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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-19 and 37 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 19, and 37 (and their respective dependents) recite ‘machine-learning models’. Here, the claims recite these machine-learning models, but the specification never discloses the necessary steps and/or flowcharts of how this occurs. The term “machine-learning model” is treated as a black box and the specification does not describe the specifics of how to achieve the above-recited function(s) with this algorithm. For example, How many and what types of layers are there? How is the data propagated? What logics are programmed to help the machine learning algorithm make a decision? Is the training supervised or unsupervised? What are the weightings? Are other training concepts used such as regression? It is not enough that a skilled artisan could devise a way to accomplish the function because this is not relevant to the issue of whether the inventor has shown possession of the claimed invention. See MPEP 2161.01(I). Therefore, adequate disclosure is needed.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-19 and 37 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the detected teeth" in Line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the identified teeth" in Line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the tooth" in Line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites ‘each tooth’ and it is unclear if this is part of the ‘plurality of teeth’ as earlier recited or not. For the purposes of examination, it will be treated as ‘each tooth of the plurality of teeth’
Claim 1 recites ‘any pathology’ after reciting ‘any dental pathology’ making it unclear if they are referring to the same element or not.
Claim 6 recites ‘each tooth’ and it is unclear if this is part of the ‘plurality of teeth’ as recited in claim 1 or not.
Claim 6 recites the limitation "the box-coordinates" in Line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the corresponding possible tooth" in Line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites ‘a tooth’ and it is unclear if this is part of the ‘plurality of teeth’ as recited in claim 1 or not.
Claim 7 recites the limitation "the plurality of detected teeth" in Lines 2 and 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the segmentation" in Line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the detected teeth" in Lines 2 and 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites ‘each localized tooth’ and it is unclear if this is part of the ‘plurality of teeth’ as recited in claim 1 or not.
Claim 11 recites ‘one or more pathologies’ and it is unclear if this is part of the ‘any pathology’ as recited in claim 1 or not.
Claim 11 recites the limitation "the tooth" in Line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites ‘one or more pathologies’ and it is unclear if this is part of the ‘any pathology’ as recited in claim 1 or not.
Claim 12 recites ‘each tooth’ and it is unclear if this is part of the ‘plurality of teeth’ as recited in claim 1 or not.
Claim 14 recites the limitation "the one or more dental structures" in Lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
The term “particular” in claim 14 is a relative term which renders the claim indefinite. The term “particular” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 15 recites the limitation "the one or more dental structures" in Lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the required alignment" in Line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the determined degree" in Lines 6-7. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites ‘a parallel manner’ twice in the claim making it unclear if each recitation refers to the same element or not.
Claim 17 recites ‘a plurality of teeth’ and it is unclear if this is part of the ‘plurality of teeth’ as recited in claim 1 or not.
Claim 17 recites the limitation "the detected teeth" in Line 11. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the identified teeth" in Line 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the tooth" in Line 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites ‘each tooth’ and it is unclear if this is part of the ‘plurality of teeth’ as earlier recited or not. For the purposes of examination, it will be treated as ‘each tooth of the plurality of teeth’
Claim 17 recites ‘any pathology’ after reciting ‘any dental pathology’ and is dependent back to claim 1 which also recites ‘any pathology’ making it unclear if they are referring to the same element or not.
Claim 19 recites the limitation "the detected teeth" in Line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "the identified teeth" in Line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "the tooth" in Line 9. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites ‘each tooth’ and it is unclear if this is part of the ‘plurality of teeth’ as earlier recited or not. For the purposes of examination, it will be treated as ‘each tooth of the plurality of teeth’
Claim 19 recites ‘any pathology’ after reciting ‘any dental pathology’ making it unclear if they are referring to the same element or not.
Claim 37 recites the limitation "the processors" in Line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 37 recites the limitation "the detected teeth" in Line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 37 recites the limitation "the identified teeth" in Line 9. There is insufficient antecedent basis for this limitation in the claim.
Claim 37 recites the limitation "the tooth" in Line 10. There is insufficient antecedent basis for this limitation in the claim.
Claim 37 recites ‘each tooth’ and it is unclear if this is part of the ‘plurality of teeth’ as earlier recited or not. For the purposes of examination, it will be treated as ‘each tooth of the plurality of teeth’
Claim 37 recites ‘any pathology’ after reciting ‘any dental pathology’ making it unclear if they are referring to the same element or not.
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 1-19 and 37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claimed invention in claims 1-19 and 37 are directed to statutory subject matter as the claims recite a system (claims 19 and 37) and a method (claims 1-18).
Step 2A, Prong One
Regarding claims 1, 18, and 19, the recited steps are directed mental process of performing concepts in a human mind or by a human using a pen and paper (see MPEP 2106.04(a)(2) subsection (III)).
Specifically from claim 1:
accessing a first image depicting an oral cavity associated with an animal;
detecting, based on one or more machine-learning models, a plurality of teeth associated with the animal from the first image;
identifying, based on the one or more machine-learning models, each of the detected teeth based on a numbering protocol;
determining, for each of the identified teeth based on the one or more machine-learning models, whether the tooth is healthy or has any dental pathology;
localizing each tooth that has any pathology based on the numbering protocol; and
generating a first report comprising a localization of each tooth that has any pathology.
Specifically from claims 19 and 37:
access a first image depicting an oral cavity associated with an animal;
detect, based on one or more machine-learning models, a plurality of teeth associated with the animal from the first image;
identify, based on the one or more machine-learning models, each of the detected teeth based on a numbering protocol;
determine, for each of the identified teeth based on the one or more machine-learning models, whether the tooth is healthy or has any dental pathology;
localize each tooth that has any pathology based on the numbering protocol; and
generate a first report comprising a localization of each tooth that has any pathology.
These underlined limitations above describe a mental process (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard, as a skilled practitioner is capable of performing the recited limitations and making a mental assessment thereafter. Examiner notes that nothing from the claims suggests that the limitations cannot be practically performed by a medical, biomedical or engineering professional with the aid of a pen and paper; their knowledge gained from education, background, or experience; or by using a generic computer as a tool to perform mental process steps in real time. Examiner additionally notes that nothing from the claims suggests and undue level of complexity that the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform the mental process steps.
Examples of ineligible claims that recite mental processes include:
• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
• claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
Step 2A, Prong Two
This judicial exceptions (abstract ideas) in claims 1-19 and 37 are not integrated into a practical application because:
•The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for performing the abstract ideas merely invoke a computer as a tool.
•The data-gathering steps do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
•There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for performing the abstract ideas
•The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to provide a medical measurement.
•The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer to perform the abstract ideas. The claims do not apply the obtained measurements to a particular machine. Rather, the data is merely output in a post-solution step.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Step 2B
The additional elements are identified as follows: ‘one or more computing systems’ in claim 1, ‘one or more machine-learning models’ in claim 1, ‘a first machine learning model and ‘a second machine-learning model’ in claim 10, ‘a cloud computing system’ in claim 17, ‘one or more finite state machines’ in claim 18, ‘one or more computer-readable non-transitory storage media’ and ‘one or more machine learning models’ in claim 19, ‘one or more processors’, ‘a non-transitory memory’, ‘one or more machine learning models’ in claim 37
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
Applicant's specification (Page 9, Lines 7-22 and Page 22, Line 16-Page 26, Line 29) which discloses that the processor and memory comprise generic computer components that are configured to perform the generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry; and
The prior art provided by the Applicant in the IDS and by the Examiner in PTO-892 which disclose each of the elements as being known and conventional in the art elements;
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3. Furthermore, the court decisions discussed in MPEP § 2106.05(d)(ll) note the well-understood, routine and conventional nature of such additional elements as those claimed. See option III. A. 2. in the Berkheimer memorandum.
Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception into a practical application or provide significantly more. See Bilski, 561 U.S. at 610, 95 USPQ2d at 1009 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 197 (1978)), and CyberSource v. Retail Decisions, 654 F.3d 1366, 1370, 99 USPQ2d 1690 (Fed. Cir. 2011). See MPEP 2106.05(b).
Regarding the dependent claims, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry or 3) further recite additional elements at a high level of generality which are conventional in the art.
Claims 10 and 17 recites additional elements at a high level of generality which are conventional in the art
Claims 2-18 are steps that are also abstract as a mental process through additional data gathering or analysis
Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 9, 11, 13, 19, and 37 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Johnson (US 2021/0279871).
Regarding claim 1, Johnson teaches a method (Abstract) comprising, by one or more computing systems (Claim 1; computer 102):
accessing a first image depicting an oral cavity associated with an animal (Paragraphs 0018-0020; “the dental imaging system can automatically determine what teeth are present in an intraoral X-Ray or camera image” ; Paragraph 0048);
detecting, based on one or more machine-learning models, a plurality of teeth associated with the animal from the first image (Paragraphs 0019 and 0022; “the output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, via a standard method of object localization or segmentation utilized in machine vision (i.e. a Mask R-CNN/Bayesian Optimization/Semantic Segmentation).”; Paragraph 0048);
identifying, based on the one or more machine-learning models, each of the detected teeth based on a numbering protocol (Paragraphs 0019 and 0022; “the output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, via a standard method of object localization or segmentation utilized in machine vision (i.e. a Mask R-CNN/Bayesian Optimization/Semantic Segmentation).”; “Such an embodiment improves the process of labeling teeth in an image” ; Paragraph 0048);
determining, for each of the identified teeth based on the one or more machine-learning models, whether the tooth is healthy or has any dental pathology (Paragraph 0022; “the dental imaging system can analyze an acquired image and automatically detect and highlight/indicate the presence of abnormalities in dentition (for example but not limited to caries and cavities). A typical embodiment uses a convolutional neural network to train a model. Such a model can be trained using a statistically significant sample of images that have been pre-labeled by dental experts as having or not having particular abnormalities/afflictions in the dentition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, which can be achieved via a method of object localization or segmentation in machine vision using deep learning (i.e. an R-CNN/Bayesian Optimization/Semantic Segmentation).” ; Paragraph 0048);
localizing each tooth that has any pathology based on the numbering protocol (Paragraph 0022; “the dental imaging system can analyze an acquired image and automatically detect and highlight/indicate the presence of abnormalities in dentition (for example but not limited to caries and cavities). A typical embodiment uses a convolutional neural network to train a model. Such a model can be trained using a statistically significant sample of images that have been pre-labeled by dental experts as having or not having particular abnormalities/afflictions in the dentition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, which can be achieved via a method of object localization or segmentation in machine vision using deep learning (i.e. an R-CNN/Bayesian Optimization/Semantic Segmentation).” ; Paragraph 0048); and
generating a first report comprising a localization of each tooth that has any pathology (Paragraph 0022; “Labels for abnormalities/afflictions can be attached or annotated on the images reflecting the ADA standard nomenclature and automate portions of the treatment planning and insurance filing process based on the detected affliction.” ; Paragraphs 0048-0049).
Regarding claim 2, Johnson teaches wherein the first image comprises an X-ray image (Paragraph 0019; “the dental imaging system can automatically determine what teeth are present in an intraoral X-Ray or camera image”).
Regarding claim 9, Johnson teaches wherein identifying each of the detected teeth is based on contextual information associated with each of the detected teeth (Paragraphs 0019 and 0022; “he model can optionally use accelerometer or gyroscope data provided by hardware in the intraoral sensor/camera or sensor holders/bite-blocks that were used to acquire the image for increasing accuracy in tooth position recognition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, via a standard method of object localization or segmentation utilized in machine vision (i.e. a Mask R-CNN/Bayesian Optimization/Semantic Segmentation).”).
Regarding claim 11, Johnson teaches further comprising: determining, for each localized tooth, one or more pathologies associated with the tooth (Paragraph 0022; “the dental imaging system can analyze an acquired image and automatically detect and highlight/indicate the presence of abnormalities in dentition (for example but not limited to caries and cavities). A typical embodiment uses a convolutional neural network to train a model. Such a model can be trained using a statistically significant sample of images that have been pre-labeled by dental experts as having or not having particular abnormalities/afflictions in the dentition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, which can be achieved via a method of object localization or segmentation in machine vision using deep learning (i.e. an R-CNN/Bayesian Optimization/Semantic Segmentation).” ; Paragraph 0048).
Regarding claim 13, Johnson teaches further comprising: determining, based on the one or more machine-learning models, the first image comprises diagnostic information associated with dental pathology detection, wherein the diagnostic information is based on one or more dental structures (Paragraph 0022; “the dental imaging system can analyze an acquired image and automatically detect and highlight/indicate the presence of abnormalities in dentition (for example but not limited to caries and cavities). A typical embodiment uses a convolutional neural network to train a model. Such a model can be trained using a statistically significant sample of images that have been pre-labeled by dental experts as having or not having particular abnormalities/afflictions in the dentition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, which can be achieved via a method of object localization or segmentation in machine vision using deep learning (i.e. an R-CNN/Bayesian Optimization/Semantic Segmentation).”; Paragraph 0048).
Regarding claim 15, Johnson teaches wherein the one or more dental structures are associated with a particular dental pathology (Paragraph 0022; “Labels for abnormalities/afflictions can be attached or annotated on the images reflecting the ADA standard nomenclature and automate portions of the treatment planning and insurance filing process based on the detected affliction.” ; Paragraphs 0048-0049).
Regarding claim 19, Johnson teaches one or more computer-readable non-transitory storage media embodying software that is operable when executed (Abstract; Claim 1) to:
access a first image depicting an oral cavity associated with an animal (Paragraphs 0018-0020; “the dental imaging system can automatically determine what teeth are present in an intraoral X-Ray or camera image” ; Paragraph 0048);
detect, based on one or more machine-learning models, a plurality of teeth associated with the animal from the first image (Paragraphs 0019 and 0022; “the output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, via a standard method of object localization or segmentation utilized in machine vision (i.e. a Mask R-CNN/Bayesian Optimization/Semantic Segmentation).”; Paragraph 0048);
identify, based on the one or more machine-learning models, each of the detected teeth based on a numbering protocol (Paragraphs 0019 and 0022; “the output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, via a standard method of object localization or segmentation utilized in machine vision (i.e. a Mask R-CNN/Bayesian Optimization/Semantic Segmentation).”; “Such an embodiment improves the process of labeling teeth in an image” ; Paragraph 0048);
determine, for each of the identified teeth based on the one or more machine-learning models, whether the tooth is healthy or has any dental pathology (Paragraph 0022; “the dental imaging system can analyze an acquired image and automatically detect and highlight/indicate the presence of abnormalities in dentition (for example but not limited to caries and cavities). A typical embodiment uses a convolutional neural network to train a model. Such a model can be trained using a statistically significant sample of images that have been pre-labeled by dental experts as having or not having particular abnormalities/afflictions in the dentition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, which can be achieved via a method of object localization or segmentation in machine vision using deep learning (i.e. an R-CNN/Bayesian Optimization/Semantic Segmentation).” ; Paragraph 0048);
localize each tooth that has any pathology based on the numbering protocol (Paragraph 0022; “the dental imaging system can analyze an acquired image and automatically detect and highlight/indicate the presence of abnormalities in dentition (for example but not limited to caries and cavities). A typical embodiment uses a convolutional neural network to train a model. Such a model can be trained using a statistically significant sample of images that have been pre-labeled by dental experts as having or not having particular abnormalities/afflictions in the dentition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, which can be achieved via a method of object localization or segmentation in machine vision using deep learning (i.e. an R-CNN/Bayesian Optimization/Semantic Segmentation).” ; Paragraph 0048); and
generate a first report comprising a localization of each tooth that has any pathology (Paragraph 0022; “Labels for abnormalities/afflictions can be attached or annotated on the images reflecting the ADA standard nomenclature and automate portions of the treatment planning and insurance filing process based on the detected affliction.” ; Paragraphs 0048-0049).
Regarding claim 37, Johnson teaches a system (Abstract) comprising:
one or more processors (Claim 1; computer 102); and
a non-transitory memory coupled to the processors comprising instructions executable by the processors (Paragraph 0025), the processors operable when executing the instructions to:
access a first image depicting an oral cavity associated with an animal (Paragraphs 0018-0020; “the dental imaging system can automatically determine what teeth are present in an intraoral X-Ray or camera image” ; Paragraph 0048);
detect, based on one or more machine-learning models, a plurality of teeth associated with the animal from the first image (Paragraphs 0019 and 0022; “the output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, via a standard method of object localization or segmentation utilized in machine vision (i.e. a Mask R-CNN/Bayesian Optimization/Semantic Segmentation).”; Paragraph 0048);
identify, based on the one or more machine-learning models, each of the detected teeth based on a numbering protocol (Paragraphs 0019 and 0022; “the output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, via a standard method of object localization or segmentation utilized in machine vision (i.e. a Mask R-CNN/Bayesian Optimization/Semantic Segmentation).”; “Such an embodiment improves the process of labeling teeth in an image” ; Paragraph 0048);
determine, for each of the identified teeth based on the one or more machine-learning models, whether the tooth is healthy or has any dental pathology (Paragraph 0022; “the dental imaging system can analyze an acquired image and automatically detect and highlight/indicate the presence of abnormalities in dentition (for example but not limited to caries and cavities). A typical embodiment uses a convolutional neural network to train a model. Such a model can be trained using a statistically significant sample of images that have been pre-labeled by dental experts as having or not having particular abnormalities/afflictions in the dentition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, which can be achieved via a method of object localization or segmentation in machine vision using deep learning (i.e. an R-CNN/Bayesian Optimization/Semantic Segmentation).” ; Paragraph 0048);
localize each tooth that has any pathology based on the numbering protocol (Paragraph 0022; “the dental imaging system can analyze an acquired image and automatically detect and highlight/indicate the presence of abnormalities in dentition (for example but not limited to caries and cavities). A typical embodiment uses a convolutional neural network to train a model. Such a model can be trained using a statistically significant sample of images that have been pre-labeled by dental experts as having or not having particular abnormalities/afflictions in the dentition. The output of this embodiment is both a probability map indicating the likelihood of a pixel or sub-region of pixels belonging to the target structure (teeth), as well as locations for the teeth, which can be achieved via a method of object localization or segmentation in machine vision using deep learning (i.e. an R-CNN/Bayesian Optimization/Semantic Segmentation).” ; Paragraph 0048); and
generate a first report comprising a localization of each tooth that has any pathology (Paragraph 0022; “Labels for abnormalities/afflictions can be attached or annotated on the images reflecting the ADA standard nomenclature and automate portions of the treatment planning and insurance filing process based on the detected affliction.” ; Paragraphs 0048-0049).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Campbell (US 2012/0057673).
Regarding claim 3, Johnson is silent on the image format. Campbell teaches wherein the first image is based on PNG format or DICOM format (Paragraph 0038). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Campbell because Campbel teaches them as being standard formats that are widely available (Paragraph 0038 of Campbell) and thus would provide predictable results.
Claim(s) 4-5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Reynard et al. (US 2020/0015943).
Regarding claim 4, Johnson is silent on the quadrant. Reynard teaches further comprising: determining a quadrant for the first image based on the numbering protocol (Paragraphs 0066-0068). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Reynard because Reynard teaches it as being the standard for labeling teeth (Paragraph 0066 of Reynard) and thus would yield predictable results.
Regarding claim 5, Johnson is silent on the quadrant. Reynard teaches further comprising: determining a view for the first image based on whether there is a composition of quadrants or not, wherein the view comprises a lateral view or an occlusal view (Paragraph 0050). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Reynard because Reynard teaches it as being the standard for labeling teeth (Paragraph 0066 of Reynard) and thus would yield predictable results.
Regarding claim 14, Johnson is silent on the quadrant. Reynard teaches wherein the one or more dental structures are associated with a particular quadrant (Paragraphs 0066-0068). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Reynard because Reynard teaches it as being the standard for labeling teeth (Paragraph 0066 of Reynard) and thus would yield predictable results.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Tuzoff et al. (US 2020/0146646).
Regarding claim 6, Johnson is silent on determining box coordinates. Tuzoff teaches wherein detecting the plurality of teeth comprises: determining a plurality of box-coordinates for all possible teeth on the first image; and calculating a probability score for each of the possible teeth based on the box-coordinates, wherein the probability score indicates a likelihood of the corresponding possible tooth being a tooth (Paragraphs 0020-0024). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Tuzoff because Tuzoff teaches these techniques as being known in the art (Paragraphs 0016, 0020, and 0022 of Tuzoff) and thus would yield predictable results.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Souza et al. (US 2014/0314291).
Regarding claim 7, Johnson is silent on segmenting the teeth. Souza teaches further comprising: segmenting the plurality of detected teeth based on the one or more machine-learning model, wherein the segmentation comprises generating a tooth boundary and a masked tooth without background for each of the plurality of detected teeth (Paragraphs 0077 and 0080). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Souza because Souza teaches these techniques as being conventional in the art (Paragraphs 0077 and 0080 of Souza) and thus would yield predictable results.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Haws et al. (US 2019/0244693).
Regarding claim 8, Johnson is silent on the Triadan system. Haws teaches wherein the numbering protocol is based on Triadan system (Paragraph 0073). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Haws because Haws teaches it as being an international standard tooth numbering convention (Paragraph 0073 of Haws) and thus would yield predictable results.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Abraham et al. (US 2020/0175678).
Regarding claim 10, Johnson is silent on the multiple models for identifying different types of teeth. Abraham teaches wherein the one or more machine-learning models comprise a first machine-learning model configured for identifying maxilla teeth and a second machine-learning model configured for identifying mandible teeth (Paragraphs 0030-0031 and 0040 and 0046). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Abraham because Abraham teaches the maxilla and mandible as being known segments that dentists would want to look at for further examination (Paragraph 0006 of Abraham). Further should Abraham be found silent on not teaching multiple machine learning models, it would have been obvious to one of ordinary skill in the art to have modified Johnson in view of Abraham to use multiple machine-learning models since it has been held that mere duplication of the essential working parts of a device involves only routine skill in the art. St. Regis Paper Co. v. Bemis Co., 193 USPQ 8. MPEP 2144.04(VI-B).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Raby et al. (US 2022/0110723).
Regarding claim 12, Johnson is silent on a level of grading. Raby teaches further comprising: determining, for at least one of the one or more pathologies associated with each tooth, a level of grading (Paragraph 0107). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Raby because Raby teaches it as being an objective grading from the American Board of Orthodontics (Paragraph 0107) and thus would yield predictable results.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Kopelman et al. (US 2015/0320320).
Regarding claim 16, Johnson is silent on the alignment of the images. Kopelman teaches further comprising:
determining, based on the one or more machine-learning models, that the first image requires an alignment (Paragraphs 0037 and 0074);
determining, based on the one or more machine-learning models, a degree to rotate the first image for the required alignment (Paragraphs 0037 and 0074); and
rotating, based on the one or more machine-learning models, the first image by the determined degree (Paragraphs 0037 and 0074).
It would have been obvious to one of ordinary skill in the art to have modified Johnson with Kopelman because it aids in the ability to compare images to identify areas of concern (Paragraph 0074 of Kopelman).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Johnson (US 2019/0333627; hereinafter JohnsonII)
Regarding claim 17, Johnson is silent on the cloud computing system. JohnsonII teaches wherein the one or more computing systems are associated with a cloud computing system (Paragraph 0044), and wherein the method further comprises:
receiving, at the cloud computing system, a plurality of second images depicting the oral cavity associated with the animal (Paragraph 0044);
processing the plurality of second images in a parallel manner, wherein processing each of the plurality of second images comprises (Paragraph 0044):
using the one or more machine-learning models in a parallel manner to (Paragraph 0044):
detect a plurality of teeth associated with the animal from each second image (Paragraph 0044);
identify each of the detected teeth based on the numbering protocol (Paragraph 0044);
determine, for each of the identified teeth, whether the tooth is healthy or has any dental pathology (Paragraph 0044); and
localize each tooth that has any pathology based on the numbering protocol (Paragraphs 0018-0019 and 0044); and
generating a second report based on the first report and processing results of the plurality of second images (Paragraph 0044).
It would have been obvious to one of ordinary skill in the art to have modified Johnson with JohnsonII because JohnsonII teaches this as being a design choice (Paragraph 0044 of JohnsonII) and further essentially would only involve duplication of parts which would be obvious to one of ordinary skill in the art since it has been held that mere duplication of the essential working parts of a device involves only routine skill in the art. St. Regis Paper Co. v. Bemis Co., 193 USPQ 8. MPEP 2144.04(VI-B).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (US 2021/0279871) in view of Jelicich et al. (US 2021/0233233).
Regarding claim 18, Johnson is silent the use of finite state machines. Jelicich teaches wherein processing the plurality of second images in the parallel manner is based on logic generated based on one or more finite state machines (Paragraph 0100). It would have been obvious to one of ordinary skill in the art to have modified Johnson with Jelicich because Jelicich teaches the use of a finite state machine as being known in the art and would be considered a design choice (Paragraph 0100 of Jelicich) and thus one of ordinary skill in the art could choose the manner of processing through routine experimentation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sabina et al. (US 2019/0231490) and Falkel (US 2018/0078347)
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/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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