DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
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 9 and 24 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 9 recites “… if another of the first or second set of potential attachments …” which renders the claim indefinite because it is unclear whether the “if another of the first or second set” refers to another potential attachment within the same first or second set, or a potential attachment from the other set between the first set and the second set. For the purpose of substantive examination, the examiner presumes that “if another of the first or second set” refers to a comparison between the first set of potential attachments and the second set of potential attachments, such as a potential attachment of the first set lacks matching potential attachments in the second set, or a potential attachment of the second set lacks matching potential attachments in the first set.
Claim 24 also recites similar elements, and is rejected for the same reason.
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.
The claim(s) 1-31 are rejected under 35 USC § 101 because the claimed invention
is directed to judicial exception an abstract idea, it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated
the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance
published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates,
and has provided such analysis below.
Step 1: Are the claims to a process, machine, manufacture or composition of matter?"
Yes, Claims 1-15 are directed to method and fall within the statutory category of process;
Yes, Claims 16-30 are directed to non-transitory computer-readable medium and fall within the statutory category of product;
Yes, Claim 31 is directed to system and falls within the statutory category of product.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
The limitation of claim 1: “receiving model data of a dental structure of a patient, the model data including one or more attachments on the dental structure; detecting, from the model data, the one or more attachments on the dental structure; modifying the model data to remove the detected one or more attachments; and presenting the modified model data,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing a representation of the patient’s dental structure, mentally identifying any attachments on the teeth, mentally determining how the dental structure would appear after removing the identified attachments, and mentally visualizing or sketching the modified dental structure. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
If a claim limitation, under its broadest reasonable interpretation in light of specification, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea under step 2A, Prong One. See MPEP 2106.04(a)(2)(III).
Claims 16 and 31 recite the similar elements as claim 1, and are rejected for the same reasons
under 35 U.S.C. 101.
Therefore, claims 1, 16 and 31 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims as a whole integrates the exception into a practical application of that exception.
Step 2A Prong 2: Claims 1, 16 and 31: The judicial exception is not integrated into a practical application.
In particular, the claims recite the following additional elements: “A non-transitory computer-readable medium comprising one or more computer- executable instructions that, when executed by at least one processor of a computing device, cause the computing device to perform the method of:” and “A system comprising: one or more processors; a memory coupled to the one or more processors, the memory storing computer-program instructions, that, when executed by the one or more processors, perform a computer- implemented method comprising:” and “A method for adjusting three-dimensional (3D) dental model data,” which are mere instruction to implement an abstract idea on a computer, or merely uses a computer as tool to perform an abstract idea with the broad reasonable interpretation, which does not integrate a judicial exception into practical application. See MPEP § 2106.05(f)).
Further, the following additional elements: “receiving model data of a dental structure of a patient, …” and “presenting the modified model data,” are merely a recitation of insignificant extra-solution activity such as data gathering (i.e., receiving model data) and data output (displaying modified model data), which do not integrate a judicial exception into practical application. See MPEP 2106.05(g).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1, 16 and 31 not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1, 16 and 31: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components which do not amount to significantly more than the abstract idea. Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, … iii. Electronic recordkeeping, … (updating an activity log). iv. Storing and retrieving information in memory,…
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include: i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016); iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014); v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015); and vi. A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1, 16 and 31 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-15 and 17-30 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (and/or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-15 and 17-30 are also rejected for incorporating the deficiency of their independent claims 1 and 16.
Claim 2 recites “The method of claim 1, wherein detecting the one or more attachments further comprises: retrieving a previous model data of the dental structure; matching one or more teeth of the previous model data with respective one or more teeth of the model data; identifying one or more previous attachments from the previous model data; detecting one or more shape discrepancies from the model data; and identifying the one or more attachments from the model data using the one or more previous attachments and the one or more shape discrepancies,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind. A person, for example, is capable of observing a prior representation of the patient’s teeth and a current representation of the patient’s teeth, mentally comparing corresponding teeth between the two representations, mentally identifying prior attachment locations on the prior representation, mentally determining one or more difference in shape based on current representations, and identifying attachments in the current representation based on the prior attachment locations and the determined shape difference. The steps include observation, comparison, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 2 is ineligible under 35 USC 101.
Claim 3 recites “The method of claim 2, wherein modifying the model data further comprises, for each of the detected one or more attachments: calculating a depth from the attachment to a corresponding tooth based on the previous model data; and adjusting a surface of the attachment towards a direction inside the tooth based on the calculated depth,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind. A person, for example, is capable of observing a representation of a dental structure including an attachment on a tooth, mentally estimating or determining a depth of the attachment relative to the tooth surface based on prior information, and mentally determining how the surface of the tooth would appear after moving or removing the attachment toward the interior of the tooth based on the estimated depth. The steps include observation, comparison, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 3 is ineligible under 35 USC 101.
Claim 4 recites “The method of claim 3, wherein adjusting the surface of the attachment further comprises moving scan vertices inside a detected area corresponding to the attachment in the direction inside the tooth,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind. A person, for example, is capable of observing a representation of a dental structure including an attachment on a tooth, mentally identifying points or locations within the area corresponding to the attachment, and mentally determining how the points would move inward toward the tooth to represent removal or reduction of the attachment. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 4 is ineligible under 35 USC 101.
Claim 5 recites “The method of claim 1, wherein presenting the modified model data further comprises displaying visual indicators of the removed one or more attachments”
This limitation merely further specifies displaying indicators of removed attachments. It is merely a recitation of insignificant extra-solution activity such as data output or insignificant application (displaying indicators), which does not integrate a judicial exception into practical application. See MPEP 2106.05(g). Therefore, the office finds that the claim 5 is ineligible under 35 USC 101.
Claim 6 recites “The method of claim 1, wherein detecting the one or more attachments further comprises: detecting, using a machine learning model, extra material on the dental structure; and identifying the extra material as the one or more attachments,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing a representation of the patient’s dental structure, mentally identifying any attachments on the teeth. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). The limitation of “using a machine learning model” is mere adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and applying a computing component to perform generic predicating/detecting function at high level of generality, which does not integrate judicial exception into practical applicant and amount to significantly more. See MPEP 2106.05(f). The limitation of “using a machine learning model” is also generally linking the use of judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Therefore, the office finds that the claim 6 is ineligible under 35 USC 101.
Claim 7 recites “The method of claim 6, wherein modifying the model data further comprises, for each of the detected one or more attachments: predicting, using the machine learning model, a depth from the attachment to a corresponding tooth; and adjusting a surface of the attachment towards a direction inside the tooth based on the predicted depth,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing a representation of the patient’s dental structure including an attachment on a tooth, mentally estimating or determining a depth of the attachment relative to the tooth surface based on available information, and mentally determining how the surface of the tooth would appear after moving or reducing the attachment depth toward the interior of the tooth. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). The limitation of “using the machine learning model” is mere adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and applying a computing component to perform generic predicating function at high level of generality, which does not integrate judicial exception into practical applicant and amount to significantly more. See MPEP 2106.05(f). The limitation of “using a machine learning model” is also generally linking the use of judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Therefore, the office finds that the claim 7 is ineligible under 35 USC 101.
Claim 8 recites “The method of claim 1, wherein detecting the one or more attachments further comprises: identifying a first set of potential attachments using previous model data; identifying a second set of potential attachments using a machine learning model; and identifying the one or more attachments based on cross-validating the first set of potential attachments with the second set of potential attachments,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing the prior and the current representation of the patient’s dental structure, mentally identifying candidate locations of attachments from each of representations, comparing or cross-checking the two sets of candidate attachment locations, and determining which locations correspond to actual attachment. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). The limitation of “using the machine learning model” is mere adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and applying a computing component to perform generic predicating function at high level of generality, which does not integrate judicial exception into practical applicant and amount to significantly more. See MPEP 2106.05(f). The limitation of “using a machine learning model” is also generally linking the use of judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h). Therefore, the office finds that the claim 8 is ineligible under 35 USC 101.
Claim 9 recites “The method of claim 8, wherein identifying the one or more attachments based on cross-validating further comprises discarding potential attachments of the first or second set of potential attachments that are close to interproximal or occlusal tooth areas if another of the first or second set of potential attachments lacks matching potential attachments,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing candidate attachment locations identified from multiple representations, recognizing that certain candidate attachments are located near interproximal or occlusal areas of teeth, comparing whether correspond matches exist between the sets of potential attachments, mentally deciding to disregard or discard certain candidate attachments when a corresponding match is lacking. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 9 is ineligible under 35 USC 101.
Claim 10 recites “The method of claim 8, wherein identifying the one or more attachments based on cross-validating further comprises discarding, from the second set of potential attachments, potential attachments for areas that do not have significant deviations in the model data compared to the previous model data,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing the prior and the current representation of the patient’s dental structure, mentally comparing area of the current and prior representations to determine whether significant differences or deviations exist, and mentally deciding to disregard candidate attachments in areas where little or no deviation is observed. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 10 is ineligible under 35 USC 101.
Claim 11 recites “The method of claim 8, wherein identifying the one or more attachments based on cross-validating further comprises discarding, from the first set of potential attachments, potential attachments having a small distance to a corresponding tooth surface that do not intersect with potential attachments of the second set of potential attachments,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing candidate attachment locations identified from multiple representations, mentally determining whether certain candidate attachments are located close to a corresponding tooth surface, mentally determining whether the potential attachments intersect or correspond with candidate attachments identified from another representation, and mentally deciding to disregard candidate attachments that are both close to the tooth surface and lack correspondence with another set. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 11 is ineligible under 35 USC 101.
Claim 12 recites “The method of claim 1, wherein presenting the modified model data further comprises displaying, with corresponding confidence values, a plurality of attachment removal options based on the detected one or more attachments.”
This limitation merely further specifies displaying attachment removal options corresponds to confidence value. It is merely a recitation of insignificant extra-solution activity such as data output or insignificant application (i.e., displaying options), which does not integrate a judicial exception into practical application. (see MPEP 2106.05(g)). Therefore, the office finds that the claim 12 is ineligible under 35 USC 101.
Claim 13 recites “The method of claim 12, wherein the confidence values are based on a degree of similarity between corresponding attachments detected via a plurality of detection approaches.”
This limitation merely further defines confidence values are based on a degree of similarity between corresponding attachments detected via a plurality of detection approaches. It is merely a mathematical concepts (See MPEP 2106.04(a)(2)(I); For example, mathematical relationships disclosed in instant specification [0117]. Therefore, the office finds that the claim 13 is ineligible under 35 USC 101.
Claim 14 recites “The method of claim 1, further comprising updating a treatment plan for the patient using the modified model data,” as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation (BRI) in light of specification, covers performance of the limitation in the human mind. A person, for example, is capable of observing a representation of a patient’s dental structure after modification, evaluating how the modified dental structure affects treatment considerations, and mentally determining or updating a treatment plan based on the evaluation. The steps include observation, evaluation, judgment, and decision-making processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).). See MPEP 2106.04(a)(2)(III). Therefore, the office finds that the claim 14 is ineligible under 35 USC 101.
Claim 15 recites “The method of claim 14, further comprising fabricating an orthodontic appliance based on the treatment plan.”
This limitation merely further specifies producing an orthodontic appliance based on the treatment plan. It is merely a recitation of insignificant extra-solution activity such as post solution (i.e., producing appliance based on determined plan), which does not integrate a judicial exception into practical application. See MPEP 2106.05(g), Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016). Therefore, the office finds that the claim 15 is ineligible under 35 USC 101.
Claims 17-30 recite the similar elements as claims 2-15, and are rejected for the same reasons under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 14-16 and 29-31 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by
Kopelman US20200022584A1.
Claim 1, Kopelman teaches A method for adjusting three-dimensional (3D) dental model data ([0058] … a computer based method for modifying a virtual model of a physical structure, … [0065] For example, said physical structure comprises any one of an intra-oral cavity of a patient and a physical dental model representative of said intra-oral cavity. [0066] Additionally or alternatively, said virtual model includes a first three dimensional (3D) virtual model representative of a first physical part of the physical structure … ), the method comprising:
receiving model data of a dental structure of a patient, the model data including one or more attachments on the dental structure ([0059] (A) providing to the computer system said virtual model obtained from the physical structure. [0066] Additionally or alternatively, said virtual model includes a first three dimensional (3D) virtual model representative of a first physical part of the physical structure … [0081] … said first portion of said first physical part was obscured with an artifact, … said artifact comprises an impression abutment or any other structure mounted onto a dental implant that is embedded in said physical structure.);
detecting, from the model data, the one or more attachments on the dental structure ([0061] (C) identifying at least one portion of the virtual model that is desired to be modified with at least a part of said additional 3D data. [0081], … said first portion of said first physical part was obscured with an artifact, and wherein said second portion of said second physical part corresponds to said first portion of said first physical part wherein said artifact has been removed. Examiner note: identifying a portion of the virtual model that is obscured by an artifact and is to be replaced or modified necessarily involves detecting the attachment from the model data, because the system must first determine the presence and location of the artifact within the model data before selecting the portion for modification.);
modifying the model data to remove the detected one or more attachments ([0062] (D) modifying the virtual model in the computer system at least by replacing said at least one identified portion of said virtual model with said at least part of said 3D data to provide a modified virtual model. [0068] causing the computer system to at least one of delete, remove and replace said portion of said virtual model … See also [0081].); and
presenting the modified model data ([0070] virtually registering said second 3D virtual model with respect to said modified first 3D virtual model to provide said modified virtual model wherein said portion of said virtual model is replaced with a corresponding part of said second 3D virtual model representative of a second physical portion of said outputting said modified virtual model from said computer system. [0075] causing the computer system to at least one of delete, remove and replace said portion of said virtual model by applying a corresponding function (i.e. a delete function, a remove function or a replace function, respectively) to said first display image portion via interaction with said first display image on said display, to provide said modified virtual model, … outputting said modified virtual model from said computer system).
Claim 14, Kopelman teaches The method of claim 1, further comprising updating a treatment plan for the patient using the modified model data ([0086] … the method further comprises designing an orthodontic treatment plan based on said modified virtual model.).
Claim 15, Kopelman teaches The method of claim 14, further comprising fabricating an orthodontic appliance based on the treatment plan ([0088] Additionally or alternatively, the method further comprises manufacturing orthodontic appliances based on said modified virtual model.).
The elements of claims 16 and 29-31 are substantially the same as those of claims 1 and 14-15. Therefore, the elements of claims 16 and 29-31 are rejected due to the same reasons as outlined above for claims 1 and 14-15. Further, additional limitations of claims 16 and 31, “A non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to perform the method of:” and “A system comprising: one or more processors; a memory coupled to the one or more processors, the memory storing computer-program instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising:” (see [0101] For example, the program can be configured for applying a computer implemented method as defined above for the second aspect of the invention, mutatis mutandis. Additionally or alternatively, the computer readable medium comprises any one of optical discs, magnetic discs, magnetic tapes, or solid state memory storage.).
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(s) 2, 5, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman
US20200022584A1 in view of Kopelman US20180168780A1.
Claim 2, Kopelman ‘584 teaches detecting the one or more attachments to teach as discussed above with respect to claim 1. However, Kopelman ‘584 fails to teaches, but Kopelman ‘780 teaches
retrieving a previous model data of the dental structure ([0101] … by comparing image data 162 to prior image data included in previous patient data 188. Patient data 188 may include past data regarding the patient (e.g., medical records), previous or current scanned images or models of the patient, current or past X-rays, 2D intraoral images, 3D intraoral images, virtual 2D models, virtual 3D models, or the like.);
matching one or more teeth of the previous model data with respective one or more teeth of the model data ([0102] Prior data comparator 180 may perform image registration between the image data 162 and the prior image data of a patient's oral cavity, dental arch, individual teeth, or other intraoral regions. [0107] This may include performing … recognition techniques to identify features in the previous image data and corresponding features in the current image data. For example, prior data comparator 180 may … to identify a dental arch, individual teeth, a gum line, gums, etc. in the current image data 162 and previous image data.);
identifying one or more previous attachments from the previous model data ([0108] Additionally, prior data comparator 180 may determine whether an attachment was previously attached to a tooth but is no longer attached to the tooth (e.g., was lost). Additionally, prior data comparator 180 may determine whether an attachment has moved out of position (e.g., currently has a different position than it had when initially placed). If there has been a change, the prior data comparator 180 may identify the change as an area of interest.);
detecting one or more shape discrepancies from the model data ([0107] once prior image data from previous patient data 188 has been registered to the current image data 162 and transformed accordingly, prior data comparator 180 compares the two images to determine differences between the prior image data and the current image data 162. … Differences between the two images may be determined, and prior data comparator 180 may generate contours of those differences … differences may include gum discoloration, tooth decay, tooth discoloration, gum recession, etc. that are shown in the current image data 162); and
identifying the one or more attachments from the model data using the one or more previous attachments and the one or more shape discrepancies ([0107] and [0108]. Examiner note: The reference teaches comparing prior image data (previous model data) with current image data (model data) to determine difference between the two datasets, which correspond to detected shape discrepancies [0107]. The reference further teaches determining whether an attachment has moved relative to its prior position, based on the comparison results [0108]. Under BRI, determining whether an attachment is present, absent , or displaced relative to its previous state constitutes identifying one or more attachments from the model data using information regarding previous attachments and detected difference between the previous and current model data. Although the determination is based on a comparison between prior and current datasets, the identification necessarily reflects the state of the attachment in the current model data because the comparison outcome indicates the presence, absence, or positional change of the attachment in the current dataset).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 to incorporate the teachings of Kopelman ‘780, and apply prior patient data registration and comparison techniques to determine difference between prior model data and current model data, including determining changes associated with attachments and generating contours of those differences in order to improve the accuracy and reliability of evaluating changes in a dental structure by using temporally earlier patient data as a reference when analyzing a current model. The combinations of teachings would predictably provide benefit of more robust and consistent identification of additional structures and structural changes across scans by using historical baseline information to guide analysis of the current model.
Claim 5, Kopelman ‘584 teaches presenting the modified model data as discussed above with respect to claim 1. However, Kopelman ‘584 fails to teaches, but Kopelman ‘780 teaches displaying visual indicators of the removed one or more attachments ([0211] In block 1830, the AR system overlays an indication of the AOI on an AR display … The indication may mark the AOI with a color or other indicator to highlight the AOI for the dental practitioner. [0203] … the AR system may update the visual overlay to provide an indicator of a new amount of material to remove … includes an indication 1525 of an amount of material to remove and an indication of the amount of material that has been removed … Examiner note: the reference teaches displaying visual overlays on a dental model or patient view that include indicators identifying areas of interest and indicators of material that has been removed from a tooth surface. Because attachments correspond to material present on the tooth surface that may be removed during dental processing, a visual indicator showing an amount of material removed from the tooth surface corresponds to a virtual indicator of removed attachments, under BRI).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 to incorporate the teachings of Kopelman ‘780, and apply virtual overlay and removal indicator techniques in order to provide visual feedback regarding portions removed from the dental model during the modification process. The combinations of teachings would predictably provide benefit of improving user understanding and verification of modification results by visually indicating removed structures in the modified dental model.
The elements of claims 17 and 20 are substantially the same as those of claims 2 and 5. Therefore, the elements of claims 17 and 20 are rejected due to the same reasons as outlined above for claims 2 and 5.
Claim(s) 3-4 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman
‘584 and Kopelman ‘780 as applied to claims 2 and 17 above, and further in view of Chen US20190159868A1.
Claim 3, Kopelman ‘584 teaches modifying the model data as discussed above with respect to claims 1 and 2. However, Kopelman ‘584 and Kopelman ‘780 fail to teach, but Chen teaches
for each of the detected one or more attachments:
calculating a depth from the attachment to a corresponding tooth based on the previous model data ([0089] As shown in FIG. 11, 3D dentition mesh 1100 can include brackets 1102, gingival 1104 and teeth 1106. [0098] a 3D detention mesh from an intra-oral scan of the dentition before the braces were attached can be used in tooth surface reconstruction. [0092] … remove the brackets from surfaces of the separated teeth 1202, each individually segmented tooth (or crown) is examined and processed … bracket boundary detection can use an automated curvature-based algorithm that computes the curvatures of vertices in the mesh of tooth surfaces, and then uses a thresholding algorithm to identify margin vertices that have large negative curvatures … mesh vertices within the closed 3D boundary are removed in the 3D dentition mesh 1100, which results in a hole 1304 on the tooth surface. Examiner note: the reference teaches the 3D dentition mesh includes brackets attached to teeth and that a prior intra-oral scan obtained before the braces were attached may be used for tooth surface reconstruction, and computing geometric properties of vertices on the tooth surface using a curvature algorithm to identify the boundary of the bracket region relative to the underlying tooth surface. Determining the boundary and spatial relationship between the bracket and the underlying tooth surface requires computing geometric relationships, including a distance (i.e., depth) between the attachment and the corresponding tooth surface.); and
adjusting a surface of the attachment towards a direction inside the tooth based on the calculated depth (Fig.13A-13C; [0092] In step 1008, to automatically remove the brackets from surfaces of the separated teeth 1202, each individually segmented tooth (or crown) is examined and processed … Then, mesh vertices within the closed 3D boundary are removed in the 3D dentition mesh 1100, which results in a hole 1304 on the tooth surface. Examiner note: The reference teaches automatically removing brackets from the tooth surfaces by detecting a boundary of the bracket region and removing mesh vertices within the detected boundary, which results in a hole on the tooth surface. Because the boundary and spatial extent of the bracket relative to the underlying tooth surface, removing mesh vertices within that boundary modifies the attachment geometry in a direction toward the tooth interior (i.e., from surface of bracket toward the underlying tooth surface), which constitutes adjusting a surface of the attachment toward a direction inside of the tooth.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 and Kopelman ‘780 to incorporate the teachings of Chen, and apply the geometric relationship determination and mesh vertex modification techniques in order to enable more accurate adjustment of attachment surfaces relative to underlying tooth structures within the dental model processing system. The combinations of teachings would predictably provide benefit of improving the precision and reliability of modifying attachment regions based on calculated spatial relationships relative to corresponding tooth surfaces.
Claim 4, Kopelman ‘584 and Kopelman ‘780 fail to teach, but Chen teaches The method of claim 3, wherein adjusting the surface of the attachment further comprises moving scan vertices inside a detected area corresponding to the attachment in the direction inside the tooth ([0092] In step 1008, to automatically remove the brackets from surfaces of the separated teeth 1202, each individually segmented tooth (or crown) is examined and processed … Then, mesh vertices within the closed 3D boundary are removed in the 3D dentition mesh 1100, which results in a hole 1304 on the tooth surface. Examiner note: The reference teaches automatically removing brackets from the tooth surfaces by detecting a boundary of the bracket region and removing mesh vertices within the detected boundary, which results in a hole on the tooth surface. Because the boundary and spatial extent of the bracket relative to the underlying tooth surface, removing mesh vertices within that boundary modifies the attachment geometry in a direction toward the tooth interior (i.e., toward the underlying tooth surface), which constitutes moving scan vertices inside a detected area corresponding to the attachment in the direction inside the tooth.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 and Kopelman ‘780 to incorporate the teachings of Chen, and apply the geometric relationship determination and mesh vertex modification techniques in order to enable more accurate adjustment of attachment surfaces relative to underlying tooth structures within the dental model processing system. The combinations of teachings would predictably provide benefit of improving the precision and reliability of modifying attachment regions based on calculated spatial relationships relative to corresponding tooth surfaces.
The elements of claims 18-19 are substantially the same as those of claims 3-4. Therefore, the elements of claims 18-19 are rejected due to the same reasons as outlined above for claims 3-4.
Claim(s) 6 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman
US20200022584A1 in view of Xue US20190180443A1.
Claim 6, Kopelman teaches detecting the one or more attachments as discussed above with respect to claim 1. However, Kopelman fails to teach, but Xue teaches
detecting, using a machine learning model, extra material on the dental structure ([0038] …The model application workflow 147 is to apply the one or more trained machine learning models to label one or more properties and/or areas in images of teeth. [0036] The method further includes processing the image comprising the edge data using a trained machine learning model to determine edge classifications for edges in the edge data, wherein one of the edge classifications is a tooth edge classification. Other edge classifications may include an aligner edge classification, a gingival edge classification, an overlapping tooth and aligner edge classification); and
identifying the extra material as the one or more attachments ([0036] … Other edge classifications may include an aligner edge classification, a gingival edge classification, an overlapping tooth and aligner edge classification.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman to incorporate the teachings of Xue, and apply machine learning detection and classification of dental objects in order to automatically detect and identify extra material attached to tooth surfaces in the dental model data. The combinations of teachings would predictably provide benefit of improving the accuracy , robustness, and automation of attachment detection in dental models, thereby reducing manual intervention and improving process efficiency.
The elements of claim 21 is substantially the same as those of claim 6. Therefore, the elements of claim 21 is rejected due to the same reasons as outlined above for claim 6.
Claim(s) 7 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman and Xue
as applied to claims 6 and 21 above, and further in view of Chen US20190159868A1.
Claim 7, Kopelman teaches modifying the model data as discussed above with respect to claims 1. However, Kopelman fails to teach, but Xue teaches
for each of the detected one or more attachments:
predicting, using the machine learning model, a depth from the attachment to a corresponding tooth ([0036] … using a trained machine learning model to determine edge classifications for edges in the edge data, wherein one of the edge classifications is a tooth edge classification. Other edge classifications may include an aligner edge classification, a gingival edge classification, an overlapping tooth and aligner edge classification, and a miscellaneous edge classification… [0037] … the edge data using a second trained machine learning model to label edges in the cropped image. Once the edges are labeled, the edge data may be processed to make determinations about the teeth in the image. For example, if the edge labels include a tooth edge and an aligner edge, then a distance between tooth edges and nearby aligner edges may be computed and compared to a threshold. Examiner note: the reference teaches using a trained machine learning model to classify edges corresponding to dental structures (e.g., tooth edges and aligner edges) and subsequently computing spatial relationships, including distance between e classified structures. A POSITA would understand that once the machine learning model identifies structural boundaries correspond to dental components, determining a distance between those structures include predicting or estimating a spatial relationship (i.e., depth) between the structures. Under BRI, “predicting … a depth from the attachment to a corresponding tooth” includes determining a distance between identified structural feature using outputs generated by the trained machine learning mode.); and
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman to incorporate the teachings of Xue, and apply machine learning prediction of geometric relationships between dental structures in order to automatically predict a distance or depth between an attachment and a corresponding tooth surface within the dental model data. The combinations of teachings would predictably provide benefit of improving the accuracy and automation of determining attachment to tooth spatial relationships, thereby enabling more precise modification of the dental model and reducing manual measurement effort.
However, Kopelman and Xue fail to teach adjusting a surface of the attachment towards a direction inside the tooth based on the predicted depth.
Chen teaches adjusting a surface of the attachment towards a direction inside the tooth based on the predicted depth (Fig.13A-13C; [0092] In step 1008, to automatically remove the brackets from surfaces of the separated teeth 1202, each individually segmented tooth (or crown) is examined and processed … Then, mesh vertices within the closed 3D boundary are removed in the 3D dentition mesh 1100, which results in a hole 1304 on the tooth surface. Examiner note: The reference teaches automatically removing brackets from the tooth surfaces by detecting a boundary of the bracket region and removing mesh vertices within the detected boundary, which results in a hole on the tooth surface. Because the boundary and spatial extent of the bracket relative to the underlying tooth surface, removing mesh vertices within that boundary modifies the attachment geometry in a direction toward the tooth interior (i.e., from surface of bracket toward the underlying tooth surface), which constitutes adjusting a surface of the attachment toward a direction inside of the tooth.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman and Xue to incorporate the teachings of Chen, and apply the geometric relationship determination and mesh vertex modification techniques in order to enable more accurate adjustment of attachment surfaces relative to underlying tooth structures within the dental model processing system. The combinations of teachings would predictably provide benefit of improving the precision and reliability of modifying attachment regions based on calculated spatial relationships relative to corresponding tooth surfaces.
The elements of claim 22 is substantially the same as those of claim 7. Therefore, the elements of claim 22 is rejected due to the same reasons as outlined above for claim 7.
Claim(s) 8-10 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman
US20200022584A1 in view of Kopelman US20180168780A1 and Xue US20190180443A1 and Kopelman US20190029524A1.
Claim 8, Kopelman ‘584 teaches detecting the one or more attachments as discussed above with respect to claims 1. However, Kopelman ‘584 fails to teach, but Kopelman ‘780 teaches
identifying a first set of potential attachments using previous model data ([0108] Additionally, prior data comparator 180 may determine whether an attachment was previously attached to a tooth but is no longer attached to the tooth (e.g., was lost). Additionally, prior data comparator 180 may determine whether an attachment was previously attached to a tooth but is no longer attached to the tooth (e.g., was lost). Additionally, prior data comparator 180 may determine whether an attachment has moved out of position (e.g., currently has a different position than it had when initially placed). If there has been a change, the prior data comparator 180 may identify the change as an area of interest. Examiner note: identification of an area of interest correspond to attachment changes based on prior data constitutes identifying candidate regions that potentially correspond to attachments. Under BRI, the candidate regions represent a set of potential attachments the first set of potential attachments ([0108])
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 to incorporate the teachings of Kopelman ‘780, and apply prior patient data registration and comparison techniques in order to identify candidate regions corresponding to potential attachments based on differences between previous model data and current model data. The combinations of teachings would predictably provide benefit of improving the accuracy and reliability of evaluating attachment changes by using temporally earlier patient data as a reference when analyzing a current model, thereby enable more robust and consistent identification of potential attachment strictures across scans.
However, Kopelman ‘584 and Kopelman ‘780 fail to teach identifying a second set of potential attachments using a machine learning model.
Xue teaches identifying a second set of potential attachments using a machine learning model; and ([0038] The model application workflow 147 is to apply the one or more trained machine learning models to label one or more properties and/or areas in images of teeth. [0036] The method further includes processing the image comprising the edge data using a trained machine learning model to determine edge classifications for edges in the edge data, wherein one of the edge classifications is a tooth edge classification. Other edge classifications may include an aligner edge classification, a gingival edge classification, an overlapping tooth and aligner edge classification. Examiner note: labeling properties and/or area in images using a trained machine learning model would produces candidate regions corresponding to potential anatomical or appliance structures prior to confirmation, which reasonably corresponds to identifying a set of potential attachments under the BRI.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 and Kopelman ‘780 to incorporate the teachings of Xue, and apply machine learning detection and classification of dental objects in order to generate candidate or potential attachment regions within dental model data that may correspond to material attached to tooth surfaces. automatically detect and identify extra material attached to tooth surfaces in the dental model data. The combinations of teachings would predictably provide benefit of improving the accuracy , robustness, and automation of identifying potential attachment regions in dental models, thereby reducing manual intervention and improving process efficiency.
However, Kopelman ‘584 and Kopelman ‘780 and Xue fail to teach identifying the one or more attachments based on cross-validating the first set of potential attachments with the second set of potential attachments.
Kopelman ‘524 teaches identifying the one or more attachments based on cross-validating the first set of potential attachments with the second set of potential attachments (Fig. 4 and Fig. 5 shows areas of interest as attachments. [0074] At block 230, processing logic compares the second intraoral image to the first intraoral image. [0075] if a candidate intraoral area of interest does not correspond to a region of a surface from another intraoral image, then the candidate intraoral image may be verified as an actual intraoral area of interest. Accordingly, the second intraoral image may be used to confirm or dismiss candidate intraoral areas of interest from the first intraoral image. Examiner note: the reference teaches candidate intraoral areas of interest generated from a first intraoral image and using a second intraoral image to confirm or dismiss those candidates, such that candidates satisfy the comparison are verified as actual intraoral area of interest. Under BRI, the candidate intraoral areas of interest correspond to a first set of potential attachments, the information derived from the second intraoral image corresponds to a second set of potential attachments, and the confirmation or dismissal process corresponds to cross-validating the first set with the second set to identify the one or more areas of interest (i.e., attachments).).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 and Kopelman ‘780 and Xue to incorporate the teachings of Kopelman ‘524, and apply candidate region validation across multiple intraoral datasets in order to cross-validate potential attachment candidates derived from prior model data and machine learning based detection to determine a final set of attachment. The combinations of teachings would predictably provide benefit of improving the accuracy , robustness, and automation of identifying attachments in dental model data by reducing false positives and ensuing consistency across multiple data sources, thereby reducing manual intervention and improving process efficiency.
Claim 9, Kopelman ‘584 and Kopelman ‘780 and Xue fail to teach, but Kopelman ‘524 teaches The method of claim 8, wherein identifying the one or more attachments based on cross-validating further comprises discarding potential attachments of the first or second set of potential attachments that are close to interproximal or occlusal tooth areas if another of the first or second set of potential attachments lacks matching potential attachments ([0075] … candidate intraoral areas of interest from an intraoral image are dismissed if they correspond to a surface (e.g., of a dental site) from another intraoral image. Alternatively, if a candidate intraoral area of interest does not correspond to a region of a surface from another intraoral image, then the candidate intraoral image may be verified as an actual intraoral area of interest. Accordingly, the second intraoral image may be used to confirm or dismiss candidate intraoral areas of interest from the first intraoral image. Examiner note: the reference teaches identifying candidate intraoral areas of interest from a first intraoral dataset and using a second intraoral dataset to confirm or dismiss the candidates based on correspondence between datasets. Under the BRI, the candidate intraoral areas of interest correspond to potential dental feature (e.g., attachments, foreign objects, or surface anomalies) detected in model data. The comparison between intraoral datasets corresponds to cross-validating a first set of potential attachments with a second set of potential attachments derived from another source. Candidates lacking correspondence between dataset are dismissed, which corresponds to discarding potential attachments when another set lacks matching potential attachments. A POSITA would understand that the cross dataset validation evaluates candidate feature located in tooth regions, including interproximal and occlusal areas, and removes unmatched candidates as false positives.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 and Kopelman ‘780 and Xue to incorporate the teachings of Kopelman ‘524, and apply candidate region validation across multiple intraoral datasets in order to cross-validate potential attachment candidates derived from prior model data and machine learning based detection to determine a final set of attachment. The combinations of teachings would predictably provide benefit of improving the accuracy , robustness, and automation of identifying attachments in dental model data by reducing false positives and ensuing consistency across multiple data sources, thereby improving automation and improving process efficiency.
Claim 10, Kopelman ‘584 and Kopelman ‘780 and Xue fail to teach, but Kopelman ‘524 teaches The method of claim 8, wherein identifying the one or more attachments based on cross- validating further comprises discarding, from the second set of potential attachments, potential attachments for areas that do not have significant deviations in the model data compared to the previous model data ([0075] … candidate intraoral areas of interest from an intraoral image are dismissed if they correspond to a surface (e.g., of a dental site) from another intraoral image. Alternatively, if a candidate intraoral area of interest does not correspond to a region of a surface from another intraoral image, then the candidate intraoral image may be verified as an actual intraoral area of interest. Accordingly, the second intraoral image may be used to confirm or dismiss candidate intraoral areas of interest from the first intraoral image. Examiner note: he reference teaches comparing candidate intraoral regions across multiple intraoral images and dismissing candidates when the regions correspond to surfaces in another image, which indicates the regions unchanged dental structures rather than new feature of interest. Under the BRI, the candidate intraoral areas of interest correspond to potential dental feature (e.g., attachments, foreign objects, or surface anomalies) detected in model data, and the determination the candidate regions “correspond to a surface from another intraoral image” corresponds to determining that there are no significant deviations between the first and second images. A POSITA would understand that attachments or anomalies are identified based on deviations from dental structures, and candidates located in regions lacking significant deviation would be discarded as false positives.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 and Kopelman ‘780 and Xue to incorporate the teachings of Kopelman ‘524, and apply candidate region validation across multiple intraoral datasets to confirm or dismiss candidate intraoral areas of interest based on correspondence between datasets in order to cross-validate potential attachment candidates derived from prior model data and machine learning based detection, and discard potential attachments corresponding to regions that do not have deviations relative to the prior model data when a final set of attachment. The combinations of teachings would predictably provide benefit of improving the accuracy , robustness, and automation of identifying attachments in dental model data by reducing false positives associated with unchanged anatomical regions and ensuring consistency across multiple data sources, thereby improving process efficiency.
The elements of claims 23-25 are substantially the same as those of claims 8-10. Therefore, the elements of claims 23-25 are rejected due to the same reasons as outlined above for claims 8-10.
Claim(s) 11 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman ‘584 and Kopelman ‘780 and Xue and Kopelman ‘524 as applied to claims 8 and 23 above, and further in view of Jung (“Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation,” published in 2018).
Claim 11, Kopelman ‘584 and Kopelman ‘780 and Xue fail to teach, but Kopelman ‘524 teaches The method of claim 8, wherein identifying the one or more attachments based on cross- validating further comprises discarding, from the first set of potential attachments, potential attachments ([0075] … candidate intraoral areas of interest from an intraoral image are dismissed if they correspond to a surface (e.g., of a dental site) from another intraoral image. Alternatively, if a candidate intraoral area of interest does not correspond to a region of a surface from another intraoral image, then the candidate intraoral image may be verified as an actual intraoral area of interest. Accordingly, the second intraoral image may be used to confirm or dismiss candidate intraoral areas of interest from the first intraoral image. Examiner note: the reference teaches comparing candidate intraoral regions across multiple intraoral images and dismissing candidates when the regions correspond to surfaces in another image, which indicates the regions do not have new feature of interest. Under the BRI, the candidate intraoral areas of interest correspond potential dental feature (e.g., attachments, foreign objects, or surface anomalies) detected in model data, and the determination the candidate regions “correspond to a surface from another intraoral image” corresponds to determining that there are no significant deviations between the first and second images. A POSITA would understand that candidate dental features that do not intersect or correspond with features confirmed from another dataset would be discarded as false positives).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 and Kopelman ‘780 and Xue to incorporate the teachings of Kopelman ‘524, and apply candidate region validation across multiple intraoral datasets to confirm or dismiss candidate intraoral areas of interest based on correspondence between datasets in order to cross-validate potential attachment candidates derived from prior model data and machine learning based detection, and discard potential attachments corresponding to regions that do not have deviations relative to the prior model data when a final set of attachment. The combinations of teachings would predictably provide benefit of improving the accuracy , robustness, and automation of identifying attachments in dental model data by reducing false positives associated with unchanged anatomical regions and ensuring consistency across multiple data sources, thereby improving process efficiency.
However, Kopelman ‘584 and Kopelman ‘780 and Xue and Kopelman ‘524 fail to teach discarding potential attachments having a small distance to a corresponding tooth surface.
Jung teaches discarding potential attachments having a small distance to a corresponding tooth surface (page.5, 2.2.1. Defining an Edge Point on Intensity Profile, “For this intensity profile generating process, two parameters are needed, the maximum distance and the interval. In this study, we used 10 voxels as the maximum distance …” page.6, 2.2.2. Dynamic Segmentation, “For a single point on the surface data, a corresponding point in the volume data can be found by intensity profile analysis … a set of corresponding points … can be obtained … Then, the sum of the distance between the corresponding points is minimized …” Page.7, 2.3.2. Length Value to Generate Intensity Profile, “… if the intensity profile is set to a long length value, unintended edge points can be detected and wrong correspondences lead to inaccurate registration results. Edge points of gums, tissue regions or metal artifact regions can be ignored automatically by the proper length value.” Examiner note: the reference teaches determining corresponding points between surface data and volumetric data and evaluating distances between the corresponding points during segmentation and registration, and improper distance parameters may lead to unintended edge detections and that not relevant edge points (e.g., gums, tissue regions, or artifacts) can be ignored automatically by selecting an appropriated length or distance parameter. Under the BRI, determining that candidate features lie within a small distance threshold relative to a corresponding surface and ignoring the features corresponds to discarding potential attachments having a small distance to a corresponding tooth surface. A POSITA would understand that features located very close to an underlying anatomical surface (e.g., tooth) as surface noise or artifacts rather than real attachments and would be discarded during model processing).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman ‘584 and Kopelman ‘780 and Xue and Kopelman ‘524 to incorporate the teachings of Jung, and apply distance correspondence and filtering techniques in order to improve the reliability of feature validation by filtering out detection that are likely caused by surface noise, segmentation artifacts, or registration inaccuracies. The combinations of teachings would predictably provide benefit of improving the accuracy , robustness, and automation of identifying attachments in dental model data by reducing false positives associated with unchanged anatomical regions and ensuring consistency across multiple data sources, thereby improving process efficiency.
The elements of claim 26 is substantially the same as those of claim 11. Therefore, the elements of claim 26 is rejected due to the same reasons as outlined above for claim 11.
Claim(s) 12-13 and 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman
US20200022584A1 in view of Blankenbecler US 20230142509A1 and Lints US20200160122A1.
Claim 12, Kopelman teaches presenting the modified model data as discussed above with respect to claims 1. However, Kopelman fails to teach, but Blankenbecler teaches displaying, ([0048] … a 3D image 12 obtained from an intraoral scan of a patient and the various tools available for the user to manipulate the 3D image 12, … The bracket removal dashboard 10 may be displayed … In FIG. 1, the 3D image 12 is seen as representing the upper jaw 18 and corresponding teeth 20 of a patient, where each of the patient's teeth comprises an orthodontic bracket 22. [0050] The user then selects the “Select Brackets” option 30 within a bracket removal tool 28 which itself is a portion of the suite of texture manipulation tools 16. [0064] The user then selects the Select Brackets option 30 within the bracket removal tool 28 and then in FIG. 15A begins to draw a line 32 around the outer perimeter of the band 72 until a complete circle is formed around the band 72 (FIG. 15B). Next, the user actuates the Remove Brackets option 34 from the bracket removal tool 28 which, in the same manner discussed above with regard to the removal of a bracket 22, causes the algorithm underlying the bracket removal dashboard 10 to remove the image data contained within the circle formed by the line 32.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman to incorporate the teachings of Blankenbecler, and apply displaying a bracket removal dashboard including selectable bracket removal tools and options associated with detected brackets in order to enable presentation of multiple attachment removal options corresponding to detected attachments within the dental model processing environment. The combinations of teachings would predictably provide benefit of improving user interaction efficiency and flexibility by allowing a user to select among alternative attachment removal operations based on detected attachment locations.
However, Kopelman and Blankenbecler fail to teach corresponding confidence values.
Lints teaches corresponding confidence values ([0075] … confidence score data 460 can be computed for each entry and/or an overall confidence score, for example, corresponding to consensus diagnosis data, can be based on calculated distance or other error and/or discrepancies between the entries.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman and Blankenbecler to incorporate the teachings of Lints, and apply confidence score determination corresponding to detected features in order to provide quantitative reliability information associated with the plurality of attachment removal options generated in the dental model processing system. The combinations of teachings would predictably provide benefit of improving decision reliability and user guidance by presenting attachment removal options together with corresponding confidence values indicating the accuracy of the detected attachments.
Claim 13, Kopelman and Blankenbecler fail to teach, but Lints teaches The method of claim 12, wherein the confidence values are based on a degree of similarity between corresponding attachments detected via a plurality of detection approaches ([0074] In some embodiments, if a medical scan was reviewed by multiple entities, multiple, separate diagnosis data entries 440 can be included in the medical scan entry 352, … [0042] Annotation similarity data can be generated by comparing the first annotation data to the second annotation data, and consensus annotation data can be generated based on the first annotation data and the second annotation data … [0075] … confidence score data 460 can be computed for each entry and/or an overall confidence score, for example, corresponding to consensus diagnosis data, can be based on calculated distance or other error and/or discrepancies between the entries.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kopelman and Blankenbecler to incorporate the teachings of Lints, and apply similarity confidence determination using multiple detection sources in order to determine confidence values based on agreement between corresponding detected structures. The combinations of teachings would predictably provide benefit of improving the reliability and robustness of attachment identification by basing confidence values on agreement between multiple detecting approaches, thereby improving decision accuracy and reducing uncertainty in dental model processing.
The elements of claims 27-28 are substantially the same as those of claims 12-13. Therefore, the elements of claims 27-28 are rejected due to the same reasons as outlined above for claims 12-13.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be
directed to YI HAO whose telephone number is (571)270-1303. The examiner can normally be reached Monday - Friday.
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, Emerson Puente can be reached at (571)272-3652. 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.
/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187