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 .
DETAILED ACTION
This is in response to applicant’s amendment/response filed on 09/11/2026, which has
been entered and made of record. Claim 1 are amended. Claims 9-10 are cancelled. Claims 1-8 are pending in the application.
Response to Arguments
Applicant arguments regarding claim rejections under 103 are considered, but are moot in view of new ground of rejections.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abu-Tarif et al. (US 2021/0186667 A1) in view of Weiss et al. (US 2021/0059796 A1) .
Regarding claim 1, Abu-Tarif teaches:
A 3D scanner system (FIG. 3) comprising:
- an intraoral scanner ([0033], “intraoral scanning system (1) may include a scanning wand”) comprising:
- an elongated housing comprising a distal end for being inserted into an oral cavity, (FIG. 3, the end near 13 can be inserted into oral cavity.) wherein the housing comprises an aperture in a sidewall of the distal end of the housing; (FIG. 3, observation window 23). and
- a window arranged in the aperture of the housing; (FIG. 3, observation window 23). and/or
- a sleeve mounted on the outside of the elongated housing, wherein the sleeve covers the aperture; ([0033], “An observation window (23) is defined in housing (19) at first end (13) and may include a transparent cover (not shown).”)
- a projector unit configured to project a predefined spatial pattern through the aperture and onto at least a part of a surface of a three-dimensional object, wherein the spatial pattern comprises a plurality of pattern features;(FIG. 4, [0037], “ For example, illumination module (25) or a processor associated therewith may select data associated with a particular coded light pattern and actuate light modulator (33) in accordance with selected data to project the selected coded light pattern. In some versions of light modulator (33), this actuation is manifested by angling or turning a series or grid of micro-mirrors within light modulator (33) whereby the desired coded light pattern is projected along a light pathway when illuminated by light source (31). Light modulator (33) may take the form of spatial or temporal light modulation. Light modulator (33) may comprise a digital light processing (DLP) element or a digital micromirror device (DMD).”[0064]) and
- one or more camera unit(s) configured to acquire one or more two- dimensional image(s) of the three-dimensional object, wherein at least a part of the spatial pattern comprising the plurality of pattern features is present in the two-dimensional imaqe(s); ([0038], “Once the known coded light pattern is projected and reflected back into acquisition system (5), digital models of the scanned object of interest (3) are built and provided to the user. Inasmuch as unencoded general beams of light result in poor reflection on objects such as teeth, coded light beams such as a black and white checkerboard grid pattern are used. The features of the projected coded light patterns are known prior to projection. Therefore, any bending or warping of the projected coded light patterns are associated with three-dimensional features of object of interest (3) and recreated spatially in the digital model of object of interest (3).”) and
- one or more processor operatively connected to the intraoral scanner, ([FIG. 4”) said processor configured to:
- receive the two-dimensional image comprising the plurality of pattern features , wherein the image comprise one or more artifacts arising from the part of the projected spatial pattern being reflected by the window and/or sleeve; ([0032], “As discussed above, intraoral scanning system (1) is generally configured to capture digital images of object of interest (3) whereby those images may be used to create a digital three-dimensional surface structure model of object of interest ” [0034], “acquisition system (5) may include an illumination module (25), an imaging module (27), and/or a sensor module (29) to facilitate projecting light patterns onto object of interest (3) and capturing the resulting reflected images for further downstream processing.”) and
However, Abu-Tarif does not, but Weiss teaches:
- provide the two-dimensional image(s) as input to a parameterized model trained to determine the position of at least a subset of the pattern features in the image(s), while suppressing or ignoring the artifacts.([0277], “In an embodiment, the machine learning model may be trained to output an identification of a margin line as well as separate information indicating one or more of the above (e.g., path of insertion, model orientation, teeth identification, gum identification, excess material identification, etc.). In one embodiment, the machine learning model (or a different machine learning model) is trained to perform one or more of: identify teeth represented in height maps, identify gums represented in height maps, identify excess material (e.g., material that is not gums or teeth) in height maps, and/or identify margin line in height maps. In some instances, the margin line identified by such a machine learning model that is trained to identify teeth, gums, excess material and margin line may have increased accuracy sine the machine learning model may learn what the tooth/gum boundaries and what artifacts to ignore.”)
Abu-Tarif teaches an intraoral scanner which can scan images and generate dental images. Weiss teaches using a trained model to generate dental images.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have combined the teachings of Abu-Tarif with the trained model method of Weiss to accurately generate dental images.
Regarding claim 2, Abu-Tarif in view of Weiss teaches:
The 3D scanner system according to claim 1, wherein one or more parameters of the parameterized model have been chosen or adjusted such that the artifacts are suppressed or ignored by the parameterized model. (Weiss [0277], “In an embodiment, the machine learning model may be trained to output an identification of a margin line as well as separate information indicating one or more of the above (e.g., path of insertion, model orientation, teeth identification, gum identification, excess material identification, etc.). In one embodiment, the machine learning model (or a different machine learning model) is trained to perform one or more of: identify teeth represented in height maps, identify gums represented in height maps, identify excess material (e.g., material that is not gums or teeth) in height maps, and/or identify margin line in height maps. In some instances, the margin line identified by such a machine learning model that is trained to identify teeth, gums, excess material and margin line may have increased accuracy sine the machine learning model may learn what the tooth/gum boundaries and what artifacts to ignore.” The combination of claim 1 is incorporated here.)
Regarding claim 3, Abu-Tarif in view of Weiss teaches:
The 3D scanner system according to claim 1, wherein the parameterized model is a machine learning model, such as a neural network. (Weiss [0277], “In an embodiment, the machine learning model may be trained to output an identification of a margin line as well as separate information indicating one or more of the above (e.g., path of insertion, model orientation, teeth identification, gum identification, excess material identification, etc.). In one embodiment, the machine learning model (or a different machine learning model) is trained to perform one or more of: identify teeth represented in height maps, identify gums represented in height maps, identify excess material (e.g., material that is not gums or teeth) in height maps, and/or identify margin line in height maps. In some instances, the margin line identified by such a machine learning model that is trained to identify teeth, gums, excess material and margin line may have increased accuracy sine the machine learning model may learn what the tooth/gum boundaries and what artifacts to ignore.” [0133], “. In one embodiment, the excess material identification algorithm is a trained machine learning model (e.g., a neural network such as a convolutional neural network) that has been trained to identify such excess material.” The combination of claim 1 is incorporated here.)
Regarding claim 4, Abu-Tarif in view of Weiss teaches:
The 3D scanner system according to claim 1, wherein the parameterized model has been trained on the basis of a training data set including two different sets of two-dimensional images, wherein a first set of the images comprises artifacts arising from reflections from the window and/or sleeve, and a second set of the images does not comprise said artifacts. (Weiss FIG. 13 shows an original and a corrected images are used to train the machine learning model. Weiss also teaches in [0277] that the machine learning model will generate output image where artifacts are ignored based on input image that has artifacts. It has been well-known in the art, in order to train a machine learning model, training data and correct ground truth data are used. It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to have combined the teachings of Abu-Tarif in view of Weiss with the well-known knowledge to use training data and correct ground truth data (in this case the data that have no artifacts) to train the model in order to generate an output that ignore the artifacts. The combination of claim 1 is incorporated here.)
Regarding claim 5, Abu-Tarif in view of Weiss teaches:
The 3D scanner system according to claim 4, wherein the training data set further includes ground truth data, said ground truth data including the position of the pattern features.(Weiss [0267], “Training of a neural network may be achieved in a supervised learning manner, which involves feeding a training dataset consisting of labeled inputs through the network, observing its outputs, defining an error (by measuring the difference between the outputs and the label values), and using techniques such as deep gradient descent and backpropagation to tune the weights of the network across all its layers and nodes such that the error is minimized. In many applications, repeating this process across the many labeled inputs in the training dataset yields a network that can produce correct output when presented with inputs that are different than the ones present in the training dataset.” The combination of claim 1 is incorporated here.)
Regarding claim 6, Abu-Tarif in view of Weiss teaches:
The 3D scanner system according to claim 4, wherein the training data set includes the ground truth data and two-dimensional training images comprising artifacts arising from reflections from the window and/or sleeve, wherein said training images have been acquired through the window and/or sleeve, wherein there is no object in focus except for the window and/or the sleeve.( Weiss [0277], “] In an embodiment, the machine learning model may be trained to output an identification of a margin line as well as separate information indicating one or more of the above (e.g., path of insertion, model orientation, teeth identification, gum identification, excess material identification, etc.). In one embodiment, the machine learning model (or a different machine learning model) is trained to perform one or more of: identify teeth represented in height maps, identify gums represented in height maps, identify excess material (e.g., material that is not gums or teeth) in height maps, and/or identify margin line in height maps. In some instances, the margin line identified by such a machine learning model that is trained to identify teeth, gums, excess material and margin line may have increased accuracy sine the machine learning model may learn what the tooth/gum boundaries and what artifacts to ignore.” Claim 1 teaches the images are getting from scanner through the window part. The combination of claim 1 is incorporated here.)
Regarding claim 7, Abu-Tarif in view of Weiss teaches:
The 3D scanner system according to claim 4, wherein the training data set further includes two-dimensional rendered images of digital 3D models of teeth, wherein the rendered images have been generated using a geometric virtual model of the intraoral scanner.( Weiss [0002], “ Once sufficient intraoral scans are generated, these are then used to generate a virtual three-dimensional (3D) model of a dental site including the preparation tooth and the surrounding teeth and gingiva. For example, a virtual 3D model of a patient's dental arch may be generated. The virtual 3D model may then be sent to a lab.” The combination of claim 1 is incorporated here.)
Regarding claim 8, Abu-Tarif in view of Weiss teaches:
The 3D scanner system according to claim 1, wherein the parameterized model or neural network is translation invariant such that the neural network produces the same response regardless of how its input is shifted.(Weiss [0147], “In one embodiment, a class of machine learning model called a MobileNet is used. A MobileNet is an efficient machine learning model based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. MobileNets may be convolutional neural networks (CNNs) that may perform convolutions in both the spatial and channel domains.” The combination of claim 1 is incorporated here.)
Conclusion
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 YANNA WU whose telephone number is (571)270-0725. The examiner can normally be reached Monday-Thursday 8:00-5:30 ET.
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/YANNA WU/Primary Examiner, Art Unit 2615
