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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/23/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); 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); 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) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 11,726,209 B2.
For claim 1, although this claim is not identical to Claims 1,7 and 13 of U.S. Patent No. 11,726,209 B2, this claim is not patentably distinct from Claim 1,7 and 13 of U.S. Patent No. 11,726,209 B2 because Claim 1 is broader than and fully encompassed by Claim 1,7 and 13 of U.S. Patent No. 11,726,209 B2.
Application 18/339,620 (US-20240004076 A1)
U.S. Patent No. 11,726,209 B2
A method performed by a three-dimensional (3D) measuring device for improving scan data, the method comprising:
storing in an electronic memory, for use by an artificial intelligence (AI) module having at least one processor, training data for identifying artifacts in scan data that correspond to moving objects using at least one of depth values and intensity values of individual pixels in the scan data; scanning an environment to collect live scan data;
analyzing at least one of depth values and intensity values in a plurality of individual pixels in the live scan data to identify live artifacts using the training data; identifying an artifact in the live scan data based on the analysis of the training data; filtering the artifact from the live scan data to generate clear scan data; and outputting the clear scan data for generating a point cloud corresponding to the environment based on the clear scan data in place of the live scan data.
A method for generating clear scan data comprising: receiving, by one or more processors, training data used to train an artificial intelligence (AI) module, the AI module comprising an image classifier; training, by the one or more processors, the AI module to identify a pattern by analyzing the training data according to changes in depth data, the pattern comprising an incomplete representation of an object that is moving during an acquisition of the first 3D coordinate data, the training data input to the AI module having labels that designate the pattern comprising changes in the depth data; identifying, using the AI module having been trained, the pattern according to changes in the depth data in live data generated by a 3D measuring device based on the training of the one or more processors; generating, by the one or more processors, clear scan data by filtering the pattern having been identified according to changes in the depth data from the live data; and outputting, by the one or more processors, the clear scan data.
For claim 9, although this claim is not identical to Claim 1,7 and 13 of U.S. Patent No. 11,726,209 B2, this claim is not patentably distinct from Claim 1,7 and 13 of U.S. Patent No. 11,726,209 B2 because Claim 9 is broader than and fully encompassed by Claim 1,7 and 13 of U.S. Patent No. 11,726,209 B2.
Application 18/339,620 (US-20240004076 A1)
U.S. Patent No. 11,726,209 B2
A method for generating and using trained scan data performed by at least one processor in communication with a three-dimensional (3D) measuring device, the method comprising: receiving training data to train a stored artificial intelligence (AI) module, the training data including a plurality of patterns corresponding to moving objects that have been identified by a human, the training data used to identify a pattern corresponding to a moving object according to changes in depth data, the pattern comprising an incomplete representation of the moving object during an acquisition of 3D coordinate data; capturing live scan data, including at least one of two-dimensional (2D) coordinate data and 3D coordinate data, using the 3D measuring device;
identifying the pattern in live scan data, using the AI module, according to changes in the depth data in the live scan data generated by the 3D measuring device;
generating clear scan data by filtering the pattern from the live scan data; and outputting the clear scan data in order to generate a point cloud corresponding to the environment based on the clear scan data without requiring a second separate scan of the environment.
A method for generating clear scan data comprising: receiving, by one or more processors, training data used to train an artificial intelligence (AI) module, the AI module comprising an image classifier; training, by the one or more processors, the AI module to identify a pattern by analyzing the training data according to changes in depth data, the pattern comprising an incomplete representation of an object that is moving during an acquisition of the first 3D coordinate data, the training data input to the AI module having labels that designate the pattern comprising changes in the depth data; identifying, using the AI module having been trained, the pattern according to changes in the depth data in live data generated by a 3D measuring device based on the training of the one or more processors; generating, by the one or more processors, clear scan data by filtering the pattern having been identified according to changes in the depth data from the live data; and outputting, by the one or more processors, the clear scan data.
For claim 17, although this claim is not identical to Claim1,7 and 13 of U.S. Patent No. 11,726,209 B2, this claim is not patentably distinct from Claim 1,7 and 13 of U.S. Patent No. 11,726,209 B2 because Claim 17 is broader than and fully encompassed by Claim 1,7 and 13 of U.S. Patent No. 11,726,209 B2.
Application 18/339,620 (US-20240004076 A1)
U.S. Patent No. 11,726,209 B2
A method for generating and using trained scan data performed by at least one processor in communication with a three-dimensional (3D) measuring device, the method comprising: receiving training data to train a stored artificial intelligence (AI) module, the training data including a plurality of patterns corresponding to moving objects that have been identified by a human, the training data used to identify a pattern corresponding to a moving object according to changes in intensity data, the pattern comprising an incomplete representation of the moving object during an acquisition of 3D coordinate data; capturing live scan data, including at least one of two-dimensional (2D) coordinate data and 3D coordinate data, using the 3D measuring device; identifying the pattern in live scan data, using the AI module, according to changes in the intensity data in the live scan data generated by the 3D measuring device; generating clear scan data by filtering the pattern from the live scan data; and outputting the clear scan data in order to generate a point cloud corresponding to the environment based on the clear scan data without requiring a second separate scan of the environment.
A method for generating clear scan data comprising:
[Claim 1: A system comprising: one or more processors; and a 3D measuring device operably coupled to the one or more processors, the 3D measuring device comprising:]
[Claim 13: A non-transitory computer readable medium having program instructions embodied therewith, the program instructions readable by one or more processors to cause the one or more processors to perform a method for generating clear scan data comprising:]
receiving, by one or more processors, training data used to train an artificial intelligence (AI) module, the AI module comprising an image classifier; training, by the one or more processors, the AI module to identify a pattern by analyzing the training data according to changes in depth data, the pattern comprising an incomplete representation of an object that is moving during an acquisition of the first 3D coordinate data, the training data input to the AI module having labels that designate the pattern comprising changes in the depth data; identifying, using the AI module having been trained, the pattern according to changes in the depth data in live data generated by a 3D measuring device based on the training of the one or more processors; generating, by the one or more processors, clear scan data by filtering the pattern having been identified according to changes in the depth data from the live data; and outputting, by the one or more processors, the clear scan data.
Allowable Subject Matter
Claims 1-20 are allowed if they are overcome the Double Patenting rejection
Rogan et al (U.S. 20140368493 A1), “Object Removal Using Lidar-Based Classification”, teaches about an automated evaluation of images of an environment to detect the objects present in the environment and depicted in the images, and, more particularly, to identify the position, size, orientation, velocity, and/or acceleration of the objects in field of machine vision. It also teaches about Many scenarios involving the evaluation of object movement may be achieved through devices that also have access to data from a laser imaging ("lidar") capturing device, which may emit a set of focused, low-power beams of light of a specified wavelength, and may detect and record the reflection of such wavelengths of light from various objects. The detected lidar data may be used to generate a lidar point cloud, representing the lidar points of light reflected from the object and returning to the detector, thus indicating specific points of the objects present in the environment. By capturing and evaluating lidar data over time, such a device may build up a representation of the relative positions of objects around the lidar detector. These representations may be used while generating reconstructions of the environment to omit the depictions of the objects.
Hausler (U.S. 20100303341 A1), “Method and Device for Three-Dimensional Surface Detection with A Dynamic Reference Frame”, teaches about method and a device for scanning and digitizing three-dimensional surfaces wherein the surface shape of a three-dimensional object is acquired with an optical sensor. The sensor, which has a projection device and a camera, is configured to generate three-dimensional data from a single exposure, and the sensor is moved relative to the three-dimensional object, or vice versa. It also teaches about the system and method apply in range from intraoral measurement of teeth to the 3D acquisition of larger objects such as human body, crime scene acquisition, the interior of rooms or even buildings, and quality testing in manufacturing assemblies. Since the sensor allows to be moved over the object surface, it enables the acquisition of the 3D topography of complex objects by moving the sensor freely around the object. It is also possible to move the object relative to the sensor.
Johannes, Schauer and Andreas, Nuchter (“The Peopleremover—Removing Dynamic Objects From 3-D Point Cloud Data by Traversing a Voxel Occupancy Grid” ; Schauer), teaches about the method for removing dynamic portions of 3D point cloud data. It also teaches about algorithm is registered set of 3-D point clouds, we build a regular voxel occupancy grid and then traverse it along the lines of sight between the sensor and the measured points to find the differences in volumetric occupancy between the scans. Our approach works for scan slices from mobile mapping as well as for the more general scenario of terrestrial scan data. The result is a clean point cloud free of dynamic objects.
Regarding independent claim 1, “Rogan et al”, “Hausler” and “Schauer et al” either singularly or in combination, fails to anticipate or render the following limitations obvious:
storing in an electronic memory, for use by an artificial intelligence (AI) module having at least one processor, training data for identifying artifacts in scan data that correspond to moving objects using at least one of depth values and intensity values of individual pixels in the scan data;
scanning an environment to collect live scan data;
analyzing at least one of depth values and intensity values in a plurality of individual pixels in the live scan data to identify live artifacts using the training data;
identifying an artifact in the live scan data based on the analysis of the training data;
filtering the artifact from the live scan data to generate clear scan data; and
outputting the clear scan data for generating a point cloud corresponding to the environment based on the clear scan data in place of the live scan data.
Regarding independent claims 7 and 13, “Rogan et al”, “Hausler” and “Schauer et al” either singularly or in combination, fails to anticipate or render the following limitations obvious:
receiving training data to train a stored artificial intelligence (AI) module, the training data including a plurality of patterns corresponding to moving objects that have been identified by a human, the training data used to identify a pattern corresponding to a moving object according to changes in intensity data, the pattern comprising an incomplete representation of the moving object during an acquisition of 3D coordinate data;
capturing live scan data, including at least one of two-dimensional (2D) coordinate data and 3D coordinate data, using the 3D measuring device;
identifying the pattern in live scan data, using the AI module, according to changes in the intensity data in the live scan data generated by the 3D measuring device;
generating clear scan data by filtering the pattern from the live scan data; and
outputting the clear scan data in order to generate a point cloud corresponding to the environment based on the clear scan data without requiring a second separate scan of the environment.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Newcombe et al (U.S. 20120194516 A1), “Three-Dimensional Environment Reconstruction”, teaches about three-dimensional environment reconstruction. In an example, a 3D model of a real-world environment is generated in a 3D volume made up of voxels stored on a memory device. The model is built from data describing a camera location and orientation, and a depth image with pixels indicating a distance from the camera to a point in the environment. A separate execution thread is assigned to each voxel in a plane of the volume. Each thread uses the camera location and orientation to determine a corresponding depth image location for its associated voxel, determines a factor relating to the distance between the associated voxel and the point in the environment at the corresponding location, and updates a stored value at the associated voxel using the factor.
Wheeler et al (U.S. 20180188045 A1) , “HIGH DEFINITION MAP UPDATES BASED ON SENSOR DATA COLLECTED BY AUTONOMOUS VEHICLES” , teaches about an online system build a high definition (HD) map for a geographical region based on sensor data captured by a plurality of autonomous vehicles driving through a geographical region. The autonomous vehicles detect map discrepancies based on differences in the surroundings observed using sensor data compared to the high definition map and send messages describing these map discrepancies to the online system. The online system updates existing landmark maps to improve the accuracy of the landmark maps (LMaps), and to thereby improve passenger and pedestrian safety.
Petrovskaya et al (U.S. 9754419 B2), “ Systems And Methods For Augmented Reality Preparation, Processing, And Application”, teaches about systems and methods for acquiring and applying a depth determination of an environment in e.g., various augmented reality applications. A user may passively or actively scan a device (e.g., a tablet device, a mobile phone device, etc.) about the environment acquiring depth data for various regions. The system may integrate these scans into an internal three-dimensional model.
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/DUY TRAN/ Examiner, Art Unit 2674
/ONEAL R MISTRY/ Supervisory Patent Examiner, Art Unit 2674