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 .
Statue of claims: claims 1-20 and 22-23 are pending below. Claim 21 is cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 10th, 2026, has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 and 22-23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Updated search found that Elazar et al (2019/0015177) teach the new claim amendment sin figure 4B, thus the combination teaching of Atiya et al (US 2019/0388194) in view of ELBAZ et al (US 2019/0269485) and Elazar et al (2019/0015177) teaches the new claim amendments.
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, 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.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Atiya et al (US 2019/0388194) in view of ELBAZ et al (US 2019/0269485) and Elazar et al (2019/0015177).
Claim 1:
Atiya et al (US 2019/0388194) teaches the following subject matter:
An intraoral scanning system (0010 teaches intraoral scanning with one or more cameras), comprising:
an intraoral scanner comprising a plurality of cameras configured to generate a first set of intraoral images, each intraoral image from the first set of intraoral images during intraoral scanning being associated with a respective camera of the plurality of cameras (0010-0013 teaches scanner with one or more cameras for plurality of images); and
a computing device configured to (figure 1 part 96 processor):
receive the first set of intraoral images (0013-0015, teaches each camera capture plurality of images);
select a camera of the plurality of cameras that is associated with an intraoral image of the set of intraoral images that satisfies one or more criteria (0013-0015 teaches plurality of camera, where each camera capture plurality of images (first set of images)); and
output the intraoral image associated with the camera to a display (figure 28b teaches display of image).
Atiya et al teach all the following subject matter above but not the following:
as a viewfinder image of the intraoral scanner showing a current field of view of the selected camera during the intraoral scanning.
ELBAZ et al (US 2019/0269485) teach the following subject matter:
as a viewfinder image of the intraoral scanner showing a current field of view of the selected camera during the intraoral scanning (paragraphs 0154, 160-162 detail display of selected dental picture based on confidence score and above threshold (meeting criteria); figure 10A-B and 0252 detail application to intraoral scanning wand with light source and cameras adjacent to each other).
Atiya et al and ELBAZ et al are both in the field of image analysis, especially intraoral scanner/imaging using a portable/wand with plurality of camera such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Atiya et al by ELBAZ et al such displaying on or more actionable dental feature such as cracks, gum recess, tartar, enamel thickness, pits, caries, pits, fissures, evidence of grinding, and interproximal voids disclosed by ELBAZ et al in paragraph 0160, would provide the dentist better diagnosis of the dental health.
Atiya et al and ELBAZ et al do not teach the following subject matter:
wherein each intraoral image in the first set of intraoral images is generated by a different camera of the plurality of cameras at a same time; select, based on the first set of intraoral images that were generated at the same time; without outputting other intraoral images of the first set of intraoral images that were generated at the same time.
Elazar et al (2019/0015177) teaches the following subject matter: wherein each intraoral image in the first set of intraoral images is generated by a different camera of the plurality of cameras at a same time; select, based on the first set of intraoral images that were generated at the same time; without outputting other intraoral images of the first set of intraoral images that were generated at the same time (figure 4B and 0079 detail different image sensors (different cameras) capture images 452 and 454 that are all captured simultaneously in an intraoral environment).
Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) are in the field of image analysis, especially intraoral scanner/imaging using a portable/wand with plurality of camera such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Atiya et al and ELBAZ et al by Elazar et al (2019/0015177) such imaging to the illumination conditions for a better image capture quality. In some embodiments, image control module 551 processes a set of time-successive images to create a single output image which has an improved visual quality, for example, but not limited to by selecting one image out of the set, or by combining portions of images, each portion from an image in the set. In some embodiments, values indicating the acceleration of image sensor 503 when an image was captured are used to improved the quality of an output image as disclosed by Elazar et al (2019/0015177) in paragraph 0097
Claim 2:
Atiya et al teach:
The intraoral scanning system of claim 1, wherein the plurality of cameras comprises an array of cameras, each camera in the array of cameras having a different position and orientation in the intraoral scanner with respect to other cameras in the array of cameras (figure 2A and 0288 teach each camera with angle θ (theta) between two respective optical axes 46 of at least two cameras 24 is 90 degrees or less, e.g., 35 degrees or less).
Claim 3:
Atiya et al teach:
The intraoral scanning system of claim 1, wherein the computing device is further to: receive a second set of intraoral images generated by the intraoral scanner at a second time; select a different camera of the plurality of cameras that is associated with(Atiya et al teach 0013-0015 teaches plurality of camera, where each camera capture plurality of images (first set of images) and next camera capture second set…..x camera capture x set of images, where figure 28b teaches display of image) as the viewfinder image of the intraoral scanner showing an updated current field of view of the different camera during the intraoral scanning (ELBAZ et al: figure 43A and 0438-0441 detail updating the image corresponding in real-time).
Claim 4:
Atiya et al teach:
The intraoral scanning system of claim 1, wherein the first set of intraoral images comprises at least one of near infrared (NIR) images or color images (paragraph 0319 teaches two-dimensional color images of object 32; 0368 teaches further color of the intraoral scanner 1020; 0374 teaches using of infrared).
Claim 5:
Atiya et al teach:
The intraoral scanning system of claim 1, wherein the computing device is further configured to: determine, for each intraoral image of the first set of intraoral images, a tooth area depicted in the intraoral image; and select the camera responsive to determining that the intraoral image associated with the first camera has a largest tooth area as compared to a remainder of the first set of intraoral images (0018 detail various camera with field of view output images such as tooth features such as curve, where use of 3-D feature (regardless of size) to improve accuracy for stitching of the overlap between scans; 0191-0195 teaches light field camera and tooth imaging).
Claims 6-20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Atiya et al (US 2019/0388194) in view of ELBAZ et al (US 2019/0269485) and Elazar et al (2019/0015177) as applied to claim 5 above, and further in view of Minchenkov et al (US 2020/0349698).
Claim 6:
Atiya et al (US 2019/0388194) and ELBAZ et al and Elazar et al (2019/0015177) teaches the following subject matter above:
The intraoral scanning system of claim 5, wherein the computing device is further configured to perform the following for each intraoral image of the first set of intraoral images.
Atiya et al (US 2019/0388194) and ELBAZ et al and Elazar et al (2019/0015177) do not teach the following:
input the intraoral image into a trained machine learning model that performs classification of the intraoral image to identify teeth in the intraoral image, wherein the tooth area for the intraoral image is based on a result of the classification.
Minchenkov et al (US 2020/0349698) teaches the following:
input the intraoral image into a trained machine learning model that performs classification of the intraoral image to identify teeth in the intraoral image, wherein the tooth area for the intraoral image is based on a result of the classification (figure 2 block 238 and paragraph 0080 teaches use of trained machine learning for classifying to predict dental (tooth) class with height maps, probability map; 0077 detail the use of recurrent neural network as neural network).
Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) and Minchenkov et al are in the field of image analysis, especially scanning of intraoral with cameras for image data sets such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) by Minchenkov et al regarding use of neural network improve the accuracy of 3D models of dental arches or other dental sites produced from an intraoral scan as disclosed by Minchenkov et al in 0067.
Claim 7:
Minchenkov et al teach:
The intraoral scanning system of claim 6, wherein the classification comprises pixel-level classification or patch-level classification, and wherein the tooth area for the intraoral image is determined based on a number of pixels classified as teeth (0005-0006 which pixel belongs to dental classes).
Claim 8:
Minchenkov et al teach:
The intraoral scanning system of claim 6, wherein the computing device is configured further to: input the first set of intraoral images into a trained machine learning model, wherein the trained machine learning model outputs an indication to select the camera associated with the intraoral image (0053, 0055, figure 8 and 0083 teaches inputting images to neural network/recurrent for classification).
Claim 9:
Minchenkov et al teach:
The intraoral scanning system of claim 6, wherein the trained machine learning model comprises a recurrent neural network (figures A-B and 0077 detail the use of recurrent neural network as neural network).
Claim 10:
Atiya et al (US 2019/0388194) and ELBAZ et al and Elazar et al (2019/0015177) do not teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is configured further to: determine that the first intraoral image associated with the first camera satisfies the one or more criteria; output a recommendation for selection of the first camera; and receive user input to select the first camera
Minchenkov et al teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is further to: determine that the first intraoral image associated with the first camera satisfies the one or more criteria; output a recommendation for selection of the first camera; and receive user input to select the first camera (above teaches camera image to display and criteria, where 0047-0049 detail user interface to controls and input to enable viewing of model from any desire direction, and automatic segmentation image generated (recommendation) for operation of workflow).
Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) and Minchenkov et al are in the field of image analysis, especially scanning of intraoral with cameras for image data sets such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) by Minchenkov et al regarding such user input would assist in provide acceptable and accurate representation of 3D model as disclosed by Minchenkov et al in paragraph 0048.
Claim 11:
Atiya et al (US 2019/0388194) and ELBAZ et al and Elazar et al (2019/0015177) do not teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is configured further to: determine that the first intraoral image associated with the first camera satisfies the one or more criteria, wherein the first camera is automatically selected without user input
Minchenkov et al teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is further to: determine that the first intraoral image associated with the first camera satisfies the one or more criteria, wherein the first camera is automatically selected without user input (0047-0049 detail user interface to controls and input to enable viewing of model from any desire direction).
Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) and Minchenkov et al are in the field of image analysis, especially scanning of intraoral with cameras for image data sets such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Atiya et al and ELBAZ et al by Minchenkov et al regarding such user input would assist in provide acceptable and accurate representation of 3D model as disclosed by Minchenkov et al in paragraph 0048.
Claim 12:
Atiya et al (US 2019/0388194) and ELBAZ et al and Elazar et al (2019/0015177) do not teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is configured further to: determine, for each intraoral image of the first set of intraoral images, a score based at least in part on a number of pixels in the intraoral image classified as teeth, wherein the one or more criteria comprise one or more scoring criteria
Minchenkov et al teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is further to: determine, for each intraoral image of the first set of intraoral images, a score based at least in part on a number of pixels in the intraoral image classified as teeth, wherein the one or more criteria comprise one or more scoring criteria (0069-0070, 0078-079, 0085-0086, 0107 detail pixel related to value (score) for probability for dental classification relating pixel to height).
Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) and Minchenkov et al are in the field of image analysis, especially scanning of intraoral with cameras for image data sets such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) by Minchenkov et al regarding the use of pixel and size to probability map or mask to provide accurate than traditional image and signal processing as disclosed by Minchenkov et al in 0069-0070.
Claim 13:
Minchenkov et al teach:
The intraoral scanning system of claim 12, wherein the computing device is configured further to: adjust scores for one or mor intraoral images of the first set of intraoral images based on scores of one or more other intraoral images of the first set of intraoral images (0112 detail further adjustment of probability threshold regarding value for pixel with classification; figure 5 and 0125 and 0132 teaches other adjustment of values such as height values for pixel to indicate surface).
Claim 14:
Minchenkov et al teach:
The intraoral scanning system of claim 13, wherein the scores for the one or more (0062 teaches building light weight for deep neural network; 0065 detail tune weights of backpropagation across all the layers and nodes for error minimizing; figure 3 block 312 and 0086 detail values apply to weight to generate output values).
Claim 15:
Minchenkov et al teach:
The intraoral scanning system of claim 14, wherein the computing device is configured further to: determine an area of an oral cavity being scanned based on processing of the first set of intraoral images; and select the weighting matrix based on the area of the oral cavity being scanned (figure 3 block 312 and 0086 teaches weight of nodes (weight matrix) across layers for different class such as excess material, teeth, gum…etc).
Claim 16:
Minchenkov et al teach:
The intraoral scanning system of claim 15, wherein the computing device is configured further to: input the first set of intraoral images into a trained machine learning model, wherein the trained machine learning model outputs an indication of the area of the oral cavity being scanned (figure 3 block 312 and 0086 teaches weight of nodes (weight matrix) across layers for different class such as excess material, teeth, gum…etc).
Claim 17:
Minchenkov et al teach:
The intraoral scanning system of claim 15, wherein the area of the oral cavity being scanned comprises one of an upper dental arch, a lower dental arch, or a bite (figure 3A and 0083 detail classification region for dental sites such as dental arch).
Claim 18:
Minchenkov et al teach:
The intraoral scanning system of claim 15, wherein the computing device is configured further to: determine, for each intraoral image of the first set of intraoral images, a restorative object area depicted in the intraoral image; and select the camera responsive to determining that the intraoral image associated with the camera has a largest restorative object area as compared to a remainder of the first set of intraoral images (figure 3C and 0098-0099 teaches processing with correcting (restorative) of the 3D model generated of soft tissue as well as removal of artifacts).
Claim 19:
Minchenkov et al teach:
The intraoral scanning system of claim 15, wherein the computing device is configured further to: determine, for each intraoral image of the first set of intraoral images, a margin line area depicted in the intraoral image; and select the camera responsive to determining that the intraoral image associated with the camera has a largest margin line area as compared to a remainder of the first set of intraoral images (0048 detail margin line accurately represent model; 0091-0092 teaches threshold (margin lines area) for accuracy for machine learning improvement from the processing of dataset (set of images); paragraph 0112 also teaches threshold for probability map to pixel classification; figure 5A and 0114 teaches use of threshold for classifying regions; paragraph 0123-0124 detail threshold for pixel classification to identify points (image data)).
Claim 20:
Atiya et al (US 2019/0388194) and ELBAZ et al and Elazar et al (2019/0015177) do not teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is further to: select
Minchenkov et al teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is further to: select an additional camera of the plurality of cameras that is associated with an additional intraoral image of the first set of intraoral images that satisfies the one or more criteria; generate a combined image based on the (0013-0015 teaches plurality of camera, where each camera (first, second, third….etc) capture plurality of images; 0018 detail various camera with field of view output images such as tooth features such as curve, where use of 3-D feature (regardless of size) to improve accuracy for stitching of the overlap between scans; paragraph 0071-0072 and 0102 teaches multiple individual intraoral images generated sequentially during the intraoral scan are combined to form a blended image outputting a color image as well; 28b teaches display of image).
Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) and Minchenkov et al are both in the field of image analysis, especially scanning of intraoral with cameras for image data sets such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) by Minchenkov et al where particular blended scan allow distinguishing of different dental classes for good accuracy as disclosed by Minchenkov et al in 0072.
Claim 22:
Atiya et al (US 2019/0388194) and ELBAZ et al and Elazar et al (2019/0015177) do not teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is configured further to: determine a score for each image of the first set of intraoral images; determine that the
Minchenkov et al teach the following subject matter:
The intraoral scanning system of claim 1, wherein the computing device is further to: determine a score for each image of the first set of intraoral images; determine that the for the second intraoral image; and select the (above teaches images, determined value (score); figure 5A and 0114 teaches improving quality with threshold difference between blended scan (first and second set of intraoral images) values).
Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) and Minchenkov et al are both in the field of image analysis, especially scanning of intraoral with cameras for image data sets such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify Atiya et al and ELBAZ et al and Elazar et al (2019/0015177) by Minchenkov et al where threshold difference calculated between enable less computational resource of the machine learning model in real time as disclosed by Minchenkov et al in 0114.
Allowable Subject Matter
Claim 23 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. At the time of the examination unable to find claim/teaching/concept regarding,”…for each intraoral image of the first set of intraoral images, at least one of a tooth area, a restorative object area, or a margin line area depicted in the intraoral image, wherein the one or more criteria comprise the intraoral image associated with the selected camera having at least one of a largest tooth area, a largest restorative object area, or a largest margin line area.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Meyer et la (US 2021/0090272) teaches METHOD, SYSTEM AND COMPUTER READABLE STORAGE MEDIA FOR REGISTERING INTRAORAL MEASUREMENTS teaches use a dental camera to scan teeth and a trained deep neural network may automatically detect portions of the input images that can cause registration errors and reduce or eliminate the effect of these sources of registration errors (abstract).
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656
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