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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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970);and, In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,921,210. Although the conflicting claims are not identical, they are not patentably distinct from each other because both comprising substantially the same elements:
US application 18/429,174
1. A light detection and ranging (LIDAR) sensor system for a vehicle, comprising: a sensor configured to: transmit a plurality of transmit beams at a plurality of angles relative to the sensor; receive a plurality of return beams from reflection by an object of the plurality of transmit beams; and output a point cloud to represent the object based on the plurality of return beams; and one or more processors configured to: determine a plurality of classification statistics regarding the object based on the point cloud, wherein a first classification statistic of the plurality of classification statistics is different from a second classification statistic of the plurality of classification statistics; and output a class of the object based on the plurality of classification statistics.
2. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to determine the class of the object based on a comparison of the point cloud with a model point cloud corresponding to the class.
3. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to: determine a first distance in a first plane defined by a first point of the point cloud that corresponds to a first return beam of the plurality of beams and a second point of the point cloud that corresponds to a second return beam of the plurality of beams; determine a second distance in a second plane defined by the first point and the second point; and determine the first classification statistic as a histogram based on the first distance and the second distance.
4. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to control the vehicle to avoid collision with the object based on the class of the object.
5. The LIDAR sensor system of claim 1, wherein the sensor is configured to generate the point cloud to include a first data point representing a first range to the object determined from a first return beam of the plurality of return beams and to include a second data point representing a second range to the object determined from a second return beam of the plurality of return beams.
6. The LIDAR sensor system of claim 1, wherein the sensor comprises: a laser source configured to output a carrier wave; a modulator configured to modulate the carrier wave to provide the carrier wave as the plurality of transmit beams; and one or more scanning optics configured to scan the plurality of transmit beams over the plurality of angles.
7. The LIDAR sensor system of claim 1, wherein the transmit beam is a chirp signal.
8. The LIDAR sensor system of claim 1, wherein the sensor is configured to output the point cloud for use as training data.
9. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to determine the class from a predetermined number of classes.
10. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to determine the object class from a vehicle class and a roadside structure class.
11. An autonomous vehicle control system, comprising: one or more processors configured to: receive a data signal comprising a three-dimensional (3D) point cloud representing an object; determine a plurality of classification statistics regarding the object based on the point cloud, wherein a first classification statistic of the plurality of classification statistics is different from a second classification statistic of the plurality of classification statistics; determine a class of the object based on the plurality of classification statistics; and generate a control signal to control operation of an autonomous vehicle based on the class of the object.
12. The autonomous vehicle control system of claim 11, wherein the 3D point cloud comprises a first data point corresponding to a first range to the object and a second data point corresponding to a second range to the object.
13. The autonomous vehicle control system of claim 11, wherein the one or more processors are configured to generate the control signal to avoid collision with the object.
14. The autonomous vehicle control system of claim 11, wherein the class of the object comprises at least one of a vehicle class or a roadside structure class.
15. The autonomous vehicle control system of claim 11, wherein the one or more processors are configured to: determine one or more distances defined relative to a first data point of the 3D point cloud and a second data point of the 3D point cloud; determine one or more angles defined relative to the first data point and the second data point; and determine the plurality of classification statistics based on the one or more distances and the one or more angles.
16. A LIDAR sensor system for a vehicle, comprising: a laser source configured to generate a carrier wave; an optic configured to output the carrier wave as a plurality of transmit signals; one or more detectors configured to detect a plurality of return signals from reflection of the plurality of transmit signals by an object; and one or more processors configured to: determine a point cloud to represent the object based on the plurality of return signals; determine a plurality of classification statistics regarding the object based on the point cloud, wherein a first classification statistic of the plurality of classification statistics is different from a second classification statistic of the plurality of classification statistics; and output a class of the object based on the plurality of classification statistics.
17. The LIDAR sensor system of claim 16, further comprising a modulator configured to modulate at least one of a phase or a frequency of the carrier wave.
18. The LIDAR sensor system of claim 16, wherein the plurality of classification statistics include a spin image determined from the point cloud and a covariance matrix determined from the point cloud.
19. The LIDAR sensor system of claim 16, wherein the one or more processors are configured to select the class based on a match between the plurality of classification statistics and the class.
20. The LIDAR sensor system of claim 16, wherein the class represents a plurality of object types.
US Patent 11,921,210
1. A light detection and ranging (LIDAR) sensor system for a vehicle, comprising: a sensor configured to: transmit a transmit beam, the transmit beam having at least one of a frequency or a phase that is modulated over time; receive a return beam from reflection or scattering of the transmit beam by an object; and output a data signal comprising at least one point cloud data point representing the object; and one or more processors configured to: determine at least one feature variable based on the at least one point cloud data point; determine a first classification statistic based on the at least one feature variable; determine a second classification statistic based on the at least one feature variable; and determine an object class corresponding to the object based on the first classification statistic and the second classification statistic.
2. The LIDAR sensor system of claim 1, wherein the sensor comprises: a laser source configured to output a carrier wave; a modulator configured to modulate the carrier wave to provide the carrier wave as the transmit beam; and one or more scanning optics configured to transmit the transmit beam.
3. The LIDAR sensor system of claim 2, wherein the modulator is configured to modulate the at least one of the phase or the frequency by applying modulation to a current driving the laser source.
4. The LIDAR sensor system of claim 2, wherein the modulator is an electro-optic modulator.
5. The LIDAR sensor system of claim 2, wherein the modulator is an acoustic-optic modulator.
6. The LIDAR sensor system of claim 1, wherein the transmit beam is a chirp signal.
7. The LIDAR sensor system of claim 1, wherein the sensor is further configured to output the data signal as a three-dimensional (3D) point cloud comprising the at least one point cloud data point.
8. The LIDAR sensor system of claim 7, wherein the sensor is configured to output the 3D point cloud for use as training data.
9. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to determine the object class from a predetermined number of object classes.
10. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to determine the object class from a vehicle class and a roadside structure class.
11. An autonomous vehicle control system, comprising: one or more processors configured to: receive a data signal comprising at least one point cloud data point representing an object; determine at least one feature variable based on the at least one point cloud data point; determine a first classification statistic based on the at least one feature variable; determine a second classification statistic based on the at least one feature variable; determine an object class corresponding to the object based on the first classification statistic and the second classification statistic; and generate a control signal to control operation of an autonomous vehicle based on the determined object class.
12. The autonomous vehicle control system of claim 11, wherein the data signal comprises a three-dimensional (3D) point cloud comprising the at least one point cloud data point.
13. The autonomous vehicle control system of claim 11, wherein the one or more processors are configured to determine the object class from a predetermined number of object classes.
14. The autonomous vehicle control system of claim 11, wherein the one or more processors are configured to determine the object class from a vehicle class and a roadside structure class.
15. A LIDAR sensor system for a vehicle, comprising: a laser source configured to generate a carrier wave having at least one of a frequency or a phase that is modulated over time; an optic configured to output the carrier wave as a transmit signal; one or more detectors configured to detect a return signal from reflection or scattering of the transmit signal by an object; and one or more processors configured to: determine a 3D point cloud representative of the object based on the return signal; determine at least one feature variable based on the 3D point cloud; determine a first classification statistic based on the at least one feature variable; determine a second classification statistic based on the at least one feature variable; and determine an object class corresponding to the object based on the first classification statistic and the second classification statistic.
16. The LIDAR sensor system of claim 15, further comprising a modulator configured to modulate the carrier wave to provide the carrier wave as the transmit beam.
17. The LIDAR sensor system of claim 16, wherein the modulator is configured to modulate the at least one of the phase or the frequency by changing a current driving the laser source.
18. The LIDAR sensor system of claim 16, wherein the modulator is an electro-optic modulator.
19. The LIDAR sensor system of claim 16, wherein the modulator is an acoustic-optic modulator.
20. The LIDAR sensor system of claim 15, wherein the one or more processors are configured to determine the object class from a vehicle class and a roadside structure class.
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 11-20 of U.S. Patent No. 11,537,808. Although the conflicting claims are not identical, they are not patentably distinct from each other because both comprising substantially the same elements:
US application 18/429,174
1. A light detection and ranging (LIDAR) sensor system for a vehicle, comprising: a sensor configured to: transmit a plurality of transmit beams at a plurality of angles relative to the sensor; receive a plurality of return beams from reflection by an object of the plurality of transmit beams; and output a point cloud to represent the object based on the plurality of return beams; and one or more processors configured to: determine a plurality of classification statistics regarding the object based on the point cloud, wherein a first classification statistic of the plurality of classification statistics is different from a second classification statistic of the plurality of classification statistics; and output a class of the object based on the plurality of classification statistics.
2. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to determine the class of the object based on a comparison of the point cloud with a model point cloud corresponding to the class.
3. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to: determine a first distance in a first plane defined by a first point of the point cloud that corresponds to a first return beam of the plurality of beams and a second point of the point cloud that corresponds to a second return beam of the plurality of beams; determine a second distance in a second plane defined by the first point and the second point; and determine the first classification statistic as a histogram based on the first distance and the second distance.
4. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to control the vehicle to avoid collision with the object based on the class of the object.
5. The LIDAR sensor system of claim 1, wherein the sensor is configured to generate the point cloud to include a first data point representing a first range to the object determined from a first return beam of the plurality of return beams and to include a second data point representing a second range to the object determined from a second return beam of the plurality of return beams.
6. The LIDAR sensor system of claim 1, wherein the sensor comprises: a laser source configured to output a carrier wave; a modulator configured to modulate the carrier wave to provide the carrier wave as the plurality of transmit beams; and one or more scanning optics configured to scan the plurality of transmit beams over the plurality of angles.
7. The LIDAR sensor system of claim 1, wherein the transmit beam is a chirp signal.
8. The LIDAR sensor system of claim 1, wherein the sensor is configured to output the point cloud for use as training data.
9. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to determine the class from a predetermined number of classes.
10. The LIDAR sensor system of claim 1, wherein the one or more processors are configured to determine the object class from a vehicle class and a roadside structure class.
11. An autonomous vehicle control system, comprising: one or more processors configured to: receive a data signal comprising a three-dimensional (3D) point cloud representing an object; determine a plurality of classification statistics regarding the object based on the point cloud, wherein a first classification statistic of the plurality of classification statistics is different from a second classification statistic of the plurality of classification statistics; determine a class of the object based on the plurality of classification statistics; and generate a control signal to control operation of an autonomous vehicle based on the class of the object.
12. The autonomous vehicle control system of claim 11, wherein the 3D point cloud comprises a first data point corresponding to a first range to the object and a second data point corresponding to a second range to the object.
13. The autonomous vehicle control system of claim 11, wherein the one or more processors are configured to generate the control signal to avoid collision with the object.
14. The autonomous vehicle control system of claim 11, wherein the class of the object comprises at least one of a vehicle class or a roadside structure class.
15. The autonomous vehicle control system of claim 11, wherein the one or more processors are configured to: determine one or more distances defined relative to a first data point of the 3D point cloud and a second data point of the 3D point cloud; determine one or more angles defined relative to the first data point and the second data point; and determine the plurality of classification statistics based on the one or more distances and the one or more angles.
16. A LIDAR sensor system for a vehicle, comprising: a laser source configured to generate a carrier wave; an optic configured to output the carrier wave as a plurality of transmit signals; one or more detectors configured to detect a plurality of return signals from reflection of the plurality of transmit signals by an object; and one or more processors configured to: determine a point cloud to represent the object based on the plurality of return signals; determine a plurality of classification statistics regarding the object based on the point cloud, wherein a first classification statistic of the plurality of classification statistics is different from a second classification statistic of the plurality of classification statistics; and output a class of the object based on the plurality of classification statistics.
17. The LIDAR sensor system of claim 16, further comprising a modulator configured to modulate at least one of a phase or a frequency of the carrier wave.
18. The LIDAR sensor system of claim 16, wherein the plurality of classification statistics include a spin image determined from the point cloud and a covariance matrix determined from the point cloud.
19. The LIDAR sensor system of claim 16, wherein the one or more processors are configured to select the class based on a match between the plurality of classification statistics and the class.
20. The LIDAR sensor system of claim 16, wherein the class represents a plurality of object types.
US 11,537,808
11. A light detection and ranging (LIDAR) system, comprising: a sensor configured to: generate a transmitted signal using a laser source; output the transmitted signal; receive a return signal responsive to the transmitted signal; and output a data signal representing at least one point cloud data point representing an object corresponding to the return signal; and a processing circuit configured to: determine at least one feature variable based on the at least one point cloud data point; determine a first classification statistic based on the at least one feature variable; determine a second classification statistic based on the at least one feature variable; assign a selected object class from a plurality of object classes to the object by determining a closest match between the object and the selected object class using the first classification statistic and the second classification statistic; and generate a control signal to control operation of an autonomous vehicle based on the selected object class.
12. The LIDAR system of claim 11, wherein the sensor is configured to provide the transmitted signal as an optical signal that comprises an optical pulse in an optical frequency band.
13. The LIDAR system of claim 12, wherein the processing circuit is configured to assign the first object class as the selected object class by: determining a first candidate closest match between a first object class of the plurality of object classes and the object using the first classification statistic; determining a second candidate closest match between a second object class of the plurality of object classes and the object using the second classification statistic; and assigning the first object class as the selected object class to the object responsive to the first object class being the same as the second object class.
14. The LIDAR system of claim 13, wherein the processing circuit is configured to assign the first object class as the selected object class by: determining, responsive to the first object class not being the same as the second object class, a closest fit between the 3D point cloud and one of a first model point cloud of the first object class or a second model point cloud of the second object class; and assigning one of the first object class or the second object class as the selected object class to the object based on the one of the first object class or the second object class corresponding to the closest fit.
15. The LIDAR system of claim 11, wherein the processing circuit is configured to: determine the first classification statistic based on a histogram of at least a subset of the at least one point cloud data point in each of a plurality of bins corresponding to a range of values of the at least one feature variable; and determine the second classification statistic based on a covariance matrix based on the at least one feature variable.
16. The LIDAR system of claim 11, wherein a number N of the plurality of object classes is less than one hundred.
17. The LIDAR system of claim 11, further comprising a display device configured to present information based on the selected object class.
18. The LIDAR system of claim 11, wherein the sensor is configured to perform at least one of frequency modulation or phase modulation to generate the transmitted signal.
19. An autonomous vehicle control system, comprising: a processing circuit configured to: determine at least one feature variable based on the at least one point cloud data point; determine a first classification statistic based on the at least one feature variable; determine a second classification statistic based on the at least one feature variable; assign a selected object class from a plurality of object classes to the object by determining a closest match between the object and the selected object class using the first classification statistic and the second classification statistic; and generate a control signal to control operation of an autonomous vehicle based on the selected object class.
20. The autonomous vehicle control system of claim 19, wherein the processing circuit is configured to receive the at least one point cloud data point from a light detecting and ranging (LIDAR) sensor.
Other Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lu et al. (“Recognizing objects in 3D point clouds with multi-scale local features”, IDS record) disclose a method, comprising: retrieving a three dimensional (3D) point cloud representing an object (page 24157 first paragraph: using 3D point cloud acquisition for object recognition to determine the location and orientation for recognizing objects in a cluttered environment, second paragraph: They are frequently investigated in the area of shape classification and model retrieval), the 3D point cloud comprising a plurality of point cloud data points (page 24164 section 3.4 for using hypothesis verification for ICP algorithms based on the number of points in a scene); determining at least one feature variable based on at least one point cloud data point of the 3D point cloud (page 24160 last paragraph for having a key point or one point in the cloud with figure 2 for showing feature description for the item in four different scales with means to rotate the local surface and extract statistics such as rotational projection statistic features).
Samadzadegant et al. ("A Multiple Classifier System for Classification of LIDAR Remote Sensing Data Using Multi-class SVM", 7 April 2010, IDS record) disclose a light detection and ranging (LIDAR) system, comprising: a sensor configured to: generate a transmitted signal using a laser source; output the transmitted signal; receive a return signal responsive to the transmitted signal; and output a data signal representing at least one point cloud data point representing an object corresponding to the return signal (Samadzadegant et al., page 257, first paragraph of chapter 3: a multi-class SVM is applied on LIDAR Data. This renders the above features of a known LIDAR sensor implicit. It is noted that the caption of Fig. 2 in page 260, which details the type of data captured, further shows data representing at least one point cloud data representing an object corresponding to the return signal); a processing circuit configured to: determine at least one feature variable based on the at least one point cloud data point (Chapter 3, first paragraph of the section Feature Extraction: intensity and range are the feature variables of the point cloud data), the sensor is configured to provide the transmitted signal as an optical signal that comprises an optical pulse in an optical frequency band (Samadzadegant disclose components and operations performed by a Lidar sensor and these features are considered implicit in the context of data captured by a LIDAR sensor).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857
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