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 .
Priority
Applicant’s claim for the benefit of as a continuation of U.S. Patent Application Number 15/660,073 filed on July 26, 2017, which claims priority to, and the benefit of U.S. Provisional Application No. 62/367,242 filed on July 27, 2016 and U.S. Provisional Application No. 62/504,213 filed on May 10, 2017. The noted priority is acknowledge by the examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/27/2025 was considered by the examiner.
Drawings
The drawings were received on 03/31/2022. These drawings are acceptable.
Double Patenting
The rejection made in the office action mailed 10/27/2022 and has been overcome by the Terminal Disclaimer filed, 3/27/2023.
Response to Arguments
Applicant's arguments filed 02/10/2026, directed to the rejection of claims under 35 USC 101: Abstract Idea, have been fully considered but they are not persuasive.
Regarding remarks directed to Step 2A Prong One
Applicant’s remarks regarding the rejections of claims under 35 USC 101 are directed to amended claim language; See current rejection below.
Applicant argues that the claimed invention is directed to patent eligible subject matter because the amended claim 1, 15, and 20 does not cover a mental process . Specifically, applicant argues generating, using the trained generative adversarial deep neural network, a contour of a crown for the prepared tooth based on the identified one or more dental features and the locations of the identified one or more dental features in the patient's scan data, in exemplar claim 1, is not directed to a mental process because generating has been established as an observation, evaluation, judgement or opinion.
Examiner disagrees. The analysis for determining a judicial exception is provided in MPEP 2106. Applicant’s argument has no legal basis and there is no legal basis for establishing how the term “generating” should be interpreted under the 35 USC 101 statue.
Regarding remarks directed to Step 2A Prong Two:
Applicant argues that the amended limitation directed to training does not recite an abstract idea and the claims as a whole are directed to significantly more than the alleged judicial exceptions because the claim limitations recited a particular solution (i.e. recognizing dental information and design a dental restoration from the recognized dental information using DNNs) to achieve a desired outcome (i.e. a contour of a crown for the prepared tooth) is a particular way, as claimed by the claimed invention.
Examiner respectfully disagrees. The guidance for the making determination under 35 USC 101 subject matter eligibility, is found in MPEP 2106 and this is the guidance used by the examiner in the previous and current office actions.
Regarding the amended limitations, the training process is not considered an abstract idea but mere instructions invoking the computer as a tool (i.e. a computer training/pre-training a deep neural network (DNN)) where the limitation invokes the use of a computer or other machinery in its ordinary capacity for training a machine learning model using training data. The courts have deemed that these types of limitations do not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field. See MPEP 2016.05(f)
Examiner noted the following regarding the analysis under 35 USC 101 abstract idea. The MPEP 2106.04(d)(1) discloses the evaluation of claimed improvements in the functioning of a computer or improvement to a technical field in step 2A prong two. The MPEP section discloses “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification…”
The examiner has considered the claims as a whole and in light of the specification. The applicant specification does not appear to set forth an improvement in non-conclusory manner (i.e., applicant specification appear only to recite a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art). Therefore, the claims were determined to no be directed to an improvement in technology. See full rejection in the current office action.
The rejection under 35 USC 101 has been maintained.
Regarding remarks directed to Step 2B
Applicant argues that the amended limitation directed to training does not recite an abstract idea and the claims as a whole are directed to significantly more than the alleged judicial exceptions because the claim limitations recites elements that are sufficient to transform the judicial exception to a patentable invention.
Examiner respectfully disagrees. As noted above and in the noted analysis of the amended limitations below, in the current office action, the claims when considered as individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claims were determined to no be directed to an improvement in technology. See full rejection in the current office action.
The rejection under 35 USC 101 has been maintained.
Examiner Remarks Regarding Effective Filing Dates
Examiner notes that the effective filing date of limitations directed to the use of generative adversarial neural network models is July 26, 2017, as they were fully supported in the US Non-Provisional Application No. filed July 26, 2017.
In examining applications, the effective filing date of the claimed invention is the actual filing date of the U.S. application, unless situation (A), (B), or (C) as set forth below applies:
(A) If the application is a continuation or divisional of one or more earlier U.S. applications or international applications and if the requirements of 35 U.S.C. 120 or 365(c) have been satisfied, the effective filing date of the claimed invention is the same as the earliest filing date in the line of continuation or divisional applications.
(B) If the application is a continuation-in-part of an earlier U.S. application or international application, any claims in the new application not supported by the specification and claims of the parent application have an effective filing date equal to the actual filing date of the new application. Any claims which are fully supported under 35 U.S.C. 112 by the earlier parent application have the effective filing date of that earlier parent application.
(C) If the application properly claims benefit under 35 U.S.C. 119(e) to a provisional application, the effective filing date of the claimed invention is the filing date of the provisional application for any claims which are fully supported under the first paragraph of 35 U.S.C. 112 by the provisional application.
In the instant case, the claimed subject matter directed to the use of a generative adversarial network (GAN) model appears to not be fully supported by the provisional applications:
No. 62/367,242 filed July 7th 2016, which is silent regarding the use of deep generative models and generative adversarial network. It merely provides some support for applying trained deep neural networks to pre-processed scan data for classification task, in 0051-0061.
No. 62/504,213 filed May 10th 2017, which is silent regarding the use of deep generative models and generative adversarial network. It merely provides some support for applying trained deep neural networks to pre-processed scan data for recognizing dental information and features of the dental models, in 0060-0072.
The recitation directed to the use of generative neural network and deep generative models appears in the filing of the US Non-Provisional Application No. dated July 26, 2017. In paragraphs, 0059-0079, 0095-00116, and 00146-0153, some paragraphs are highlighted below to help clarify the record:
[0059] In one embodiment, the present systems and methods may employ a generative adversarial network (GAN) to generate a 3D model of a dental restoration such as a crown, a bridge, or an implant. For example, the generator of the GAN can be trained to output a 3D model of a crown using random (noise) dentition scan data sets as inputs. Although purely random dentition scan data sets can be used to train the generator, a faster convergence time may be achieved by using scan data sets specific to the type of dental features and/or restorations of interest. For example, the training data sets can be certain types of dentition data such as crown data, implant data, margin line data, cusp data, tooth surface anatomy data, dental restorations data, etc. The discriminator of the GAN may be trained, using real dental restoration (e.g., crown) models, to recognize whether the output 3D model from the generator is a real or fake model. If the output 3D model is determined to be a fake by the discriminator, the generator creates another model that can fool the discriminator to think that the output model is the same as the input (real/ideal) model.
[0079] Once the one or more deep neural networks are trained, scan recognition module 125a can use the trained deep neural network to generate a 3D model of a crown using one or more of the patient's dentition data sets. If the generated model is satisfactory to the evaluation module 135,dental restoration server 101 can send the generated model to client device 107. In one embodiment, qualitative evaluation module 135 may be a component of a generative adversarial network. For example, qualitative evaluation module 135 can be a discriminator network of the generative adversarial network. Once the discriminator cannot differentiate between the generated/simulated 3D model of a crown and a real/ideal 3D crown model, the 3D crown modeling process is completed. [0080]Client 175 may then upload the generated model received from restoration server 101 to a fabricator such as a 3D printer, which then can fabricate the crown on site at the dental office. In this way, the entire process can be accomplished within an hour rather than days or weeks as possible with conventional technologies.
[00114] In one embodiment, training module 123 can simultaneously train two adversarial networks, generator 510 and discriminator 520. Training module 123 can train generator 510 using one or more of a patient's dentition scan data sets to generate a sample model of one or more dental features and/or restorations. For example, the patient's dentition scan data can be 3D scan data of a lower jaw including a prepared tooth and its neighboring teeth. Simultaneously, training module 123 can train discriminator 520 to distinguish a generated sample of a crown for the prepared tooth (generated by generator 510) against a sample from a crown from a real data set (a collection of multiple scan data set having crown images). In one embodiment, GANs are designed for unsupervised learning, thus input 505 and real data 525 (e.g., the dentition training data sets) can be unlabeled.
[00151] In one embodiment, to pre-train the generative deep neural network (generator 510), specifically designed dentition training data sets can be used. In the illustrated example of FIG. 18, to pre-train the generative deep neural network identifies and characterizes a prepared tooth and to generate a crown contour, the training data sets can include numerous real-life examples of before and after pair images
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 a judicial exception (i.e. an abstract idea) without significantly more.
Claim 1: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
…. identifying (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
computer-implemented method for recognizing dental information using DNNs, … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
training, using one or more training data sets…, a generative adversarial deep neural network; … wherein the generative adversarial deep neural network comprises a generator and a discriminator (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… one or more training data sets comprising images of real-life prepared teeth and real-life crowns installed on the real-life prepared teeth, … patient's scan data representing at least one portion of the patient's dentition data set … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
receiving, by one or more computing devices, a patient's scan data (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
a patient's scan data representing at least one portion of the patient's dentition data set; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
… identifying, using a trained deep neural network,… based on one or more output probability values of the trained deep neural network. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
…generating, using the trained generative adversarial deep neural network, …(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… one or more dental features in the patient's scan data …(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
wherein the crown is to be attached to the prepared tooth, wherein the trained generative adversarial deep neural network has been trained with scan data sets specific to a type of dental features of interest (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
the discriminator is trained using … to recognize whether the output … from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
Alternatively, wherein the crown is to be attached to the prepared tooth (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant Extra-Solution Activity. See MPEP 2106.05(g).)
Alternatively, wherein the trained generative adversarial deep neural network has been trained with scan data sets …(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
Alternatively, when not considered as part of the abstract idea the limitation: and generating, deemed insufficient to transform the judicial exception to a patentable invention because the recitation the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (generating a contour of a crown based on claimed features). The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). See MPEP 2106.05(f)(1). Examiner notes that mere linking the use of a computer as tool (i.e. generating, using the trained generative neural network) fails to resolve the issues that claim limitation elements are recited at a high level of generality that fails to recite details of how a solution to a problem is accomplished.
Alternatively, generating, using the trained generative adversarial deep neural network, a contour of a crown for the prepared tooth based on the identified one or more dental features and the locations of the identified one or more dental features in the patient's scan data, wherein the crown is to be attached to the prepared tooth, wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
Examiner notes that by claiming to use a generative adversarial network (GAN) to generate a contour of a crown for the prepared tooth where the GAN is also claimed to be trained to generate a 3D model of a crown and classify if the 3D model is fake or real … it is unclear how the GAN is used to achieve the claimed outcome i.e. generating, using the trained generative adversarial deep neural network, a contour of a crown for the prepared tooth based on the identified one or more dental features and the locations of the identified one or more dental features in the patient's scan data, wherein the crown is to be attached to the prepared tooth, wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model.
Thus the limitation recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.)
Alternatively, wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is has been trained with …data sets specific to a type … ,wherein the discriminator is trained … to recognize whether the output … from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the claimed elements merely recite well-understood, routine, conventional activity previously known to the industry; See 2106.05(d)).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation are directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."
Regarding the limitation deemed insignificant extra-solution activity (i.e. wherein the crown is to be attached to the prepared tooth), the examiner provides evidence under the Berkheimer Memorandum Option 3: A citation to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s).
The examiner provides citations to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s) to demonstrate that the additional elements are widely prevalent or in common use in the relevant field (crowns, prepared/created, to be attached on a prepared tooth), as cited below:
Loudon (US 4834656): discloses in 1:31-41: … The manufacture and application of dental crowns to prepared or altered natural teeth is, of course, well known as disclosed by way of example in U.S. Pat. Nos. 504,126, 2,154,499 and 4,504,230 to Durr, Eisenstein and Patch, respectively…
Jones et al. (US 20020197583): discloses in [0020] By way of example, FIG. 1 depicts a prepared tooth 11 (shown partially in phantom lines) having a crown 10 affixed thereto. Crown 10 is affixed to prepared tooth 11 in any manner conventional with dental restorations, including cementatious and/or adhesive bonding.
Ray et al. (US 20130010080): discloses in [0003] One common dental application for which surface imaging would be particularly advantageous relates to obtaining the profile of a tooth surface that has been prepared for the insertion of a dental crown. The typical method for fitting the crown is to take an impression of the prepared tooth and to forward the impression to a laboratory for fabrication…
Regarding the limitation deemed well-understood, routine, conventional activity previously known to the industry (i.e. wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is has been trained with …data sets specific to a type … , wherein the discriminator is trained … to recognize whether the output … from the generator is a real or fake model.), the examiner provides evidence under the Berkheimer Memorandum Option 3: A citation to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s).
Prior art also teaches that a GAN model comprises a generator and discriminator components, as claimed in elements as well-known as that is the defined operation of a GAN model as known in machine learning:
Goodfellow et al. (NPL: Generative Adversarial Nets): teaches in abstract “We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G… Sec. 1 ... In the proposed adversarial nets framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency.
Wu et al. (NPL: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling): teaches in Sec. 3: As proposed in Goodfellow et al. [2014], the Generative Adversarial Network (GAN) consists of a generator and a discriminator, where the discriminator tries to classify real objects and objects synthesized by the generator, and the generator attempts to confuse the discriminator. In our 3D Generative Adversarial Network (3D-GAN), the generator G maps a 200-dimensional latent vector z, randomly sampled from a probabilistic latent space, to a 64 × 64 × 64 cube, representing an object G(z) in 3D voxel space. The discriminator D outputs a confidence value D(x) of whether a 3D object input x is real or synthetic… Training details A straightforward training procedure is to update both the generator and the discriminator in every batch. However, the discriminator usually learns much faster than the generator, possibly because generating objects in a 3D voxel space is more difficult than differentiating between real and synthetic objects [Goodfellow et al., 2014, Radford et al., 2016]…
Burnap et al. (NPL: Estimating and Exploring the Product Form Design Space Using Deep Generative Models): teaches in Sec. 2.4: Deep generative models refer to a class of hierarchical statistical models (referred to as “deep learning" in the computer science community;… The generative adversarial network (GAN) is a generative model that has a unique parameter estimation approach [51]. The model is divided into two parts, a generator and a discriminator; the generator is trained to generate images so that the discriminator cannot distinguish them from the ground truth images, while the discriminator is trained to discriminate generated images from the known “ground truth" images. The two parts are trained simultaneously to force the generator to produce images as similar to the ground truth images as possible, where similarity is defined by the discriminator…
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 2: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein …maps one or more dental features (Mental process for evaluating and making judgements for generating an opinion; and mathematical concepts for process the making mathematical relationships using probability values associated with portions of data)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
the trained deep neural network maps one or more dental features… (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
…one or more dental features in at least one portion of each dentition training data set from a plurality of dentition training data sets.. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 3: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the … maps locations of the one or more dental features(Mental process for evaluating and making judgements for generating an opinion; and mathematical concepts for process the making mathematical relationships using probability values associated with portions of data)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
the trained deep neural network maps one or more dental features… (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
…the one or more dental features in the at least one portion of each dentition training data set… the identified one or more dental features one or more dental features in the patient's scan data.. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 4: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
further comprising comparing, … , (Mental process for evaluating and making judgements for making comparisons)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
…using a discriminating deep neural network… and wherein the deep neural network is configured to generate a second contour of the crown based on the output loss function of the second discriminating deep neural network; (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
… the generated contour of the crown for the prepared tooth in the patient's scan data … an image of a real crown contour from one or more real-sample data sets... (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 5: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Incorporates abstract ideas recited in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein one or more dental features in the at least one portion of each dentition training data set comprise one or more of a tooth surface anatomy, a tooth dentition, and a restoration type. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 6: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Recites the abstract idea of claim 2.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the probability vector comprises a plurality of probability values, wherein each probability value indicating a probability that the one or more dental features in at least one portion of each dentition training data set belonging to one or more of a tooth surface anatomy, a tooth dentition, and a restoration type. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 7: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
(Mental process for making observations and evaluated portions)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
…, wherein each portion represents a characteristic of a tooth surface anatomy, a tooth dentition, or a restoration type. And wherein each of the training data sets is segmented into different portions… (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 8: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the …data is segmented into different portions prior to identifying the one or more dental features (Mental process for making observations and evaluated portions for further evaluations and making judgements)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
the one or more dental features the patient's scan data …, wherein each different portion represents an aspect of a tooth surface anatomy, a tooth dentition, or a restoration type. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 9 Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Incorporates abstract ideas recited in claim 5.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the tooth surface anatomy comprises one or more features selected from a group of buccal and lingual cusps, distobuccal and mesiobuccal inclines, distal and mesial cusp ridges, distolingual and mesiolingual inclines, occlusal surface, and buccal and lingual arcs. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 10 Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Incorporates abstract ideas recited in claim 5.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the tooth dentition comprises one or more classifications selected from a group consisting of upper and lower jaws, prepared and opposing jaws, prepared tooth, and tooth numbers. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 11: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Incorporates abstract ideas recited in claim 5.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the restoration type comprises one or more restoration selected from a group consisting of crown, inlay, bridge, and implant. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 12: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Incorporates abstract ideas recited in claim 2.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the plurality of dentition training data sets have been preprocessed to generate a depth map for each training data set, wherein each training data set comprises three dimensional (3D) data. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 13: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the plurality of dentition training data sets have been preprocessed to generate a depth map for each training data set, (Mental process for making observations and judgements for dataset observations; mathematical concepts as mathematical relationships as depth map of datasets)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
Alternatively: wherein the plurality of dentition training data sets have been preprocessed to generate a depth map for each training data set, (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
wherein each training data set comprises three dimensional (3D) data. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 14: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the depth map is generated by converting 3D coordinates of each point of the 3D data into a distance value from a given plane to each point. (Mathematical concepts making mathematical relationships as depth map as 3D coordinates of data into distance values )
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the plurality of dentition training data sets are preprocessing prior to the training of the deep neural network. (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 15: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
a computer-readable storage medium having computer program logic recorded thereon for enabling a processor-based system to recognize dental information and to design a dental restoration from the recognized dental information, the computer program product, … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
the computer program product comprising: a first program logic module for enabling the processor-based system to train, …, a generative adversarial deep neural network ; a second program logic module for enabling the processor-based system to receive … and a third program logic module for enabling the processor-based system to use a first deep neural network to identify … based on one or more output probability values of the deep neural network, wherein the third program logic module comprises: logic for enabling the processor-based system to determine … and logic for enabling the processor-based system to generate, using the trained generative adversarial deep neural network, … wherein the trained generative adversarial deep neural network has been trained with scan data sets specific to a type of dental features of interest. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… one or more training data sets comprising images of real-life prepared teeth and real-life crowns installed on the real-life prepared teeth, … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
a second program logic module for enabling the processor-based system to receive a patient's scan data (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
a patient's scan data representing at least one portion of the patient's dentition data set; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
… one or more dental features in the patient's scan data …(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
wherein the crown is to be attached to the prepared tooth, wherein the generative adversarial deep neural network comprises a generator and a discriminator, (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model . (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
Alternatively, wherein the crown is to be attached to the prepared tooth (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant Extra-Solution Activity. See MPEP 2106.05(g).)
Alternatively, when not considered as part of the abstract idea the limitation: generate, deemed insufficient to transform the judicial exception to a patentable invention because the recitation the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (generating a contour of a crown based on claimed features). The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). See MPEP 2106.05(f)(1).
Examiner notes that mere linking the use of a computer as tool (i.e. generating, using the trained generative neural network) fails to resolve the issues that claim limitation elements are recited at a high level of generality that fails to recite details of how a solution to a problem is accomplished.
Alternatively, logic for enabling the processor-based system to generate, using the trained generative adversarial deep neural network, a contour of a crown for the prepared tooth based on the identified one or more dental features in the patient's scan data, wherein the crown is to be attached to the prepared tooth, wherein the crown is to be attached to the prepared tooth, wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
Examiner notes that by claiming to use a generative adversarial network (GAN) to generate a contour of a crown for the prepared tooth where the GAN is also claimed to be trained to generate a 3D model of a crown and classify if the 3D model is fake or real … it is unclear how the GAN is used to achieve the claimed outcome i.e. enabling the processor-based system to generate, using the trained generative adversarial deep neural network, a contour of a crown for the prepared tooth based on the identified one or more dental features in the patient's scan data, wherein the crown is to be attached to the prepared tooth, wherein the crown is to be attached to the prepared tooth, wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model.
Thus the limitation recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.)
Alternatively, wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is has been trained with …data sets specific to a type … ,wherein the discriminator is trained … to recognize whether the output … from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the claimed elements merely recite well-understood, routine, conventional activity previously known to the industry; See 2106.05(d)).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation are directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."
Regarding the limitation deemed insignificant extra-solution activity (i.e. wherein the crown is to be attached to the prepared tooth), the examiner provides evidence under the Berkheimer Memorandum Option 3: A citation to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s).
The examiner provides citations to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s) to demonstrate that the additional elements are widely prevalent or in common use in the relevant field (crowns, prepared/created, to be attached on a prepared tooth), as cited below:
Loudon (US 4834656): discloses in 1:31-41: … The manufacture and application of dental crowns to prepared or altered natural teeth is, of course, well known as disclosed by way of example in U.S. Pat. Nos. 504,126, 2,154,499 and 4,504,230 to Durr, Eisenstein and Patch, respectively…
Jones et al. (US 20020197583): discloses in [0020] By way of example, FIG. 1 depicts a prepared tooth 11 (shown partially in phantom lines) having a crown 10 affixed thereto. Crown 10 is affixed to prepared tooth 11 in any manner conventional with dental restorations, including cementatious and/or adhesive bonding.
Ray et al. (US 20130010080): discloses in [0003] One common dental application for which surface imaging would be particularly advantageous relates to obtaining the profile of a tooth surface that has been prepared for the insertion of a dental crown. The typical method for fitting the crown is to take an impression of the prepared tooth and to forward the impression to a laboratory for fabrication…
Regarding the limitation deemed well-understood, routine, conventional activity previously known to the industry (i.e. wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is has been trained with …data sets specific to a type … , wherein the discriminator is trained … to recognize whether the output … from the generator is a real or fake model.), the examiner provides evidence under the Berkheimer Memorandum Option 3: A citation to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s).
Prior art also teaches that a GAN model comprises a generator and discriminator components, as claimed in elements as well-known as that is the defined operation of a GAN model as known in machine learning:
Goodfellow et al. (NPL: Generative Adversarial Nets): teaches in abstract “We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G… Sec. 1 ... In the proposed adversarial nets framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency.
Wu et al. (NPL: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling): teaches in Sec. 3: As proposed in Goodfellow et al. [2014], the Generative Adversarial Network (GAN) consists of a generator and a discriminator, where the discriminator tries to classify real objects and objects synthesized by the generator, and the generator attempts to confuse the discriminator. In our 3D Generative Adversarial Network (3D-GAN), the generator G maps a 200-dimensional latent vector z, randomly sampled from a probabilistic latent space, to a 64 × 64 × 64 cube, representing an object G(z) in 3D voxel space. The discriminator D outputs a confidence value D(x) of whether a 3D object input x is real or synthetic… Training details A straightforward training procedure is to update both the generator and the discriminator in every batch. However, the discriminator usually learns much faster than the generator, possibly because generating objects in a 3D voxel space is more difficult than differentiating between real and synthetic objects [Goodfellow et al., 2014, Radford et al., 2016]…
Burnap et al. (NPL: Estimating and Exploring the Product Form Design Space Using Deep Generative Models): teaches in Sec. 2.4: Deep generative models refer to a class of hierarchical statistical models (referred to as “deep learning" in the computer science community;… The generative adversarial network (GAN) is a generative model that has a unique parameter estimation approach [51]. The model is divided into two parts, a generator and a discriminator; the generator is trained to generate images so that the discriminator cannot distinguish them from the ground truth images, while the discriminator is trained to discriminate generated images from the known “ground truth" images. The two parts are trained simultaneously to force the generator to produce images as similar to the ground truth images as possible, where similarity is defined by the discriminator…
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 16: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the… maps one or more dental features … to one or more highest probability values of a probability vector. (Mental process for evaluating and making judgements for generating an opinion; and mathematical concepts for process the making mathematical relationships using probability values associated with portions of data)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
the trained deep neural network maps one or more dental features… (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
… one or more dental features in at least one portion of each dentition training data set from a plurality of dentition training data sets... (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 17: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein a … outputs a loss function based on a comparison of the generated contour of the crown (Mental process for evaluating and making judgements for comparing observations; and mathematical concepts for process the making mathematical relationships using loss functions for making comparisons)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
… the generated contour of the crown for the prepared tooth in the patient's scan data … an image of a real crown contour from a real-sample data sets… (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
a second trained deep neural network outputs a loss function based on a comparison … and wherein the first neural network is configured to generate a second contour of the crown based on the output loss function of the second trained deep neural network. (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 18: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Incorporates abstract ideas recited in claim 15.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the one or more dental features in the at least one portion of each dentition training data set comprise one or more of a tooth surface anatomy, a tooth dentition, and a restoration type; (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 19: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… generate a depth map for … data set, (Mental process for making observations and judgements for dataset observations; mathematical concepts as mathematical relationships as depth map of datasets)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the plurality of dentition training data sets have been preprocessed to generate a depth map for each training data set, (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
the plurality of dentition training data sets … a depth map for each training data set (Claimed limitations are generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h))
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception. These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 20: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… selects a deep neural network ….; … and generates, …, an output restoration model …. (Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
the system comprising: a dental restoration client wherein the dental restoration client receives, …, (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
a training module, wherein the training module trains, using one or more training data sets …, a generative adversarial deep neural network; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
a training module, wherein the training module trains, …, a generative adversarial deep neural network; a 3D modeling module, wherein the 3D modeling module: receives … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
…an input indicating a selection of a dental restoration type to be fabricated. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
… one or more training data sets comprising images of real-life prepared teeth and real-life crowns installed on the real-life prepared teeth, … uses the patient's dentition data set as an input to the selected pre-trained deep neural network; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
receives, from a user interface, an input…(Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
…a 3D modeling module, wherein the 3D modeling module: receives a dentition data set of a patient; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
wherein the patient's dentition data set is generated by scanning a 3D impression or model of the patient's teeth (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
… a deep neural network pre-trained by a group of training data sets designed to model a specific restoration type that matches the selected dental restoration type; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
… one or more dental features in the patient's scan data … using one or more training data sets comprising images of real-life prepared teeth and real-life crowns installed on the real-life prepared teeth … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
uses the patient's dentition data set as an input to the selected pre-trained deep neural network; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
and generates, using the trained generative adversarial deep neural network, an output restoration model using the selected pre-trained deep neural network based on the patient's dentition data, … wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
wherein the restoration model is to be used on a preparation site, wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, ... wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
Alternatively, wherein the restoration model is to be used on a preparation site (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant Extra-Solution Activity. See MPEP 2106.05(g).)
Alternatively, receives, from a user interface, an input … uses the patient's dentition data set as an input to the selected pre-trained deep neural network; (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
Alternatively, when not considered part of the abstract idea the limitation: generates, udeemed insufficient to transform the judicial exception to a patentable invention because the recitation the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (generating an output restoration mode based on claimed features). The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). See MPEP 2106.05(f)(1).
Examiner notes that mere linking the use of a computer as tool (i.e. generating, using the trained generative neural network) fails to resolve the issues that claim limitation elements are recited at a high level of generality that fails to recite details of how a solution to a problem is accomplished.
Alternatively, generates, using the trained generative adversarial deep neural network, an output restoration model using the selected pre-trained deep neural network based on the patient's dentition data, wherein the restoration model is to be used on a preparation site, wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
Examiner notes that by claiming to use a generative adversarial network (GAN) to generate a contour of a crown for the prepared tooth where the GAN is also claimed to be trained to generate an output restoration model using the selected pre-trained deep neural network based on the patient's dentition data and classify if the 3D model is fake or real … it is unclear how the GAN is used to achieve the claimed outcome i.e. generates, using the trained generative adversarial deep neural network, an output restoration model using the selected pre-trained deep neural network based on the patient's dentition data, wherein the restoration model is to be used on a preparation site, wherein the generator is trained with scan data sets specific to a type of dental features of interest to output a 3D model of a crown, wherein the discriminator is trained using real dental restoration models to recognize whether the output 3D model from the generator is a real or fake model.
Thus the limitation recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.)
Alternatively, wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is has been trained with …data sets specific to a type … ,wherein the discriminator is trained … to recognize whether the output … from the generator is a real or fake model. (Deemed insufficient to transform the judicial exception to a patentable invention because the claimed elements merely recite well-understood, routine, conventional activity previously known to the industry; See 2106.05(d)).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation are directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."
Regarding the limitation deemed insignificant extra-solution activity (i.e. wherein the restoration model is to be used on a preparation site such as a restoration model fabricated as a crown is to be attached to the prepared tooth as a preparation site), the examiner provides evidence under the Berkheimer Memorandum Option 3: A citation to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s).
The examiner provides citations to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s) to demonstrate that the additional elements are widely prevalent or in common use in the relevant field (crowns, prepared/created, to be attached on a prepared tooth), as cited below:
Loudon (US 4834656): discloses in 1:31-41: … The manufacture and application of dental crowns to prepared or altered natural teeth is, of course, well known as disclosed by way of example in U.S. Pat. Nos. 504,126, 2,154,499 and 4,504,230 to Durr, Eisenstein and Patch, respectively…
Jones et al. (US 20020197583): discloses in [0020] By way of example, FIG. 1 depicts a prepared tooth 11 (shown partially in phantom lines) having a crown 10 affixed thereto. Crown 10 is affixed to prepared tooth 11 in any manner conventional with dental restorations, including cementatious and/or adhesive bonding.
Ray et al. (US 20130010080): discloses in [0003] One common dental application for which surface imaging would be particularly advantageous relates to obtaining the profile of a tooth surface that has been prepared for the insertion of a dental crown. The typical method for fitting the crown is to take an impression of the prepared tooth and to forward the impression to a laboratory for fabrication…
Regarding the limitation deemed well-understood, routine, conventional activity previously known to the industry (i.e. wherein the generative adversarial deep neural network comprises a generator and a discriminator, wherein the generator is has been trained with …data sets specific to a type … , wherein the discriminator is trained … to recognize whether the output … from the generator is a real or fake model.), the examiner provides evidence under the Berkheimer Memorandum Option 3: A citation to a publication that demonstrates the well-understood, routine, or conventional nature of the element(s).
Prior art also teaches that a GAN model comprises a generator and discriminator components, as claimed in elements as well-known as that is the defined operation of a GAN model as known in machine learning:
Goodfellow et al. (NPL: Generative Adversarial Nets): teaches in abstract “We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G… Sec. 1 ... In the proposed adversarial nets framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency.
Wu et al. (NPL: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling): teaches in Sec. 3: As proposed in Goodfellow et al. [2014], the Generative Adversarial Network (GAN) consists of a generator and a discriminator, where the discriminator tries to classify real objects and objects synthesized by the generator, and the generator attempts to confuse the discriminator. In our 3D Generative Adversarial Network (3D-GAN), the generator G maps a 200-dimensional latent vector z, randomly sampled from a probabilistic latent space, to a 64 × 64 × 64 cube, representing an object G(z) in 3D voxel space. The discriminator D outputs a confidence value D(x) of whether a 3D object input x is real or synthetic… Training details A straightforward training procedure is to update both the generator and the discriminator in every batch. However, the discriminator usually learns much faster than the generator, possibly because generating objects in a 3D voxel space is more difficult than differentiating between real and synthetic objects [Goodfellow et al., 2014, Radford et al., 2016]…
Burnap et al. (NPL: Estimating and Exploring the Product Form Design Space Using Deep Generative Models): teaches in Sec. 2.4: Deep generative models refer to a class of hierarchical statistical models (referred to as “deep learning" in the computer science community;… The generative adversarial network (GAN) is a generative model that has a unique parameter estimation approach [51]. The model is divided into two parts, a generator and a discriminator; the generator is trained to generate images so that the discriminator cannot distinguish them from the ground truth images, while the discriminator is trained to discriminate generated images from the known “ground truth" images. The two parts are trained simultaneously to force the generator to produce images as similar to the ground truth images as possible, where similarity is defined by the discriminator…
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception and does not recite, when claim elements are examined or as an ordered combination, that are directed to what have the courts have identified as "significantly more”, than the identified abstract idea, see MPEP 2106.05.
Allowable Subject Matter
Claims 1-20 have been fully considered by the examiner and no prior art could be found to maintain a rejection under 35 USC 102 and/or 35 USC 103.
The following is an examiner's statement of reasons for allowance:
Claims1-20 are considered allowable since when reading the claims in light of the specification, as per MPEP 2111.01, none of the references of record alone or in combination disclose or suggest the limitations found within the independent claims 1, 15 and 20 as a whole as recited by the claim limitations. The noted limitations were deemed allowable over the cited prior art:
Claim 1: “training, using one or more training data sets comprising images of real-life prepared teeth and real-life crowns installed on the real-life prepared teeth, a generative adversarial deep neural network; … identifying, using a trained deep neural network, one or more dental features in the patient's scan data based on one or more output probability values of the trained deep neural network; determining locations of the identified one or more dental features including the locations of a prepared tooth; and generating, using the trained generative adversarial deep neural network, a contour of a crown for the prepared tooth based on the identified one or more dental features and the locations of the identified one or more dental features in the patient's scan data, …”
Claim 15: “…train, using one or more training data sets comprising images of real-life prepared teeth and real-life crowns installed on the real-life prepared teeth, a generative adversarial deep neural network; … use a first deep neural network to identify one or more dental features in the patient's scan data based on one or more output probability values of the deep neural network, … determine the locations of a prepared tooth; … generate, using trained generative adversarial deep neural network, a contour of a crown for the prepared tooth based on the identified one or more dental features in the patient's scan data, …”
Claim 20: “ a training module, wherein the training module trains, using one or more training data sets comprising images of real-life prepared teeth and real-life crowns installed on the real-life prepared teeth, a generative adversarial deep neural network; … selects a deep neural network pre-trained by a group of training data sets designed to model a specific restoration type that matches the selected dental restoration type; uses the patient's dentition data set as an input to the selected pre-trained deep neural network; and generates, using the trained generative adversarial deep neural network, an output restoration model using the selected pre-trained deep neural network based on the patient's dentition data, …,”
The closest prior art, listed below, discloses:
Kuo et al. (U.S. Pub. No. 2005/0192835): teaches employing a data mining technique for interrogating said database for generating an output data stream, the output data stream correlating a patient malocclusion with an orthodontic treatment using machine learning models.
Denton et al. (NPL: “Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks”, hereinafter ‘Denton’): teaches neural networks include generative network trained with imaged for developing image models. And Generative Adversarial Networks machine learning approach is a framework for training generative models, where the method pits two networks against one another: a generative model G that captures the data distribution and a discriminative model D that distinguishes between samples drawn from G and images drawn from the training data.
Lehmann et al. (US 20070026363) teaches capturing real-life dental data when a crown is finally prepared, digital images can be taken and compared to the digital images of the patient's teeth that were previously obtained to assure that the closest color match has been achieved. Any necessary color corrections can be made by the laboratory or the technician before permanent placement of the crown.
In summary, the references made of record, fail to disclose the required claimed technical features as recited by the independent claim limitations as a whole.
Furthermore, the references of record alone or in combination disclose or suggest the combination of limitations found within the independent claims as a whole without hindsight reasoning.
The dependent claims, being further limiting to the independent claims, definite, and enable by the Specification are also allowed.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled "Comments on Statement of Reasons for Allowance."
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Zhang et al. (10460231) : teaches a system includes a neural network trained by inputting a set of raw data images and a correlating set of desired quality output images; the neural network including an input for receiving input image data and providing processed output; wherein the processed output includes input image data that has been adjusted for at least one image quality attribute.
Asch et al. (US 9877700): teaches a imaging station may also be used to image dental restorations to identify internal features that may affect the longevity of the restoration such as bubbles and cracks (which may show as unexpected surfaces within a three-dimension image of a restoration). Restorations may be images before or after being placed in the oral cavity (e.g., to evaluate placement of the restoration within the patient's mouth and to determine when to replace an existing restoration before a failure occurs).
Gole (US 20130172731): teaches capturing real-life scan images before and after placement of a dental implant.
Cunningham et al. (US 20170046486): teaches capturing digital images of crowns on a prepared tooth prior to additional surgery. And discloses in order for the dental patient to be ready for dental implant surgery, a dental professional may need certain images such as cone beam computed tomography scans. Various types of components, such as the surgical guides, scan guides, dental implants, healing abutments, bone graft material, bio-membranes, temporary crowns, screws, and temporary dentures may also be obtained prior to surgery.
Boerjes et al. (US 20090298017): teaches , a digital model, such as a digital surface representation obtained using the image capture system described above, of a surface prepared for a restoration such as a crown, or any other dental object. And taking a scan, either as a result of prior dental work (e.g., a previously restored tooth) or during an evaluation of fit and other aspects of a current procedure. Dental objects may include "restorations", which may be generally understood to include components that restore the structure or function of existing dentition, such as crowns, bridges, veneers, inlays, onlays, amalgams, composites, and various substructures such as copings and the like, as well as temporary restorations for use while a permanent restoration is being fabricated
Hu et al. (NPL: “Enhancing Dental CBCT Image by Semi-coupled Generative Adversarial Networks”): teaches that Generative Adversarial Networks (GANs) (Goodfellow et al. 2014) have been widely utilized to improve the quality of CBCT images (You et al. 2019), remove metal artifacts (Koike et al. 2020), and synthesize high-quality CT images across multiple modalities (Sun et al. 2021).
Hsieh et al. (US 20180144466): teaches the list of possible neural network types in 0110: …The AI catalog 1326 can include one or more AI models such as good old fashioned artificial intelligence (GOFAI) (e.g., expert systems, etc.), machine learning (ML) (e.g., SVM, RF, etc.), deep learning (DL) (e.g., convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LS™), generative adversarial network (GAN), etc.), paradigms (e.g., supervised, unsupervised, reinforcement, etc.), etc.
Kuo et al. (US 20170100213): teaches processing image data using neural network and machine learning models; where the data is used for supporting orthodontic assessment of patient data.
Jojic et al. (US 6701016): teaches the use of generative networks for learning pattern for image processing and matching facial images.
Weng et al. (US Pub. No. 20170008168): teaches the use of deep neural networks and generative networks for control tasks using sensor data.
Farag et al. (20020028418): generating digital reconstruction of an oral cavity using deep neural network as a multi-layer neural network based on image data captured using a scanning device such as x-ray, tomographic imaging, sonographic imaging, and other techniques for obtaining information about the position and structure of the teeth, jaws, gums and other orthodontically relevant tissue.
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/OLUWATOSIN ALABI/Primary Examiner, Art Unit 2129