DETAILED ACTION
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 1-15 and 37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) recite(s) limitations that fall under the grouping of abstract idea of “Certain Methods of Organizing Human Activity”, e.g. Concepts Relating To Managing Human Behavior (Step 2A, Prong One) and “Mental Processes”, e.g. concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (step 2A). Specifically, the claim recites various steps of human activity that can be performed in the mind or with pen and paper, such as determining reference points, reference lines, reference planes, and ratios, which constitute an evaluation or analysis of information concerning the patient's facial anatomy. Such evaluation and analysis of information, when performed mentally, falls within the category of mental processes, which is an abstract idea.
Under step 2A, prong two, this judicial exception is not integrated into a practical application. In particular, the limitation of receiving facial scan data comprising image data and depth data amounts to obtaining information for use in the claimed analysis. Data gathering that merely provides information for an otherwise abstract process does not, by itself, integrate the exception into a practical application. Furthermore, the judicial exception is not integrated into a practical application because the claims are directed to an abstract idea with additional generic computer elements (e.g. processor, memory, computer storage medium, etc.), which are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim does not recite an improvement to the operation of the computer itself, an improvement to facial scanning technology, an improvement to image or depth sensing, or any particular improvement in computer functionality. The claim also does not require the determined reference points, lines, planes, or ratios to control a dental device, perform a dental treatment, manufacture a dental appliance, or otherwise cause a physical transformation or other particular technological result. Rather, the claimed process ends with determining information relevant to dental treatment planning. Thus, the additional elements, individually and in combination, do not integrate the abstract idea into a practical application.
Under step 2B, the claims does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). The claims do not include an inventive concept that is sufficient to transform the abstract idea into a patent-eligible application. The claimed computer system and the receipt of facial scan data comprising image and depth data are recited at a high level of generality and are used in their ordinary capacities to obtain and analyze information. The claim does not recite any unconventional computer architecture, unconventional scanning technique, or particular technological improvement that would transform the abstract information analysis into patent-eligible subject matter.
Regarding claims 2-15, the claims depend, directly or indirectly, from claim 1 and therefore include all of the limitations of claim 1. As discussed above, claim 1 is directed to the abstract idea of evaluating and analyzing information concerning a patient's facial anatomy to determine reference points, lines, planes, and ratios relevant to dental treatment planning. The additional limitations of claims 2-15 do not integrate the abstract idea into a practical application or otherwise provide significantly more.
Claim 2 merely specifies particular anatomical reference points, including infraorbital, condylar, pupillary, nose wing, subnasal, gnathion, trichion, ophryon, gonion, pronasal, upper lip, lower lip, ectocanthion, tragion, cutaneous nasion, and summit of a tragus angle points. These limitations merely identify particular types of information to be determined in the claimed facial analysis.
Claims 3-6 further specify techniques for generating a low-dimensional representation of the patient's face and using a reference point recognition model, including using a two-dimensional projection, depth data, or image data. These limitations merely specify particular computerized techniques for representing and analyzing the facial information and do not recite an improvement to the underlying computer technology or facial-scanning technology.
Claim 7 recites applying and deforming a deformable mask to reduce a difference between the deformable mask and the facial scan data. This limitation merely specifies an additional computational technique for analyzing the facial scan and identifying facial reference points. It does not recite an improvement to computer graphics, mesh deformation, or another underlying technology.
Claims 8-9 recite receiving motion information and determining dynamic characteristics, including detecting movement of a patient's mandible. These limitations continue to analyze information concerning the patient and determine characteristics of the patient. They do not require the computer to control a physical device or otherwise produce a particular technological or therapeutic result.
Claims 10-12 recite receiving a dental model, co-registering the dental model with facial scan data or a facial model, and determining a mandibular range-of-motion limit based on a closure amount at which digital maxillary and mandibular models collide. These limitations constitute further analysis of information represented by digital models. The claimed collision is a computational relationship between digital representations and does not require a physical collision or physical movement of the patient's mandible. The claims do not recite an improvement to the underlying computer registration or collision-detection technology, nor do they require the resulting information to control a dental apparatus or perform treatment.
Claim 13 recites determining a condition associated with the patient. This limitation merely specifies an additional result of the analysis of patient information and does not integrate the abstract idea into a practical application.
Claim 14 recites determining Eigenfaces and associated weights to describe the patient's face as a linear combination of Eigenfaces. This limitation merely applies a mathematical/computational facial representation technique to the claimed analysis and does not recite an improvement to the underlying computer technology.
Claim 15 recites cage deformation, skeleton animation, or mesh interpolation. These limitations merely identify particular computational techniques for deforming the facial representation and do not recite an improvement to the underlying computer graphics or deformation technology.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 13, and 37 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kochel et al (NPL: 3D Soft Tissue Analysis – Part 2, see IDS).
Regarding claim 1, Konchel discloses a method for determining characteristics of a patient comprising:
receiving, by a computer system, facial scan data of a patient (pg. 209 Measuring Equipment and Method: The 3D recording of facial surfaces was made with the optical 3D sensor FaceScan3D (3D-Shape GmbH, Erlangen, Germany). Data acquisition is based on the phase-measuring triangulation method [11, 46]. This involves the projection of a pattern of stripes of sinusoidal light intensity distributed onto the surface being examined. Images of these stripes are assembled in four stages using a specialized algorithm which then creates a 3D image of the object’s surface), the facial scan data comprising image data and depth data (pg. 209 Measuring Equipment and Method: Measurement error is 0.1 mm in the z-axis with this equipment; it takes 0.3 s to record the data; see Fig. 1 3D reference frame.);
determining, by the computer system based on the facial scan data, a plurality of reference points (Fig. 2 Landmarks for 3D soft tissue analysis; Fig. 1a-3c 3D soft tissue measurements. Measurement V1 (a), Measurement V2 (b), Measurement V3 (c))
determining, by the computer system, one or more reference points, lines, or planes (pg. 211 Table 1b. 3D soft tissue measurements);; and
determining, by the computer system, one or more ratios (pg. 211 Table 1b. 3D soft tissue measurements, which includes ratios) relevant for dental treatment planning (pg. 207-208 abstract: 3D soft tissue analysis provides information about vertical skeletal parameters, allowing assessment of vertical craniofacial morphology. Further investigation will be required so that 3D soft tissue diagnosis can be integrated into treatment planning and assessment as a supportive diagnostic tool in the future; pg. 218 discussion: Thus the further development of precise 3D soft tissue diagnostics is required to enhance current diagnosis techniques and treatment planning. Our 3D soft tissue analyses may be regarded as a first step towards determining reliable reference values and designing comprehensive 3D diagnostics).
Regarding claim 2, Kochel discloses the method of claim 1, wherein the plurality of reference points comprises at least one of an infraorbital point, a condylar point, a pupillary point, a nose wing point, a subnasal point, a gnathion point, a trichion point, an ophryon point, a gonion point, a pronasal point, an upper lip point, a lower lip point, an ectocanthion point, a tragion point, a cutaneous nasion point, or a summit of a tragus angle (pg. 210 Table 1a. 3D soft tissue landmarks, definitions according to Farkas, which includes Subnasale (e.g. Midpoint on the nasolabial soft tissue contour between the nasal columella’s base and upper lip)).
Regarding claim 13, Kochel discloses the method of claim 1, further comprises: determining, by the computer system, a condition associated with the patient (pg. 217 discussion: 3D soft tissue images and lateral cephalograms of 100 adult patients were analyzed in this study to investigate correlations between vertical 3D soft tissue parameters and skeletal variables).
Regarding claim(s) 37 (drawn to a system):
The rejection/proposed combination of Kochel, explained in the rejection of method claim(s) 1, anticipates/renders obvious the steps of the system of claim(s) 37 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1 is/are equally applicable to claim(s) 37.
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.
Claim(s) 3-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kochel as applied to claim 1 above, and further in view of Chen (US 10839481 B1).
Regarding claim 3, Kochel discloses the method of claim 1, but fails to teach where Chen teaches wherein determining the plurality of reference points comprises: generating, based on the facial scan data, a low-dimensional representation of a face of the patient (col. 8 lines 5-10 Given some number of 3D points and their corresponding 2D image points, PnP estimates a rigid body transformation (translation and rotation) that projects the 3D points to match the 2D image points; col 8 lines 35-40 projectPoints takes as input at 3XN/NX3 array of object points and returns a 2XN/NX 2 array of corresponding points, or pixels, in an image plane); and determining, using a reference point recognition model, one or more reference points (col 8 lines 35-40 projectPoints takes as input at 3XN/NX3 array of object points and returns a 2XN/NX 2 array of corresponding points, or pixels, in an image plane).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein determining the plurality of reference points comprises: generating, based on the facial scan data, a low-dimensional representation of a face of the patient and determining, using a reference point recognition model, one or more reference points from Chen into the method as disclosed by Kochel. The motivation for doing this is to improve the aligning of a digital face model.
Regarding claim 4, the combination of Kochel and Chen discloses the method of claim 3, wherein the low-dimensional representation is based on a two-dimensional projection of at least a part of the facial scan data (Chen col 8 lines 35-40 projectPoints takes as input at 3XN/NX3 array of object points and returns a 2XN/NX 2 array of corresponding points, or pixels, in an image plane). The motivation to combine the references is discussed above in the rejection for claim 3.
Regarding claim 5, the combination of Kochel and Chen discloses the method of claim 3, wherein the low-dimensional representation is based on the depth data (Chen col 8 lines 35-40 projectPoints takes as input at 3XN/NX3 array of object points and returns a 2XN/NX 2 array of corresponding points, or pixels, in an image plane). The motivation to combine the references is discussed above in the rejection for claim 3.
Regarding claim 6, the combination of Kochel and Chen discloses the method of claim 3, wherein the low-dimensional representation is based on the image data (Chen col 8 lines 21-40 intraoral scanner 2 and face scanner 4 are calibrated and that the scans are taken with the subject facing the camera; projectPoints takes as input at 3XN/NX3 array of object points and returns a 2XN/NX 2 array of corresponding points, or pixels, in an image plane). The motivation to combine the references is discussed above in the rejection for claim 3.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kochel as applied to claim 1 above, and further in view of Hodges et al (US 20230079478).
Regarding claim 7, Kochel discloses the method of claim 1, but fails to teach where Hodges teaches wherein determining the plurality of reference points comprises: applying, by the computer system, a deformable mask to the facial scan data (¶52 At step 202, the system receives a neutral mesh based on a scan of a face, as well as initial control point positions on the neutral mesh); and deforming the deformable mask, wherein deforming the deformable mask comprises adjusting the deformable mask to reduce a difference between the deformable mask and the facial scan data (¶54 At step 206, the system generates a radial basis function (hereinafter “RBF”) deformed mesh based on RBF interpolation of the initial control point positions and the user-defined control point positions. RBF interpolation as used herein refers to constructing a new mesh deformation by using radial basis function networks).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein determining the plurality of reference points comprises: applying, by the computer system, a deformable mask to the facial scan data and deforming the deformable mask, wherein deforming the deformable mask comprises adjusting the deformable mask to reduce a difference between the deformable mask and the facial scan data from Hodges into the method as disclosed by Kochel. The motivation for doing this is to improve methods and apparatuses for providing face mesh deformation.
Regarding claim 15, the combination of Kochel and Hodges discloses the method of claim 7, wherein deforming the deformable masks comprises one of or more of cage deformation, skeleton animation, or mesh interpolation (Hodges ¶72 the RBF deformed example will represent a smooth RBF interpolation from the neutral mesh). The motivation to combine the references is discussed above in the rejection for claim 7.
Claim(s) 8-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kochel as applied to claim 1 above, and further in view of Jaisson (US 20180104036).
Regarding claim 8, Kochel discloses the method of claim 1, but fails to teach where Jaisson teaches wherein the facial scan data comprises motion information, wherein the method further comprises: determining, by the computer system, dynamic characteristics of the patient (¶38 to record the mandibular kinetics, the patient is equipped with markers attached directly to the teeth of the mandibular arch or to the mandible through a support.).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein the facial scan data comprises motion information, wherein the method further comprises: determining, by the computer system, dynamic characteristics of the patient from Jaisson into the method as disclosed by Kochel. The motivation for doing this is to improve creating 3D models of the dental arches.
Regarding claim 9, the combination of Kochel and Jaisson discloses the method of claim 8, wherein determining the dynamic characteristics comprises: detecting, by a motion detection model, movement of a mandible of the patient (Jaisson ¶38 to record the mandibular kinetics, the patient is equipped with markers attached directly to the teeth of the mandibular arch or to the mandible through a support; ¶68 The model of the maxillary arch and the reference planes are associated with the animation of the mandible in movement.). The motivation to combine the references is discussed above in the rejection for claim 8.
Regarding claim 10, Kochel discloses the method of claim 1, but fails to teach where Jaisson teaches receiving, by the computer system, a dental model of the patient; and co-registering the dental model and the facial scan data (¶5 three-dimensional models of the dental arches of the patient are obtained. Said models, positioned with respect to each other during their creation, are registered with the volumetric image of the facial skeleton or the reference planes determined beforehand).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of receiving, by the computer system, a dental model of the patient; and co-registering the dental model and the facial scan data from Jaisson into the method as disclosed by Kochel. The motivation for doing this is to improve creating 3D models of the dental arches.
Regarding claim 11, the combination of Kochel and Jaisson discloses the method of claim 10, wherein the dental model comprises a maxillary model (Jaisson ¶67 e.g. 3D maxillary model) and a mandibular model (Jaisson ¶58-68 Recording of the Mandibular Kinetics). The motivation to combine the references is discussed above in the rejection for claim 10.
Regarding claim 12, the combination of Kochel and Jaisson discloses the method of claim 11, further comprising: generating a facial model of the patient (Jaisson ¶4 obtaining a volumetric image of the facial skeleton); co-registering the dental model and the facial model (Jaisson ¶5 three-dimensional models of the dental arches of the patient are obtained. Said models, positioned with respect to each other during their creation, are registered with the volumetric image of the facial skeleton or the reference planes determined beforehand); and determining a range of motion limit for a mandible of the patient, the range of motion limit determined by determining a closure amount at which the maxillary model collides with the mandibular model (Jaisson ¶94 To calculate the maxillary FGP, it is considered that the mandible is immobile and the movement is transposed to the maxillary arch).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of generating a facial model of the patient, co-registering the dental model and the facial model, and determining a range of motion limit for a mandible of the patient, the range of motion limit determined by determining a closure amount at which the maxillary model collides with the mandibular model from Jaisson into the method as disclosed by Kochel. The motivation for doing this is to improve creating 3D models of the dental arches.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Kochel and Chen as applied to claim 3 above, and further in view of Coughlan et al (US 8165282 B1).
Regarding claim 14, the combination of Kochel and Chen discloses the method of claim 3, but fail to teach where Coughlan teaches wherein generating the low-dimensional representation comprises: determining a set of Eigenfaces and a set of associated weights, wherein a face of the patient is described by a linear combination of Eigenfaces and their associated weights (col 7 lines 40-65 Image-based systems are normally based on eigenfaces or eigenimages, which generally use two-dimensional, global grayscale images representing distinctive characteristics of a facial image; When properly weighted, eigenfaces can be summed together to create an approximate gray-scale rendering of a human face. Variants using eigenfaces include parametric model-based approaches, such as the Active Shape Model.).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein generating the low-dimensional representation comprises: determining a set of Eigenfaces and a set of associated weights, wherein a face of the patient is described by a linear combination of Eigenfaces and their associated weights from Coughlan into the method as disclosed by the combination of Kochel and Chen. The motivation for doing this is to permit computers to record someone's face using less memory space.
Conclusion
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/KEVIN KY/ Primary Examiner, Art Unit 2671
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