DETAILED ACTION
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 § 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.
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 21 is directed to a computer-readable storage medium. According to MPEP 2106 (I), machine readable storage media can encompass non-statutory transitory forms of signal transmission, such as, a propagating electrical or electromagnetic signal per se. When the broadest reasonable interpretation of machine readable storage media in light of the specification as it would be interpreted by one of ordinary skill in the art encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable storage medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 of this title, 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.
Claim 1-9, 12, 16 and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Akashi U.S. Patent Application 20220005203 in view of Saphier U.S. Patent Application 20210321872.
Regarding claim 1, Akashi discloses a system, the system comprising:
one or more cameras (visible light camera 41, depth camera 42, near infrared light camera 43);
one or more processors (CPU 1000); and
a memory (memory 1002) storing a set of instructions, that, when executed by the one or more processors, cause the one or more processors to perform a method (paragraph [0100]: the CPU 1000 executes processing on the basis of the program in the memory 1002) comprising:
identifying edges in an un-patterned illumination image taken from a scan (paragraph [0057]: The second edge detection unit 134 detects edges for each small region in the visible light image (step S23); paragraph [0003]: FIG. 14, in an object detection device 50, a projection unit (light source) 51 emitting near infrared light irradiates light on the region (irradiation region) where the target object exists; paragraph [0075]: near infrared images from near infrared image acquisition means (for example, near infrared light camera 43));
generating a depth map for the one or more cameras corresponding to the illumination image; identifying edges in the depth map (paragraph [0003]: The ranging unit 52 generates a background depth map by measuring the distance based on the received light... The ranging unit 52 generates a foreground depth map by measuring the distance based on the received light; paragraph [0057]: The first edge detection unit 133 detects edges for each small region in the depth image (step S24)).
Akashi discloses all the features with respect to claim 1 as outlined above. However, Akashi fails to disclose an intraoral scanner comprising one or more cameras; determining a location of the one or more cameras corresponding to a patterned illumination image taken during the intraoral scan; determining an alignment transform to align edges identified from the un-patterned illumination image with edges identified from the depth map; and modifying a 3D model that is derived from patterned illumination images of intraoral scan using the alignment transform and the un-patterned illumination image.
Saphier discloses an intraoral scanner comprising one or more cameras (intraoral scanner 150) comprising one or more cameras (paragraph [0244]: Intraoral objects will start appearing in a field of view (FOV) of the scanner 150 (e.g., in the FOV of front cameras of the scanner 150 or a front of a FOV of the scanner 150), and will then be shown to move towards a back of the camera when the scanner 150 enters an oral cavity);
determining a location of the one or more cameras corresponding to a patterned illumination image taken during the intraoral scan (paragraph [0217]: the user may apply scanner 150 to one or more patient intraoral locations; paragraph [0586]: processing logic may compare patient case details (e.g., existence of preparation tooth, location or preparation tooth, upper or lower jaw, etc.) of a current 3D model to patient case details associated with multiple stored virtual camera trajectories; paragraph [0635]: The scans may be generated by generating coherent light or non-coherent light by an intraoral scanner, which is reflected off of an intraoral object back into the intraoral scanner and detected to generate the intraoral scans and/or 2D images. The light may include structured light (patterned illumination) and/or unstructured light);
determining an alignment transform to align edges identified from the un-patterned illumination image with edges identified from the depth map (paragraph [0226]: determination of the transformations which align one scan with the other scan and/or with the 3D model; Saphier’s teaching of aligning can be combined with Akashi’s device, such that to align edges in depth map); and
modifying a 3D model that is derived from patterned illumination images of intraoral scan using the alignment transform and the un-patterned illumination image (paragraph [0312]: correcting a surface of a tooth in an image and/or 3D model of the tooth and/or for modifying a margin line of a preparation tooth that is unacceptable; paragraph [0313]: modify an image and/or 3D model of a preparation tooth, such as to correct a margin line of the preparation tooth (e.g., to sculpt or perform virtual cleanup of the margin line)).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 2, Akashi as modified by Saphier discloses the system of claim 1, wherein the steps of identifying edges in the un-patterned illumination image, determining the location of the one or more cameras, generating the depth map, identifying edges in the depth map, calculating the alignment transform, and modifying the 3D model are performing while scanning (Saphier’s paragraph [0312]: Intraoral scan application 115 may additionally or alternatively include logic for automatically correcting a surface of a tooth in an image and/or 3D model of the tooth and/or for modifying a margin line of a preparation tooth that is unacceptable; paragraph [0387]: Multiple problems can be solved simultaneously: role classification, teeth/gums/restorative object segmentation, view determination, etc.; paragraph [0447]: At block 716, processing logic determines whether scanning is complete. Such a determination may be made based on analysis of a completeness of the segments for the upper and lower dental arches and existence of one or more intraoral scans associated with a bite role, based on detection of removal of a scanner from a patient's mouth).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 3, Akashi as modified by Saphier discloses the system of claim 1, wherein identifying the edges of the un-patterned illumination image comprises identifying edges from the un-patterned illumination image comprising one or more of: a tooth-air boundary, a tooth-tooth boundary, a tooth-gum boundary, and/or a scan-body/air boundary (Saphier’s paragraph [0039]: determining a first region of the second portion that depicts a tooth to gum boundary or a tooth to tooth boundary).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 4, Akashi as modified by Saphier discloses the system of claim 1, wherein the set of instructions is further configured to cause the one or more processors to label the identified edges as either: a tooth-air boundary, a tooth-gum boundary, a tooth-tooth boundary, and/or a scan-body/air boundary (Saphier’s paragraph [0576]: processing logic generates a matrix that identifies, for each point (e.g., edge, vertex, voxel, etc. on a surface of the 3D model), a probability that the point represents a margin line; paragraph [0039]: determining a first region of the second portion that depicts a tooth to gum boundary or a tooth to tooth boundary).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 5, Akashi as modified by Saphier discloses the system of claim 1, wherein identifying the edges of the un-patterned illumination image comprises using a trained machine-learning agent to identify the edge of the un-patterned illumination image (Saphier’s paragraph [0576]: the 3D model or projections of the 3D model onto one or more planes may be input into a trained ML model that outputs at least a first class indicating a representation of a margin line and a second class indicating a representation of something other than a margin line… processing logic generates a matrix that identifies, for each point (e.g., edge, vertex, voxel, etc. on a surface of the 3D model), a probability that the point represents a margin line).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 6, Akashi as modified by Saphier discloses the system of claim 1, wherein determining the location of the one or more cameras corresponding to the patterned illumination image taken during the intraoral scan comprises determining the location the one or more cameras corresponding the patterned illumination image that corresponds to the un-patterned illumination image (Saphier’s paragraph [0217]: the user may apply scanner 150 to one or more patient intraoral locations; paragraph [0586]: processing logic may compare patient case details (e.g., existence of preparation tooth, location or preparation tooth, upper or lower jaw, etc.) of a current 3D model to patient case details associated with multiple stored virtual camera trajectories; paragraph [0215]: Intraoral scan data 135A-N may also include color 2D images and/or images of particular wavelengths (e.g., near-infrared (NIRI) images, infrared images, ultraviolet images, etc.) of a dental site).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 7, Akashi as modified by Saphier discloses the system of claim 6, wherein the patterned illumination image that corresponds to the un-patterned illumination image is a patterned illumination image that was taken either immediately before or immediately after the un-patterned illumination image was taken while scanning (Saphier’s paragraph [0245]: for scanners 150 that use structured light (SL) projectors, the intraoral scan application 115 and/or intraoral scanner 150 can automatically turn on and off the structured light (SL) projectors; paragraph [0635]: The scans may be generated by generating coherent light or non-coherent light by an intraoral scanner, which is reflected off of an intraoral object back into the intraoral scanner and detected to generate the intraoral scans and/or 2D images. The light may include structured light (patterned illumination) and/or unstructured light).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 8, Akashi as modified by Saphier discloses the system of claim 1, wherein the location of the one or more cameras is determined relative to a 3D model derived from the patterned illumination image (Saphier’s paragraph [0217]: the user may apply scanner 150 to one or more patient intraoral locations; paragraph [0586]: processing logic may compare patient case details (e.g., existence of preparation tooth, location or preparation tooth, upper or lower jaw, etc.) of a current 3D model to patient case details associated with multiple stored virtual camera trajectories).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 9, Akashi as modified by Saphier discloses the system of claim 1, wherein generating the depth map comprises generating the depth map from a viewpoint of the one or more cameras (Akashi’s paragraph [0003]: The ranging unit 52 generates a background depth map by measuring the distance based on the received light... The ranging unit 52 generates a foreground depth map by measuring the distance based on the received light; paragraph [0057]: The first edge detection unit 133 detects edges for each small region in the depth image (step S24); Saphier’s paragraph [0217]: the user may apply scanner 150 to one or more patient intraoral locations; paragraph [0586]: processing logic may compare patient case details (e.g., existence of preparation tooth, location or preparation tooth, upper or lower jaw, etc.) of a current 3D model to patient case details associated with multiple stored virtual camera trajectories).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 12, Akashi as modified by Saphier discloses the system of claim 1, wherein creating the alignment transform comprises identifying points in the depth map corresponding to the edges identified from the un-patterned illumination image (Saphier’s paragraph [0226]: determination of the transformations which align one scan with the other scan and/or with the 3D model. Registration may involve identifying multiple points in each scan (e.g., point clouds) of a scan pair (or of a scan and the 3D model), surface fitting to the points, and using local searches around points to match points of the two scans; Akashi’s paragraph [0003]: The ranging unit 52 generates a background depth map by measuring the distance based on the received light... The ranging unit 52 generates a foreground depth map by measuring the distance based on the received light; paragraph [0057]: The first edge detection unit 133 detects edges for each small region in the depth image (step S24)).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 16, Akashi as modified by Saphier discloses the system of claim 1, wherein calculating the alignment transform comprises using a trained machine-learning agent to align edges identified from the un-patterned illumination image with edges identified from the depth map (Saphier’s paragraph [0226]: determination of the transformations which align one scan with the other scan and/or with the 3D model. Registration may involve identifying multiple points in each scan (e.g., point clouds) of a scan pair (or of a scan and the 3D model), surface fitting to the points, and using local searches around points to match points of the two scans; paragraph [0576]: the 3D model or projections of the 3D model onto one or more planes may be input into a trained ML model that outputs at least a first class indicating a representation of a margin line and a second class indicating a representation of something other than a margin line… processing logic generates a matrix that identifies, for each point (e.g., edge, vertex, voxel, etc. on a surface of the 3D model), a probability that the point represents a margin line).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 18, Akashi as modified by Saphier discloses the system of claim 1, wherein modifying the 3D model using the alignment transform and the un-patterned illumination image comprises correcting a surface of the 3D model (Saphier’s paragraph [0312]: correcting a surface of a tooth in an image and/or 3D model of the tooth and/or for modifying a margin line of a preparation tooth that is unacceptable; paragraph [0313]: modify an image and/or 3D model of a preparation tooth, such as to correct a margin line of the preparation tooth (e.g., to sculpt or perform virtual cleanup of the margin line)).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 19, Akashi as modified by Saphier discloses the system of claim 1, wherein the set of instructions is further configured to cause the one or more processors to display the modified 3D model (Saphier’s paragraph [0230]: Intraoral scan application 115 may generate one or more 3D models from intraoral scans, and may display the 3D models to a user (e.g., a doctor) via a user interface).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Regarding claim 20, Akashi discloses a method, the method comprising:
identifying edges in an un-patterned illumination image taken from a scan (paragraph [0057]: The second edge detection unit 134 detects edges for each small region in the visible light image (step S23); paragraph [0003]: FIG. 14, in an object detection device 50, a projection unit (light source) 51 emitting near infrared light irradiates light on the region (irradiation region) where the target object exists; paragraph [0075]: near infrared images from near infrared image acquisition means (for example, near infrared light camera 43));
generating a depth map for the one or more cameras corresponding to the illumination image; identifying edges in the depth map (paragraph [0003]: The ranging unit 52 generates a background depth map by measuring the distance based on the received light... The ranging unit 52 generates a foreground depth map by measuring the distance based on the received light; paragraph [0057]: The first edge detection unit 133 detects edges for each small region in the depth image (step S24)).
Akashi discloses all the features with respect to claim 20 as outlined above. However, Akashi fails to disclose determining a location of the one or more cameras corresponding to a patterned illumination image taken during the intraoral scan; determining an alignment transform to align edges identified from the un-patterned illumination image with edges identified from the depth map; modifying a 3D model that is derived from patterned illumination images of intraoral scan using the alignment transform and the un-patterned illumination image; and outputting the modified 3D model.
Saphier discloses determining a location of the one or more cameras corresponding to a patterned illumination image taken during the intraoral scan (paragraph [0217]: the user may apply scanner 150 to one or more patient intraoral locations; paragraph [0586]: processing logic may compare patient case details (e.g., existence of preparation tooth, location or preparation tooth, upper or lower jaw, etc.) of a current 3D model to patient case details associated with multiple stored virtual camera trajectories; paragraph [0635]: The scans may be generated by generating coherent light or non-coherent light by an intraoral scanner, which is reflected off of an intraoral object back into the intraoral scanner and detected to generate the intraoral scans and/or 2D images. The light may include structured light (patterned illumination) and/or unstructured light);
determining an alignment transform to align edges identified from the un-patterned illumination image with edges identified from the depth map (paragraph [0226]: determination of the transformations which align one scan with the other scan and/or with the 3D model; Saphier’s teaching of aligning can be combined with Akashi’s device, such that to align edges in depth map);
modifying a 3D model that is derived from patterned illumination images of intraoral scan using the alignment transform and the un-patterned illumination image (paragraph [0312]: correcting a surface of a tooth in an image and/or 3D model of the tooth and/or for modifying a margin line of a preparation tooth that is unacceptable; paragraph [0313]: modify an image and/or 3D model of a preparation tooth, such as to correct a margin line of the preparation tooth (e.g., to sculpt or perform virtual cleanup of the margin line)); and
outputting the modified 3D model (paragraph [0230]: Intraoral scan application 115 may generate one or more 3D models from intraoral scans, and may display the 3D models to a user (e.g., a doctor) via a user interface).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi’s to use intraoral scanner as taught by Saphier, to automate the process of performing intraoral scans with machine learning.
Claim 21 recites the functions of the method recited in claim 20 as medium steps. Accordingly, the mapping of the prior art to the corresponding functions of the method in claim 20 applies to the medium steps of claim 21.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Akashi U.S. Patent Application 20220005203 in view of Saphier U.S. Patent Application 20210321872, and further in view of Bormet U.S. Patent Application 20170230556.
Regarding claim 10, Akashi as modified by Saphier discloses identifying edges in the depth map from the un-patterned illumination image (Akashi's paragraph [0057]: The first edge detection unit 133 detects edges for each small region in the depth image (step S24); paragraph [0003]: FIG. 14, in an object detection device 50, a projection unit (light source) 51 emitting near infrared light irradiates light on the region (irradiation region) where the target object exists; paragraph [0075]: near infrared images from near infrared image acquisition means (for example, near infrared light camera 43)). However, Akashi as modified by Saphier fails to disclose identifying a sub-set of edges corresponding to the edges.
Bormet discloses identifying a sub-set of edges corresponding to the edges (paragraph [0014]: (a) randomly selecting a boundary point subset having at least three of the boundary points and determining a fit for the boundary point subset... (d) selecting a best fit, the best fit being the fit having the inlier number with the greatest magnitude).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi and Saphier’s to identify subset edges as taught by Bormet, to adjust size of an active portion of a digital image to fit a monitor screen automatically.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Akashi U.S. Patent Application 20220005203 in view of Saphier U.S. Patent Application 20210321872, and further in view of Blasco U.S. Patent Application 20200134849.
Regarding claim 11, Akashi as modified by Saphier discloses all the features with respect to claim 1 as outlined above. However, Akashi as modified by Saphier fails to disclose calculating the alignment transform in six spatial degrees of freedom.
Blasco discloses calculating the alignment transform in six spatial degrees of freedom (paragraph [0031]: lines and patterns in the original images (referred to a six axis reference system [x′, y′, z′, pitch′, roll′ and yaw′] in which the moving camera is shooting after a certain amount of time t1) are mapped to align lines and patterns in the transformed images (referred to a six axis reference system [x, y, z, pitch, roll and yaw] in which the camera was at time zero), resulting in two images (initially acquired at times t1 and zero) that are comparable images as if they had been acquired by coplanar cameras with the same z, pitch, roll and yaw, and with “rectified” values of x and y that depend on the movement along those two axis (baselines in x and y between time 0 and time t1)).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi and Saphier’s to align in six spatial degrees as taught by Blasco, to estimate distances and generate depth maps accurately.
Claim 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Akashi U.S. Patent Application 20220005203 in view of Saphier U.S. Patent Application 20210321872, in view of Blasco U.S. Patent Application 20200134849, and further in view of Hirano U.S. Patent Application 20060210338.
Regarding claim 14, Akashi as modified by Saphier and Blasco discloses creating the alignment transform comprises iteratively checking alternative transforms in six degrees of freedom (Blasco’s paragraph [0031]: lines and patterns in the original images (referred to a six axis reference system [x′, y′, z′, pitch′, roll′ and yaw′] in which the moving camera is shooting after a certain amount of time t1) are mapped to align lines and patterns in the transformed images (referred to a six axis reference system [x, y, z, pitch, roll and yaw] in which the camera was at time zero), resulting in two images (initially acquired at times t1 and zero) that are comparable images as if they had been acquired by coplanar cameras with the same z, pitch, roll and yaw, and with “rectified” values of x and y that depend on the movement along those two axis (baselines in x and y between time 0 and time t1)). However, Akashi as modified by Saphier and Blasco fails to disclose minimize the difference in the sum of the squares of a distance between corresponding points of the edges.
Hirano discloses minimize the difference in the sum of the squares of a distance between corresponding points of the edges (paragraph [0055]: a sum of squares of a difference (for example, a distance) between a model (a fitting line) and a measurement point (an edge point) is minimized).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi, Saphier and Blasco’s to minimize difference as taught by Hirano, to obtain a desired fitting line.
Regarding claim 15, Akashi as modified by Saphier, Blasco and Hirano discloses the system of claim 14, wherein the alternative transforms correspond to putative positions of the camera for the un-patterned illumination image (Saphier’s paragraph [0226]: determination of the transformations which align one scan with the other scan and/or with the 3D model; paragraph [0586]: processing logic may compare patient case details (e.g., existence of preparation tooth, location or preparation tooth, upper or lower jaw, etc.) of a current 3D model to patient case details associated with multiple stored virtual camera trajectories).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi, Saphier and Blasco’s to minimize difference as taught by Hirano, to obtain a desired fitting line.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Akashi U.S. Patent Application 20220005203 in view of Saphier U.S. Patent Application 20210321872, and further in view of Jang U.S. Patent Application 20150237330.
Regarding claim 17, Akashi as modified by Saphier discloses generating the depth map, identifying edges and calculating the alignment transform (Akashi’s paragraph [0057]: The first edge detection unit 133 detects edges for each small region in the depth image (step S23). The second edge detection unit 134 detects edges for each small region in the visible light image (step S24); Saphier’s paragraph [0226]: determination of the transformations which align one scan with the other scan and/or with the 3D model). However, Akashi as modified by Saphier fails to disclose to iteratively repeat the steps and using a corrected camera position for the one or more cameras, until a maximum number of iterations has been met or until a change in the corrected camera position is equal to or less than a threshold.
Jang discloses to iteratively repeat the steps and using a corrected camera position for the one or more cameras, until a maximum number of iterations has been met or until a change in the corrected camera position is equal to or less than a threshold (paragraph [0014]: The correcting of the position of the camera may include... (d) recursively performing operations (a) to (c) until the sum of the euclidean distance is equal to or less than the preset threshold).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Akashi and Saphier’s to iteratively repeat steps as taught by Jang, to align by minimizing points using a correspondence between multi-images and reduce error.
Allowable Subject Matter
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 13 is about creating the alignment transform comprises using a subset of the edges identified from the un-patterned illumination image that correspond to a tooth-air boundary, a tooth-gum boundary, a tooth-tooth boundary, and/or a scan-body/air boundary in six degrees of freedom to minimize the difference in the sum of the squares of a distance between corresponding points of the edges.
Akashi 20220005203, Saphier 20210321872, Bormet 20170230556, Blasco 20200134849 and Hirano 20060210338 combined cannot teach these features perfectly. These limitations when read in light of the rest of the limitations in the claim and the claims to which it depends make the claim allowable subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Yi Yang whose telephone number is (571)272-9589. The examiner can normally be reached on Monday-Friday 9:00 AM-6:00 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Hajnik can be reached on 571-272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YI YANG/
Primary Examiner, Art Unit 2616
Prosecution Insights