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 .
Response to Amendment
The amendment filed 06/09/2026 has been entered. As directed, claims 1, 7, 9, 16, 22, 24 and 31
have been amended, claims 6 and 21 have been canceled, no claim has been added. Thus claims 1-5, 7-20 and 22-31 remain pending in the application.
Response to Arguments
With respect to the Applicant’s argued rejection under 35 U.S.C 101 in “Applicant Arguments/Remarks Made in an Amendment”:
Applicant argues:
…
The Applicant submits that the claims are not directed to an abstract idea, and even if the claims were viewed as involving an abstract concept, they recite additional elements that amount to significantly more than the purported abstract idea.
During orthodontic treatment, a patient may be prescribed to wear a series of patient- removable appliances (e.g., "aligners") to move some or all of their teeth in accordance with a treatment plan. The treatment plan may treat a variety of dental problems such as, for example, malocclusions. The treatment plan may be used to manufacture a number of aligners that are worn in sequence to sequentially move the patient's teeth. Sometimes a patient's teeth do not move according to the treatment plan, and the treatment plan may be adjusted. A dental professional rescans the patient's teeth to revise the treatment plan. The rescan can include artifacts, such as attachments that have been applied to the patient's teeth. The artifacts may adversely affect treatment plans moving forward.
The claims here solve this problem by 1) detecting dental attachments that appear in a dental model of a patient and 2) removing from the model the detected attachment. The claims accomplish this by receiving model data of a dental structure of the patient, the model data including one or more attachments on the dental structure, wherein the one or more attachments engage with an aligner worn by the patient over the dental structure, detecting, using a machine learning model, extra material on the dental structure, identifying the extra material as the one or more attachments, and modifying the model data to remove the detected one or more attachments. In this manner, the model data can be free of attachments that were placed on the patient's teeth as part of a previous treatment plan.
I. The claims are directed to a specific technological improvement, not an abstract idea
The Office Action characterizes the claims as allegedly, except for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the human mind, and is therefore an abstract idea. This characterization oversimplifies the claims and ignores their technical focus. Instead, the claims are directed to a concrete technological solution. The claims are directed to a specific, practical improvement in orthodontic treatment technology, namely:
" receiving dental model data of a patient that includes one or more attachments,
" detecting, using a machine learning model, extra material on the dental structure,
" identifying the extra material as the one or more attachments, and
" modifying the model data to remove the detected one or more attachments.
Thus, the claims improve revision treatment planning by automatically detecting and removing attachments from a patient's dental model. Dental models, including 3D dental models, that are based on intraoral scans can be large and unwieldy. Persons having skill in the art will recognize that dental models have hundreds or thousands of vertices and triangles. Modifying the dental model is more than just the identification of an attachment, the dental model must be edited or modified by editing or modifying the underlying data. This is not something that can be done with pen and paper simply because the model data is too large. The claims recite a specialized and technological solution to a clinical and computational problem unique to digital orthodontic treatment systems.
Accordingly, the claims are patent-eligible under Alice Step One.
II. The claims recite significantly more than the purported abstract idea
Even if the claims were viewed as involving an abstract concept, the claims nevertheless recite additional elements that amount to significantly more than the purported abstract idea. The claims recite a specific sequence of operations that is not routine or conventional:
" automatically detecting extra material in a dental model of a patient's dental
structure,
" identifying attachments within the extra material, and
" removing the attachments from the dental model.
The recited order combination produces a new and useful capability that addresses a technological problem in a non-conventional way, which amount to significant more than the purported abstract idea (mental process). Accordingly, the claims are patent-eligible under Alice Step Two.
For at least the reasons set forth above, the Applicant submits that the claims are patent- eligible and requests withdrawn of the rejection under 35 U.S.C. § 101
(see Response filed 06/09/2026 [pages 10-13]).
Applicant’s arguments regarding § 101 have been considered but are not persuasive.
First, Applicant argues that the limitations of detecting extra material, identifying the extra material as one or more attachments, and modifying the model data to remove the detected attachments provide a practical improvement in orthodontic treatment technology. However, these limitations recite observation, evaluation, judgement, and alteration that fall within the mental process grouping of abstract idea. The alleged improvement in orthodontic treatment technology results from these limitations constituting the judicial exception, rather than from additional elements or combination of additional elements. The receiving limitation merely gathers the model data for the mental process, and the machine learning model is broadly recited as a tool to perform the detection. As explained in MPEP 2106.05(a), II.: "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited satisfying persona design goals and persona capability) is not an improvement in technology." (emphasis added). Accordingly, the additional elements, considered individually and in combination, do not integrate the judicial exception into a practical application.
Second, Applicant argues that a 3D dental model may include hundreds or thousands of vertices and triangles and that modifying the dental model cannot be performed mentally or with pen and paper. This argument is not commensurate with the scope of the claims. The claims do not require any particular model format, mesh representation, number of vertices or triangles, model size, or computational workload. Rather, under the broadest reasonable interpretation, the claims encompass identifying a depicted attachment and altering the representation to omit, erase, or redraw the identified attachment, which may be performed in the human mind or with the aided of pen and paper. Accordingly, under the broadest reasonable interpretation (BRI) in light of specification, the modifying limitation, but for the recitation of generic computing component, covers performance of the recited operation in the human mind or by a human using a pen and paper.
Third, Applicant argues that the specific ordered combination of limitations is not routine or conventional and produces a new and useful capability that addresses a technological problem in a nonconventional manner, thereby amounting to significantly more than the judicial exception. This argument is not persuasive because these limitations constitute the identified mental process, rather than additional elements evaluated under Step 2B for an inventive concept. As previously discussed, the remaining additional elements merely recite receiving the model data by using generic computing components, and a machine learning model as a tool for performing the detection. Therefore, the additional elements, when considered individually and in combination, perform their ordinary functions and do not add significantly more than the judicial exception.
Accordingly, as discussed above, the amended claims remain directed to an abstract idea including mental processes. The additional elements, considered individually and in combination, do not integrate the abstract idea into a practical application and do not amount to significantly more than the judicial exception. Therefore, the rejection of claims 1, 16 and 31, and the claims depend thereon, under 35 U.S.C. 101 is maintained.
Applicant’s arguments with respect to rejection of claim(s) 1, 16 and 31 based on Kopelman ‘584
and Xue have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The amendments changed the scope of claims and necessitated a new ground of rejection set forth in this Office Action. The current rejection newly applies Kuo (US20190090982A1) to teach dental model data including aligner engaging attachments and detecting and identifying portions of the model corresponding to the attachments. Kopelman (US20180168780A1), although previously cited against dependent claims, is now relied upon to teach using machine learning to analyze dental data and identify areas of interest, including foreign objects. Accordingly, the combined teachings of Kuo and Kopelman ‘780 teach or suggest the amended limitations of claims 1, 16 and 31. Therefore, the rejection of claims 1, 16 and 31 under 35 U.S.C. 103 is maintained.
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 1-5, 7-20 and 22-31 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.
Claims 1, 16 and 31 recite the limitation "the detected one or more attachments". There is insufficient antecedent basis for this limitation in the claim.
Claims 8 and 23 recite “a machine learning model,” which renders the claim indefinite because it is unclear if the “a machine learning model” refers to the “a machine learning model” recited in claims 1 and 16 or a separate machine learning model. For the purpose of substantive examination, the examiner presumes that “a machine learning model” has an antecedent basis in claims 1 and 16 as “the machine learning model”.
The remaining claims are dependent upon one of the claims listed above and 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) 1-5, 7-20, and 22-31 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 1-5 and 7-15 are directed to method and fall within the statutory category of process;
Yes, Claims 16-20 and 22-30 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 machine.
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: “detecting … extra material on the dental structure; identifying the extra material as the one or more attachments; modifying the model data to remove the detected 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. For example, a person is capable of observing a representation of the patient’s dental structure, determining which portions of the representation depict extra material, identifying the extra martial as one or more attachments, and altering the representation to depict the dental structure without the identified attachment, such as by omitting, erasing, or redrawing the identified attachments. The steps include observation, evaluation, judgment, and alteration 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, the model data including one or more attachments on the dental structure, wherein the one or more attachments engage with an aligner worn by the patient over the dental structure” 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).
Further, the following additional elements: “detecting … using a machine learning model …” which is merely 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. See MPEP 2106.05(f).
In particular, the claim does not recite a particular machine learning model architecture, algorithm, training technique, feature extraction technique, inference operation, or a manner in which the machine learning model improves computer, machine learning functionality, or any other technology or technical field. Rather, the machine learning model is merely recited at a high level as a tool to perform detect function.
Alternatively, 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 additional limitation merely uses a machine learning model as a tool to perform the recited mental process and does not integrate the judicial exception into a practical application.
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)); …
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,…
In particular, the claim recites the additional elements including generic computing components, receiving and presenting model data, and a machine learning model. These elements merely perform their ordinary functions of receiving, processing, and presenting information, and apply a machine learning model to dental model data to detect extra martial. Therefore, the additional elements, when considered individually and in combination, merely apply the judicial exception using
generic computing components functionality and do not provide significantly more than the judicial
exception.
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 2-5, 7-15, 17-20, 22-30 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 2-5, 7-15, 17-20, 22-30 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 7 recites “The method of claim 1, 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: identifying attachments from the first and second set of potential attachments that are close to interproximal or occlusal tooth areas; determining whether each of the identified attachments in the first set of potential attachments has a corresponding attachment in the second set of potential attachments; and discarding the identified attachments that lack a corresponding attachment,” 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 two sets of potential attachment locations, identifying which attachments are close to interproximal or occlusal tooth areas, comparing each identified attachment in the first set with the second set to determine whether a corresponding attachment exists, and deciding to disregard an identified attachment of candidate attachments when no corresponding attachment is found. 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 mental process (e.g., determining a confidence values based on similarity comparison between corresponding attachments) and/or 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 evaluating how the altered 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 17-20 and 22-30 recite the similar elements as claims 2-5 and 7-15, and are rejected for the same reasons under 35 U.S.C. 101.
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) 1-2, 5, 14-17, 20 and 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over
Kuo US20190090982A1 in view of Kopelman US20180168780A1.
Claim 1, Kuo teaches A method for adjusting three-dimensional (3D) dental model data ([0019] A new digital dental model of the aligned teeth (either fully or partially) can be created based on a model of the current teeth with the current dental appliance present … For example, the new digital dental model may be created by removing the current dental appliance from the current digital dental model … [0053] … a current digital dental model 210 (FIGS. 2b-6b ) and the new digital dental models 220-620 (FIGS. 2c-6c ) are three dimensional models.), 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, wherein the one or more attachments engage with an aligner worn by the patient over the dental structure ([0024] Example 120 depicts the patient's set of physical teeth 100 with dental attachments 122 that are suitable for use with a removable plastic positioning dental appliance, such as an aligner. [0025] The dental brackets and dental attachments as depicted in FIGS. 1b, 1c, 1d, 1f, 1g are considered some examples of and shall be referred to a “current dental appliance” since the dental appliances 112, 122, 132, 132, 152, 162 are currently attached to the patient's physical teeth 100. The dental attachments 122 may be aligner attachments for use with an aligner. [0056] The current-digital-dental-model-receiving-component 910 is suitable for receiving a current digital dental model 210 (FIGS. 2b-6b ) that includes a representation of the set of physical teeth 100 (FIG. 1a ) for the patient with the current dental appliance 112, 122, 132, 152, 162, 800, 820 (FIGS. 1b, 1c, 1d, 1f, 1g, 8a, 8b ) attached to the physical teeth 100 or oral cavity 810 a, 810 b (FIGS. 8a, 8b ) …);
detecting, ([0030] Each of the segmented digital teeth 200a -200d can be superimposed on the corresponding digital teeth 210a - 210d associated with the current digital dental model 210 … The non-superimposed portion includes any portion of the current digital dental model 210 that is not part of the original digital dental model as represented by the segmented digital teeth 200. [0065] At 1130, a non-superimposed portion of the current digital dental model 210 is determined based on the superimposing 1120. Examiner note: the reference teaches determining a non-superimposed portion of the current digital dental model by comparing the current model with segmented digital teeth representing the patient’s teeth without the dental appliance. The non-superimposed portion corresponds to the extra material because it is the additional material or geometry present on the dental structure beyond the underlying tooth geometry);
identifying the extra material as the one or more attachments; ([0030] For example, the non-superimposed portion can include the current dental appliance 112 and any cement that is used for attaching the current dental appliance 112 to the patient's physical teeth 100 (FIG. 1b ).);
modifying the model data to remove the detected one or more attachments ([0030] The new digital dental model 220 can be created either by removing the non superimposed portion of the current digital dental model 210.); and
presenting the modified model data ([0064] At 1030, a new digital dental model 220-620 (FIGS. 2c-6c) that includes the representation of the set of physical teeth 100 (FIG. 1a ) without including the current dental appliance 112, 122, 132, 152, 162, 800, 820 (FIGS. 1b, 1c, 1d, 1f, 1g, 8a, 8b ) is created based on the current digital dental model 210 (FIG. 2b ).).
However, Kuo fails to teach detecting, using a machine learning model, extra material on the dental structure.
Kopelman teaches detecting, using a machine learning model, extra material on the dental structure ([0065] AOI identifying modules 115 are responsible for identifying areas of interest (AOIs) from image data 135 received from image capture device 160 … Areas of interest may also include areas indicative of foreign objects (e.g., studs, bridges, etc.), areas for the dental practitioner to perform planned treatment, or the like … The analysis may involve direct analysis (e.g., pixel-based and/or other point-based analysis), the application of machine learning, the application of image registration, and/or the application of image recognition. [0096] The dental condition identifier 174 may then provide an image or an extracted representation of a dentition feature to the dental condition profile 192 and receive an indication of potential AOIs. [0097] A dental condition profile 192 may be trained by extracting contents from a training data set and performing machine-learning analysis on the contents to generate a classification model and a feature set for the particular dental condition. Examiner note: The reference teaches apply machine learning to identify an area indicative of a foreign object on the patient’s dental structure. The area indicative of a foreign object corresponds to the extra material because it represents material or an object present on the dental structure in addition to the underlying dental anatomy. The trained dental condition profile and its classification model correspond to the machine learning model).
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 Kuo to incorporate the teachings of Kopelman, and apply machine learning based analysis of dental data to identify areas indicative of foreign objects in order to improve the accuracy and reliability of detecting extra material on the dental structure . In this case, Ye teaches determining a non-superimposed portion of a current digital dental model, wherein the non-superimposed portion may include the current dental appliance and cement. Kopelman teaches using a trained machine learning classification model to identify areas indicative of foreign objects in dental data and determine a confidence level for the classification. The combinations of teachings would predictably provide benefit of more accurately and reliably detecting the extra material for subsequent identification and removal from the dental model.
Claim 2, Kuo fails to teach, but Kopelman teaches The method of claim 1, wherein detecting the one or more attachments further comprises: 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 Kuo to incorporate the teachings of Kopelman, 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, Kuo fails to teach, but Kopelman teaches The method of claim 1, wherein presenting the modified model data further comprises 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 Kuo to incorporate the teachings of Kopelman, 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.
Claim 14, Kuo teaches The method of claim 1, further comprising updating a treatment plan for the patient using the modified model data ([0073] Therefore, according to one embodiment, when the current digital dental model 210 (FIGS. 2b-6b ) is created when the patient's physical teeth 100 are close but not at the desired teeth arrangement, positions of one or more digital teeth in the new digital dental model 220-620 (FIGS. 2c-6c ) are adjusted to the desired teeth arrangement … Information pertaining to one or more dimensions of the feature or descriptions of the feature can be used to determine how to adjust the positions of the one or more digital teeth in the new digital dental model 220-620 (FIGS. 2c-6c ) … A subsequent dental appliance that is manufactured based on a new digital dental model 220-620 (FIGS. 2c-6c ) adjusted to the desired teeth arrangement can be used to move the physical teeth 100 (FIG. 1a ) to the desired teeth arrangement. See also [0074]. Examiner note: the reference teaches modifying the new digital dental model by adjusting the positions of one or more digital teeth to a desired teeth arrangement and using the adjusted model to move the patient’s teeth to that desired arrangement. Under the broadest reasonable interpretation, adjusting the planned arrangement of the patient’s teeth in the modified model data constitutes updating the treatment plan using the modified model data).
Claim 15, Kuo teaches The method of claim 14, further comprising fabricating an orthodontic appliance based on the treatment plan ([0074] At 1040, digital data suitable for use in manufacturing the subsequent dental appliance is provided based on electronic data included in the new digital dental model 220-620 (FIGS. 2c-6c ) prior to removal of the current dental appliance 112, 122, 132, 152, 162, 800, 820 (FIGS. 1b, 1c, 1d, 1f, 1g, 8a, 8b ) from the set of physical teeth 100 or the oral cavity 810 a, 810 b (FIGS. 1a, 8a, 8b ) … In the event that treatment is a combination between braces and removable aligners, the subsequent device may be a clear removable aligner or series of aligners similar to a retainer, but designed to continue with orthodontic movement of the teeth. [0076] According to one embodiment, a subsequent dental appliance can be manufactured based on the provided digital data, such as electronic data included in the new digital dental model, prior to removal of all or part of the current dental appliance from the set of physical teeth. For example, the electronic data included in the new digital dental model can be used to fabricate a mold using a rapid-prototyping machine or milling machine and forming the subsequent dental appliance over the mold. Examiner note: the reference teaches fabricating the subsequent orthodontic appliance, including a clear removable aligner or series of aligners designed to continue orthodontic movement, based on the digital dental model representing the desired teeth arrangement.).
The elements of claims 16-17, 20 and 29-31 are substantially the same as those of claims 1-2, 5 and 14-15. Therefore, the elements of claims 16-17, 20 and 29-31 are rejected due to the same reasons as outlined above for claims 1-2, 5 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 Kuo [0083]).
Claim(s) 3-4 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo and
Kopelman as applied to claims 2 and 17 above, and further in view of Chen US20190159868A1.
Claim 3, Kuo fails to teach, but Kopelman teaches 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 ([0102] For example, prior data comparator 180 may match points of one image with the closest points interpolated on the surface of the other image, and iteratively minimize the distance between matched points … Other techniques that may be used for image registration include those based on determining point-to-point correspondences using other features and minimization of point-to-surface distances, for example. [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. [0109] Prior data comparator 180 may determine a magnitude of a change in a dental condition based on the determined differences between the current image data 162 and the previous image data. Examiner note: the reference teaches comparing current dental data with previous dental data, including determining a change in an attachment previously attached to a corresponding tooth, and further teaches matching corresponding points of the current and previous dental surfaces, calculating point to surface distances, and determining a magnitude of the identified difference. Under BRI, the calculated distance or magnitude representing the extent of the attachment related surface difference relative to the corresponding tooth constitutes a depth from the attachment to the corresponding tooth based on the previous model data);
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 Kuo to incorporate the teachings of Kopelman, and apply matching corresponding current and previous dental surfaces and calculating point-to -surface distances and a magnitude of the identified difference to the attachment removal method of Kuo in order to quantify the extent by which the detected attachment surface differs from the corresponding tooth surface represented by the previous model data. The combinations of teachings would predictably provide benefit of more accurately determining the amount of attachment material to be digitally removed and thereby improve the precision and reliability of modifying the attachment region relative to the corresponding tooth surface.
However, Kuo and Kopelman 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 calculated depth (Fig.13A-13C; [0092] … 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 , which results in a hole 1304 on the tooth surface. [0093] In step 1010, tooth surfaces of the segmented tooth 1202 having the bracket removed are automatically reconstructed … hole-filling procedures (e.g., tooth or crown surface reconstruction) can include a first step to generate an initial patch to fill the hole and a second step to smooth the reconstructed mesh to obtain better quality polygons (e.g., triangles) therein. [0094] A closed polygon 1303′ represents a boundary of the (removed) bracket. A region 1308 enclosed by the closed polygon 1303′ is the hole left by bracket removal. First in step 1010, an initial patch is generated to fill the tooth surface or hole 1308 (e.g., within the closed polygon 1303′). [0095] … the second part of step 1010 can correct positions of points created in the initial patch using local information globally. Thus, the 3D mesh including the initial patch … can be smoothed using a Laplacian smoothing method that adjusts the location of each mesh vertex to the geometric center of its neighbor vertices. [0096] … a patch refinement algorithm using the Poisson equation with Dirichlet boundary conditions … [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. Examiner note: The reference teaches that removal of the outward bracket region mesh creates a 3D hole extending from the bracket surface toward the underlying tooth surface, and constructs the tooth surface by generating a patch within the 3D hole and correcting the positions of the patch vertices based on the boundary and surrounding mesh geometry. The reference further teaches that an earlier intraoral scan obtained before the braces were attach may be used for the tooth surface reconstruction. The spatial extent between the outward bracket surface and the reconstructed underlying tooth surface corresponds to a depth of the attachment region. Because the reconstructed patch is positioned and adjusted within that spatial extent toward the underlying tooth surface, the reference teaches adjusting the attachment region surface toward a direction inside the tooth base on the depth).
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 Kuo and Kopelman to incorporate the teachings of Chen, and apply bracket region removal and tooth surface reconstruction technique in order to adjust the detected attachment surface toward the underlying tooth surface based on the attachment depth determined from the current and previous dental model data. The combinations of teachings would predictably provide benefit of more accurately removing the attachment geometry and restoring the attachment region to the corresponding tooth contour.
Claim 4, Kuo and Kopelman 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 ([0089] As shown in FIG. 10, a virtual or digital 3D dentition mesh model is obtained in step 1002. For example, a digital 3D dentition mesh model can be obtained by using an intraoral scanner. [0092] In one exemplary embodiment, 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. As shown in FIG. 13A, these identified margin vertices form a closed 3D curve or bracket boundary 1303 (or the boundary vertices of the bracket) that surrounds the bracket 1302. 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. [0094]. A closed polygon 1303′ represents a boundary of the (removed) bracket. A region 1308 enclosed by the closed polygon 1303′ is the hole left by bracket removal. First in step 1010, an initial patch is generated to fill the tooth surface or hole 1308 (e.g., within the closed polygon 1303′). [0095] In one embodiment, the second part of step 1010 can correct positions of points created in the initial patch using local information globally. Thus, the 3D mesh including the initial patch … can be smoothed using a Laplacian smoothing method that adjusts the location of each mesh vertex to the geometric center of its neighbor vertices. Examiner note: The reference teaches detecting a closed 3D boundary surrounding the bracket, generating an initial mesh path within the region enclosed by the detected bracket boundary, and smoothing the reconstructed surface by correcting point positions and adjusting the location of each mesh vertex. Therefore, the reference teaches moving scan vertices inside a detected area corresponding to the attachment in the direction toward the reconstrued tooth surface, i.e., 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 Kuo and Kopelman to incorporate the teachings of Chen, and apply mesh patch generation and smoothing technique in order to reconstruct the tooth surface within the attachment region after removal of the attachment geometry. The combinations of teachings would predictably provide benefit of producing a complete and accurately reconstructed tooth surface while improving the mesh within the modified attachment region.
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) 7 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo and Kopelman
as applied to claims 1 and 16 above, and further in view of Xue 20190180443A1 and Chen US20190159868A1.
Claim 7, Kuo teaches attachments engage with an aligner worn by the patient over the dental structure recited in claim 1. However, Kuo and Kopelman fail to teach, but Xue teaches The method of claim 1, 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 ([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. See also [0124] and [0137]. Examiner note: the reference teaches using a trained machine learning model to classify edges corresponding to dental structures and subsequently computing spatial relationships, including distance between 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 Kuo and 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, Kuo and 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] … 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 , which results in a hole 1304 on the tooth surface. [0093] In step 1010, tooth surfaces of the segmented tooth 1202 having the bracket removed are automatically reconstructed … hole-filling procedures (e.g., tooth or crown surface reconstruction) can include a first step to generate an initial patch to fill the hole and a second step to smooth the reconstructed mesh to obtain better quality polygons (e.g., triangles) therein. [0094] A closed polygon 1303′ represents a boundary of the (removed) bracket. A region 1308 enclosed by the closed polygon 1303′ is the hole left by bracket removal. First in step 1010, an initial patch is generated to fill the tooth surface or hole 1308 (e.g., within the closed polygon 1303′). [0095] … the second part of step 1010 can correct positions of points created in the initial patch using local information globally. Thus, the 3D mesh including the initial patch … can be smoothed using a Laplacian smoothing method that adjusts the location of each mesh vertex to the geometric center of its neighbor vertices. [0096] … a patch refinement algorithm using the Poisson equation with Dirichlet boundary conditions … [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. Examiner note: The reference teaches that removal of the outward bracket region mesh creates a 3D hole extending from the bracket surface toward the underlying tooth surface, and constructs the tooth surface by generating a patch within the 3D hole and correcting the positions of the patch vertices based on the boundary and surrounding mesh geometry. The reference further teaches that an earlier intraoral scan obtained before the braces were attach may be used for the tooth surface reconstruction. The spatial extent between the outward bracket surface and the reconstructed underlying tooth surface corresponds to a depth of the attachment region. Because the reconstructed patch is positioned and adjusted within that spatial extent toward the underlying tooth surface, the reference teaches adjusting the attachment region surface toward a direction inside the tooth base on the depth).
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 Kuo and Kopelman and Xue to incorporate the teachings of Chen, and apply bracket region removal and tooth surface reconstruction technique in order to adjust the detected attachment surface toward the underlying tooth surface based on the attachment depth determined from the current and previous dental model data. The combinations of teachings would predictably provide benefit of more accurately removing the attachment geometry and restoring the attachment region to the corresponding tooth contour.
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 Kuo and
Kopelman as applied to claims 1 and 16 above, and further in view of Kopelman US20190029524A1.
Claim 8, Kuo teaches 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 ([0029] … a digital scan of the patient's physical teeth 100 is taken without any dental appliance being on the patient's physical teeth 100. [0030] Each of the segmented digital teeth 200 a-200 d can be superimposed on the corresponding digital teeth 210 a-210 d associated with the current digital dental model 210 … The non-superimposed portion includes any portion of the current digital dental model 210 that is not part of the original digital dental model as represented by the segmented digital teeth 200. For example, the non-superimposed portion can include the current dental appliance 112 and any cement that is used for attaching the current dental appliance 112 to the patient's physical teeth 100 … [0032] Examples of a portion 300 a, 300 b of the current dental appliance 112 (FIG. 1b ) are a bracket, a wire, a band, a tube, a cleat, a button, a ligature wire, a hook, an aligner attachment, and an O-ring. Examiner note: the earlier digital tooth model without appliance corresponds to the previous model data. The non-superimposed portions identified by comparing the current dental model with the earlier digital tooth model represent dental appliance geometry and may include aligner attachments Therefore, The identified non-superimposed portions reasonably correspond to a first set of potential attachments identified using the previous model data);
However, Kuo fails to teach identifying a second set of potential attachments using a machine learning model.
Kopelman ‘780 teaches identifying a second set of potential attachments using a machine learning model ([0065] The AOI identifying modules 115 may also identify AOIs from reference data 138, which may include patient history, virtual 3D models generated from intraoral scan data, or other patient data … Areas of interest may also include areas indicative of foreign objects (e.g., studs, bridges, etc.) … The analysis may involve direct analysis (e.g., pixel-based and/or other point-based analysis), the application of machine learning, the application of image registration, and/or the application of image recognition. [0096] The dental condition identifier 174 may then provide an image or an extracted representation of a dentition feature to the dental condition profile 192 and receive an indication of potential AOIs. In some embodiments, the dental condition identifier 174 may perform additional analysis to confirm the AOIs identified by a dental condition profile. Examiner note: the reference teaches applying machine learning to dental image or model data to identify potential areas indicative of foreign objects. Under the Broadest reasonable interpretation, an attachment affixed to and protruding from a tooth is an additional foreign object or dental structure on the tooth. The machine learning identified potential foreign object areas correspond to a second set of potential 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 Kuo to incorporate the teachings of Kopelman ‘780, and apply machine learning identification of potential areas indicative of foreign objects in dental image or model data in order to generate an additional set of potential regions that may correspond to attachments. The combinations of teachings would predictably provide benefit of improving the accuracy and reliability of attachment detection by supplementing Kuo’s previous model based identification with an independent machine learning based identification approach, thereby reducing missed attachments and improving the robustness of the detected candidate attachment regions.
However, Kuo and Kopelman ‘780 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 ([0106] At block 372 of method 370 the processing logic may analyze one or more first 3D intraoral images to yield a candidate intraoral area of interest. [0107] At block 374, the processing logic may identify one or more second 3D intraoral images which may be relevant to the candidate intraoral area of interest … The processing logic may determine such shared geometrical relation by identifying common surface features … [0109] At block 378, the processing logic may determine whether the first 3D intraoral images and the second 3D intraoral images, taken together, agree, disagree, or agree in part with the candidate intraoral area of interest. [0115] Where the processing logic finds agreement, the processing logic may promote the candidate AOI to an indication of the sort discussed hereinabove … Where the processing logic finds disagreement, the processing logic may reject the candidate AOI. [0116] Where there is disagreement the processing logic may proceed to block 384, in which the processing rejects the candidate indication. Examiner note: the reference teaches validating a candidate identified from a first source by determining whether information from a second source agrees with that candidate. Candidates supported by the second source are promoted, while candidates not supported by the second source are rejected.).
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 Kuo and Kopelman ‘780 to incorporate the teachings of Kopelman ‘524, and apply candidate validation based on determine whether information from a second source agrees with a candidate identified from a first source, in order to improve the accuracy and reliability of identifying attachments by confirming candidate detections and rejecting unsupported candidates. The combinations of teachings would predictably provide benefit of identifying a final set of attachments based on corroboration between the previous model based detection and the machine learning detection, thereby reducing false positive detections and improving the robustness of attachment identifying.
Claim 9, Kuo and Kopelman ‘780 teaches first and second sets of potential attachments as recited in claim 8. However, Kuo and Kopelman ‘780 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:
identifying attachments from the first and second set of potential attachments that are close
to interproximal or occlusal tooth areas ( [0081] At block 260, processing logic identifies one or more voxels from the intraoral images and/or the virtual models that satisfy a criterion. [0082] At block 265, one or more subsets of the identified voxels that are in close proximity to one another are identified. At block 270, these subsets are grouped into candidate intraoral areas of interest. [0084] At block 280, classifications are determined for the intraoral areas of interest. For example, AOIs may be classified as voids, conflicting surfaces, changes in a dental site, foreign objects, and so forth. [0094] The origin of the rank-altering weighting factors considered by the processing logic may be set by processing logic which accesses pooled patient data and/or pedagogical patient data which includes correlations between foreseeable indications regarding foreign object recognition assistance (e.g., concerning fillings and/or implants) and importance … [0095] One such weighting factor may specify that an indication relating to the vicinity of (e.g., to the interproximal areas of) one or more preparation teeth have its rank raised by a specified value. Examiner note: the reference teaches identifying candidate intraoral areas of interest, classifying the candidate areas as foreign objects, and increasing the significance of an indication located in the vicinity of an interproximal tooth area.);
determining whether each of the identified attachments in the first set of potential
attachments has a corresponding attachment in the second set of potential attachments ([0106] At block 372 of method 370 the processing logic may analyze one or more first 3D intraoral images to yield a candidate intraoral area of interest. [0107] At block 374, the processing logic may identify one or more second 3D intraoral images which may be relevant to the candidate intraoral area of interest … The processing logic may determine such shared geometrical relation by identifying common surface features … [0109] At block 378, the processing logic may determine whether the first 3D intraoral images and the second 3D intraoral images, taken together, agree, disagree, or agree in part with the candidate intraoral area of interest. Examiner note: the reference teaches identifying a candidate from a first source, identifying relevant information from a second source based on shared geometric feature, and determining whether the second source agrees with the candidate); and
discarding the identified attachments that lack a corresponding attachment ([0115] Where
the processing logic finds agreement, the processing logic may promote the candidate AOI to an indication of the sort discussed hereinabove … Where the processing logic finds disagreement, the processing logic may reject the candidate AOI. [0116] Where there is disagreement the processing logic may proceed to block 384, in which the processing rejects the candidate indication. Examiner note: the reference teaches retaining or promoting a candidate when it is supported by the second source and rejecting the candidate when the second source disagrees).
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 Kuo and Kopelman ‘780 to incorporate the teachings of Kopelman ‘524, and apply candidate validation based on determining whether information from a second intraoral data source agrees with a candidate identified from a first intraoral data source, and promoting or rejecting the candidate based on the determined agreement, in order to improve the accuracy and reliability of identifying attachment by filtering unsupported candidate detections. The combinations of teachings would predictably provide benefit of reducing false positive attachment identifications and producing a more reliable final set of attachments.
Claim 10, Kuo fails to teach, but Kopelman ‘780 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 ([0150] At block 215, processing logic identifies previous image data associated with the dental arch … At block 220, processing logic registers the image of the dental arch to previous image data associated with the dental arch. [0151]
At block 230, processing logic compares one or more areas of the dental arch from the image to one or more corresponding areas of the dental arch from the previous image data … In one embodiment, processing logic may identify those changes that are over a threshold value for an amount of change. [0152] The area of interest may be an area of the identified differences or an area of a tooth or gum for which the difference was identified. [0153] At block 310 of method 300, processing logic determines that previous image data comprises a three-dimensional (3-D) model of a dental arch. Examiner note: the reference teaches comparing corresponding areas in current dental data and previous three dimensional dental model data and identifying as an area of interest a difference that exceeds a threshold value for an amount of change. A difference that exceeds the threshold corresponds to a significant deviation. Conversely, an area whose difference does not exceed the threshold is not identified or retained as an area of interest).
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 Kuo to incorporate the teachings of Kopelman ‘780, and apply a comparison of corresponding areas in current dental data and previous dental model data, including identifying changes that exceed a threshold amount, in order to improve the reliability of dental feature identification and reduce false positive identifications caused by insignificant variations between current and previous dental data. The combinations of teachings would predictably provide benefit of retaining candidates associated with meaningful changes while excluding candidates associated with insignificant variations, thereby improving the accuracy and reliability of the final identified attachments.
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 Kuo and
Kopelman ‘780 and Kopelman ‘524 as applied to claims 8 and 23 above, and further in view of Chishti US20050244782A1.
Claim 11, Kuo and Kopelman ‘780 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 ([0106] At block 372 of method 370 the processing logic may analyze one or more first 3D intraoral images to yield a candidate intraoral area of interest …The processing logic may identify one or more points (e.g., one or more pixels and/or groups of pixels) corresponding to the candidate intraoral area of interest. [0107] At block 374, the processing logic may identify one or more second 3D intraoral images which may be relevant to the candidate intraoral area of interest. The one or more second 3D intraoral images may be ones which are intraorally proximal to the first one or more 3D intraoral images and/or ones which share geometrical relation to the first one or more 3D intraoral images … The processing logic may determine such shared geometrical relation by identifying common surface features … [0108] At block 376, the processing logic may perform analysis with respect to one or more of the first 3D intraoral images and the second 3D intraoral images taken together. In so doing the processing logic may or may not align the one or more first 3D intraoral images with the one or more second 3D intraoral images (e.g., the processing logic may align one or more point clouds corresponding to the first one or more 3D intraoral images with one or more point clouds corresponding to the second one or more 3D intraoral images). [0109] At block 378, the processing logic may determine whether the first 3D intraoral images and the second 3D intraoral images, taken together, agree, disagree, or agree in part with the candidate intraoral area of interest. [0115] Where the processing logic finds disagreement, the processing logic may reject the candidate AOI. [0116] Where there is disagreement the processing logic may proceed to block 384, in which the processing rejects the candidate indication. Examiner note: the reference teaches identifying a candidate area of interest from a first three dimensional intraoral data source, identifying a second three dimensional intraoral data source having an intraoral proximity or shared geometrical relationship with the first source, aligning point clouds from the first and second sources, and determining whether the two sources agree or disagree with the candidate. The reference further teaches rejecting (discarding) the candidate when the analysis finds disagreement. Therefore, the first set candidate that does not spatially overlap or intersect a candidate from the second set is unsupported by the second set and represents disagreement between the two candidate sources. ).
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 Kuo and Kopelman ‘780 to incorporate the teachings of Kopelman ‘524, and apply candidate validation based on jointly analyzing geometrically related three dimensional intraoral data and rejecting a candidate when the data disagree, in order to improve the reliability of candidate identification and reduce false positive detections that are not corroborated by another dental data source. The combinations of teachings would predictably provide benefit of requiring geometric corroboration between independently identified candidate regions before retaining a candidate as an attachment, thereby improving the accuracy and reliability of the final identified attachments.
However, Kuo and Kopelman ‘780 and Kopelman ‘524 fail to teach discarding potential attachments having a small distance to a corresponding tooth surface.
Chishti teaches discarding potential attachments having a small distance to a corresponding tooth surface ([0079] Cusp detection: In a preferred embodiment, the software provides the ability to detect cusps for a tooth. Cusps are pointed projections on the chewing surface of a tooth … The algorithm used for cusp detection is composed of two stages: (1) “detection” stage, during which a set of points on the tooth are determined as candidates for cusp locations; and (2) “rejection” stage, during which candidates from the set of points are rejected if they do not satisfy a set of criteria associated with cusps. [0080] … In the detection stage, a possible cusp is viewed as an “island” on the surface of the tooth, with the candidate cusp at the highest point on the island. “Highest” is measured with respect to the coordinate system of the model, but could just as easily be measured with respect to the local coordinate system of each tooth if detection is performed after the cutting phase of treatment. [0082] Since the plane is lowered a finite distance at each step, very small local maxima that can occur due to noisy data are skipped over. [0083] Cusp candidates that exhibit “non-cusp-like features” are removed from the list of cusp candidates. [0084] As depicted in FIG. 6B, the local curvature of the surface around the cusp candidate is approximated, and then analyzed to determine if it is too large (very pointy surface) or too small (very flat surface), in which case the candidate is removed from the list of cusp candidates. Conservative values are used for the minimum and maximum curvatures values to ensure that genuine cusps are not rejected by mistake. Examiner note: the reference teaches identifying candidate projections on a 3D tooth surface and subsequently rejecting candidates that do not satisfy the criteria for the dental feature being detected. The skipping very small local maxima caused by noisy data and removing a candidate when the surrounding tooth surface is to flat. Under BRI, a very small local maximum or a candidate that is too flat has only a small prominence or height relative to the surrounding tooth surface and therefore constitutes a candidate having a small distance to the corresponding tooth surface).
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 Kuo and Kopelman ‘780 and Kopelman ‘524 to incorporate the teachings of Chishti, and apply dental surface candidate filtering based on whether a candidate projection is very small or has a surface geometry that is too flat, in order to reduce false positive detections caused by noisy data or insignificant tooth surface variations. The combinations of teachings would predictably provide benefit of preventing small or shallow tooth surface variations from being incorrectly retained as potential attachments, thereby improving the accuracy and reliability of attachment detection.
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 Kuo and
Kopelman as applied to claims 1 and 16 above, and further in view of Blankenbecler US 20230142509A1 and Lints US20200160122A1.
Claim 12, Kuo and Kopelman fails to teach, but Blankenbecler teaches The method of claim 1, wherein presenting the modified model data further comprises 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 Kuo and 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, Kuo and 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 Kuo and 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, Kuo and 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 Kuo and 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37
CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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