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 .
Notice to Applicants
This communication is in response to the Application filed on 09/27/2024.
Claims 1-20 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an input device that receives a user input” in claim 14, “a control device that controls the display based on the user input … the control device is configured to cause the display to show an oral cavity image three-dimensionally showing inside of an oral cavity … set, in the oral cavity image, a first spot … , a second spot … , and a third spot … calculate a ratio between a first distance … and a second distance …” in claim 14, “the control device is further configured to move the first spot, the second spot, and the third spot … and calculate the ratio between the first distance and the second distance …” in claim 18, and “the control device is further configured to set the tooth axis designated by a user … , or set the tooth axis with an estimation model …” in claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1, 5 and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Kearney et al. (U.S. Publication No. 2021/0353393) (hereafter, "Kearney") in view of FARKASH et al. (U.S. Publication No. 2022/0189611) (hereafter, "FARKASH").
Regarding claim 1, Kearney teaches a data generation method of generating image data ([0139] The method 100 may include receiving 102 an image; [0142] Step 108 may further include classifying the image, such as classifying which portion of the patient's teeth and jaw is in the field of view of the image; [0144] The method 100 may further include processing 110 the image to identify patient anatomy; [0169] the system 400 may be used to train a machine learning model to estimate the view of an image. Accordingly, the output of the machine learning model for a given input image will be a view label indicating an anatomic region sequence, anatomic region sequence modifier, and laterality visualized by the image; [0175] the images 404 are 3D images, such as a CT scan) for checking a state of a biological tissue including a tooth and a gingiva ([0175] The output of the machine learning model 410 in such embodiments may be a mapping of the CT scan to one of a number of regions within the oral cavity, such as the upper right quadrant, upper left quadrant, lower left quadrant, and lower right quadrant; [0348] Each tooth 2000 may have a CEJ 2002 that can be measured at various points around the tooth 2000. A GM, e.g., gum line, 2004 may also be represented along with the bone level 2006. Parts of the teeth 2000 such as pulp 2008 and dentin 2010 may also be identified. Carious lesions (e.g., caries or cavities) 2012 may also be represented), the data generation method comprising ([0142] Step 108 may further include classifying the image; [0144] processing 110 the image to identify patient anatomy; [0145] detecting 112 features present in the anatomy identified at step 110; [0167] The system 400 may be used to train a machine learning model to classify the view an image represents for use in pre-processing an image at step 108 of the method 100; [0175] the images 404 are 3D images, such as a CT scan), as processing to be performed by a processing circuitry ([0804] Computing device 6500 includes one or more processor(s) 6502; [0809] A graphics-processing unit (GPU) 6532 may be coupled to the processor(s) 6502 and/or to the display device 6530, such as by the bus 651): displaying, on a display, an oral cavity image ([0494] FIG. 36 illustrates an interface 3600 that may be used to receive inputs from a user ... A computer system may display the interface 3600. [0495] The image 3602 may have corresponding masks 3414 as described above that indicate pixels of the image 3602 corresponding to particular features. The masks 3414 may or may not be displayed or may be selectively displayed in response to an input from a user; [0497] The interface element 3608 may list some or all masks 3414 for some or all of the dental features (e.g., anatomy and treatments as defined above) for which masks 3414 are defined … one shape 3606 corresponds to a caries and the may select the caries mask 3414 using interface element 3608 for that shape 3606; [0498] The user may then instruct the computer system to synthesize an image, such as by selecting user interface element 3610 ... The output of the generator 3402 will be a synthetic image 3416 generated using the one or more modified masks. As a result, representations of features added to the one or more modified masks will be present in the synthetic image 3416) … setting, in the oral cavity image, a first spot indicating a top of a crown portion, a second spot indicating a top of an alveolar bone or a junction between the tooth and the gingiva, and a third spot indicating a root apex portion, based on a user input or machine learning; and ([0144]; [0145] detecting 112 features present in the anatomy identified at step 110 ... identifying caries, measuring clinical attachment level (CAL), measuring pocket depth (PD) … The identifying step may include generating a pixel mask defining pixels in the image corresponding to the detected feature; [0224] the system 800 may be used to label anatomical features such as the cementum enamel junction (CEJ), bony points on the maxilla or mandible that are relevant to the diagnosis of periodontal disease, gingival margin, junctional epithelium, or other anatomical feature; [0225] A training algorithm 802 takes as inputs training data entries that each include an image 804a and labels 804b for teeth represented in that image, e.g., pixel masks indicating portions of the image 804a corresponding to teeth; [0227] The training algorithm 802 may operate with respect to one or more loss functions 808 and modify a machine learning model 810 in order to train the machine learning model 810 to label the anatomical feature of interest in a given input image; [0228] the machine learning model 810 includes a GAN including a generator 812 and a discriminator 814 ... The output of the generator 812 may also be input to a classifier 818 trained to produce an output 820 embodied as a label of the anatomical feature of interest, e.g. pixel mask labeling a portion of an input image estimated to correspond to the anatomical feature of interest; [0245] A training algorithm 802 takes as inputs training data entries that each include an image 904a and labels 904b, e.g., pixel masks indicating portions of the image 904a corresponding to teeth, CEJ, JE, B, or other anatomical features. The labels 904b for an image 904a may be generated by a licensed dentist or automatically generated using … the labeling system 800 of FIG. 8; [0538] FIG. 39B illustrates orthodontic points 3932a-3932h that may be identified for each tooth number ... Using the orthodontic points 3932a-3932h, distances between them may be estimated ... These examples are non-limiting, distances between any pair of orthodontic points may be calculated; 3932a and 3932b in FIG. 39B show the spot indicating a top of a crown portion. 3932g and 3932h in FIG. 39B show the spot indicating a root apex portion) calculating a ratio between ([0350] one or more machine learning models may be trained to measure that item of dental anatomy. Measurements of an item of dental anatomy may include its center of mass, relative distance to other anatomy, size distortion, and density; [0353] Machine learning models may be trained to identify and measure dental anatomy that may be used to determine the appropriateness of root canal therapy at a given tooth position such as crown-to-root-ratio; [0392] The machine learning model 2802 may be multiple models, each being trained to output a particular measurement or group of measurements. The measurements of an item of anatomy may include its center of mass, relative distance to other anatomy; [0765] FIG. 60a ... patient data, including one or more input images 6004, may be processed by a machine learning model 6006 to obtain a dental measurement 6008, such as a measurement of dental anatomy (CAL, PD, crown-to-root ratio, crown height, root length, orthodontic landmark spacing, occlusion, tooth size, tooth width, etc.)) a first distance between the first spot and the second spot and a second distance between the second spot and the third spot ([0245] A training algorithm 802 takes as inputs training data entries that each include an image 904a and labels 904b, e.g., pixel masks indicating portions of the image 904a corresponding to teeth, CEJ, JE, B, or other anatomical features. The labels 904b for an image 904a may be generated by a licensed dentist or automatically generated using … the labeling system 800; [0538] FIG. 39B illustrates orthodontic points 3932a-3932h … Using the orthodontic points 3932a-3932h, distances between them may be estimated … distance 3936b between a point 3932d and a point 3932f (a point on the CEJ) of the same tooth may be calculated ... a distance 3936c between a root tip 3932h and a point 3932f on the CEJ may be calculated. These examples are non-limiting, distances between any pair of orthodontic points may be calculated; 3932a and 3932b in FIG. 39B show the spot indicating a top of a crown portion. 3932g and 3932h in FIG. 39B show the spot indicating a root apex portion).
Kearney does not expressly teach three-dimensionally showing an oral cavity including the biological tissue.
However, FARKASH teaches three-dimensionally showing an oral cavity including the biological tissue ([0062] The scanning system 254 may include a computer system configured to scan a patient's oral cavity, including the periodontium and/or the teeth ... The scanning system 254 may be configured to produce 3D and/or 2D scans of the patient's dental arch. The scanning system 254 may be configured to receive 2D or 3D scan data taken previously or by another system. The display system 256 may include a computer system configured to display at least a portion of the periodontium and/or teeth; [0065] the displayed images (and/or 3D model) includes color-coded features based on the identified features indicative of a disease or condition. For example, gums effected by gingivitis, cancerous lesions, precancerous lesions, tooth cavities, tooth cracks and/or plaque may each be identified with distinctive colors; FIG. 11A-C).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the device and method of Kearney to incorporate the step/system of generating and displaying a 3D scan of dental arch by scanning the oral cavity including the periodontium and the teeth taught by FARKASH.
The suggestion/motivation for doing so would have been to improve the accuracy for evaluating an oral health ([0007] The apparatuses (e.g., devices, systems, etc.) and methods described herein solve the above-described problems by providing improved techniques for evaluating a subject's oral health and visualizing and screening individuals for early detection of oral conditions, resulting in improved outcomes; [0077] the algorithm may use the reference to the known 3D surface scan to improve the accuracy of the internal feature data, and/or color data). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predicted results. Therefore, it would have been obvious to combine Kearney and FARKASH to obtain the invention as specified in claim 1.
Regarding claim 5, the combination of Kearney and FARKASH teaches all the limitations of claim 1 above. Kearney teaches wherein the setting further includes setting the first spot, the second spot, and the third spot designated by a user based on the user input, or setting the first spot, the second spot, and the third spot with an estimation model for estimation of the first spot, the second spot, and the third spot based on the oral cavity image ([0533] The output of the generator 3902 may be a set of predicted orthodontic points 3922. To facilitate training, the orthodontic points 3922 as output from the generator 3902 may be dilated, e.g., each orthodontic point may be represented as a 2D (circle or rectangle) or 3D region (sphere or cuboid) in which the generator 3902 estimates the actual orthodontic point of the patient to lie; [0538] the predicted orthodontic points 3922 and target orthodontic points 3924 may include values for each point 3932a-3932h on each tooth number of the patient ; [0224] the system 800 may be used to label anatomical features such as the cementum enamel junction (CEJ), bony points on the maxilla or mandible that are relevant to the diagnosis of periodontal disease, gingival margin, junctional epithelium; 3932a and 3932b in FIG. 39B show the spot indicating a top of a crown portion. 3932g and 3932h in FIG. 39B show the spot indicating a root apex portion).
Regarding claim 11, the combination of Kearney and FARKASH teaches all the limitations of claim 1 above. Kearney teaches wherein the oral cavity image is generated based on optical data obtained by an optical scanner ([0187] the machine learning model 610 may transform between any two of the following three-dimensional imaging modalities, such as a CT scan, magnetic resonance imaging (MM) image, a three-dimensional optical image, LIDAR (light detection and ranging) point cloud, or other three-dimensional imaging modality; [0189] Transformation using the machine learning model 610 may be performed to obtain a transformed image), the optical scanner data including position information of each point in a point group indicating a surface of the biological tissue ([0187] a three-dimensional optical image, LIDAR (light detection and ranging) point cloud, or other three-dimensional imaging modality; [0188] Deciphering dental pathologies on an image may be facilitated by establishing absolute measurements between anatomical landmarks (e.g., in a standard unit of measurement), and CT data obtained by computed tomography (CT) scan of the biological tissue ([0187] the machine learning model 610 may transform between any two of the following three-dimensional imaging modalities, such as a CT scan, magnetic resonance imaging (MM) image, a three-dimensional optical image, LIDAR (light detection and ranging) point cloud, or other three-dimensional imaging modality; [0175] the images 404 are 3D images, such as a CT scan. Accordingly, the 3×3 convolutional kernels of the multi-scale stages 412 may be replaced with 3×3×3 convolutional kernels. The output of the machine learning model 410 in such embodiments may be a mapping of the CT scan to one of a number of regions within the oral cavity, such as the upper right quadrant, upper left quadrant, lower left quadrant, and lower right quadrant).
Regarding claim 12, the combination of Kearney and FARKASH teaches all the limitations of claim 1 above. Kearney teaches further comprising performing segmentation for each anatomical element shown in the oral cavity image ([0440] The data input to the LSTM networks 3110 may be further augmented with other items of information such as semantically segmented anatomical labels of anatomy represented in an input image 3102. These labels may be manually generated or generated according to a machine learning model, such as any of the machine learning models described herein for labeling dental and periodontal anatomy and pathologies. Data augmentation may be conducted by automatically generated distances from and relationships to semantically segmented anatomy. In particular, any of the measurements of anatomy and pathologies (caries, pockets, and the like) described herein may be used as augmented information input to the LSTM model 3106; [0618] The reference image 4600 and the input image 4604 may be processed by a segmentation network 4606. The output of the segmentation network 4606 may be labels 4608 of reference points labeling points on the input image 4604 corresponding to the reference point labels 4602 in the reference image; [0438] removing features from the image 3102 to obtain the modified image, such as representations of one or more teeth, caries, endodontic lesions).
With respect to claim 13, arguments analogous to those presented for claim 1, are applicable.
With respect to claim 14, arguments analogous to those presented for claim 1, are applicable.
Claim 2-4, 6, 8, 10 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kearney et al. (U.S. Publication No. 2021/0353393) (hereafter, "Kearney") in view of FARKASH et al. (U.S. Publication No. 2022/0189611) (hereafter, "FARKASH") in further in view of Tsuji et al. (U.S. Publication No. 2018/0303441) (hereafter, "Tsuji").
Regarding claim 2, the combination of Kearney and FARKASH teaches all the limitations of claim 1 above. Kearney teaches wherein the calculating the ratio further includes ([0350] Measurements of an item of dental anatomy may include its center of mass, relative distance to other anatomy; [0353] Machine learning models may be trained to identify and measure dental anatomy that may be used to determine the appropriateness of root canal therapy at a given tooth position such as crown-to-root-ratio; [0392] The measurements of an item of anatomy may include its center of mass, relative distance to other anatomy; [0765] FIG. 60a ... patient data, including one or more input images 6004, may be processed by a machine learning model 6006 to obtain a dental measurement 6008, such as a measurement of dental anatomy (CAL, PD, crown-to-root ratio, crown height, root length, orthodontic landmark spacing … etc.)).
Kearney does not expressly teach moving the first spot, the second spot, and the third spot in a measurement direction based on the user input and calculating the ratio between the first distance and the second distance depending on changes in positions of the first spot, the second spot, and the third spot.
However, Tsuji teaches moving the first spot, the second spot, and the third spot in a measurement direction based on the user input and ([0091] Since the A tomographic image, the C tomographic image and the S tomographic image are linked to one another, the user is allowed to specify the center position of the tooth crown top in the S tomographic image on the screen where the tooth crown can be observed in the A tomographic image; [0119] If the displayed image is appropriately set, the user is allowed to very easily specify the tooth crown top, the tooth cervix, the root apex and the alveolar bone crest on the image, and thus allowed to select as appropriate a plurality of places therearound to manually perform selection; [0096] <Correction of Main Axis> (S15); [0097] the image is rotated by main-axis correction means such that a main axis, which connects the center of the tooth crown top and the center of the tooth part in the region of interest, is vertical on the display) calculating the ratio ([0108] <Calculation of Tooth Root Adhesion Length Ratio> (S20); [0109] in the tooth root adhesion length ratio calculating means 22, “(distance between alveolar bone crest and root apex):(distance between cement-enamel junction and root apex)” is calculated. Specifically, each of the distances is calculated from the positions of the cement-enamel junction, the alveolar bone crest and the root apex, and a ratio of the distances is obtained) between the first distance and the second distance depending on changes in positions of the first spot, the second spot, and the third spot ([0109] in the tooth root adhesion length ratio calculating means 22, “(distance between alveolar bone crest and root apex):(distance between cement-enamel junction and root apex)” is calculated. Specifically, each of the distances is calculated from the positions of the cement-enamel junction, the alveolar bone crest and the root apex, and a ratio of the distances is obtained; [0100] <Specification of Cement-Enamel Junction> (S17); [0101] the user is allowed to specify that portion from the operation part; [0102] <Specification of Alveolar Bone Crest> (S18); [0103] The operator is allowed to specify this portion from the operation part; [0106] <Specification of Root Apex> (S19); [0107] The operator is allowed to specify this portion from the operation part).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the device and method of Kearney to incorporate the step/system of specifying any anatomical locations including tooth crown top, alveolar bone crest, root apex by manually performing selection on images by a user and calculating the tooth root ratio by using two distinct distances related to the specified anatomical locations taught by Tsuji.
The suggestion/motivation for doing so would have been to improve the diagnostic reliability by enhancing measurement accuracy ([0053] In the periodontal disease diagnosis supporting device according to the present invention, diagnosis of periodontal disease is supported by use of a distance between a cement-enamel junction and an alveolar bone crest, which can be objectively measured with high accuracy and reproducibility, thus allowing provision of highly reliable support information). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predicted results. Therefore, it would have been obvious to combine Kearney and FARKASH with Tsuji to obtain the invention as specified in claim 2.
Regarding claim 3, the combination of Kearney and FARKASH with Tsuji teaches all the limitations of claim 2 above. Tsuji teaches wherein the measurement direction is a direction along a tooth axis ([0096] <Correction of Main Axis> (S15); [0097] by an instruction from the operation part 11, the image is rotated by main-axis correction means such that a main axis, which connects the center of the tooth crown top and the center of the tooth part in the region of interest, is vertical on the display).
Regarding claim 4, the combination of Kearney and FARKASH with Tsuji teaches all the limitations of claim 3 above. Tsuji teaches further comprising setting the tooth axis designated by a user based on the user input, or setting the tooth axis with an estimation model for estimation of the tooth axis based on the oral cavity image ([0096] <Correction of Main Axis> (S15); [0097] Next, by an instruction from the operation part 11, the image is rotated by main-axis correction means such that a main axis, which connects the center of the tooth crown top and the center of the tooth part in the region of interest, is vertical on the display. FIG. 12 shows a conceptual diagram of this operation; [0025] the tooth root adhesion degree measuring part may include main-axis correction means for correcting an image by a main axis defined by a unit vector of a line segment from center coordinates of a tooth crown top to center coordinates of an entire tooth; [0021] the device including a tooth root adhesion degree measuring part for measuring a degree of adhesion between a tooth root and alveolar bone by use of the image; This automated geometric algorithm serves as the algorithmic "estimation model" defined in the claim to locate the This automated geometric algorithm serves as the algorithmic "estimation model" defined in the claim to locate the tooth axis).
Regarding claim 6, the combination of Kearney and FARKASH teaches all the limitations of claim 5 above. Tsuji teaches wherein the estimation model has a measurement target tooth surrounded by a bounding box, and ([0095] When the center coordinates of the entire tooth are specified, a cubic region centered on the center coordinates of the entire tooth is displayed ... This region indicates a region of interest of the measurement target tooth; [0097] the image is rotated by main-axis correction means such that a main axis, which connects the center of the tooth crown top and the center of the tooth part in the region of interest, is vertical on the display; FIG. 12 shows a target tooth surrounded by a 3D bounding box) estimates the tooth axis ([0097] the image is rotated by main-axis correction means such that a main axis, which connects the center of the tooth crown top and the center of the tooth part in the region of interest, is vertical on the display; [0099] a measurement cross section including the main axis is specified. While the measurement cross section can be set in any position within 360 degrees, ... the measurement cross section is set at six points), the first spot ([0025] the tooth root adhesion degree measuring part may include main-axis correction means for correcting an image by a main axis defined by a unit vector of a line segment from center coordinates of a tooth crown top to center coordinates of an entire tooth; [0038] the tooth root adhesion degree measuring part may include; [0039] tooth crown top specifying means for specifying a tooth crown top), and the third spot ([0031] the tooth root adhesion degree measuring part may include; [0034] root apex specifying means for specifying a root apex) with a shape of the bounding box being defined as a reference ([0025] the tooth root adhesion degree measuring part may include main-axis correction means for correcting an image by a main axis defined by a unit vector of a line segment from center coordinates of a tooth crown top to center coordinates of an entire tooth; [0095] When the center coordinates of the entire tooth are specified, a cubic region centered on the center coordinates of the entire tooth is displayed ... This region indicates a region of interest of the measurement target tooth; FIG. 12 shows a 3D bounding box utilized as a reference for object rotation).
Regarding claim 8, the combination of Kearney and FARKASH teaches all the limitations of claim 1 above. Kearney teaches setting the first spot, the second spot, and the third spot in the oral cavity image ([0533] The output of the generator 3902 may be a set of predicted orthodontic points 3922. To facilitate training, the orthodontic points 3922 as output from the generator 3902 may be dilated, e.g., each orthodontic point may be represented as a 2D (circle or rectangle) or 3D region (sphere or cuboid) in which the generator 3902 estimates the actual orthodontic point of the patient to lie; [0538] the predicted orthodontic points 3922 and target orthodontic points 3924 may include values for each point 3932a-3932h on each tooth number of the patient ; [0224] the system 800 may be used to label anatomical features such as the cementum enamel junction (CEJ), bony points on the maxilla or mandible that are relevant to the diagnosis of periodontal disease, gingival margin, junctional epithelium; 3932a and 3932b in FIG. 39B show the spot indicating a top of a crown portion. 3932g and 3932h in FIG. 39B show the spot indicating a root apex portion) viewed from a specific point of view selected from among the plurality of points of view ([0169] the system 400 may be used to train a machine learning model to estimate the view of an image ... the output of the machine learning model for a given input image will be a view label indicating an anatomic region sequence, anatomic region sequence modifier, and laterality visualized by the image; [0170] A training algorithm 402 takes as inputs training data entries that each include an image 404 according to any of the imaging modalities described herein and a view label 406 indicating which of the view the image corresponds to (anatomic region sequence, anatomic region sequence modifier, and laterality). The view label 406 for an image may be assigned by a human observing the image and determining which of the image views it is; [0225] Each training data entry may further include a feature label 806 that may be embodied as a pixel mask indicating pixels in the image 804a that correspond to an anatomical feature of interest. The image 804a may be an image that has been reoriented according to the approach of FIG. 3 … a machine learning model 810 may be trained for each view of the FMX such that the machine learning model 810 is used to label teeth in an image that has previously been classified using the approach of FIG. 4 as belonging to the FMX view for which the machine learning model 810 was trained).
Kearney does not expressly teach wherein the setting further includes displaying the oral cavity image viewed from a plurality of points of view on the display based on the user input, and.
However, Tsuji teaches wherein the setting further includes displaying the oral cavity image viewed from a plurality of points of view on the display based on the user input, and ([0091] the A tomographic image of the tooth is observed while the cross-sectional position is sequentially changed, to select an image where a tooth crown can be observed ... Since the A tomographic image, the C tomographic image and the S tomographic image are linked to one another, the user is allowed to specify the center position of the tooth crown top in the S tomographic image on the screen where the tooth crown can be observed in the A tomographic image. FIG. 8 shows a screen in a state where the center of the tooth crown top has been specified; [0061] FIG. 8 is an example of the image in the periodontal disease diagnosis supporting system according to one embodiment of the present invention; [0093] the center coordinates of the entire tooth are found displaced to the right in the A tomographic image, and hence an appropriate position for the center coordinates is newly allowed to be specified in the A tomographic image).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the device and method of Kearney to incorporate the step/system of displaying multiple tomographic views which are different points of view based on the user input taught by Tsuji.
Motivation for this combination has been stated in claim 2.
Regarding claim 10, the combination of Kearney and FARKASH teaches all the limitations of claim 1 above. Tsuji teaches further comprising displaying a numerical value in accordance with the ratio ([0111] In the periodontal disease diagnosis supporting part 30, the numerical value of the bone attachment level or the tooth root adhesion length ratio as the index of the tooth root adhesion degree as has been described is checked with a determination reference for periodontal disease, to provide information that supports diagnosis) between the first distance and the second distance on the tooth shown in the oral cavity image ([0109] in the tooth root adhesion length ratio calculating means 22, “(distance between alveolar bone crest and root apex):(distance between cement-enamel junction and root apex)” is calculated. Specifically, each of the distances is calculated from the positions of the cement-enamel junction, the alveolar bone crest and the root apex, and a ratio of the distances is obtained; [0021] supports diagnosis of periodontal disease by use of a captured three-dimensional image of a tooth part).
With respect to claim 15, arguments analogous to those presented for claim 2, are applicable.
With respect to claim 16, arguments analogous to those presented for claim 3, are applicable.
With respect to claim 17, arguments analogous to those presented for claim 4, are applicable.
With respect to claim 18, arguments analogous to those presented for claim 2, are applicable.
With respect to claim 19, arguments analogous to those presented for claim 3, are applicable.
With respect to claim 20, arguments analogous to those presented for claim 4, are applicable.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kearney et al. (U.S. Publication No. 2021/0353393) (hereafter, "Kearney") in view of FARKASH et al. (U.S. Publication No. 2022/0189611) (hereafter, "FARKASH") in further in view of Ezhov et al. (U.S. Publication No. 2025/0322521) (hereafter, "Ezhov").
Regarding claim 9, the combination of Kearney and FARKASH teaches all the limitations of claim 1 above. Kearney teaches … the ratio between the first distance and the second distance ([0350] one or more machine learning models may be trained to measure that item of dental anatomy. Measurements of an item of dental anatomy may include its center of mass, relative distance to other anatomy; [0353] Machine learning models may be trained to identify and measure dental anatomy that may be used to determine the appropriateness of root canal therapy at a given tooth position such as crown-to-root-ratio; [0765] obtain a dental measurement 6008, such as a measurement of dental anatomy (CAL, PD, crown-to-root ratio, crown height, root length, orthodontic landmark spacing.); [0538] FIG. 39B illustrates orthodontic points 3932a-3932h … Using the orthodontic points 3932a-3932h, distances between them may be estimated … distance 3936b between a point 3932d and a point 3932f (a point on the CEJ) of the same tooth may be calculated ... a distance 3936c between a root tip 3932h and a point 3932f on the CEJ may be calculated. These examples are non-limiting, distances between any pair of orthodontic points may be calculated; 3932a and 3932b in FIG. 39B show the spot indicating a top of a crown portion. 3932g and 3932h in FIG. 39B show the spot indicating a root apex portion.).
Kearney does not expressly teach further comprising displaying a color with … on the tooth shown in the oral cavity image.
However, Ezhov teaches wherein further comprising displaying a color ([0229] visualizing the calculated characteristic distance as a gradient color map ... visualizing the calculated distance as a gradient color map with a spectrum ranging from green to dark red 2614) in accordance with … the … distance and the … distance on the tooth shown in the oral cavity image ([0229] receiving aligned point cloud of at least one volumetric image and at least one intra-oral surface scan image 2602; determining a baseline on a root by identifying CEJ vertices within a predefined distance to a crown 2604; identifying the boundary on the root by identifying determining bone attachment vertices 2606; identifying the boundary on the root by determining gingiva attachment vertices 2608; assigning each vertex of the gingiva and bone boundaries to each vertex of the CEJ baseline 2610; calculating a predetermined distance from each vertex of the gingiva attachment to each vertex of the bone attachment to calculate the distance between the gingiva and the bone, calculating a predetermined distance from each vertex of the gingiva attachment to the CEJ baseline to calculate the distance between the gingiva and the CEJ, or calculating a predetermined distance from each vertex of bone attachment to the CEJ baseline to calculate the distance between the bone and the CEJ 2612).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the device and method of Kearney to incorporate the step/system of calculating specific distances (such as gingiva-to-bone or gingiva-to-CEJ) and visualizing them as a color map gradient taught by Ezhov. Ezhov inherently determines and displays relative distance proportions (ratios) across the tooth surface.
The suggestion/motivation for doing so would have been to improve the dental diagnostics by enhancing visualization ([0001] This invention relates generally to medical diagnostics, and more specifically to a system and method for determining a dental condition based on the alignment of different image formats for improving medical/dental diagnostics; [0011] there is a need for tools that can integrate gradient color mapping and measurement lines to enhance visualization). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predicted results. Therefore, it would have been obvious to combine Kearney and Ezhov to obtain the invention as specified in claim 9.
Allowable Subject Matter
Claim 7 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL C. CHANG whose telephone number is (571)270-1277. The examiner can normally be reached Monday-Thursday and Alternate Fridays 8:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan S. Park can be reached at (571) 272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL C CHANG/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
Prosecution Insights