DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
Figures 1-5 should be designated by a legend such as --Prior Art-- because only that which is old is illustrated. See MPEP § 608.02(g). Corrected drawings in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The flow chart in MPEP 2106, Subject Matter Eligibility Test For Products and Processes, will be referenced to establish that the subject matter is ineligible.
Step 1: claim 1 recites a method, claim 11 recites a computer-readable medium and claim 12 recites a system. Claims 1, 11 and 12 fall under one of the four recognized statutory categories.
Step 2A Prong One: However, claims 1, 11 and 12 are further directed to the abstract idea of projecting a three-dimensional image as a two-dimensional view. See MPEP 2106.04(a)(2). Furthermore, the claims do not preclude the limitations from being performed in the human mind. The limitations are mental processes that can be performed by a human using pen and paper. While the claims positively recite localizing dental relevant anatomical structures, automatically placing a guide curve and defining a projection region, the limitation is simply appending well-understood, routine and conventional activities previously known in the industry. Generating a reprojection panoramic view from a dental DVT volume is commonplace in the art. The Applicant's Background describes a clinician performing this process as predating the Applicant’s claimed invention.
Step 2A Prong Two: Additional elements include a computer performing machine learning. The involvement of a generic computer components does not provide additional elements that are sufficient to amount to significantly more than the judicial exception because the recitations to hardware involve no more than a generic computer performing generic computer functions that are well understood, routine and conventional activities previously known in the industry. That is, other than reciting “by a processor,” nothing in the claim precludes the steps from practically being performed in the human mind. See MPEP 2106.05(d)).
Step 2B: The claims do not provide an inventive concept as they do not provide an improvement to any type of particular machine. Automating the manual process of generating a panoramic tomogram does not constitute a patentable improvement in computer technology. The claims do not improve the computer system that is implementing the abstract idea. Merely automating or otherwise making efficient traditional methods do not constitute an inventive concept.
Furthermore, “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025). Therefore claims 1-12 are non-statutory.
Claim Rejections - 35 USC § 112
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the position" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the following variants" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "the guide curve results" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites “wherein the guide curve results are as follows: a curve definable by freely selectable knot points and an interpolation rule; another curve which is selected from a set of predetermined curve shapes and can be adapted under geometric transformations.” It is unclear whether the claim requires that the guide curve results include both (1) a curve definable by freely selectable knot points and an interpolation rule, and (2) another curve which is selected from a set of predetermined curve shapes and can be adapted under geometric transformations, or either (1) or (2).
Claim 4 recites the limitations " the following criteria" in lines 2-3, “the distance measures” in line 5, “the aesthetics” and “the resulting RPV” in line 12, and “the following criteria” in line 13. There are insufficient antecedent bases for these limitations in the claim.
Claim 5 recites the limitation "the following structures" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation “the longitudinal axis” in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitations " the transverse sectional planes" in line 2 and “the respective projection direction” in lines 3-4. There are insufficient antecedent bases for these limitations in the claim.
Claim 8 recites the limitations "the necessary displacement " in lines 1-2 and “the full displacement” in line 2. There are insufficient antecedent bases for these limitations in the claim.
Claim 9 recites the limitations "the thickness (D) " in line 2 and “the most part” in line 4. There are insufficient antecedent bases for these limitations in the claim.
Claim 11 recites the limitation "the position" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the position" in line 6. There is insufficient antecedent basis for this limitation in the claim.
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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Arai et al. US Publication 2021/0104039 (hereafter “Arai”) and Chen et al. US Publication 2024/0099812 (hereafter “Chen”).
Referring to claims 1, 11 and 12, Arai discloses a method for automatically generating a reprojection panoramic view (RPV) from a dental volume of a patient, comprising:
(S1) localizing dental relevant anatomical structures in the volume by using a machine learning method (paragraph 106, In Step S2a1, the calculation unit 34 acquires segmentation data of a tooth region (a region of interest in the biologically normal region) by inputting the data of the constituent maxillofacial region input in Step S1 (see FIG. 9) described above to the learning model LM1);
(S2) automatically placing of a guide curve by optimizing the guide curve based on the position of the localized dental relevant anatomical structures (paragraph 107, In Step S2a2, the calculation unit 34 sets a curved line along the curve of the dental arch for the segmentation data of the tooth region acquired in Step S2a1);
(S3) defining a projection region of the reprojection panoramic view using the placed guide curve without manual steps in the volume so that the localized dental relevant anatomical structures are encompassed (paragraph 108, In Step S2a3, the calculation unit 34 generates at least one image of a panoramic tomographic image along the spline curve SP set in Step S2a2 and a cross-section image crossing the spline curve SP);
(S4) creating the reprojection panoramic view (1) by reprojecting the volume in the defined projection area (paragraph 108, In Step S2a3, the calculation unit 34 generates at least one image of a panoramic tomographic image along the spline curve SP set in Step S2a2 and a cross-section image crossing the spline curve SP).
While Arai discloses a dental volume comprising an X-ray CT or MRI, Arai does not disclose expressly the dental volume is a dental DVT volume.
Chen discloses a dental DVT of a patient (paragraph 127, The schematic diagram of FIG. 1 shows an imaging apparatus 100 for 3-D CBCT cephalometric imaging).
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to obtain a dental volume of a patient via DVT. The motivation for doing so would have been to obtain an accurate and detailed 3d image of the patient using standardized dental equipment. Therefore, it would have been obvious to combine Chen with Arai to obtain the invention as specified in claims 1, 11 and 12.
Referring to claim 2, Arai discloses wherein said localizing comprises one of the following variants:
- (S1.1) localizing centers of the dental relevant anatomical structures by applying at least one trained CNN to transform the volume into heat maps indicating the position of the dental relevant anatomical structures by voxels lying above a threshold value;
- (S1.2) localizing and determining dimensions of the dental relevant anatomical structures using a trained machine learning method that outputs bounding boxes; or
- (S 1.3) localizing and determining an exact shape of the dental relevant anatomical structures (2) using a trained machine learning method that outputs segmentation masks (paragraph 89, FIG. 5B is an image in which a region of interest (specifically a tooth region) in the image illustrated in FIG. 5A is segmented and masked).
Referring to claim 3, Arai discloses wherein the guide curve results are as follows:
a curve definable by freely selectable knot points and an interpolation rule;
another curve which is selected from a set of predetermined curve shapes and can be adapted under geometric transformations (paragraph 119, A spline curve SP is set for the segmentation data SGJ of the jawbone region. The spline curve may be set to pass through the center in the buccolingual direction of the jawbone similarly to the spline curve set for the dental arch).
Referring to claim 4, Arai discloses optimizing a guide curve, but does not disclose expressly minimizing a distance measure between the guide curve and the localized dental relevant anatomical structures.
Chen discloses wherein the optimizing is performed with respect to one or more of the following criteria:
- minimizing a distance measure between the guide curve and the localized dental relevant anatomical structures, the distance measures being:
a) sum of distances between the structures and their nearest perpendicular points on the guide curve (paragraph 299, The AI engine detects an arch rotation that is a function ƒ(t) of tooth vector t representing the set {t1, . . . tN}, wherein N is the number of teeth in the arch. The positions (in an exemplary 2D space) of t can be corrected by the AI engine inverse operation by rearranging the teeth t to minimize the arch rotation in a systematic and automated manner: min ƒ(t); this expression is subject to an exemplary function g(t)=4th order polycurve, which, in turn, leads to solving an over-determined system in an exemplary 2D space: XTβ=y wherein XT signifies a matrix that contains all the teeth's 0 to nth order x positions in the exemplary 2D space);
b) weighted sum of the distances between said structures and their nearest perpendicular points on the guide curve, using a different weight depending on the anatomical structure and/or curve region, the distances being calculated by any arbitrary distance metric;
- maintaining the aesthetics of the resulting RPV, wherein the measure of aesthetics is based on at least one of the following criteria: Avoidance of local distortions of the RPV, Reduction of imaging-induced asymmetry of the RPV;
- in a case of curves spanned by freely selected knot points, limitation of a curve complexity, which is determined by a number of knot points or degree of a polynomial.
Before the effective filing date of the claimed invention, it would have obvious to a person of ordinary skill in the art to minimize a distance measure between the guide curve and the localized dental relevant anatomical structures. The motivation for doing so would have been to correct inaccuracies and inconsistencies in dental images. Therefore, it would have been obvious to combine Chen with Arai to obtain the invention as specified in claim 4.
Referring to claim 5, Arai discloses wherein the dental relevant anatomical structures are at least one of the following structures: Temporomandibular joint, jawbone, Teeth, Root tips, Implants, Foramen Mandibulae, Foramen Mentale, Foramen incisivum, Foramen Palatinum Majus, Foramen infraorbitale, Processus coronoideus, Spina Nasalis Anterior, Spina Nasalis Posterior, canalis mandibularis, canalis incisivus (paragraph 106, In Step S2a1, the calculation unit 34 acquires segmentation data of a tooth region (a region of interest in the biologically normal region) by inputting the data of the constituent maxillofacial region input in Step S1 (see FIG. 9) described above to the learning model LM1).
Referring to claim 6, Arai discloses wherein in (S3) the projection region is determined by extending, in a sectional plane transverse with respect to the patient, the guide curve to a two- dimensional surface having a fixed thickness or a thickness profile predetermined along the curve in the transverse plane, and subsequently extruding said surface along the longitudinal axis of the patient (paragraph 117, As illustrated in FIG. 11B, an area which extends two-dimensionally to sufficiently include the tooth region in the upper and lower sides of the spline curve SP can be set as a panoramic sectional layer PN1).
Referring to claim 7, Arai discloses wherein in (S3), the projection region in the transverse sectional planes in which dental-relevant anatomical structures are found in the localizing step (S1) is locally displaced along the respective projection direction in each case in such a way that the projection region runs through the dental-relevant anatomical structures (paragraph 117, As illustrated in FIG. 11B, an area which extends two-dimensionally to sufficiently include the tooth region in the upper and lower sides of the spline curve SP can be set as a panoramic sectional layer PN1).
Referring to claim 8, Arai discloses wherein the necessary displacement of the projection region is interpolated between the full displacement in the transversal sectional planes with dental relevant structures and no displacement starting from a suitably chosen distance between the relevant structures and the guide curve (paragraph 107, Accordingly, various methods are used to set the spline curve SP. For example, the spline curve SP may be set along the curve of the dental arch to pass through the centers in a buccolingual direction of upper and lower teeth)
Referring to claim 9, Arai discloses wherein in (S3) the thickness (D) of the projection region is automatically selected either locally or globally such that the dental relevant anatomical structures lie completely or for the most part within the projection region (paragraph 117, As illustrated in FIG. 11B, an area which extends two-dimensionally to sufficiently include the tooth region in the upper and lower sides of the spline curve SP can be set as a panoramic sectional layer PN1).
Referring to claim 10, Arai discloses wherein in (S1) for training data pairs comprise volumes and annotations, said annotations having
- in (S 1.1), heat maps
- in (S1.2) bounding boxes
- in (S1.3) segmentation masks (paragraph 8, The learning model may be a learning model which is generated using the training data such that segmentation data of a region of interest in a biologically normal region which is a region outside of the biologically important region in the constituent maxillofacial region is additionally output when the data of the constituent maxillofacial region is input, and the calculation unit may be configured to perform segmentation of the region of interest) (paragraph 89, FIG. 5B is an image in which a region of interest (specifically a tooth region) in the image illustrated in FIG. 5A is segmented and masked. An image other than the masked part is removed).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER K HUNTSINGER whose telephone number is (571)272-7435. The examiner can normally be reached Monday - Friday 8:30 - 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Q Tieu can be reached at 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER K HUNTSINGER/ Primary Examiner, Art Unit 2682