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 .
Notice of Pre-AIA or AIA Status
This communication is in response to application filed 12/13/2024. It is noted that application is a 371 of PCT/IB2023/056142 filed 06/14/2023 which claims priority to Provisional Application No. 63/366,507 filed 06/16/2022. Claims 1-20 are pending.
Information Disclosure Statement
Information disclosure statement dated 12/13/2024 has been acknowledged and considered.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-16 are drawn to a method for validating the placement and generation of components for dental restoration appliances, which is within the four statutory categories (i.e. process). Claims 17-20 are drawn to a system for validating the placement and generation of components for dental restoration appliances, which is within the four statutory categories (i.e. machine).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
(Original) A computer-implemented method for using one or more machine learning models to automatically validate one or more characteristics of a digital representation of a dental restoration appliance component, the method comprising:
receiving, by one or more computer processors, a first digital 3D oral care representation of an appliance component;
receiving, by the one or more computer processors, a first digital 3D oral care representation of one or more of a patient's teeth;
using, by the one or more computer processors, a machine learning model to assign one or more result labels to the first digital 3D oral care representation of the appliance component, wherein the one or more result labels specify whether the appliance component is incorrectly formed or incorrectly placed;
analyzing, by the one or more computer processors, the one or more result labels; and
automatically generating, by the one or more computer processors, output that specifies whether the appliance is incorrectly formed or incorrectly placed.
These recited underlined limitations fall within the "Certain Methods of Organizing Human Activities" grouping of abstract ideas as it relates to certain methods of organizing human activity –managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II).
The limitations of receiving 3D data; assigning labels to the 3D data; analyzing the labels and outputting whether the application is placed or formed correctly as drafted and detailed above, are steps that, under its broadest reasonable interpretation, recites steps for organizing human interactions. The claimed invention is directed to receiving data; analyzing the data and outputting a conclusion from the analysis which is a concept relating to tracking or filtering information related to a3D digital representation. Tracking information or filtering content has been found to be an abstract idea and a method of organizing human behavior. See MPEP 2106.04(a)(2)(II)(C). This is a method of organizing patient data thus falling into one category of abstract idea. That is other than reciting “a processor”; and “machine learning” language, nothing in the claim element precludes the steps from describing concepts related to receiving, organizing and analyzing imaging data between people. If a claim limitation, under its broadest reasonable interpretation, covers concepts related to interpersonal and intrapersonal activities then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
(Original) A computer-implemented method for using one or more machine learning models to automatically validate one or more characteristics of a digital representation of a dental restoration appliance component, the method comprising:
receiving, by one or more computer processors, a first digital 3D oral care representation of an appliance component;
receiving, by the one or more computer processors, a first digital 3D oral care representation of one or more of a patient's teeth;
using, by the one or more computer processors, a machine learning model to assign one or more result labels to the first digital 3D oral care representation of the appliance component, wherein the one or more result labels specify whether the appliance component is incorrectly formed or incorrectly placed;
analyzing, by the one or more computer processors, the one or more result labels; and
automatically generating, by the one or more computer processors, output that specifies whether the appliance is incorrectly formed or incorrectly placed.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
The additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f).
the recitations performing the functions by the computer processor amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0036], [0037] of the present Specification.
the recitation of using machine learning to assign labels recites only the idea of a solution or outcome (i.e. claim fails to recite details of how a solution to a problem is accomplished).
in order to transform a judicial exception into a patent-eligible application, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’".
Examiner submits that these limitations amount to merely using software to tailor information and provide it to the user on a generic computer. Applicant’s claims recite the machine learning module in a generic manner. Applicant does not provide adequate evidence or technical reasoning on how the process improves the efficiency of the computer and is beyond conventional use of components, as opposed to the efficiency of the process, or of any other technological aspect of the computer.
generally link the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h)– for example, the recitation of performing the functions by the computer processor merely limits the abstract idea the environment of a computer.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Independent claim 1 does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally linking the abstract idea to a particular technological environment or field of use and the same analysis applies with regards to whether they amount to “significantly more.” Therefore, the additional elements do not add significantly more to the at least one abstract idea.
As per claim 17, the claim teaches limitations similar to claim 1 and the same abstract idea (“certain methods of organizing human activity”) for the same reasons as stated above. Claim 17 further teaches a system containing processors and a non-transitory computer readable medium to store instructions executable by the processor to perform the functionality taught by claim 1. These limitations of a processor and storage as generally recited, amount to mere instructions to apply an exception, see MPEP 2106.05(f) and generally link the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h). Independent claim 11 is directed to an abstract idea.
Furthermore, for similar reasons as representative independent claim 1, analogous independent claim 17 does not recite additional elements that integrate the judicial exception into a practical application nor add significantly more.
The following dependent claims further the define the abstract idea or are also directed to an abstract idea itself:
In relation to claim 14 this claim specifies that based on analyzing the digital representation is not correctly formed, sending a command to re-run a process which is a certain method of organizing human activity, under its broadest reasonable interpretation, covers interactions between people or managing personal behavior or relationships
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 15 and 18: These claims recite performing the method in real-time which thus does no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the at least one abstract idea is performed (see MPEP § 2106.05(e)).
Claims 2-13, 16, and 19-20: These claims recite generally applying the machine learning to achieve a purpose which thus amount to mere instructions to apply an exception by invoking the computer as a tool OR reciting the idea of a solution (i.e. claim fails to recite details of how a solution to a problem is accomplished) or outcome (see MPEP § 2106.05(f)).
The dependent claims further do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Subject Matter free from Prior Art
Azernikov (2022/0218449), the closest domestic prior art of record teaches, discloses deep neural networks trained to detect restoration design defects and provide feedback to a restoration design module, including qualitative evaluation and iterative improvement (Azernikov; paras. [0022], [145]). Azernikov teaches processing a combined restoration context or a design state object rather than two separately received 3D representations as claimed. Furthermore Azernikov teaches detecting defects and providing feedback/grades. A “grade” or “feedback” is not necessarily the same as assigning result labels that specify incorrectly formed/placed.
Claessen (2021/0082184) is teaches predicting 3D tooth root shape from crown/scan data using a 3D deep neural network. However, Claessen does not expressly teach validating a restoration/appliance component as “incorrectly formed/incorrectly placed.”
Claessen (EP3462373A1), the closest foreign prior art of record teaches automated classification/taxonomy of 3D teeth data using deep learning. Claessen teaches classifying teeth data not validating a restoration appliance component as in correctly formed/ place. Further, Claessen does not disclose result labels that specify whether a restoration component is incorrectly formed or incorrectly placed.
Xu (Xu X, Liu C, Zheng Y. 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. IEEE Trans Vis Comput Graph. 2019 Jul;25(7):2336-2348. doi: 10.1109/TVCG.2018.2839685. Epub 2018 May 22. PMID: 29994311.) the closest non-patent literature of record teaches a classic deep-learning approach to segment/label dental 3D models. Xu does not teach validation of a restoration appliance component as incorrectly formed/placed and does not disclose result labels that specify whether a restoration component is incorrectly formed or incorrectly placed.
No final decision on patentability has been made in light of pending rejections.
Conclusion
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LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/Primary Examiner, Art Unit 3686 1/23/2026