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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
1. Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8, and 9, each recite multiple instances of “a point group” and it is not clear if they are all the same “point group(s)” or different “point group(s)”. If the Applicant means for the point groups to be the same point groups, the Examiner suggests that the second and third recitations of “point group” are changed to “the point group”.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
2. Claims 1, 4-6, and 8-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by JP 2013196171 A: Seiji Hosoya et al., (herein after “Hosoya”).
Regarding claim 1, as best understood, A mobile object control device comprising (Hosoya, §Abstract: “To provide a vehicle detection method and a vehicle detection device”, and P[37]: “sending this determination result to, for example, a car navigation system”):
a storage device that has stored a program (where the device must have storage and a way to process instructions to operate); and
a hardware processor (where the device must have storage and a way to process instructions to operate), wherein the hardware processor executes a program stored in the storage device, thereby recognizing a point group corresponding to an object present around the mobile object based on an image of surrounding situations of a mobile object (Hosoya, P[4]: “in the detection of the vehicle using the three-dimensional laser radar, when the moving body is a vehicle, the number of reflection points (measurement points) of the laser beam ranges from several to several tens, but such a large number Since the position of the moving body is estimated based on the measurement point group that is a set of measurement points, the shape of the moving body cannot be accurately captured as in the image.”, where the output image of the three-dimensional laser radar, for example LiDAR, generates an image using point groups/clouds),
determining a moving route of the mobile object based on the surrounding situations, and controlling the mobile object so that the object moves along the determined moving route (Hosoya, P[37]: “it is possible to recognize the existence of a two-wheeled vehicle that is about to pass through the left side of the vehicle or a vehicle that is changing the lane, and by sending this determination result to, for example, a car navigation system that is a host system, And it will be possible to safely stop at the shoulder.”), and
executing noise determination processing for calculating a distribution in a height direction of a point group recognized as a candidate for the object in a predetermined area around the mobile object (Hosoya, Fig. 3A-3B show point cloud distributions within particular areas, P[19]: “The measurement points bn above the height threshold H are removed, and the measurement points set on the basis of the vehicle interval, as shown in FIG. 3B, for a large number of measurement points b by the same scanning. Sorting processing is performed based on an interval threshold (for example, 1 m), and the measurement points b in which the interval d between adjacent measurement points b is equal to or less than the measurement point interval threshold are grouped and handled as being included in the same measurement point group G1 to G5.”), and
determining whether light source noise is included in a point group in the predetermined area based on the distribution (Hoyosa, P[20]: “the measurement point groups G1 to G5 formed as a group are subjected to a selection process with a threshold value (for example, two) of the number of measurement points b constituting the measurement point groups G1 to G5, and measurement below this threshold value is performed. The point group G5 is removed as noise, and the remaining measurement point groups G1 to G4 are detected as vehicles existing in the lane regions L1 and L2.”, and the three-dimensional laser radar provides light, where the outliers are the “light noise”, see P[16]: “the time measuring unit 32 may have a discrimination function for selecting a light reception signal Sr having a desired light reception intensity from the light reception signal Sr, and a gate function for excluding those having a short flight time from the light reception signal Sr. Good. Noise can be efficiently eliminated by these discrimination function and gate function.”).
Claims 8 and 9 recites features nearly identical to those recited in claim 1. Claims 8 and 9 are rejected for reasons analogous to those discussed above in conjunction with claim 1.
Regarding claim 4, wherein the hardware processor recognizes surroundings of the mobile object as a set of unit areas of the same size, manages a presence or absence of the object in the unit area based on an occupancy state of the unit area, and executes the noise determination processing for each unit area (Hoyosa, Fig. 3B showing the clusters, and see P[20]: “the measurement point groups G1 to G5 formed as a group are subjected to a selection process with a threshold value (for example, two) of the number of measurement points b constituting the measurement point groups G1 to G5, and measurement below this threshold value is performed. The point group G5 is removed as noise, and the remaining measurement point groups G1 to G4 are detected as vehicles existing”, where the areas are the two identical rectangles that contain the grouped point clusters.).
Regarding claim 5, wherein the hardware processor recognizes candidate areas for the object by clustering of point groups recognized from the image, and recognizes candidate areas of a predetermined size or smaller as candidate areas for the light source noise (Hoyosa, Fig. 3B showing the clusters, and see P[20]: “the measurement point groups G1 to G5 formed as a group are subjected to a selection process with a threshold value (for example, two) of the number of measurement points b constituting the measurement point groups G1 to G5, and measurement below this threshold value is performed. The point group G5 is removed as noise, and the remaining measurement point groups G1 to G4 are detected as vehicles existing”, where G5 is removed due to its small size.).
Regarding claim 6, wherein the hardware processor executes the noise determination processing for each unit area included in the candidate area for the light source noise (Hoyosa, Fig. 3B showing the clusters, and see P[20]: “the measurement point groups G1 to G5 formed as a group are subjected to a selection process with a threshold value (for example, two) of the number of measurement points b constituting the measurement point groups G1 to G5, and measurement below this threshold value is performed. The point group G5 is removed as noise, and the remaining measurement point groups G1 to G4 are detected as vehicles existing”).
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.
3. Claims 2-3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Hoyosa in view of “Point Cloud Library: Three-Dimensional Object Recognition and 6 DoF Pose Estimation” by Aitor Aldoma et al., (herein after “Aldoma”).
Regarding claim 2, Hoyosa does not explicitly disclose “wherein the hardware processor calculates a curve representing the distribution and determines whether light source noise is included in a point group in the predetermined area based on the number of maximum points that the curve has in the noise determination processing.”
However, Aldoma discloses wherein the hardware processor calculates a curve representing the distribution and determines whether light source noise is included in a point group in the predetermined area based on the number of maximum points that the curve has in the noise determination processing in §Clustered Viewpoint Feature Histogram: “Each of these smooth regions is then used independently to compute a set of N VFH histograms … The smooth regions for CVFH are easily computed by 1) removing points on the object with high curvatures, indicating noise or borders between smooth regions and 2) performing region growing on the remaining points on xyz and normal space. CVFH outputs a multivariate representation of the object of interest and the histogram k”, where the points showing high curvatures on the histogram are removed which are the maximum points of the curve.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hoyosa to generate a histogram using light data and remove the noise by eliminating the peaks/outliers, as taught by Aldoma, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have provided the benefit of reducing light noise in the final processed “image”, increasing the images quality.
Regarding claim 3, wherein, when the hardware processor determines that light source noise is included in a point group in the predetermined area based on the number of maximum points (Hoyosa, Fig. 3B shows G5 which is removed, where G5 is represented as a histogram as provided by Aldoma, §Clustered Viewpoint Feature Histogram: “Each of these smooth regions is then used independently to compute a set of N VFH histograms … The smooth regions for CVFH are easily computed by 1) removing points on the object with high curvatures, indicating noise or borders between smooth regions and 2) performing region growing on the remaining points on xyz and normal space. CVFH outputs a multivariate representation of the object of interest and the histogram k”, where the points are included based on the number of those maximum points with “high curvatures” in the histogram), the hardware processor removes the light source noise from the point group in the predetermined area (Hoyosa, P[20]: “the measurement point groups G1 to G5 formed as a group are subjected to a selection process with a threshold value (for example, two) of the number of measurement points b constituting the measurement point groups G1 to G5, and measurement below this threshold value is performed. The point group G5 is removed as noise).
Regarding claim 7, wherein the hardware processor determines that light source noise is included in a point group in the predetermined area when the number of maximum points of the curve is two is disclosed by Hoyosa in Fig. 3B where it shows G5 containing two maximum points which are the light source noise instances represented on the curve of the histogram provided by Aldoma that are removed from the predetermined area.
Conclusion
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240418839 A1 by Felix Heide et al., is directed to a LiDAR sensor that generates temporal histograms and denoises the temporal waveform, estimates ambient light, determines noise threshold corresponding to the ambient light, determines a peak of the plurality of peaks on the histogram as the ambient light with maximum intensity, and adds the maximum peaks to the point cloud.
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TY M BEATTY whose telephone number is (703)756-5370. The examiner can normally be reached Mon-Fri: 8AM-4PM EST..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at (571) 272 - 3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TY MITCHELL BEATTY/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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