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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2024-020043, filed on 02/14/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/09/2025 is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: detection unit in claims 1 and 4.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1, 3-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (US 20230150518 A1) and further in view of Agia (US 20230267615 A1).
Regarding claim 1, Zhao discloses a detection unit mounted on a moving body and configured to irradiate surroundings of the moving body with an electromagnetic wave to detect an external environment situation in the surroundings based on a reflected wave ([0013] For example, radars and lidar emit electromagnetic signals (radio signals or optical signals) that reflect from the objects and determine distances to the objects (e.g., from the time of flight of the signals) and velocities of the objects (e.g., from the Doppler shift of the frequencies of the signals). Radars and lidars can cover an entire 360-degree view by using a series of consecutive sensing frames. Sensing frames can include numerous reflections covering the outside environment in a dense grid of return points.); and
a microprocessor, wherein the microprocessor is configured to perform ([0059] Processing device 602 (which can include processing logic 603) represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like.):
the point cloud data including position information of a measurement point on a surface of an object from which the reflected wave is obtained and speed information indicating a relative moving speed of the measurement point ([0013] For example, radars and lidar emit electromagnetic signals (radio signals or optical signals) that reflect from the objects and determine distances to the objects (e.g., from the time of flight of the signals) and velocities of the objects (e.g., from the Doppler shift of the frequencies of the signals). Radars and lidars can cover an entire 360-degree view by using a series of consecutive sensing frames. Sensing frames can include numerous reflections covering the outside environment in a dense grid of return points. Each return point can be associated with the distance to the corresponding reflecting object and a radial velocity (a component of the velocity along the line of sight) of the reflecting object.
[0035] To estimate the actual locations of reference points based on detected radius-vectors {right arrow over (r)}.sub.j(α), reference object tracking module 230 can use locations of the sensors {right arrow over (r)}.sub.α relative to the AV.);
calculating a translational motion component and a rotational motion component, based on the position information and the speed information of the measurement point corresponding to the road surface point cloud data ([0035] To estimate the actual locations of reference points based on detected radius-vectors {right arrow over (r)}.sub.j(α), reference object tracking module 230 can use locations of the sensors {right arrow over (r)}.sub.α relative to the AV. Additionally, reference object tracking module 230 can receive data about motion of the AV (both the translational and the rotational motion of the AV) from an AV motion tracking module 240 and determine how locations of the sensors {right arrow over (r)}.sub.α relative to the AV are transformed to the locations of the same sensors relative to Earth.),
the translational motion component indicating a movement amount and a moving direction of a translational motion of the moving body ([0039] Motion of AV 302 can be a combination of a translational motion of COM 330 with velocity {right arrow over (V)} and a rotational motion around COM 330 with angular velocity {right arrow over (ω)}. The components of the angular velocity along the coordinate axes are also referred to as roll angular velocity ω.sub.x≡ω.sub.roll, pitch angular velocity ω.sub.y≡ω.sub.pitch, and yaw angular velocity ω.sub.z≡ω.sub.yaw.),
the rotational motion component indicating a rotation amount and a rotating direction of a rotational motion of the moving body ([0039] a rotational motion around COM 330 with angular velocity {right arrow over (ω)}. The components of the angular velocity along the coordinate axes are also referred to as roll angular velocity ω.sub.x≡ω.sub.roll, pitch angular velocity ω.sub.y≡ω.sub.pitch, and yaw angular velocity ω.sub.z≡ω.sub.yaw.); and
updating position information indicating a position of the moving body, based on the translational motion component and the rotational motion component ([0044] The distance and direction can be adjusted in view of a known motion of the AV (e.g., from positioning data and sensing data relative to known stationary objects, e.g., roadway, road signs, etc.), including translational motion of the AV and rotational motion (turning) of the AV. The distance and the direction can be further adjusted in view of motion of the reference point(s).).
Zhao implicitly teaches extracting road surface point cloud data corresponding to a road surface from the point cloud data ([0024] For example, the perception system 130 can analyze images captured by the cameras 118 and can be capable of detecting traffic light signals, road signs, roadway layouts (e.g., boundaries of traffic lanes, topologies of intersections, designations of parking places, and so on), presence of obstacles, and the like. The perception system 130 can further receive radar sensing data (Doppler data and ToF data) to determine distances to various objects in the environment 101 and velocities (radial and, in some implementations, transverse, as described below) of such objects.).
Zhao does not explicitly teach extracting road surface point cloud data corresponding to a road surface from the point cloud data.
In a similar field of endeavor of road surface segmentation using point clouds, Agia teaches extracting road surface point cloud data corresponding to a road surface from the point cloud data ([0006] The present disclosure describes systems and methods which provide one or more efficient techniques to perform semantic segmentation of a sequence of point clouds, thereby reducing the time and resources required to detect road surfaces in the 3D point clouds and classify the detected objects in the sequence of point clouds.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to combine the known system of sensor calibration for an autonomous vehicle, as taught by Zhao, with the known teaching of detection roadways using an autonomous vehicle, as taught by Agia, in order to yield the predictable results of lane keeping and safe path planning.
Regarding claim 3, Zhao discloses the calculating including calculating the translational motion component of the moving body, based on the position information and the speed information of the measurement point corresponding to the road surface point cloud data ("[0039] Vector {right arrow over (r)}.sub.α can indicate some specific element of a respective sensor, e.g., a receiving aperture of lidar 112, an objective lens of camera 118, and so on. Motion of AV 302 can be a combination of a translational motion of COM 330 with velocity {right arrow over (V)} and a rotational motion around COM 330 with angular velocity {right arrow over (ω)}.
[0044] Multiple sensors can track (e.g., as a function of time) a distance and direction to a given reference point. The distance and direction can be adjusted in view of a known motion of the AV (e.g., from positioning data and sensing data relative to known stationary objects, e.g., roadway, road signs, etc.), including translational motion of the AV and rotational motion (turning) of the AV. The distance and the direction can be further adjusted in view of motion of the reference point(s)."),
and calculating the rotational motion component from the translational motion component, based on a predetermined correlation between the translational motion component and the rotational motion component ("[0039] Vector {right arrow over (r)}.sub.α can indicate some specific element of a respective sensor, e.g., a receiving aperture of lidar 112, an objective lens of camera 118, and so on. Motion of AV 302 can be a combination of a translational motion of COM 330 with velocity {right arrow over (V)} and a rotational motion around COM 330 with angular velocity {right arrow over (ω)}.
[0044] Multiple sensors can track (e.g., as a function of time) a distance and direction to a given reference point. The distance and direction can be adjusted in view of a known motion of the AV (e.g., from positioning data and sensing data relative to known stationary objects, e.g., roadway, road signs, etc.), including translational motion of the AV and rotational motion (turning) of the AV. The distance and the direction can be further adjusted in view of motion of the reference point(s).").
Regarding claim 4, Zhao discloses the correlation is defined based on an installation position of the detection unit in the moving body, a moving speed of the moving body, and a steering angle of the moving body ([0045] More specifically, FIG. 4 depicts an AV having a first position 402-1 at time t.sub.1 and performing a combination of a translational motion described by displacement vector Δ{right arrow over (R)} and a rotation to angle Δθ around a vertical axis to a second position 402-2 at time t.sub.2. Although a planar projection of the motion of the AV is depicted, it should be understood that the AV can also perform motion in the other two planes, e.g. by moving uphill or downhill, changing pitch and yaw angle, and the like. As depicted, the coordinate axes x and y change their orientation to x′ and y′. Sensor α 410 moves from its position at time t.sub.1 to a new position at time t.sub.2 in a combination of translation Δ{right arrow over (R)} of COM of the AV and rotational displacement {right arrow over (T)}.sub.Δ{right arrow over (θ)}[{right arrow over (r)}.sub.α] relative to COM. In the meantime, reference point j 420 can change its location by Δ{right arrow over (R)}.sub.j (which can also be a due to translational motion of the object to which the reference point j 420 belongs or a rotational motion of the object, or a combination thereof).).
Regarding claim 5, Zhao discloses the detection unit is a LiDAR or a radar ([0021] The sensing system 110 can include a lidar 112, which can be a laser-based unit capable of determining distances to the objects and velocities of the objects in the driving environment 101.).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (US 20230150518 A1), in view of Agia (US 20230267615 A1) and further in view of Yang (US 20230043061 A1).
Regarding claim 2, Zhao discloses calculating an absolute moving speed of each of a plurality of the measurement points corresponding to the point cloud data, based on the speed information of the measurement point and the translational motion component of the moving body ([0021] Hereinafter, “velocity” refers to both how fast the object is moving (the speed of the object) as well as the direction of the object's motion. The sensing system 110 can include a lidar 112, which can be a laser-based unit capable of determining distances to the objects and velocities of the objects in the driving environment 101.); and
also searching for the translational motion component and the rotational motion component minimizing a position error between corresponding measurement points of the stationary point cloud data at the current time and the stationary point cloud data after the offsetting ("[0017] For example, an unrecognized (and, therefore, uncorrected) error in a camera orientation of 1° results in an error of about 2 meters at a distance of 100 meters and can result in an incorrect lidar-camera association interpreting different objects as the same object or vice versa. A misalignment of a camera orientation can occur due to manufacturing (e.g., installation) tolerances, as a result of shaking due to road bumps, uneven heating of different components of the autonomous vehicle and the camera, and so on. The focal distance of the lens of a camera can be affected by elements of the environment precipitating on the camera or by wear of the optical and mechanical components of the camera. Correcting for various errors in alignment and inaccuracies of sensor data is referred to herein as calibration.
[0018] Calibration of the sensors can be performed by optimizing various sensor parameters (such as directions of view, focal lengths, precise locations of the sensors on the AV, etc.) in a way that minimizes errors in coordinates of reference points as determined by different sensors. Reference points can be stationary, e.g., a road sign, a parked vehicle, a trunk of a tree, a feature of a building, bridge, or any other structure.
[0037] Sensor calibration module 260 can adjust parameters of various sensors in a way that optimizes (e.g., minimizes) the loss function 250. After optimization, sensor calibration module 260 updates the sensor parameterization module 220. "),
and updating the position information of the moving body, based on the translational motion component and the rotational motion component obtained by the searching ([0044] The distance and direction can be adjusted in view of a known motion of the AV (e.g., from positioning data and sensing data relative to known stationary objects, e.g., roadway, road signs, etc.), including translational motion of the AV and rotational motion (turning) of the AV. The distance and the direction can be further adjusted in view of motion of the reference point(s).).
Zhao does not explicitly disclose but in a similar field of endeavor of object detection in lidar point clouds, Yang teaches classifying the point cloud data into stationary point cloud data in which an absolute value of the absolute moving speed is smaller than a predetermined speed and moving point cloud data in which the absolute value is equal to or higher than the predetermined speed ("fig. 3a
[0067] For ease of description, the point cloud data in FIG. 3a only involves a small number of objects, including objects A, B, C, and D. The local device is represented by a rectangular box in the middle of the point cloud data. The local device is moving forward at a constant speed, that is, the ego-motion of the local device remains unchanged. The objects A and B are tracked objects that always exist. The object A is stationary, the object B is also moving forward, and a speed of the object B is greater than the speed of the local device. Both objects C and D are stationary. The object C will disappear in a later frame, that is, a dead object. The object D is a new born object."), wherein
the microprocessor is configured to perform the updating including offsetting the stationary point cloud data at a past time before a current time by a predetermined time, based on the translational motion component and the rotational motion component ("fig. 3a
[0067] For ease of description, the point cloud data in FIG. 3a only involves a small number of objects, including objects A, B, C, and D. The local device is represented by a rectangular box in the middle of the point cloud data. The local device is moving forward at a constant speed, that is, the ego-motion of the local device remains unchanged. The objects A and B are tracked objects that always exist. The object A is stationary, the object B is also moving forward, and a speed of the object B is greater than the speed of the local device. Both objects C and D are stationary. The object C will disappear in a later frame, that is, a dead object. The object D is a new born object.").
PNG
media_image1.png
344
516
media_image1.png
Greyscale
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to combine the known system of sensor calibration and roadway detection for an autonomous vehicle, as taught by Zhao, with the known teaching of determining the difference between a stationary object and dynamic object on the road, as taught by Yang, in order to yield the predictable results of accurate future prediction of objects/pedestrians on or near the road, and for faster computer processing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220012466 A1 with similarity to claims 1 and 4: [0052] For example, the memory 126 may store sensor data including image data representing a 2D image captured by a camera that is received from the cameras 112, data points representing a 3D point cloud received from the LIDAR scanning system 114, SAR data received from the SAR units 116, odometry data from wheel odometry unit 117 or an inertial measurement unit (IMU) 118, location data from global positioning system (GPS) 119, and data from other sensors 120. The odometry data received from the wheel odometry unit 117 includes rotation data indicative of rotation of the vehicle 100 and translation data indicative of a translation of the vehicle 100. The odometry data received from the IMU 118 includes velocity data representing three-axis angular velocity of the vehicle 100 and acceleration data representing three-axis acceleration of the vehicle 100.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED A NASHER whose telephone number is (571)272-1885. The examiner can normally be reached Mon - Fri 0800 - 1700.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AHMED A NASHER/ Examiner, Art Unit 2675
/EMILY C TERRELL/ Supervisory Patent Examiner, Art Unit 2666
Prosecution Insights