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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 4, 2024 is noted. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with the “2019 Revised Patent Subject Matter Eligibly Guidance” issued January 7, 2019 and section 2106 of the MPEP the pending claims are analyzed as follows:
Step 1: In regard to claims 1-9 and 15-17, the claims are directed towards “A method of generating a dental treatment plan” that claims “obtaining a dataset” “generating a parametric model of jaw motion”, “identifying a plurality of representative segments”, “evaluating”, “identifying” and “generating the dental treatment plan”. Claims 10-14 are directed towards “A system for generating a dental treatment plan” comprising “a treatment plan generator”. The claimed “method” and “system” are within the 35 U.S.C. 101 statutory category of a process and apparatus respectively (MPEP 2106.03), but falls into the judicial expectation (MPEP 2106.04).
Step 2A: In regards to the claims, the claimed invention is directed to an abstract idea without reciting additional elements that amount to significantly more than the judicial exception (MPEP 2106.05). The claimed method is directed towards a mental process, concepts that are capable of being performed in the human mind, including observations, evaluations and judgments. More particularly, the functions of “obtaining a dataset”, “generating a parametric model”, “identifying a plurality of representative segments of the parametric model”, “evaluating a patient dentition to generate a patient jaw profile”, “identifying one of the representative segments” and “generating the dental treatment plan” are all capable of being performed mentally by the dental practitioner or simply with a piece of paper and pencil. For example the user may use their knowledge of dental treatments and most optimal outcomes to design a dental treatment. With respect to the system claims, it is noted the claims system does not require anything other than a general-purpose computer and the steps carried out by the general-purpose computer are capable of being performed mentally as discussed above. Therefore the claims do not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B: In regards to the claims, the claimed steps are all algorithms capable of being performed mentally and represent nothing more than concepts related to performing mathematical calculations which falls within the judicial exception. Implicit in the claimed invention is the intended use of a general-purpose computer or data processing device, however, there is no disclosure in the written description that the processing unit is anything more than a generic component, nor is there any disclosure that the method improves the manner in which the processing unit operates. The mere recitation in the claims of a generic data processing method and apparatus that is used in a conventional manner to perform conventional computer functions that are well understood and routine does not amount to “significantly more” than the judicial expectation. The claims do not go beyond “determining” and “calculating” numerical values based on mathematical algorithms with a general-purpose computer. It is suggested that the applicant amend the method claim to claim a transformation step, such as carrying out the tooth treatment plan, such as physically processing a tooth preparation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4, 10, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanssen et al. (2019/0332734).
Hanssen teaches with respect to claim 1, a method of generating a dental treatment plan comprising the steps of obtaining a dataset of human jaw and dentition motion and measurements (see par. 42, such DVT data set, JMT data set and optical surface data set of the patient is acquired, par. 22 regarding the different data sets), generating a parametric model of jaw motion based on the dataset of human jaw and dentition motion and measurement (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, such that the parametric model is the model 30 selected/generated based on the data obtained from the patient), identifying a plurality of representative segments of the parametric model of jaw motion (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, such that a specific model is selected from the models and a segment of the jaw model is selected to supplement for incomplete data from the data obtained from the patient), evaluating a patient dentition to generated a patient jaw profile, the evaluation including obviously measuring at least one parameter of the patient jaw (pars. 20, 38 40, such that jaw opening is measured), identifying one of the representative segments of the parametric model of jaw motion that is most similar to the patient jaw profile (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, specifically par. 20 teaches identifying similar patients, par. 21 teaching selecting the correct model based on assigning the patient to a patient population and par. 40 teaches using similarity measure), and generating a dental treatment plan based on the representative segment of the parametric model of jaw motion (pars. 101, 105, 112-113, 119-124). It is noted with respect to the evaluating step a parameter of the jaw is obviously measured since it is matched with another model similar to the patient’s jaw. It is noted that while a specific measurement, such as the jaw being so many mm, is not taught, the jaw is measured relative to other jaws in order to match them and combine data, therefore, Hansson obviously teaches measuring a parameter of the jaw.
With respect to claim 4, the representative segments are segmented by jaw motion type (see pars. 40, 43, 60-79 , such that different movements are considered when selecting the representative segments including opening angle and other movements).
Hanssen teaches with respect to claim 10, a system for generating a dental treatment plan comprising a treatment plan generator configured to determine a treatment plan based on a patient jaw profile (pars. 83, 101, 105, 112-113, 119-124), the patient jaw profile including at least one measurement of a patient’s jaw size (such that the entire jaw is imaged and a size is determined, see below for further explanation), the treatment plan generator including a parametric model of jaw motion based on a dataset of human jaw and dentition motion and measurement (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, such that the parametric model is the model 30 selected based on the data obtained from the patient), the parametric model of jaw motion divided into a plurality of representative segments (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, such that a specific model is selected from the models and a segment of the jaw model is selected to supplement for incomplete data from the data obtained from the patient) and the treatment plan generator further configured to compare the patient jaw profile to the parametric model of jaw motion to determine which of the representative segments is most similar to the patient jaw profile (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, specifically par. 20 teaches identifying similar patients, par. 21 teaching selecting the correct model based on assigning the patient to a patient population and par. 40 teaches using similarity measure) and to generate the dental treatment plan based on the representative segment identified a most similar to the patient jaw profile (pars. 101, 105, 112-113, 119-124). It is noted with respect to the patient jaw profile including at least one measurement of a patient’s jaw size, the jaw profile obviously includes a measurement of jaw size since it is matched with another model similar to the patient’s jaw. It is noted that while a specific measurement, such as the jaw being so many mm, is not taught, the jaw is measured relative to other jaws in order to match them and combine data, therefore, Hansson obviously teaches measuring a size of the jaw.
With respect to claim 15, Hanssen teaches a method of generating a dental treatment plan comprising the steps of obtaining a parametric model of jaw motion based on a dataset of human jaw and dentition motion and measurements (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, such that the parametric model is the model 30 selected based on the data obtained from the patient), identifying a plurality of representative segments of the parametric model of jaw motion (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, such that a specific model is selected from the models and a segment of the jaw model is selected to supplement for incomplete data from the data obtained from the patient), evaluating a patient dentition to generate a patient jaw profile, the evaluation including obviously measuring at least one parameter of the patient’s jaw (pars. 20, 38 40, such that jaw opening is measured, see below for further explanation), identifying one of the representative segments of the parametric model of jaw motion that is most similar to the patient jaw profile (pars. 17, 19-21, 30-31, 37-44, 79, 83, 101, 112, 119-124, specifically par. 20 teaches identifying similar patients, par. 21 teaching selecting the correct model based on assigning the patient to a patient population and par. 40 teaches using similarity measure), and generating a dental treatment plan based on the representative segment of the parametric model of jaw motion (pars. 101, 105, 112-113, 119-124). It is noted with respect to the evaluating step a parameter of the jaw is obviously measured since it is matched with another model similar to the patient’s jaw. It is noted that while a specific measurement, such as the jaw being so many mm, is not taught, the jaw is measured relative to other jaws in order to match them and combine data, therefore, Hansson obviously teaches measuring a parameter of the jaw.
Claim(s) 2-3, 5, 7-9, and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanssen et al. (2019/0332734) as applied to claim 1 above, and further in view of Mehl (2006/0063135).
Hanssen teaches the invention as substantially claimed and discussed above, including taking the size of the patient into consideration (pars. 20-21, 38, such that by having models of patients of different ages, size is taken into consideration), however, with respect to claims 2-3 and 5 does not specifically teach wherein the at least one parameter of the patient’s jaw comprises the size of the patient’s jaw, the representative segments are segmented by jaw size, the plurality of representative segments is divided into five representative segments corresponding with sizes extra small, small, normal, large, and extra-large.
Mehl teaches a method of generating a dental treatment plan comprising the steps of obtaining a dataset of human jaw and dentition motion and measurements (pars. 40), generating a parametric model a jaw (pars. 37-38, such that the generic models/average models are the parametric model), identifying a plurality of representative segments of the parametric model of jaw motion (pars. 37-38, such the size of the different models), evaluating a patient dentition to generate a patient jaw profile, the evaluation including measuring at least one parameter of the patient’s jaw (pars. 40, 41, 77, 80, such that a tooth is selected based on the size of the jaw), identifying one of the representative segments of the parametric model of the jaw motion that is most similar to the patients jaw profiles (pars. 40, 41, 77, 80) and generating the dental treatment plan based on the representative segment of the parametric model of the jaw (par. 61) and further with respect to claim 2, further comprising wherein the at least one parameter of the patient’s jaw comprises the size of the patient’s jaw (pars. 37-38, 61, such that a tooth is selected from on the data sets most like the current patient which includes selecting from models of different sizes, therefore, the size of the jaw in obvious measured in order to select a tooth most like the patients from the different data sets of different sized jaws), with respect to claim 3, wherein the representative segments are segmented by jaw size (pars. 37-38) and with respect to claim 5, wherein the plurality of representative segments are divided into segments corresponding to sizes including small, medium, and large (see pars. 37-38). It is noted that Mehl does not specifically teach 5 different sizes including extra-small and extra-large, however, it would have been obvious to one having ordinary skill in the art before the effective filling date of the invention to modify the 3 different sized of Mehl to include 5 as claimed such that the smaller the groups of the segments, the more accurate the results will be. It would have been obvious to one having ordinary skill in the art before the effective filling date of the invention to modify the method of Hanssen with the method of Mehl to include the segment information to includes sizes in order to help in selecting a model closest to the patient’s teeth. Such that the teeth of jaws of different sizes are different and therefore, a jaw of a similar size would want to be selected to provide more accurate treatment.
With respect to claims 7 and 12, Hanssen teaches the system and method wherein the representative segments comprise three dimensional digital dental models generated form scan and pre-treatment occlusion (see par. 42, such DVT data set, JMT data set and optical surface data set of the patient is acquired, par. 22 regarding the different data sets), however, does not specifically teach the pre-treatment occlusion is a pre-treatment interference boundary determined from motion data included in the dataset of human jaw and dentition motion and measurements.
Mehl further teaches the pre-treatment occlusion is a pre-treatment interference boundary determined from motion data included in the dataset of human jaw and dentition motion and measurements (par. 72, 78 such that areas on the remaining tooth structure penetrate the bite registration, such that they are too high and therefore, a boundary is determined if it is determined that point are too high, i.e. they are outside of a boundary).
With respect to claims 8 and 13, Hanssen teaches the invention as substantially claimed and discussed above including the step of predicting a post-treatment interference based on the representative segment identified as most similar to the patient jaw profile (see pars. 120-124, such that the crown would obviously have an interference with the tooth of the opposite jaw), however, does not specifically teach the interference has a boundary.
Mehl further teaches the step of predicting a post-treatment interference boundary based on the representative segment identified as most similar to the patient jaw profile (par. 61).
With respect to claim 9, Hanssen teaches predicting the post-treatment interference using a digital articulator displayed through a user interface of a computer device (pars. 47-54), the digital articulator including a three-dimensional digital dental model generated form the representative segment and simulated to include a dentition adjustment (pars. 47-54, 119-124, such that it includes the crown), however, does not specifically teach wherein the user interacts with the digital articulator using input device of the computing device to manually shift or lower jaw within the simulation.
Mehl further teaches wherein the post-treatment interference boundary can be manually determined and the adjustment can be manual (par. 36, 39, 43, 61, 76, 78). It would have been obvious to one having ordinary skill in the art before the effective filling date of the invention to modify Hanssen in order to allow for manual adjustment in order to allow for customized adjustment. Such that the user may prefer a specific alignment not automatically provided and is therefore able to adjust as desired. It is noted that the combination of the references teach the claimed limitations such that it would teach the manual adjustment of the jaw on the digital articulator.
With respect to claim 14, Hanssen/Mehl teaches the invention as substantially claimed and discussed above, including Mehl further teach wherein the treatment plan generator is configured to further determine the treatment plane based on the pre-treatment interference boundary (pars. 61, 72, 78).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the invention to modify Hanssen with the step of setting boundaries as taught by Mehl in order to provide a tooth treatment that meets the needs of the patient and will be successful such as by preventing errors in designing the tooth treatment.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanssen et al. (2019/0332734) as applied to claims 1 and 15 above, and further in view of Adams et al. (2019/0133731).
Hanssen teaches the invention as substantially claimed and discussed above including with respect to claim 16 carrying out the treatment (see pars. 105, 112-113), however, does not specifically teach the representative segments are determined using a normal probability distribution curve, wherein segments are determined by standard deviations form the mean and re-evaluating the patient dentition during implementation of the dental treatment plan to generate an updated jaw profile, the re-evaluation including remeasuring the at least one parameter of the patient’s jaw, identifying one of the representative segment of the parametric model that is most similar to the updated jaw profile, determining whether the representative segment of the parametric model of jaw motion identified at the beginning of treatment is the same as the representative segment that is most similar to the updated jaw profile and adjusting the dental treatment plan based on the representative segment that is most similar to the updated jaw profile.
Adams teaches a method of obtaining a parametric model of jaw motion based on a dataset of human jaw and dentition motion and measurement (pars. 4, 7, 81), identifying a plurality of representative segments of the parametric model of jaw motion (see pars. 4, 7, 81, such that at least one specific segment is selected that is most similar to the current patient), evaluating a patient dentition to generating a patient jaw profile, the evaluation including measuring at least one Parmenter of the patient’s jaw (such that a measure parameter of the jaw movement is used to evaluate the patient dentition to generate a jaw profile), identifying one of the representative segments of the parametric model of jaw motion that is most similar to the patient jaw profile (pars. 4, 7, 81) and generating the dental treatment plane based on the representative segment of the parameter model of jaw motion (par. 7, 77).
Adams further teaches with respect to claim 6, the representative segments are determined using a normal probability distribution curve, wherein segments are determined by standard deviations from the mean (par. 80-81). It would have been obvious to one having ordinary skill in the art before the effective filling date of the invention to modify the method of Hanssen with the step of using normal probability as taught by Adams to easily create the model using a well-known statistical calculation.
With respect to claim 16, Adams further teaches re-evaluating the patient dentition during implementation of the dental treatment plan to generate an updated jaw profile, the re-evaluation including remeasuring the at least one parameter of the patient’s jaw, identifying one of the representative segment of the parametric model that is most similar to the updated jaw profile, determining whether the representative segment of the parametric model of jaw motion identified at the beginning of treatment is the same as the representative segment that is most similar to the updated jaw profile and adjusting the dental treatment plan based on the representative segment that is most similar to the updated jaw profile (par. 77, such that post effectiveness is monitored, par. 82-83). It would have been obvious to one having ordinary skill in the art before the effective filling date of the invention to modify the method of Hanssen with the step of re-evaluating as taught by Adams in order to ensure that the treatment plan is providing the desired results and if not correct the treatment plan as needed.
Allowable Subject Matter
Claim 17 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and overcome the 101 rejection.
Response to Arguments
Applicant's arguments filed November 20, 2025 have been fully considered but they are not persuasive.
Regarding the applicant’s arguments with respect to the 101 rejection. It is noted that the relied upon 2019 guidance is still applicable and the claims were properly analyzed according to section 2106 of the MPEP. It is noted that section 2106 of the MPEP was referenced in the rejection also. While it is noted that newer guidance has been provided, the guidance has not changed and the new guidance is for analyzing claims directed towards AI and machine learning. It is noted that the claims are not directed towards software using AI and machine learning. Further it is noted that the newer guide still applies the same analysis, but just provide additional guidance in step 2A regarding mental processing with respect to AI. As mentioned above, the claims are not directed towards the use of AI or machine learning therefore, the new guidance does not change the rejection or analysis and the rejection it proper and maintained.
The applicant argues that the claims of the invention (i) cannot be practically performed in the human mind and (ii) integrate the recited concept into a practical application that improves the technical field of dental treatment planning. It is noted that as discussed above in the rejection the claims can be performed in the human mind. Such that by allowing the practitioner to evaluate a patient chew (i.e. move their jaw) and then rely on their past experience to compare to previous patients (i.e., a parametric model) and group the current patient in a group of a previous patient based on observed parameters and generate a treatment plan based on the previous patient/parametric model. The claim does not require any limitation that cannot be practically preformed in the mind. In the 2025 guidance referenced by the applicant, it mentions that anything claiming AI cannot be practically performed in the mind. As discussed above, the applicant is not claiming any limitations using AI, therefore, the claims are still properly rejected under 101 as they can be carried out in the mind.
Further with respect to the applicant’s response (ii) above, the claims do not make an improvement of the functioning computer as it only relies upon a generic general-purpose computer for carrying out the obtaining and analyzing steps, the claims doe not apply or use the judicial exception to effect treatment, the claims do not effect transformation of the judicial exception, do not implement the judicial exception with a particular machine, or apply the judicial exception in some other meaning full way beyond generally linking the use of the judicial exception to a particular technological environment. In view of the above response, the rejection of the claims under 11 is maintained as they do not pass the subject matter eligibility test.
The amendments to claims 2, 5 and 9 have overcome the 112 rejections.
With respect to claims 1, 4, 10, and 15, the applicant argues that the prior art of Hanssen is fundamentally different and describes using statistical methods to interpolate or extrapolate missing movement data for a patient, based on that patient’s own imaging and demographic information. The applicant argues that there is no teaching in Hanssen of segmenting a parametric model into groups such as “extra-small, small, normal, large, extra-large” nor of selecting “most similar” to a patient’s jaw profile for the purpose of generating a treatment plan. However, first it is noted that Hanssen has not been relied upon to teach the limitation of segmenting a parametric model into groups such as “extra-small, small, normal, large, extra-large”. It is noted that claim 5 claims that limitation which was rejected with Mehl to teach the limitation of the groups of different sizes. Therefore, the applicants’ arguments directed towards Hanssen not teaching segmenting a parametric model into groups such as “extra-small, small, normal, large, extra-large” are moot sine Hanssen was not used to teach the claimed limitation of claim 5.
Further the applicant argues that the prior art of Hanssen does not teach the limitation of identifying one of the representative segments of the parametric model of jaw motion that is most similar to that patient’s jaw profile. However, the prior art of Hanssen teaches specifically in par. 31 that the information can indicate with what probability that patient shole be assigned to a specific patient population and further if a specific significance lever is not reached, the dentist can undertake further diagnostic measures. Therefore, while the specific terminology of “most similar” is used, the prior art teach evaluating the patient’s dentition and identifying one of the representative segments of the parametric model that is most similar, such that is needs to be within the specific significance level, which is similar, as taught by the prior art.
The applicant argues that the prior art of Hanssen teaches the measurement parameters including general demographic information including age, sex, height, and BMI and not specific jaw measurements. However, while the parameters do include the information discussed above, it also includes data from imaging methods and movement data (see par. 20). Therefore, it is noted that the information from the imaging methods and the movement data includes jaw measurements. The prior art teaches the limitations are required including evaluating a patient dentition to generate a patient jaw profile, the evaluation including measuring at least one parameter of the patient’s jaw (pars. 20, 38, 40). Such that the jaw measurement of the maximum jaw opening is measured and considered in the evaluating step.
The applicant further argues that the prior art of Hanssen teaches the system generates a treatment plan or preview based on a supplemented individual movement model for the patient and not a representative segment selected form a parametric model. However, as discussed above in detail in the rejection Hanssen teaches the limitation of generating the dental treatment plan based on the representative segment of the parametric model of jaw motion. Such as discussed above, Hanssen teaches identifying one of the representative segment of the parametric model of jaw motion that is most similar to the patient jaw profile. Such that the representative segment of the parametric model of jaw motion selected is similar to the patient. Paragraph 20 discuses identifying similarities amount patients and par. 40 discusses using similarities (i.e. parameters) between the patient and patients within the parametric model and par. 83 discusses using the specific patient parameters with those of other patients (i.e. the parametric model) that are most similar to the patient’s jaw profile and with the information generating the dental treatment plan (pars. 101, 105). Therefore, the applicants’ arguments are not persuasive and the rejections are maintained.
The applicant argues that Hanssen does not teach or suggest dicing a parametric model into discreate representative segments, however, Hanssen teaches identifying segments of the parametric model of jaw motion such that the different disclosed subgroup categories/properties. For example par. 38 discusses a plurality of models 30, such that the plurality of models are the claimed segments of the parametric model. Therefore, the applicant’s arguments are moot and the rejection is maintained.
The applicant further argues that the prior art of Mehl and Adams do not cure the deficiencies discussed above, however, it is noted that Hanssen clearly teaches the limitations discussed above in detail, therefore the applicant’s arguments are not persuasive regarding the secondary references and the rejections are maintained.
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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/HEIDI M EIDE/ Primary Examiner, Art Unit 3772
12/10/2025