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 .
Response to Applicant’s arguments
Applicant’s arguments filed January 27, 2026 have been fully considered, but they are not deemed to be persuasive.
For claim 1, and similar claim 11, Applicant argues that the cited references do not teach the claimed “determine at least one cluster having voxels the number of which is equal to or smaller than a predetermined number; and remove scan data values associated with the at least one cluster." However, Saphier teaches the concept of defining a cluster based on the number of elements on that cluster (e.g., [0641] - At block 2808, processing logic determines whether an amount of points that represent a dirty region in the intraoral scan(s) satisfy one or more size threshold); furthermore, it is well known in the art that the “noise removing” from data captured by a scanner in which an effective technique removes cluster noise based on a size threshold involves connecting neighboring noise points (points/pixels/voxels) and removing groups in comparison to a specific size, or threshold (e.g., number of points, pixels, or voxels) (see also Kriegel, page 234, column 1 - 3. Evaluation of the frequency ki of each data point xi. The frequency is the number of points located within a distance r of point xi, hence it is a measure of the density. 4. Removal of the nondense (“noise”) points (where ki < k)). Noted: By defining Kriegel’s distance r large so that the circle radius r will cover the “noise” cluster, then Kriegel’s noise removing technique teaches the claimed ““determine at least one cluster having voxels the number of which is equal to or smaller than a predetermined number; and remove scan data values associated with the at least one cluster."
For claim 7, Applicant argues that the cited references do not teach “wherein the one or more processors are configured to divide the three-dimensional image model into the multiple clusters in response to reception of, through an input device, a user input for terminating the scan of the three-dimensional scanner." However, a processor starts to perform a cluster algorithm on the scanned data during the scanning or after the scanning is just a tradeoff of processing capacity and processing time; therefore, it would have been obvious to perform “clustering” when all the samples have been collected (see Kriegel, Introduction - Clustering is the problem of finding a set of groups of similar objects within a data set while keeping dissimilar objects separated in different groups or the group of noise (noisy points)… Given the data as a set of objects from a given data space D ⊂ S and a dissimilarity function dis : S × S → R+0 ,
the task is to learn a meaningful grouping of the data… Figure 1 – the collected sample points for clustering).
Accordingly, the claimed invention as represented in the claims does not represent a patentable distinction over the art of record.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 7-14, 16-24 are rejected under 35 U.S.C. 103 as being unpatentable over LEE et al (WO 2021145713 A1) in view of NIKOLSKIY et al (WO 2020/263987), SAPHIER et al (20210321872) and ROSCHIN et al (20200306012), and further in view of KRIEGEL et al (Density-based clustering).
As per claim 1, lee teaches the claimed "electronic device" comprising: "a communication circuit communicatively connected to a three-dimensional scanner" (Lee, Figure 1 - The controller 110 may control the operation of each component of the virtual model generating apparatus 100 and may process a signal transmitted between each component; page 5 - Measurement data received from the scanner 10 may be displayed on the display, and a 3D virtual model and/or a finally generated tooth model generated based on the scan data may be displayed. Here, the measurement data may include an intraoral scan image scanned with a scanner); "a display" (Lee, Figure 1 - a storage unit 140); and "one or more processors" (Lee, Figure 1 - The controller 110 may control the operation of each component of the virtual model generating apparatus 100 and may process a signal transmitted between each component), wherein the one or more processors are configured to: "obtain scan data values for a surface of a target object through a scan of the three-dimensional scanner, the scan data values including a three-dimensional coordinate value" (Lee, page 5 - Measurement data received from the scanner 10 may be displayed on the display, and a 3D virtual model and/or a finally generated tooth model generated based on the scan data may be displayed. Here, the measurement data may include an intraoral scan image scanned with a scanner); "generate a three-dimensional image model of the target object, based on the obtained scan data values" (Lee, page 5 - Measurement data received from the scanner 10 may be displayed on the display, and a 3D virtual model and/or a finally generated tooth model generated based on the scan data may be displayed); "divide the three- dimensional image model into multiple clusters" (Lee, page 6 - The image analysis unit 150 analyzes the measurement data input (received) from the scanner 10 to recognize the shape of the object. For example, the image analyzer 150 recognizes shapes of teeth, gingiva, and the like from the measurement data, respectively… For example, the image analyzer 150 may divide the image of the measurement data into a tooth image and a gingival image by using a machine learning-based semantic segmentation technique); "determine at least one cluster among the multiple clusters" (Lee, page 11 - As another example, when the third icon of the designated area selection tool is selected, the virtual model generating apparatus 100 selects a tooth area recognized by machine learning from among the 3D virtual model as the designated area. Here, the virtual model generating apparatus 100 may delete measurement data received for an area other than the selected designated area in real time, and in this process, all soft tissues of the gingival area may be removed); and "remove scan data values associated with the at least one cluster" (Lee, page 10 - When any one of the icons of the designated area selection tool is selected, the virtual model generating apparatus 100 recognizes an area set corresponding to the selected icon among the 3D virtual model as a designated area and automatically selects the designated area in the 3D virtual model. You can select, create a final model based on the selected designated area and display it on the execution screen). It is noted that Lee does not teach that the selected area "having voxels the number of which is equal to or smaller than a predetermined number" as claimed. However, Lee's designated area selection tool for removing an area from the captured 3D image (e.g., page 10 - the designated area selection tool provides a function for designating an area in which a final model is to be generated among the 3D virtual model... Accordingly, the user may select an icon corresponding to a desired area from among the icons of the designated area selection tool; pages 12-13, extract rules - When displaying the 3D virtual model on the display screen, the virtual model generating apparatus 100 selects and displays a deletion target area from among the 3D virtual model (S140)... For an embodiment of the deletion target area, refer to FIG. 3) suggests the claimed removing of "small" (i.e., "having a size equal to or smaller than a predetermined size") segmented area (see Nikolskiy, [0253] - some embodiments include receiving a digital model of a dental impression 3160, performing curvature based segmentation to identify one or more small features on one or more surface regions based on one or more from the group consisting of perimeter, volume, or surface area 3162, and removing the one or more small features from the one or more surface regions of the digital model 3164; Saphier, [0641] - Processing logic may determine one or more dirty regions in the intraoral scan(s) and/or 2D images, where a dirty region is a region of all adjacent dirty pixels that together form a contiguous dirty region. If a size of any contiguous dirty region exceeds the contiguous dirty region threshold, then this may indicate that the scanner is dirty; [0652] - Processing logic may then perform one or more of a) reject one or more of the intraoral scans that were taken while the optical surface was dirty, b) use the intraoral scans to determine a 3D surface of the dental site in the oral cavity, where those pixels associated with points that represent a dirty region of the optical surface are not used in the determination of the 3D surface, or c) discard data for points that represent the dirty region; Roschin, [0132] - the method or apparatus may verify that the outlying cluster is a tooth based on the size of the outlying cluster. This may be done quickly and computationally inexpensively, because a size check is typically fast and may filter out all small clusters This may filter out ill-defined clusters 1905, such as the example shown in FIG. 19; [0104] - the system may filter out poorly segmented digital representations. In some variations the system may indicate a pass/fail ranking; alternatively or additionally, the systems described herein may provide a score for the segmentation). it is well known in the art that the “noise removing” from data captured by a scanner in which an effective technique removes cluster noise based on a size threshold involves connecting neighboring noise points (points/pixels/voxels) and removing groups in comparison to a specific size, or threshold (e.g., number of points, pixels, or voxels) (see also Kriegel, page 234, column 1 - 3. Evaluation of the frequency ki of each data point xi. The frequency is the number of points located within a distance r of point xi, hence it is a measure of the density. 4. Removal of the nondense (“noise”) points (where ki < k)). Noted: By defining Kriegel’s distance r large so that the circle radius r will cover the “noise” cluster, then Kriegel’s noise removing technique teaches the claimed “determine at least one cluster having voxels the number of which is equal to or smaller than a predetermined number; and remove scan data values associated with the at least one cluster."
Thus, it would have been obvious, in view of Nikolskiy, Saphier, Roschin, and Kriegel, to configure Lee's device as claimed by removing the "small" (i.e., " having voxels the number of which is equal to or smaller than a predetermined number") area of the scanned 3D object. The motivation is to enhance the reliability of the scanned data by removing the noise, dirty area, poor segment, which have "small" sizes.
Claim 7 is similar to claim 1, and adds "wherein the one or more processors are configured to divide the three-dimensional image model into the multiple clusters in response to reception of, through the input device, a user input for terminating the scan of the three-dimensional scanner" (Saphier, [0243] - intraoral scan application 115 may automatically begin generating one or more 3D models of dental arches and/or performing post processing and/or diagnostics on generated 3D models of dental arches responsive to detecting removal of the intraoral scanner 150 from a patient mouth). Furthermore, a processor starts to perform a cluster algorithm on the scanned data during the scanning or after the scanning is just a tradeoff of processing capacity and processing time; therefore, it would have been obvious to perform “clustering” when all the samples have been collected (see Kriegel, Introduction - Clustering is the problem of finding a set of groups of similar objects within a data set while keeping dissimilar objects separated in different groups or the group of noise (noisy points)… Given the data as a set of objects from a given data space D ⊂ S and a dissimilarity function dis : S × S → R+0 , the task is to learn a meaningful grouping of the data… Figure 1 – the collected sample points for clustering). Thus, it would have been obvious, in view of Nikolskiy, Saphier, Roschin, and Kriegel, to configure Lee's device as claimed by performing a “clustering” when all the samples have been collected, or based on a user input for terminating the scan of the three-dimensional scanner. The motivation is to enhance the reliability of the scanned data by making sure all the scanned data of the model has been available.
Claim 8 adds into claim 1 "wherein the one or more processors are configured to determine, as one cluster, scan data values having consecutive three-dimensional coordinate values among the obtained scan data values" (Lee, page 6 - The learning unit 160 performs machine learning based on a two-dimensional or three-dimensional tooth image as a reference. At this time, the learning unit 160 divides the tooth and the gingiva from the reference tooth image and uses it as input data, and extracts a rule for distinguishing the tooth and the gingiva as a result of performing machine learning; Roschin, [0117] - In addition, or alternative to the assessment of segmentation quality described above, in some variations the assessment of quality may be based at least in part on the use of color information received by tooth scan. Although color may vary between patient dental scans, making direct comparison difficult, color information may be stable ( or may change in a continuous manner) within a single scanning session, and this color information may be used to aid in segmentation and/or to assess the quality of a segmentation; [0126] - In general, color quality is a metric that describes how well the regions ( e.g., vertices) of a scan can be divided into two groups (e.g., clusters), such as gingiva-like colored regions and teeth-like colored regions). Thus, it would have been obvious, in view of Nikolskiy, Saphier, Roschin, and Kriegel, to configure Lee's device as claimed by performing the segmentation of the scanned data by determining, as one cluster, scan data values having consecutive three-dimensional coordinate values among the obtained scan data values. The motivation is to define different regions or clusters based on the similarity of the neighboring voxels.
Claim 9 adds into claim 1 "wherein the one or more processors are configured to determine, as the multiple clusters, multiple closed curved surfaces included in the three- dimensional image model" (Nikolskiy, [0238] - The computer-implemented method can perform surface region determination. In some embodiments, the surface regions can be digital surface mesh triangles; [0241] - For example, the computer-implemented method can segment the digital surface mesh of the single-jaw impression using curvature based segmentation). Thus, it would have been obvious, in view of Nikolskiy, Saphier, and Roschin, to configure Lee's device as claimed by performing the segmentation of the scanned data by determine, as the multiple clusters, multiple closed curved surfaces included in the three- dimensional image model. The motivation is to define different regions or clusters based on the closed curved surfaces included in the three-dimensional image model.
Claim 10 adds into claim 1 "wherein the one or more processors are configured to: determine whether each of the multiple clusters corresponds to a teeth area or a gum area" (Lee, page 7 - the designated area selection tool is a first icon for designating the entire target area including the teeth and gingiva of the 3D virtual model as the model generation area, and designating the tooth area and some gingival areas around the tooth area as the model generation area); and "determine, among the multiple clusters, at least one cluster having a size equal to or smaller than a predetermined size and not corresponding to the teeth area or the gum area" which Lee suggests in the designated area selection tool for removing any selected area from the captured 3D image (e.g., pages 12-13, extract rules - When displaying the 3D virtual model on the display screen, the virtual model generating apparatus 100 selects and displays a deletion target area from among the 3D virtual model (S140)... For an embodiment of the deletion target area, refer to FIG. 3) (see also Nikolskiy, [0253] - some embodiments include receiving a digital model of a dental impression 3160, performing curvature based segmentation to identify one or more small features on one or more surface regions based on one or more from the group consisting of perimeter, volume, or surface area 3162, and removing the one or more small features from the one or more surface regions of the digital model 3164; Saphier, [0641] - Processing logic may determine one or more dirty regions in the intraoral scan(s) and/or 2D images, where a dirty region is a region of all adjacent dirty pixels that together form a contiguous dirty region. If a size of any contiguous dirty region exceeds the contiguous dirty region threshold, then this may indicate that the scanner is dirty; [0652] - Processing logic may then perform one or more of a) reject one or more of the intraoral scans that were taken while the optical surface was dirty, b) use the intraoral scans to determine a 3D surface of the dental site in the oral cavity, where those pixels associated with points that represent a dirty region of the optical surface are not used in the determination of the 3D surface, or c) discard data for points that represent the dirty region; Roschin, [0132] - the method or apparatus may verify that the outlying cluster is a tooth based on the size of the outlying cluster. This may be done quickly and computationally inexpensively, because a size check is typically fast and may filter out all small clusters This may filter out ill-defined clusters 1905, such as the example shown in FIG. 19; [0104] - the system may filter out poorly segmented digital representations. In some variations the system may indicate a pass/fail ranking; alternatively or additionally, the systems described herein may provide a score for the segmentation). Thus, it would have been obvious, in view of Nikolskiy, Saphier, Roschin, and Kriegel, to configure Lee's device as claimed by removing the "small" (i.e., "having a size equal to or smaller than a predetermined size") area of the scanned 3D object and not corresponding to the teeth area or the gum area. The motivation is to enhance the reliability of the scanned data by removing the noise, dirty area, poor segment, which have "small" sizes.
Claim 21 adds into claim 1 “wherein the one or more processors are configured to, after removing the scan data values, update the generated three-dimensional image model” (Lee, Figure 8c – the updated image without the “unwanted/removed” data). Noted: Lee’s display unit (e.g., figures 7-8) is capable to display the scanned data with, or without, all or some elements of the object.
Claim 22 adds into claim 21 “wherein the one or more processors are configured to display the updated three-dimensional image model through the display” (Lee, Figure 8c – the updated image without the “unwanted/removed” data).
Claim 23 adds into claim 21 “wherein the scan data values comprise multiple voxels, and wherein the one or more processors are configured to remove a three-dimensional image associated with at least one voxel included in the at least one cluster from the generated three- dimensional image model, so as to update the three-dimensional image model” (Saphier, Figure 10A, step 1012 - Generate map comprising, for each pixel/voxel in scan/3D surface/projection, indication(s) of dental class for pixel/voxel). Thus, it would have been obvious, in view of Nikolskiy, Saphier, Roschin, and Kriegel, to configure Lee’s device as claimed by removing the cluster with its size smaller than a threshold in which the removed cluster is associated with a 3D image. The motivation is to enhance the visual representation by removing the unwanted part of image from representation.
Claim 24 adds into claim 1 “wherein the one or more processors are configured to determine, as the at least one cluster, a cluster remaining after excluding a predetermined number of clusters from the multiple clusters in an order from a largest cluster size to a smallest” which would have been obvious in Lee’s classification of scanned data into different clusters and removing some of these clusters; furthermore, the ranking of cluster sizes (in an order from a largest cluster to a smallest) would have been obvious for the comparison of the cluster sizes to a threshold. The motivation is to simplify the removing of the clusters (when a ranking cluster having its size smaller than the threshold than each of the remaining clusters must have its size smaller than the threshold).
Claims 11-14, 16-20 claim a method based on the electronic device of claims 1, 7-10, 21-24; therefore, they are rejected under a similar rationale.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHU K NGUYEN whose telephone number is (571)272-7645. The examiner can normally be reached M-F 8-5pm.
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/PHU K NGUYEN/ Primary Examiner, Art Unit 2616