DETAILED ACTION
Claims 1-12 are currently pending.
The objection to the drawings is withdrawn due to Applicant’s amendment.
The rejections to claims 1-3 and 5-12 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, are withdrawn due to Applicant’s amendment.
Response to Arguments
Applicant's arguments filed 6/16/26 have been fully considered but they are not persuasive.
The Applicant argues on page 8 of the response in essence that: Claim 1 requires "localizing dental relevant anatomical structures in the DVT volume by using a machine learning method, the dental DVT volume comprising three-dimensional voxel data acquired by cone beam computed tomography." A DVT volume is three-dimensional voxel data acquired by cone beam computed tomography. Localizing anatomical structures within such a volume by applying a trained machine learning method ------ for example, applying at least one trained CNN to transform the DVT volume into heat maps indicating structure positions (claim 2) - cannot practically be performed in the human mind or with pen and paper.
The limitations of “localizing dental relevant anatomical structures in the DVT volume using a trained machine learning method, the dental DVT volume is three-dimensional voxel data acquired by cone beam computed tomography” is simply appending well-understood, routine and conventional activities previously known in the industry. The Applicant's Background describes a clinician generating a panoramic tomogram on a DVT volume as predating the Applicant’s claimed invention. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitations involve no more than generic equipment performing generic functions that are well understood, routine and conventional activities previously known in the industry.
The Applicant argues on page 9 of the response in essence that: Even if the claims were found to recite an abstract idea, the claims integrate it into a practical application by providing an improvement to dental imaging technology. The claimed method solves this problem through a specific sequence: machine-learning-based localization of dental relevant anatomical structures, optimization of a guide curve based on the positions of those localized structures, definition of a projection region using the guide curve so that the localized structures are encompassed, and reprojection. This is an improvement to the technical field of dental panoramic imaging and is therefore a practical application.
The recited machine-learning limitations are mental processes that can be performed by a human using pen and paper. Localizing dental relevant anatomical structures, optimizing a guide curve based on the positions of those localized structures, and defining a projection region using the guide curve so that the localized structures are encompassed are mental processes. “[P]atents 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).
The Applicant argues on pages 9 and 10 of the response in essence that: The Examiner cites Recentive Analytics, Inc. V. Fox Corp. for the proposition that applying generic machine learning to a new data environment, without improving the machine learning models, is patent ineligible. The present claims are distinguishable. The claims do not recite the bare application of a generic machine learning model to data. The claimed advance resides in this integrated process for generating a reprojection panoramic view, not in the generic application of machine learning to a new dataset.
The background of the specification describes the process of generating a reprojection panoramic view from a dental volume of a patient. Merely automating the previously performed manual process of a dentist does not constitute an inventive concept.
The Applicant argues on page 11 of the response in essence that: Arai sets the spline along the segmented arch. It does not optimize the curve based on positions of individually localized structures. Arai 's spline placement is based on curve-fitting to a region mask, not optimization against discrete localized-structure positions per the claimed criteria.
Arai discloses setting a curve line along the centers of upper and lower teeth (paragraph 107), which is optimizing the curve based on position of the teeth.
The Applicant argues on page 11 of the response in essence that: Arai’s purpose is to identify the mandibular canal and measure tooth-to-canal distance for extraction/implant risk. It is not to ensure that localized dental structures are encompassed by an optimized projection region, as is presently claimed.
In response to Applicant's argument that Arai’s purpose is not to ensure that localized dental structures are encompassed by an optimized projection region, the fact that the inventor has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
The Applicant argues on page 12 of the response in essence that: Arai does not disclose a "curve which is selected from a set of predetermined curve shapes and can be adapted under geometric transformations", which is entirely absent from both Arai and Chen.
Arai discloses setting a curve along (1) centers in a buccolingual direction of upper and lower teeth as illustrated in FIG. 11A, (2) centers of the lower teeth, or (3) along the root apexes. Each of the curves has a predetermined shape and the curve of 11A is described as a substantially horse's hoof shape. Arai further discloses that the curve may subsequently be manually corrected (paragraph 107).
The Applicant argues on pages 12 and 13 of the response in essence that: Chen minimizes arch rotation by rearranging teeth, that is, Chen moves the teeth to fit a curve in an orthodontic treatment-planning context. The present claim 4 minimizes a distance measure between the guide curve and the localized anatomical structures. The structures are fixed and the guide curve is optimized relative to them, to define a projection region for an RPV. These are different operations directed to different ends. Any combination of Arai and Chen would not result in the claimed operations.
Chen discloses minimizing a distance between the guide curve and the teeth (paragraph 299). In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which Applicant relies (i.e., the structures are fixed) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The Applicant argues on pages 13 and 14 of the response in essence that: The cited portions of Arai discloses a panoramic sectional layer PN1 that "extends two-dimensionally to sufficiently include the tooth region in the upper and lower sides of the spline curve SP. That is a uniform two-dimensional extension of a layer around the spline; it is not a local displacement of the projection region along the respective projection direction in individual transverse planes where structures are found.
Arai discloses that 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 (paragraph 108). The panoramic tomographic image as shown in FIG. 12B runs through the teeth.
Claim Objections
Claim 4 is objected to because of the following informalities: In line 12 insert “of” after “one”. Appropriate correction is required.
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, acquiring three-dimensional voxel data by cone beam computer tomography, 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
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors, at the time the application was filed, had possession of the claimed invention.
Claims 1, 11 and 12 have been amended to recite “the dental DVT volume comprising three-dimensional voxel data acquired by cone beam computed tomography”. Applicant’s specification generally discloses acquiring a dental DVT volume by rotating an X-ray around a patient’s head on page 6, lines 23-26. However, Applicant’s specification does not specify that the dental DVT volume is acquired by cone beam computed tomography.
Claim 4 is 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 4 was previously in the alternative form by previously reciting “wherein the optimizing is performed with respect to one or more of the following criteria”. This was described in the Applicant’s specification on page 8, lines 25-35 and page 9 lines 1-7. The amendment of 6/16/26 removes the previous language and is no longer in alternative form. It is unclear how optimization can be performed to (1) minimize a distance measure between the guide curve and the localized dental relevant structures, (2) maintaining a measure of aesthetics of the RPV, and (3) limitation of curve complexity. For instance, minimizing the distance between the guide curve and the localized dental relevant structures may decrease the aesthetics measure and increase curve complexity. A skilled artisan would not know how to optimize the curve with respect to each of the criteria listed in claim 4.
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:
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), the dental DVT volume comprising three-dimensional voxel data acquired by cone beam computed tomography (paragraph 57, An example of three-dimensional image data is volume data or volume-rendered image data. The reconfiguration data represents, for example, a measured value for each voxel);
automatically placing of a guide curve by optimizing the guide curve based on positions 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);
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); and
creating the reprojection panoramic view (RPV) by reprojecting the volume in the defined projection region (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,
the dental DVT volume comprising three-dimensional voxel data acquired by cone beam computed tomography (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 DVT via cone beam computed tomography. 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:
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;
localizing and determining dimensions of the dental relevant anatomical structures using a trained machine learning method that outputs bounding boxes; or
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 is defined by:
a curve definable by freely selectable knot points and an interpolation rule; or
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:
minimizing a distance measure between the guide curve and the localized dental relevant anatomical structures, the distance measure 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 a measure of aesthetics of the RPV, wherein the measure of aesthetics is based on at least one of: Avoidance of local distortions of the RPV, or Reduction of imaging-induced asymmetry of the RPV; and
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: 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 defining 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 a 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, the projection region in transverse sectional planes in which dental-relevant anatomical structures are found is locally displaced along a 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 a displacement of the projection region is interpolated between a 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 defining the projection region, a thickness (D) of the projection region is automatically selected either locally or globally such that the dental relevant anatomical structures lie completely or substantially 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 for training, data pairs comprise volumes and annotations, said annotations having: heat maps for training the at least one trained CNN; bounding boxes for training the trained machine learning method that outputs bounding boxes; or segmentation masks for the trained machine learning method that outputs 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
THIS ACTION IS MADE FINAL. 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 PETER K HUNTSINGER whose telephone number is (571)272-7435. The examiner can normally be reached Monday - Friday 8:30 - 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, 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
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