Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not
identically disclosed as set forth in section 102, if the differences between the claimed invention and the
prior art are such that the claimed invention as a whole would have been obvious before the effective filing
date of the claimed invention to a person having ordinary skill in the art to which the claimed invention
pertains. Patentability shall not be negated by the manner in which the invention was made.
1. Claims 1, 2, 9,10, & 12 are rejected under 35 U.S.C. 103 as being unpatentable over Curatu et al (US 20180284285 A1), hereinafter Curatu, in view of Li et al (CN 113138396 B), hereinafter Li.
2. Regarding Claims 1 & 9:
Curatu teaches a data processing device (1) and method for a LiDAR sensor (2) adapted to be mounted on a vehicle (10), ([0030]: An example lidar system in which these techniques can be implemented is considered next with reference to FIGS. 1-4, followed by a discussion of the techniques which the lidar system can implement to scan a field of regard and generate individual pixels (FIGS. 5-7). An example implementation in a vehicle is then discussed with reference to FIGS. 8 and 9. Then, an example photo detector and an example pulse-detection circuit are discussed with reference to FIGS. 10 and 11). Curatu further teaches, ([0113]: Data from each of the sensor heads 360 may be combined or stitched together to generate a point cloud that covers a greater than or equal to 30-degree horizontal view around a vehicle. For example, the laser 352 may include a controller or processor that receives data from each of the sensor heads 360 (e.g., via a corresponding electrical link 370) and processes the received data to construct a point cloud covering a 360-degree horizontal view around a vehicle or to determine distances to one or more targets). Curatu teaches a computer (3),
([0146]: In some cases, a computing device may be used to implement various modules, circuits, systems, methods, or algorithm steps disclosed herein. As an example, all or part of a module, circuit, system, method, or algorithm disclosed herein may be implemented or performed by a general-purpose single- or multi-chip processor, a digital signal processor (DSP), an ASIC, a FPGA, any other suitable programmable-logic device, discrete gate or transistor logic, discrete hardware components, or any suitable combination thereof. A general-purpose processor may be a microprocessor, or, any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration). Curatu teaches obtaining (110) a matrix of pixels (p) acquired by the LiDAR sensor (2), ([0005]: One example embodiment of the techniques of this disclosure is a synchronized scanning system in a vehicle including a plurality of lidar sensors disposed along a perimeter of the vehicle and configured to simultaneously scan, at a same horizontal speed, different respective fields of regard. Each lidar sensor may include a light source configured to emit light pulses, a scanner configured to scan a field of view of the light source across a field of regard of the lidar sensor, including direct the light pulses at different angles toward different points within the field of regard, and a receiver configured to detect light from some of the light pulses scattered by one or more remote targets to generate respective pixels. The light pulses from every two neighboring lidar sensors are separated by a uniform phase angle. The system further includes a processor configured to combine the pixels from each of the plurality of lidar sensors to capture a complete horizontal field of regard around the vehicle). Curatu teaches each pixel (p) of the matrix being associated with a light intensity (1) and with a position (x, y, z) in a three-dimensional space, ([0067]: The receiver 140 may include circuitry that performs signal amplification, sampling, filtering, signal conditioning, analog-to-digital conversion, time-to-digital conversion, pulse detection, threshold detection, rising-edge detection, or falling-edge detection. For example, the receiver 140 may include a transimpedance amplifier that converts a received photocurrent (e.g., a current produced by an APD in response to a received optical signal) into a voltage signal. The receiver 140 may direct the voltage signal to pulse-detection circuitry that produces an analog or digital output signal 145 that corresponds to one or more characteristics (e.g., rising edge, falling edge, amplitude, or duration) of a received optical pulse). Curatu further teaches, ([0115]: In some implementations, the vehicle controller 372 receives point cloud data from the laser 352 or sensor heads 360 via the link 370 and analyzes the received point cloud data to sense or identify targets 130 and their respective locations, distances, speeds, shapes, sizes, type of target (e.g., vehicle, human, tree, animal), etc).
Curatu does not teach:
- identifying (120) at least one group of neighboring pixels of the matrix of pixels;
and, for at least one identified group of neighboring pixels:
* determining (130) a plurality of normal vectors associated with the pixels of the group of neighboring pixels;
* identifying (140) whether the group of neighboring pixels fully or partly belongs to a cloud of particles based on a substantially homogeneous distribution of the plurality of normal vectors associated with the pixels of the group of neighboring pixels.
However, Li teaches, a vehicle based laser radar system for dust and obstacle detection, ([Abstract]: The invention relates to the technical field of automatic driving, specifically to a dust and obstacle detection method based on laser radar). Li further teaches, ([0011]: Preferably, the point cloud vector analysis method is as follows: calculating the normal vector of each point in the point cloud cluster; counting the orientation change of the adjacent normal vector in the space; If the adjacent normal orientation generally has a relatively large change, then the possibility of dust is large, forming a conclusion Q3).
It would have been obvious for one of ordinary skill in the art at the time of filing to modify Curatu with Li to include identifying (120) at least one group of neighboring pixels of the matrix of pixels; and, for at least one identified group of neighboring pixels:
determining (130) a plurality of normal vectors associated with the pixels of the group of neighboring pixels; identifying (140) whether the group of neighboring pixels fully or partly belongs to a cloud of particles based on a substantially homogeneous distribution of the plurality of normal vectors associated with the pixels of the group of neighboring pixels, since it is the same field of endeavor and results would have been predictable. One of ordinary skill in the art at the time of filing would have been motivated to modify Curatu with Li since, such detection methods and systems provide an effective way to differentiate dense, impenetrable, solid objects from a permeable porous cloud of particles (e.g., rain, fog, smoke, or dust). Hard, solid surfaces (e.g., a car, wall, or the ground) have a clear, defined boundary. The normal vectors of neighboring pixels on a solid surface typically align in a highly organized, predictable, or contiguous manner. In addition, such detection methods and systems can reduce false object detection by identifying particle clouds and improve computational efficiency by grouping pixels into regions or clusters.
3. Regarding Claims 2 & 10:
Curatu does not teach the computer (3) is also configured for determining (150), for at least one specific pixel of the group of neighboring pixels identified as fully or partly belonging to a cloud of particles, whether the specific pixel belongs to a solid element other than the cloud of particles.
However, Li teaches, ([0027]: the point cloud on the solid object is distributed on the surface of the object, and the point cloud on the dust has a certain level on the thickness. performing " peeling onion " operation to the clustering, namely removing the outermost layer point cloud towards the light core; if the remaining point cloud is less than a certain threshold value, judging it as solid object; if the remaining point cloud number exceeds the set percentage, it can continue to peel again; the possibility of dust is large. the specific process is according to the length of the cluster is high; the length d is side length; the clustering is divided into L* W* H small voxel, on the long width and high three latitude, removing the voxel of the outermost layer, if only one time or 2 times, the rest voxel number is lower than the threshold value, then the object is a solid object; otherwise, judging it as dust).
It would have been obvious for one of ordinary skill in the art at the time of filing to modify Curatu with Li to include the computer (3) is also configured for determining (150), for at least one specific pixel of the group of neighboring pixels identified as fully or partly belonging to a cloud of particles, whether the specific pixel belongs to a solid element other than the cloud of particles, since it is the same field of endeavor and results would have been predictable. One of ordinary skill in the art at the time of filing would have been motivated to modify Curatu with Li since, such a configuration can differentiate between soft aerosol reflections (particles) and hard-surface reflections, ensuring a solid obstacle is not falsely classified as a harmless cloud of particles, maintain high-accuracy 3D point clouds and scene segmentation even when operating in adverse weather (fog, heavy rain, or smoke), and ensure that boundary tracking and obstacle avoidance algorithms in autonomous navigation map the true geometry of the environment rather than atmospheric obstructions. When taken in combination these motivating advantages serve to improve the safety of autonomous vehicles.
4. Regarding Claim 12:
A non-transient computer-readable storage medium storing code instructions for implementing a method as claimed in any one of the above method claims.
Curatu teaches a non-transitory computer-readable storage medium storing code instructions for implementing a method, ([0147]: In particular embodiments, one or more implementations of the subject matter described herein may be implemented as one or more computer programs (e.g., one or more modules of computer-program instructions encoded or stored on a computer-readable non-transitory storage medium). As an example, the steps of a method or algorithm disclosed herein may be implemented in a processor-executable software module which may reside on a computer-readable non-transitory storage medium).
5. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Curatu et al (US 20180284285 A1), hereinafter Curatu in view of Li et al (CN 113138396 B), hereinafter Li, as applied to Claims 1 & 2, and further in view of Hunt et al (US 8964168 B1), hereinafter Hunt.
6. Regarding Claim 3:
Curatu as modified by Li does not teach the computer (3) is configured for determining that the specific pixel belongs to a solid element when an intensity associated with the pixel is greater than an intensity threshold associated with a determined solid element.
However, Hunt teaches a Lidar system and method for object detection, ([Col. 4, Lines 18-6]: Detector 140 receives reflected light beam 132 from input intensity control module 138 and detects an intensity of beam 132. In the exemplary embodiment, if the intensity of reflected light beam 132 is equal to, or greater than, a predetermined threshold value, detector 140 outputs a signal indicating that object 102 was detected. A distance to object 102, such as a distance from object 102 to first transmitter 108, second transmitter 110, and/or receiver 106 may be determined, as is described more fully herein). Hunt further teaches, ([Col. 5, Lines 45-53]: Memory 408 includes a computer readable storage medium, such as, without limitation, random access memory (RAM), flash memory, a hard disk drive, a solid state drive, a diskette, a flash drive, a compact disc, a digital video disc, and/or any suitable memory. In the exemplary embodiment, memory 408 includes data and/or instructions that are executable by processor 406 to enable processor 406 to perform the functions described herein).
It would have been obvious for one of ordinary skill in the art at the time of filing to modify Curatu and Li with Hunt to include the computer (3) is configured for determining that the specific pixel belongs to a solid element when an intensity associated with the pixel is greater than an intensity threshold associated with a determined solid element, since it is the same field of endeavor and results would have been predictable. One of ordinary skill in the art at the time of filing would have been motivated to modify Curatu and Li with Hunt since, such a configuration can reduce false object detection, enhance material discrimination, improve edge detection, improve computational efficiency, and improve overall safety for the operation of autonomous vehicles.
7. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Curatu et al (US 20180284285 A1), hereinafter Curatu in view of Li et al (CN 113138396 B), hereinafter Li, as applied to Claims 1 & 2, further in view of Hunt et al (US 8964168 B1), hereinafter Hunt, as applied to Claim 3, and further in view of Stanhope et al (US 20200284886 A1), hereinafter Stanhope.
8. Regarding Claim 4:
Curatu as modified by Li and Hunt does not teach the computer (3) is further configured for determining an average distance of the group of neighboring pixels relative to the LiDAR sensor (2), and in that a group of neighboring pixels is a candidate to be identified as fully or partly belonging to the cloud of particles when an average distance of the pixels of the group of neighboring pixels relative to the LiDAR sensor (2) is less than a predetermined distance threshold.
However, Stanhope teaches a Lidar system and method for dust or particle detection, ([0041]: In alternative embodiments, the controller 122 may be configured to identify the obscured region(s) within the initial three-dimensional representation of the field in any other suitable manner. For example, the controller 122 may be configured to identify the obscured region(s) based on the shape(s) of the object(s) depicted in the initial three-dimensional representation of the field. Specifically, the crops growing within the field and dust/spray clouds may generally have different shapes or profiles. As such, in one embodiment, the controller 122 may perform a classification operation on the data points of the initial three-dimensional representation of the field to extract feature parameters that may be used to identify any objects therein (e.g. using classification methods, such as k-nearest neighbors search, naïve Bayesian classifiers, convoluted neural networks, support vector machines, and/or the like). Thereafter, the controller 122 may compare the values associated with the feature parameter(s) of the identified object(s) to a predetermined range of values associated with dust/spray clouds. When the values of the feature parameter(s) of an identified object falls within the predetermined range of values, the controller 122 may identify the region of the initial three-dimensional representation of the field where such object is present as an obscured region).
One of ordinary skill in the art at the time of filing would understand that a k-nearest neighbor search would include using the average distance of the pixels of the group of neighboring pixels relative to the LiDAR sensor to classify a dust cloud in the embodiment disclosed by Stanhope.
It would have been obvious for one of ordinary skill in the art at the time of filing to modify the combination of Curatu, Li, and Hunt with Stanhope to include teach the computer (3) is further configured for determining an average distance of the group of neighboring pixels relative to the LiDAR sensor (2), and in that a group of neighboring pixels is a candidate to be identified as fully or partly belonging to the cloud of particles when an average distance of the pixels of the group of neighboring pixels relative to the LiDAR sensor (2) is less than a predetermined distance threshold, since it is the same field of endeavor and results would have been predictable. One of ordinary skill in the art at the time of filing would have been motivated to modify the combination of Curatu, Li, and Hunt with Stanhope since, such a configuration can eliminate or significantly reduce ghost targets, provide superior object distinction, reduce computational load, and improve real-time safety.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
9. Claim 11 is rejected under 35 U.S.C. § 101 as being directed to patent-ineligible subject matter.
10. Claim 11 is directed to a "computer program product comprising instructions for implementing any one of the above method claims when it is implemented by a computer." However, the claim does not recite any physical or tangible medium for storing the instructions, nor does it include any other structural limitations. The specification also does not require the computer program product to be embodied in a physical or tangible form.
Under the broadest reasonable interpretation, in light of the specification, claim 11 encompasses embodiments that lack a physical or tangible medium—such as a computer program per se, when claimed as a product without any structural recitation.
Claim Objections
11. Claims 5-8 are objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim cannot depend from any other multiple dependent claims. See MPEP § 608.01(n). Accordingly, the claims have not been further treated on the merits.
12. Claim 12 is objected to because of the following informalities: the Claim discloses the use of “non-transient” computer-readable storage medium. In this case non-transient should be replaced with “non-transitory” computer-readable storage medium.
Appropriate correction is required.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 9097800 B1: Discloses a system and method for solid object detection
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES W NAPIER whose telephone number is (571)272-7451. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm.
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, Helal Algahaim can be reached at (571) 270-5227. 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.
/J.W.N./Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645
Prosecution Insights