the disclosed calculation chain: caster pivot angle → Pm/SA → Xm/Ym → current location → steering/guidance correction.
• of said at least one pivoting wheel
See at least:
“…the change in the pivot angle … of the caster…” (Alofs, col. 5, ll. 18–22).
Rationale:
Alofs expressly discloses that the relevant angle is the pivot angle of the caster/pivoting wheel assembly.
• so that the wheeled-equipped apparatus moves
See at least:
“…determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 34–36).
Rationale:
Alofs expressly discloses directing vehicle movement.
• so as to decrease the differential in location
See at least:
“…determine how to direct the vehicle along a desired path or toward a desired location.” (Alofs, col. 5, ll. 34–36).
Rationale:
Decreasing the differential in location is inherent because Alofs’s express disclosure of directing the vehicle from its calculated current location toward a desired path/location necessarily and inevitably reduces the positional difference between the current and desired/predefined locations.
• between the current position
See at least:
“calculate the vehicle’s current location…” (Alofs, col. 5, ll. 31–34).
Rationale:
Alofs expressly discloses the current position/current location.
• and the predefined location,
See at least:
“…toward a designated location.” (Alofs, col. 4, ll. 49–53).
Rationale:
Alofs expressly discloses the predefined/designated location.
• in accordance with:
See at least:
“Once the measurements and calculations of these variables are determined…” (Alofs, col. 5, ll. 27–31).
Rationale:
Alofs discloses that vehicle-motion/current-location determination is performed in accordance with calculated variables and equations.
• a predefined function
See at least:
“Ym=Rm Cos(SA)-Pm-Sin (SA)-Cx HC, and Xm=Rm'Sin (SA)+Pm Cos(SA)+Cy He…” (Alofs, col. 11, ll. 13–20).
Rationale:
Alofs expressly discloses predefined mathematical functions for calculating vehicle movement components. Correcting OCR artifacts from the reproduced text, Alofs discloses motion-component equations of the form Ym = Rm·Cos(SA) - Pm·Sin(SA) - Cx·Hc and Xm = Rm·Sin(SA) + Pm·Cos(SA) + Cy·Hc. The cited column and line location identifies Alofs’s disclosed Xm/Ym motion-component equations.
• having as arguments
See at least:
“Rm is a calculated distance based in part on information Supplied from Said wheel rotation Sensor, SA is a calculated average caster rotation angle based in part on information Supplied by Said caster pivot Sensor, Pm is a calculated vehicle motion based in part on information Supplied by Said angular motion Sensor and Said caster pivot Sensor…” (Alofs, col. 11, ll. 20–30).
Rationale:
Alofs expressly discloses functions using variables, including Rm, SA, Pm, Cx, Cy, and Hc, as arguments.
• at least said angle of rotation
See at least:
“SA is a calculated average caster rotation angle based in part on information Supplied by Said caster pivot Sensor…” (Alofs, col. 11, ll. 23–25).
Rationale:
Alofs expressly discloses using SA, a calculated average caster rotation angle, in the predefined function.
• of said at least one pivoting wheel,
See at least:
“SA is a calculated average caster rotation angle based in part on information Supplied by Said caster pivot Sensor…” (Alofs, col. 11, ll. 23–25).
Rationale:
Alofs discloses that the function uses a caster rotation angle based on information supplied by the caster pivot sensor, corresponding to the angle of rotation of the pivoting wheel.
Claim Limitations Not Explicitly Disclosed by Alofs
Alofs does not explicitly disclose the following claim limitations:
• in such a way as to generate
• an angular velocity difference
• between said at least two drive wheels
• wherein said controller determines
• the angular velocity difference
• between said at least two drive wheels
• the function being based
• on one or more of the following algorithms:
• Kalman filter,
• Linear-Prediction,
• or Proportional-Integral Derivation control (PID);
• or
• an output signal
• of a previously trained neural network
• having as input signal
• at least said angle of rotation
• of said at least one pivoting wheel.
Disclosure by Jones
Jones discloses:
• in such a way as to generate
See at least:
“The motorS 42AM, 42BM of the main wheel Subassemblies 42A, 42B are operative to drive the main wheels … at different speeds … to effect turning patterns for the autonomous floor-cleaning robot 10…” (Jones, 0037).
Rationale:
Jones expressly discloses driving the main wheels at different speeds to generate turning patterns. The different-speed wheel control supplies the specific manner in which the drive means is controlled to generate the claimed wheel-speed difference.
• an angular velocity difference
See at least:
“The motorS 42AM, 42BM of the main wheel Subassemblies 42A, 42B are operative to drive the main wheels … at different speeds … to effect turning patterns…” (Jones, 0037).
Rationale:
Jones expressly discloses different-speed operation of the two main wheels. Different rotational speeds of the left and right drive wheels necessarily constitute an angular velocity difference between those wheels.
• between said at least two drive wheels
See at least:
“Such independent means includes right and left main wheel Subassemblies 42A, 42B, each Subassembly 42A, 42B having its own independently-operated motor 42AM, 42BM…” (Jones, 0034).
Rationale:
Jones expressly discloses two independently driven main wheel subassemblies between which the angular velocity difference is generated.
• wherein said controller determines
See at least:
“The control module 60 comprises the control circuitry … and microcontroller for the autonomous floor-cleaning robot 10 that controls the movement of the robot 10 during floor cleaning operations and in response to signals generated by the sensor Subsystem 50.” (Jones, 0043).
Rationale:
Jones expressly discloses a microcontroller controlling robot movement. Because Jones’s robot turns by different-speed operation of the right and left main wheels, it would have been obvious to one of ordinary skill in the art that the controller determines the relative wheel-speed commands needed to produce the desired movement. Differential-drive control, in which a controller determines distinct velocity commands for independently driven left and right wheels to effect steering, was a standard and well-understood robotics control technique. Implementing Jones’s independently operated motors through controller-determined angular velocity differences represents the application of a known technique to a known problem with a predictable result.
• the angular velocity difference
See at least:
“The motorS 42AM, 42BM of the main wheel Subassemblies 42A, 42B are operative to drive the main wheels … at different speeds … to effect turning patterns…” (Jones, 0037).
Rationale:
Jones discloses different-speed operation of the main wheels. The controller-determined difference between commanded wheel speeds is the angular velocity difference.
• between said at least two drive wheels
See at least:
“right and left main wheel Subassemblies 42A, 42B, each Subassembly 42A, 42B having its own independently-operated motor…” (Jones, 0034).
Rationale:
Jones expressly discloses the two drive-wheel subassemblies between which the controller-determined angular velocity difference is applied.
Motivation to Combine Alofs and Jones
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Alofs and Jones before them, to modify Alofs’s driverless-vehicle navigation and guidance system by implementing Jones’s differential-drive wheel-speed control in which independently operated right and left main wheel subassemblies are driven at different speeds to effect turning patterns.
Alofs discloses the driverless vehicle, navigation/guidance system, computer processor, caster-angle sensing, current-location calculation, and steering correction context. Alofs also renders obvious drive means operatively connected to at least two drive wheels through its disclosure of a dual-end steering driverless vehicle having front and rear steering mechanisms. Jones is relied upon for the more specific limitation of generating an angular velocity difference between at least two drive wheels because Jones expressly discloses independently operated right and left main wheel subassemblies driven at different speeds to effect turning.
A person of ordinary skill in the art would have looked to Jones specifically because Jones is in the same field of autonomous wheeled robotic control and addresses the same actuator-control problem of controlling a robot’s trajectory using independently driven wheels. Jones supplies a known implementation for a steering-control architecture that Alofs leaves open by broadly disclosing conventional steering mechanisms. The combination uses known elements according to their established functions: Alofs’s navigation/guidance system provides the location-correction guidance, and Jones’s differential-drive wheel-speed control provides a known actuator-control implementation for carrying out vehicle steering corrections by commanding different left/right wheel speeds. MPEP §2143 recognizes combining prior art elements according to known methods to yield predictable results, simple substitution of one known element for another, and applying a known technique to a known device ready for improvement as proper rationales supporting obviousness.
A person of ordinary skill in the art would have had a reasonable expectation of success because Alofs’s navigation/guidance processor generates correction information from sensor-derived motion data, and Jones’s control module actuates independently controlled wheels in response to control signals. The proposed combination does not require overcoming any technical incompatibility; it applies a known differential-drive steering implementation to Alofs’s known driverless-vehicle navigation correction system. Alofs col. 3–6 describes sensor-based navigation/guidance and steering, while Jones 0034, 0037, and 0043 describe a compatible motive subsystem, different-speed wheel operation, and control-module-based movement control. These teachings are complementary rather than discouraging: Alofs does not criticize differential-drive steering, and Jones does not criticize using sensor-derived navigation corrections or caster-angle sensing.
Claim Limitations Not Explicitly Disclosed by the Combination of Alofs and Jones
After combining the teachings of Alofs and Jones, the following claim limitations are not explicitly disclosed:
• the function being based
• on one or more of the following algorithms:
• Kalman filter,
• Linear-Prediction,
• or Proportional-Integral Derivation control (PID);
• or
• an output signal
• of a previously trained neural network
• having as input signal
• at least said angle of rotation
• of said at least one pivoting wheel.
Disclosure by Ibrahim
Ibrahim discloses:
• the function being based
See at least:
“Conventional Sensor error estimation is typically performed by developing Sensor error models, and then implementing the model parameter estimation as augmented equations in the Overall set of navigation equations, usually in a ‘Kalman’ filter.” (Ibrahim, col. 1, ll. 45–52).
Rationale:
Ibrahim expressly discloses navigation/sensor-error estimation functions based on sensor-error models and Kalman-filter-type estimation. This discloses basing the correction function on an algorithmic estimation technique.
• on one or more of the following algorithms:
See at least:
“Conventional Sensor error estimation is typically performed by developing Sensor error models, and then implementing the model parameter estimation as augmented equations in the Overall set of navigation equations, usually in a ‘Kalman’ filter.” (Ibrahim, col. 1, ll. 45–52).
Rationale:
Ibrahim discloses at least one of the claimed algorithmic alternatives, namely a Kalman filter. Because the claim recites “one or more,” the algorithmic group is satisfied by the Kalman/Kalman filter alternative. Linear-Prediction and PID are unrelied alternatives within the same “one or more” group and do not need to be separately established once the Kalman/Kalman filter alternative is taught.
• Kalman filter,
See at least:
“FIG. 9 is a graph illustrating the average bias estimates provided by a Kalman filter type System over a period of time. FIG. 10 is a graph illustrating the Scale factor estimates provided by a Kalman filter type System over a period of time.” (Ibrahim, col. 3, ll. 7–13).
Rationale:
As construed above, “Kalman filter” is treated as “Kalman filter.” Ibrahim expressly discloses a Kalman-filter-type system in the vehicle navigation sensor-error-estimation context.
• Linear-Prediction,
See at least:
“Conventional Sensor error estimation is typically performed by developing Sensor error models, and then implementing the model parameter estimation as augmented equations in the Overall set of navigation equations, usually in a ‘Kalman’ filter.” (Ibrahim, col. 1, ll. 45–52).
Rationale:
The rejection does not rely on Linear-Prediction as the selected algorithm. This limitation is accounted for as an unrelied alternative within the “one or more” algorithmic group. Because Ibrahim discloses the Kalman/Kalman-filter alternative, the “one or more” algorithmic requirement is met without separately establishing Linear-Prediction.
• or Proportional-Integral Derivation control (PID);
See at least:
“Conventional Sensor error estimation is typically performed by developing Sensor error models, and then implementing the model parameter estimation as augmented equations in the Overall set of navigation equations, usually in a ‘Kalman’ filter.” (Ibrahim, col. 1, ll. 45–52).
Rationale:
The rejection does not rely on PID as the selected algorithm. This limitation is accounted for as an unrelied alternative within the “one or more” algorithmic group. Because Ibrahim discloses the Kalman/Kalman-filter alternative, the “one or more” algorithmic requirement is met without separately establishing PID.
• or
See at least:
“Other attempts at estimating these types of Sensor errors have been made using neural networks.” (Ibrahim, col. 1, ll. 53–56).
“The System 10 utilizes linear neuron 14 to adaptively estimate Scale factor and the bias values…” (Ibrahim, col. 8, ll. 29–33).
Rationale:
Ibrahim expressly identifies neural networks as an alternative sensor-error-estimation approach and then discloses a linear-neuron estimator. These disclosures support the claim’s disjunctive “or” branch leading to the neural-estimator alternative.
• an output signal
See at least:
“The estimated Scale factor and bias is fed back into module 12 in order to predict the error of the INS heading angle for the next DGPS period.” (Ibrahim, col. 8, ll. 25–28).
Rationale:
Ibrahim discloses estimator output values fed back into the navigation module to predict heading-angle error. These values constitute an output signal of the estimator. In the Alofs/Jones/Ibrahim combination, the output signal of Ibrahim’s trained estimator corresponds to an estimated navigation correction value used in place of or in addition to Alofs’s direct caster-angle calculation to generate the corrective control signal.
• of a previously trained neural network
See at least:
“After obtaining the desired or ‘combined’ angle ψ in Eq. 17, the angle is inputted into the linear neuron 14, as shown in FIG. 1 and is used to train the linear neuron 14.” (Ibrahim, col. 6, ll. 56–60).
“The System 10 utilizes linear neuron 14 to adaptively estimate Scale factor and the bias values…” (Ibrahim, col. 8, ll. 29–33).
Rationale:
Primary construction: Under the broadest reasonable interpretation, “previously trained” encompasses any training that precedes the use of the trained output, including adaptive training occurring at each measurement cycle before the trained output is applied to navigation correction. Ibrahim discloses training the linear neuron using the combined angle and then using the trained/adapted neuron to estimate scale factor and bias values. Under the broadest reasonable interpretation, a “neural network” encompasses an adaptive neural computing element or assembly, including a single linear neuron, where the claim does not limit “neural network” to multi-layer, multi-node, or deep-learning architectures. Ibrahim expressly identifies neural networks as a known category of sensor-error estimation technique and then implements the adaptive estimation function with linear neuron 14.
Alternative construction: Even if “previously trained” is construed to require a temporally distinct training phase preceding use, Ibrahim discloses that the combined angle is used to train the linear neuron before the trained linear neuron is used to estimate scale factor and bias values for navigation correction. The training step at col. 6, ll. 56–60 temporally and functionally precedes the estimation-output step at col. 8, ll. 29–33.
Further alternative: To the extent “neural network” is construed to require more than a single linear neuron, it would have been obvious to one of ordinary skill in the art to implement Ibrahim’s neural estimator using a known multi-node neural-network architecture because Ibrahim expressly identifies neural networks as a known approach for estimating sensor errors and uses a linear neuron for the same adaptive estimation purpose. Scaling a known linear-neuron estimator to a more complex neural-network estimator would have been a routine implementation choice to improve estimation flexibility or accuracy.
• having as input signal
See at least:
“The neuron design is simply a simulation of Eq. 29, where the neuron weights are the estimated Scale factor δK and bias B, and the neuron inputs are the yaw rate Sum and time.” (Ibrahim, col. 8, ll. 17–22).
Rationale:
Ibrahim expressly discloses that the neural estimator has input signals. In the proposed combination, the specific input would be Alofs’s caster pivot angle because Alofs already uses that angle as the navigation-correction input.
• at least said angle of rotation
See at least:
“System module 12 is electrically, physically, and communicatively coupled to various Sensors … which provide input Signals to module 12…” and “module 12 receives … a yaw rate Signal ‘r’ from a conventional vehicle yaw rate Sensor…” (Ibrahim, col. 3, ll. 40–55).
“The neuron design is simply a simulation of Eq. 29 … and the neuron inputs are the yaw rate Sum and time.” (Ibrahim, col. 8, ll. 17–22).
“The average caster angle SA between two measuring intervals is the initial angle Si plus half the change in the pivot angle over the measurement interval.” (Alofs, col. 5, ll. 24–27).
Rationale:
Ibrahim does not expressly disclose using a caster pivot angle as the neural-network input. However, it would have been obvious to one of ordinary skill in the art in view of the Alofs/Jones/Ibrahim combination to use Alofs’s caster pivot angle as at least one input signal to Ibrahim’s known navigation-error estimator. Ibrahim discloses that module 12 is coupled to various sensors that provide input signals, demonstrating that the estimator architecture is designed to receive vehicle navigation sensor signals generally. The specific use of yaw rate in Ibrahim is an implementation example of processing an angular vehicle-motion sensor signal, not a teaching that the estimator is limited exclusively to yaw-rate input.
Although Ibrahim’s yaw-rate input and Alofs’s caster pivot angle are not identical physical quantities, both are vehicle-motion-related angular sensor quantities used to estimate or correct navigation error. Ibrahim’s yaw-rate signal is an angular velocity quantity, while Alofs’s caster pivot angle is an angular displacement quantity derived from an absolute shaft encoder on the caster stem. A person of ordinary skill in autonomous vehicle navigation and control would have understood that angular displacement and angular velocity are related motion quantities and that converting, scaling, differentiating, integrating, or otherwise conditioning angular sensor signals for estimator input was routine signal processing within the ordinary level of skill. Thus, adapting Ibrahim’s sensor-error estimator to process Alofs’s caster pivot-angle input would have required only routine configuration of a known estimator architecture for a compatible class of angular motion sensor signals.
This adaptation represents a predictable use of Ibrahim’s known vehicle navigation sensor-error estimator with Alofs’s known caster-angle navigation-correction input. It does not rely on hindsight because Alofs itself identifies the caster pivot angle as a navigation-correction quantity, and Ibrahim independently teaches algorithmic estimation of vehicle navigation sensor error from angular sensor inputs.
• of said at least one pivoting wheel.
See at least:
“The motion of the vehicle Pm, as sensed by the caster pivot sensor 50, is the product of the change in the pivot angle…” (Alofs, col. 5, ll. 18–22).
“The neuron design is simply a simulation of Eq. 29 … and the neuron inputs are the yaw rate Sum and time.” (Ibrahim, col. 8, ll. 17–22).
Rationale:
Alofs discloses the angle of rotation of the pivoting caster wheel, and Ibrahim discloses using navigation sensor-derived angular input signals in a trained neural estimator. It would have been obvious to one of ordinary skill in the art to provide Alofs’s caster pivot-angle signal as an input to Ibrahim’s estimator to improve odometry/navigation correction in the combined autonomous wheeled apparatus.
Motivation to Combine Alofs, Jones, and Ibrahim
Therefore, given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having Alofs, Jones, and Ibrahim before them, to further modify the Alofs/Jones autonomous-drive wheel-equipped apparatus by implementing the corrective computation using Ibrahim’s Kalman-filter-type estimation and/or trained-neural-estimator technique.
Alofs discloses using caster pivot angle and wheel-motion information to calculate current vehicle location and guide a driverless vehicle toward a selected path or desired location. Jones discloses a compatible differential-drive control implementation for independently controlled right and left drive wheels. Ibrahim discloses vehicle navigation sensor-error estimation using known algorithmic approaches, including Kalman-filter-type estimation and trained linear-neuron estimation.
One of ordinary skill in the art would have been motivated to consult Ibrahim because Ibrahim addresses the same technical field of vehicle navigation accuracy and sensor-error correction, and because Alofs’s problem of correcting motion/position error is directly compatible with Ibrahim’s sensor-error estimation techniques. The selection of a Kalman filter, Linear-Prediction, PID, or neural estimator reflects a finite set of known, predictable navigation/control solutions for implementing a corrective function. MPEP §2143 recognizes applying known techniques to known devices ready for improvement and combining prior art elements according to known methods to yield predictable results as proper rationales for obviousness. MPEP §2145 further recognizes that an obvious-to-try rationale may support obviousness when one of ordinary skill chooses from a finite number of identified, predictable solutions with a reasonable expectation of success.
A person of ordinary skill in the art would have had a reasonable expectation of success because Alofs’s caster-angle-based navigation correction is designed to work within a driverless vehicle navigation/guidance system, Jones’s differential-drive control is a standard autonomous-vehicle propulsion/control implementation with independently controlled wheels, and Ibrahim’s estimator is designed to process navigation sensor signals to improve vehicle position/heading reliability. Combining these teachings does not require overcoming any technical incompatibility because Alofs’s navigation processor generates correction information from sensor-derived motion data, Jones’s control module actuates independently controlled wheels in response to sensor/control signals, and Ibrahim’s estimator provides a known algorithmic technique for improving navigation sensor-error correction.
Further, the proposed use of Alofs’s caster pivot-angle signal in Ibrahim’s estimator does not change the principle of operation of either reference. Alofs still uses caster-angle-derived information to correct vehicle navigation, while Ibrahim’s estimator still processes vehicle-motion sensor information to estimate navigation error. The combination merely applies Ibrahim’s known estimator to Alofs’s known sensor-derived angular correction quantity. Alofs col. 3–6, Jones 0034, 0037, and 0043, and Ibrahim col. 1–8 contain complementary teachings directed to vehicle navigation, sensor-based correction, drive control, and navigation-error estimation. These passages do not criticize, discredit, or discourage combining caster-angle-based navigation correction, differential-drive control, and known navigation-error estimation algorithms.
Therefore, viewing Claim 9 as a whole, the claimed autonomous-drive wheel-equipped apparatus is the predictable combination of: (1) Alofs’s known caster-angle-based driverless vehicle navigation system, which discloses the pivoting caster wheel, caster pivot sensor, computer processor, and predefined motion-calculation functions using caster angle as an argument; (2) Jones’s known differential-drive wheel-speed control for autonomous robots, which supplies the angular velocity difference between independently operated drive wheels; and (3) Ibrahim’s known vehicle navigation sensor-error estimation algorithms, which supply the Kalman-filter and neural-estimator alternatives. Each element performs the same function in the proposed combination that it performs in the reference from which it is drawn. The combination yields the predictable result of improved autonomous vehicle navigation correction and drive control, rather than a non-obvious or unexpected result.
Response to Arguments
Applicant’s Arguments Regarding 35 U.S.C. § 112
Applicant’s amendments and remarks have been fully considered. Claims 1 and 9 have been amended. Claims 2-8 and 10-15 have been cancelled. Accordingly, claims 1 and 9 remain pending for examination.
The amendments to claims 1 and 9 have been entered. In view of Applicant’s remarks identifying support in the originally filed disclosure, including at least paragraphs [0010], [0024], and [0055]-[0065], no new matter issue is presently maintained with respect to the amendments.
With respect to the discussion of 35 U.S.C. §112(f), the prior Office Action interpreted certain claim language under §112(f). The prior §112(f) interpretation remains a matter of claim construction to the extent applicable.
Response to 35 U.S.C. §112(a) Rejection
The rejection of claims 1, 3-9, and 11-15 under 35 U.S.C. §112(a) for lack of enablement and/or lack of written description has been reconsidered in view of Applicant’s amendments and remarks.
Applicant amended claims 1 and 9 to further define the pivoting wheel, the axis of rotation, the angle of rotation, the first predefined reference direction, the second variable orientation direction, the predefined-function alternative, and the neural-network alternative. Applicant also argued that the amended claim language is supported by the cited disclosure and that a person of ordinary skill in the art would understand how to implement the recited predefined function and neural-network functionality without undue experimentation.
Upon reconsideration of the amended claim scope, the level of ordinary skill in the art, the state of the art, and the guidance identified in the specification, the rejection under 35 U.S.C. §112(a) is withdrawn as to pending claims 1 and 9.
With respect to written description, the written-description and enablement requirements are separate requirements under 35 U.S.C. §112(a). However, in view of the amended claim language and Applicant’s identified support in the originally filed disclosure, the written-description rejection is likewise withdrawn as to pending claims 1 and 9.
Because claims 3-8 and 11-15 have been cancelled, the rejection under 35 U.S.C. §112(a) is moot as to those cancelled claims.
Response to 35 U.S.C. §112(b) Rejection
The rejection of claims 1-11 under 35 U.S.C. §112(b) has been reconsidered in view of Applicant’s amendments and remarks.
Applicant amended claims 1 and 9 to clarify the pivoting-wheel language, the axis of rotation, the measured angle of rotation, the relationship between the first predefined reference direction and the second variable orientation direction, and the controller’s generation of the corrective signal. In view of these amendments, claims 1 and 9 are considered to particularly point out and distinctly claim the subject matter which Applicant regards as the invention.
Accordingly, the rejection under 35 U.S.C. §112(b) is withdrawn as to pending claims 1 and 9.
Because claims 2-8 and 10-11 have been cancelled, the rejection under 35 U.S.C. §112(b) is moot as to those cancelled claims.
112 Conclusion
The rejections under 35 U.S.C. §112(a) and §112(b) are withdrawn as to pending claims 1 and 9. The §112(a) rejection is moot as to cancelled claims 3-8 and 11-15. The §112(b) rejection is moot as to cancelled claims 2-8 and 10-11.
Response to Applicant’s 103 Arguments
Applicant’s arguments filed in response to the rejection under 35 U.S.C. § 103 have been fully considered but are not persuasive.
Applicant argues that Alofs and Jones, either independently or in combination, do not teach or suggest a controller that “generates a corrective signal that controls said drive means in such a way as to generate an angular velocity difference between said at least two drive wheels on the basis of the angle of rotation of said at least one pivoting wheel so that the wheeled-equipped apparatus moves so as to decrease the differential in location between the current position and the predefined location,” as recited in independent claims 1 and 9. This argument is not persuasive because it attacks Alofs in isolation and does not address the combination as applied in the rejection.
Alofs is relied upon for the driverless vehicle navigation and caster-angle correction framework. Alofs discloses a driverless vehicle having a navigation and guidance system equipped with a caster pivot sensor and wheel rotation sensor. Alofs expressly teaches that the vehicle’s navigation and guidance system samples sensor data and steers the vehicle responsive to those sensor inputs. See Alofs, col. 4, ll. 44–49. Alofs further teaches that, as the vehicle moves, the wheel rotation sensor and/or caster pivot sensor senses motion and transmits a corresponding signal to the navigation and guidance system’s computer processor. See Alofs, col. 5, ll. 9–15.
More specifically, Alofs teaches that the caster pivot sensor senses a change in pivot angle, namely the “final angle Sf minus initial angle Si,” and that the average caster angle SA is determined from the initial angle and the change in pivot angle. See Alofs, col. 5, ll. 18–27. Alofs then uses the calculated movement components Xm and Ym to calculate the vehicle’s current location so that the guidance system can determine how to direct the vehicle along a desired path or toward a desired location. See Alofs, col. 5, ll. 27–36. Thus, Alofs teaches the claimed causal chain in which the angle of rotation of the pivoting wheel is used as a navigation-correction input: caster pivot angle → vehicle motion/current location calculation → guidance correction toward the predefined path/location.
Applicant’s argument is further not persuasive because Jones is specifically relied upon to supply the differential-drive implementation that Alofs does not expressly disclose. Jones teaches a motive subsystem including right and left main wheel subassemblies, each having its own independently operated motor. See Jones, 0034. Jones further teaches that the motors of the right and left main wheel subassemblies drive the main wheels “at different speeds” to effect turning patterns for the autonomous robot. See Jones, 0037. Jones also teaches that each wheel encoder measures rotation of the associated wheel subassembly and transmits a corresponding signal to the control module. See Jones, 0040. Jones further teaches a control module/microcontroller that controls movement of the robot in response to signals generated by the sensor subsystem. See Jones, 0043.
Accordingly, the combination teaches or renders obvious the disputed limitation. Alofs provides the sensor-based corrective navigation determination based on the pivoting wheel angle, and Jones provides the known actuator/control implementation for carrying out the correction by independently driving left and right drive wheels at different speeds. In the combined system, the controller uses Alofs’s caster-angle-based navigation correction to determine how the apparatus should be directed toward the desired path/location, and implements that correction through Jones’s differential wheel-speed control. The claimed “angular velocity difference between said at least two drive wheels” is taught by Jones’s different-speed operation of the right and left main wheels. See Jones, 0037.
Applicant appears to require Alofs alone to disclose the full limitation. That is not the proper inquiry under § 103. The rejection is based on the combined teachings of Alofs and Jones, not on Alofs alone. A proper obviousness rejection may rely on combining prior art elements according to known methods to yield predictable results, simple substitution of one known element for another, or applying a known technique to a known device ready for improvement. MPEP § 2141 and § 2143 recognize these as appropriate rationales under KSR.
Here, the combination is technically compatible and yields predictable results. Alofs teaches a driverless vehicle guidance system that determines current location and directs the vehicle along a desired path using caster-angle and wheel-motion information. Jones teaches a compatible autonomous wheeled robot drive architecture in which independently operated left and right drive wheels are driven at different speeds to effect turning. See Alofs, col. 5, ll. 18–36; Jones, 0034, 0037, 0040, 0043. A person of ordinary skill in the art would have had reason to implement Alofs’s steering/guidance correction using Jones’s known differential-drive wheel-speed control because Jones provides a predictable actuator mechanism for carrying out steering corrections in an autonomous wheeled apparatus.
Applicant’s statement that “neither Jones nor Ibrahim cure the above deficiency of Alofs” is also not persuasive. Jones cures the alleged deficiency regarding the generation of an angular velocity difference between two drive wheels. Ibrahim is relied upon for the algorithmic correction-function limitations, including the Kalman-filter/neural-estimator alternatives, where applicable. Ibrahim teaches navigation sensor-error estimation by developing sensor-error models and implementing the model parameter estimation in navigation equations, usually in a Kalman filter. See Ibrahim, col. 1, ll. 45–52. Ibrahim also teaches Kalman-filter-type bias and scale-factor estimates. See Ibrahim, col. 3, ll. 7–13. Further, Ibrahim identifies neural networks as an approach for estimating sensor errors, teaches training a linear neuron, and teaches using the trained/adapted neuron to estimate scale factor and bias values. See Ibrahim, col. 1, ll. 53–56; col. 6, ll. 56–60; col. 8, ll. 25–33.
To the extent Applicant argues that the cited art does not expressly disclose every limitation in a single reference, that argument is unavailing because the rejection is based on obviousness, not anticipation. The issue is whether the claimed subject matter as a whole would have been obvious to a person of ordinary skill in the art in view of the combined teachings. The combination of Alofs, Jones, and, where applicable, Ibrahim provides the claimed subject matter as a predictable combination of known elements performing their known functions.
Applicant’s argument regarding claim 9 is not persuasive for the same reasons. Claim 9 recites the apparatus counterpart to the method of claim 1. Alofs discloses the driverless vehicle, pivoting caster wheel, caster pivot sensor, computer processor, current-location calculation, and guidance correction based on caster pivot angle. See Alofs, col. 3, ll. 17–34; col. 4, ll. 12–20, 44–49; col. 5, ll. 18–36. Jones discloses the independently operated right and left drive wheels and different-speed wheel control used to generate the claimed angular velocity difference. See Jones, 0034, 0037, 0043. Therefore, the apparatus of claim 9 is likewise taught or rendered obvious by the combined teachings.
Applicant’s general statement that the dependent claims are independently distinguishable is also not persuasive because Applicant has not identified specific limitations of any dependent claim that are allegedly missing from the cited prior art or explained why the specific mappings in the rejection are deficient. Unsupported attorney argument that dependent claims are distinguishable, without identifying particular claim language and explaining the alleged deficiency in the prior art, does not overcome the prima facie case of obviousness.
For these reasons, Applicant’s arguments do not overcome the rejection. The rejection of independent claims 1 and 9, and the claims depending therefrom, is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/OLUWABUSAYO ADEBANJO AWORUNSE/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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