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 .
Claims 1-12 are pending regarding this application.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application (JP2023-188628) filed in Japan on 11/02/2023. It is noted, however, that applicant has not filed a certified copy of the JP2023-188628 application as required by 37 CFR 1.55. Please see the “Document indicating retrieval request was unsuccessful” mailed on 04/02/2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/24/2024 and 12/02/2025 are
considered and attached.
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, 2, 11, and 12, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis for claim 1 is provided in the following. Claim 1 is reproduced in the following (annotation added):
A non-transitory computer-readable medium storing a program causing a computer to execute:
acquiring intraoral three-dimensional data that represents a tooth of a subject person and periodontium of the tooth;
and calculating a gingival state index value of the subject person, an alveolar bone resorption index value of the subject person, or both thereof as a state index value of the subject person by using the intraoral three-dimensional data, wherein the gingival state index value of the subject person is an index value related to a state of a gingival of the subject person, wherein the alveolar bone resorption index value of the subject person is an index value related to resorption of an alveolar bone of the subject person, and wherein the state index value is an index value related to a state of teeth of the subject person;
Step 1: Evaluating whether the claim belongs to one of the statutory categories.
Claim 1 recites at least one step or act. Thus, the claim is directed to a process, which is one of the statutory categories of invention (Step 1: YES)
Step 2A Prong One: Evaluating whether the claim recites a judicial exception (an abstract idea enumerated in 2019 PEG, a law of nature, or a natural phenomenon). If no exception is recited, the claim is eligible. This concludes the eligibility analysis. If the claim recites an exception, go to Step 2A Prong Two.
Claim 1 recites an abstract idea of a mental process. At least step b is recited at a high level of generality such that they could be practically performed by a human (The courts consider a mental process (thinking) that “can be performed in the human mind, or be a human using a pen and paper” to be an abstract idea.). These concepts fall into the “mental processes” and “mathematical concept” group of abstract ideas, which is observation, evaluation and/or judgement. The process of calculating a gingival index and alveolar bone resorption index precedes computer implementation, and can ultimately be done by hand. Regarding the claimed gingival index, please refer to the ScienceDirect definition of the “Ginigival Index” wherein the term commonly refers to the gingival index proposed by Loe and Silness in 1963. Here, the gingival index is clearly a calculation which precedes computer implementation. As such, calculating the gingival state index value is recited at a high level of generality such that they could be practically performed by a human. Furthermore, regarding the alveolar bone resorption index value, please refer to Lopez et al. (“Computer-aided system for morphometric mandibular index computation (Using dental panoramic radiographs)”), wherein the authors compare their computer implementation for calculating the “mandibular alveolar bone resorption index” to “standard manual methods”, wherein the article clearly shows typical methods of calculating alveolar bone resorption index as conventionally being done by hand. As such, calculating the alveolar bone resorption index value is recited at a high level of generality such that they could be practically performed by a human. Calculating the above indexes using computers constitutes mere automation of manual processes. MPEP, 2106.04 (a) (2) III (C): Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). The limitations, interpreted under their broadest reasonable interpretation and in consistence with the specification, cover performance of the limitations in the mind or by generic computer components. See MPEP 2106.04 and the 2019 PEG. (Step 2A Prong One YES)
Step 2A Prong Two: Evaluating whether the claim recites additional elements that integrate the exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into practical application. If the answer to (a) is YES and (b) is NO, go to Step 2B; if the answer to (a) and (b) is YES, go to PATHWAY B, i.e., the claim is not directed to a judicial exception and the claim is eligible.
The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apple, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Limitations that are indicative of integration into a practical application when recited in a claim with a judicial exception include:
Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a);
Applying or using a judicial exception to affect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05©; and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
Limitations that are not indicative of integration into a practical application when recited in a claim with a judicial exception include:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).
[Examiners should note that revised Step 2A excludes consideration of whether claim elements represent well-understood, routine, conventional activity. The question of whether claim elements represent only well-understood, routine, conventional activity is considered at Step 2B and is not a consideration in Step 2A.]
Step a can be regarded as an additional element recited in claim 1. This additional element, i.e., acquiring intraoral three-dimensional data, does not integrate the exception into a practical application of the exception. Note even if the specification discloses that the invention pertains to an improvement in the technology, the claim must be evaluated to ensure the claim itself reflects the improvement in technology. It is also important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. Therefore, the additional elements do not recite an improvement. (Step 2A Prong Two NO)
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
Step a can be regarded as an additional element recited in claim 1. This additional element, i.e., acquiring intraoral three-dimensional data is considered insignificant extra-solution activities which amounts to automating a manual human activity.
In the instant case, the recited functional limitation can be performed by a photographer (organizing human activity/mere automation of manual processes). Step a recites the features of “acquiring intraoral three-dimensional data”. Acquiring intraoral three-dimensional data is a well-understood, routine, conventional activity in the field. See Aggarwal (“History of Dental Scanners: Decade-by-Decade Advancements”), wherein 3D scanners were conventional by the 2010s. Using the broadest reasonable interpretation of the claim, the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the abstract idea itself. See MPEP 2106.05. (Step 2B: NO) The claim is not eligible.
Claim 2 recites:
“calculating a predicted value of each of one or more parameters required for the calculation of the state index value by using a region of interest, the region of interest being a three-dimensional region that is included in the intraoral three-dimensional data and that includes all or a part of a tooth of interest and periodontium of the tooth of interest, the tooth of interest being a tooth regarding which the state index value is to be calculated; and”
“calculating the state index value based on the calculated predicted value.”
Steps a-b are directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to steps a-b of claim 2. No additional elements are recited. Accordingly, claim 2 does not have eligible subject matter.
Regarding claim 11, claim 11 recites “using the gingival state index value, the alveolar bone resorption index value, or both thereof for determining presence or absence of a periodontal-related disease, for determining a state of the periodontal-related disease, or determining whether to recommend a visit to dentist”. This contains steps that are directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to the steps of claim 11. No additional elements are recited. Accordingly, claim 11 does not have eligible subject matter.
Independent claim 12 is directed to a machine, which is a statutory category of invention. Similar analysis is applicable as applied above to the method of claim 1. Claim 12 further recites other additional elements of “a memory” and “a processor”. These elements are recited at a high level of generality such that they amount to no more than mere generic computer system elements. The remainder of the claim is identical to claim 1. The similar examination analysis as applied to claim 1 is applied to the remaining steps of claim 12. Accordingly claim 12 does not have eligible subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 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.
Regarding claim 1, claim 1 recites “calculating a gingival state index value of the subject person, an alveolar bone resorption index value of the subject person, or both thereof as a state index value of the subject person by using the intraoral three-dimensional data”. Here, it is unclear whether the aforementioned recitation should be read as either:
(outline 1) “calculating
(path 1) a gingival state index value of the subject person,
(path 2) an alveolar bone resorption index value of the subject person, or
(path 3) both thereof
as a state index value of the subject person by using the intraoral three-dimensional data”
OR
(outline 2) “calculating
(path 1) a gingival state index value of the subject person,
(path 2) an alveolar bone resorption index value of the subject person, or
(path 3) both thereof as a state index value of the subject person by using the intraoral three-dimensional data”.
Please note that in outline 1, the phrase “as a state index value of the subject person by using the intraoral three-dimensional data” is interpreted as separate from path 3, whereas in outline 2, the phrase “as a state index value of the subject person by using the intraoral three-dimensional data” is interpreted as a part of path 3, and path 3 only. This lack of clarity presents an issue regarding whether the state index value can be interpreted as any of a gingival state index value, an alveolar bone resorption index value, or both thereof, or if the state index value solely represents both the gingival state index value and alveolar bone resorption index value. Applicant discusses this in para. [0010], wherein it is stated that “As the state index value, a gingival state index value 20, an alveolar bone resorption index value 30 or both are calculated”. As such, it is clear that the state index value may be equivalent to any of the paths as outlined in outline 1. However, it remains unclear if applicant intends to narrow the scope of the claims by claiming that the state index value represents both the gingival state index value and alveolar bone resorption index value. Therefore, the aforementioned limitation(s) of claim 1 fail to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. For prior art purposes, it will be assumed that applicant intended for the pathway outlined in outline 1 to be the received interpretation of claim 1.
Similar analysis is applied to claim 12.
Claims 2-11 are rejected due to their dependency upon rejected claim 1.
Regarding claim 2, claim 2 recites “calculating a predicted value of each of one or more parameters required for the calculation of the state index value” in lines 3-4 and “calculating the state index value based on the calculated predicted value” in line 9. Here, since a predicted value of each of one or more parameters required for the calculation of the state index value is calculated, it is apparent there may exist more than one predicted value. As such, it is unclear which calculated predicted value is being referenced in the limitation of “calculating the state index value based on the calculated predicted value”. Applicant discusses this process in para. [0153]-[0156], [0207], and [0236]. However, none of these sections clarify which calculated predicted value is utilized in calculating the state index value in a situation where multiple predicted values exist. As such, the aforementioned limitation(s) of claim 2 fail to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3-5 additionally reference “the parameter” as defined in claim 2. However, as shown in the above section regarding claim 2, claim 2 fails to clearly define which parameter(s) is being referenced in recitations of “the parameter”. As such, any recitation of “the parameter” as stated in claims 3-5 similarly lack clarity in regards to the parameter in question. Claims 3-5 are rejected for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3-5 and 9-10 are rejected due to their dependency upon claim 2.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Sorensen et al. (US 20240172943 A1), hereinafter Sorensen, in view of Tsuji et al. (U.S. Publication No. 2014/0234796 A1), hereinafter Tsuji.
Regarding claim 1, Sorensen teaches a non-transitory computer-readable medium storing a program causing a computer to execute (Sorensen teaches “one or more non-transitory computer readable mediums having instructions stored thereon. The instructions may cause at least one processor of an imaging analysis system/device to perform one or more operations” as shown in para. [0032]):
acquiring intraoral three-dimensional data that represents a tooth of a subject person and (Sorensen teaches “ the scan data may comprise at least a three-dimensional (3D) representation of the oral cavity” in para. [0142]); and
calculating
(path 1) a gingival state index value of the subject person (Sorensen teaches “For gingivitis assessment, the output may be an estimated modified gingival index (MGI) (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) score rather than, or in addition to, a pocket depth measurement” in para. [0088]. Here, these output index(es) are interpreted as equivalent to the claimed gingival state index value of the subject person),
(path 2) an alveolar bone resorption index value of the subject person, or
(path 3) both thereof
as a state index value of the subject person by using the intraoral three-dimensional data (Sorensen teaches “after training of the ML algorithms for pocket depth measurement and/or gingivitis indication, one or more diagnostic techniques may include receiving IOS 3D scan data. The 3D scan data may be tooth, gum/gingiva, and one or more virtual probing sites/assessment locations,” wherein “the one or more ML algorithms may return analysis estimates of pocket depth measurements for one or more of the sites/assessment locations” [or return analysis estimates of the index values as defined in para. [0088]] as shown in para. [0086]. The state index value is equivalent to the gingival state index value (MGI or pocket depth measurement) as defined in the above claim limitation),
(path 1.1) wherein the gingival state index value of the subject person is an index value related to a state of a gingival of the subject person (Sorensen teaches “the gingivitis ML algorithm analysis may function very much like the pocket depth measurement ML algorithm analysis described above. For gingivitis assessment, the output may be an estimated modified gingival index (MGI) (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) score rather than, or in addition to, a pocket depth measurement” in para. [0088]. Here, the gingival state index value is an index value which represents a gingivitis assessment (state of a gingival)),
(path 2.1) wherein the alveolar bone resorption index value of the subject person is an index value related to resorption of an alveolar bone of the subject person, and
wherein the state index value is an index value related to a state of teeth of the subject person (Sorensen teaches calculating the periodontal pocket depth and/or MGI (equivalent to the claimed gingival state index value and state index value) wherein “cross-sectional assessment may include the detection and/or diagnosis of disease at one particularly point in time” as shown in para. [0076] wherein the calculated index value can be detected or displayed and acts as “an indication of a gingivitis condition of a subject's oral cavity” as shown in para. [0138]).
Please note that, due to the “or” language in the above claim, only one path and its sub-parts need be found in the prior art. As such, path 1 has been chosen as shown in the mapping above in claim 1.
Sorensen fails to teach that the intraoral three-dimensional data specifically captures the periodontium.
However, Tsuji teaches that the intraoral three-dimensional data specifically captures the periodontium (Tsuji teaches “the measurement using intraoral radiography or panoramic x-ray imaging is a method where the bone level and the external shape of the alveolar bone 35 are grasped from an x-ray of the (three-dimensional) tooth 40 and periodontal tissue to make a measurement” as shown in para. [0006]).
Sorensen and Tsuji are both considered to be analogous to the claimed invention because they are in the same field of analyzing the state of periodontal disease of an oral cavity through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sorensen to incorporate the teachings of Tsuji and include “that the intraoral three-dimensional data specifically captures the periodontium”. The motivation for doing so would have been to “collect an indicator indicating the progression of periodontal disease of a patient by x-ray CT without causing pain to the patient and causing a dentist to spend time and effort, achieve a highly accurate calculation for obtaining an objective indicator, and improve the accuracy of automatic detection measurement”, as suggested by Tsuji in para. [0030]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sorensen with Tsuji to obtain the invention specified in claim 1.
Regarding claim 2, Sorensen and Tsuji teach the medium according to claim 1, wherein the calculation of the state index value includes:
calculating a predicted value of each of one or more parameters required for the calculation of the state index value by using a region of interest (Sorensen teaches “the processor may be configured to indicate a first assessment location proximate to a first tooth of the one or more teeth, based at least in part, on the scan data” in para. [0138], wherein “one or more machine-learning algorithms may determine the first MGI value (e.g., a first modified gingival index (MGI) value, a first gingival index (GI), and/or another first index related to a gingival condition) based, at least in part, on one or more of: an outer shape of soft tissue, a gingival color, a shape of a gingival margin, a position of the gingival margin, a position of a cemento-enamel junction (CEJ), and/or a curvature of at least the first assessment location” in para. [0149]. Here, the first assessment location is interpreted as equivalent to the claimed region of interest, and the “[] modified gingival index (MGI) value, [] gingival index (GI), and/or another [] index related to a gingival condition”/pocket depth (see para. [0137]) are interpreted as equivalent to the claimed predicted value(s)),
the region of interest being a three-dimensional region that is included in the intraoral three-dimensional data and that includes all or a part of a tooth of interest (Sorensen teaches “the scan data may comprise at least a three-dimensional (3D) representation of the oral cavity” in para. [0142], and “generat[ing] a first image from the scan data. The first image may include at least the first assessment location” in para. [0139]. As shown above, the first assessment location is interpreted as equivalent to the region of interest which includes a first tooth (para. [0138]) (tooth of interest)) and periodontium of the tooth of interest (Tsuji teaches specifically analyzing the periodontium of teeth of interest as shown in para. [0014], wherein Tsuji teaches “using a plurality of cross-sectional images provided by x-ray CT images of teeth and periodontal tissues to detect a contour of each tooth in each of the cross-sectional images”), the tooth of interest being a tooth regarding which the state index value is to be calculated (Sorensen teaches “a first assessment location proximate to a first tooth of the one or more teeth” in para. [0138], wherein “a first modified gingival index (MGI) value for the first assessment location” in para. [0140] is generated. Here, the first MGI is interpreted as equivalent to the state index value); and
calculating the state index value based on the calculated predicted value (Sorenson teaches that “one or more machine-learning algorithms may determine the first MGI value (e.g., a first modified gingival index (MGI) value, a first gingival index (GI), and/or another first index related to a gingival condition) based, at least in part, on one or more of: an outer shape of soft tissue, a gingival color, a shape of a gingival margin, a position of the gingival margin, a position of a cemento-enamel junction (CEJ), and/or a curvature of at least the first assessment location” in para. [0149]. Here, the state index value (MGI) is determined based on the calculated predicted value(s) (“e.g. a first modified gingival index (MGI) value, a first gingival index (GI), and/or another first index related to a gingival condition” as shown in para. [0140] and [0149])).
Similar motivation as applied to claim 1 can be applied here to claim 2.
Regarding claim 3, Sorensen and Tsuji teach the medium according to claim 2, comprising:
a prediction model that is trained to output the predicted value of the parameter in response to an input of the region of interest (Sorensen teaches “the machine learning algorithms may return estimates of periodontal pocket depth and/or MGI (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) scores for one or more, or each, of those sites/assessment locations” as shown in para. [0075], wherein “one or more convolutional neural networks/models (CNNs) may be a part of one or more ML algorithms” as shown in para. [0096]. Here, the “of periodontal pocket depth and/or MGI (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) scores” are the predicted values and the site/assessment locations are interpreted as equivalent to the region of interest. See also para. [0077]. This ML algorithm/CNN is interpreted as equivalent to the claimed prediction model), wherein the calculation of the state index value includes:
inputting the region of interest into the prediction model to calculate the predicted value of the parameter (Sorensen teaches determining predicted values (“estimates of periodontal pocket depth and/or MGI (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) scores”) of parameters based on a site/assessment location in para. [0075]); and
calculating the state index value using the calculated predicted value of the parameter (Sorensen teaches determining the MGI (state index value) by using “a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition” or “pocket depth measurement” (predicted values of the parameters) as shown in para. [0075], [0090] and [0140]. Here, the term “calculate” can be interpreted broadly, as the processor as taught by Sorensen determines which value(s) (of the index values and/or pocket depth measurement value) to use to represent the gingival condition. The chosen value(s) is interpreted as the calculated state index value).
Regarding claim 4, Sorensen and Tsuji teach the medium according to claim 2, comprising:
a prediction model that is trained to output the predicted value of the parameter in response to an input of the region of interest and a relevant region, the relevant region being a three-dimensional region that is included in the intraoral three-dimensional data and that is relevant to the region of interest (Sorensen teaches “the ML algorithm analysis may provide measurements/estimates for an MGI values (e.g., a MGI values, a gingival index (GI) values, and/or another index value related to a gingival condition) and/or pocket depth measurements for the one or more sites/assessment locations 60” in para. [0090]. Here, the site/assessment location is interpreted as equivalent to the region of interest and the MGI/pocket depth measurement is interpreted as equivalent to the state index value. Sorensen additionally teaches “The processor may be configured to indicate a second assessment location proximate to the first tooth, based at least in part, on the scan data” as shown in para. [0150]. Here, the second assessment location is interpreted as equivalent to the claimed relevant region. Sorensen further teaches generating a second image including “at least the second assessment location and/or one or more second data channels” as shown in para. [0151]. Additionally, Sorensen teaches “determining, via one or more machine-learning algorithms, a first differential periodontal pocket depth value for the first assessment location based, at least in part, on the first image, the second image, the first set of one or more first data channels, and/or the second set of one or more first data channels” in para. [0136]. Here, the first image represents the region of interest, the second image represents the region of interest, and the first differential periodontal pocket depth value represents the predicted parameter value. Here, the claimed ML model is interpreted as equivalent to the claimed prediction model), wherein the calculation of the state index value includes:
extracting the relevant region corresponding to the region of interest from the intraoral three-dimensional data (Sorensen teaches “the processor may be configured to indicate a second assessment location proximate to the first tooth, based at least in part, on the scan data” as shown in para. [0150]. Here, the second assessment location is interpreted as equivalent to the claimed extracted relevant region since it corresponds to the first tooth which acts as the claimed region of interest. Sorensen further teaches generating a second image including “at least the second assessment location and/or one or more second data channels” as shown in para. [0151]);
inputting the region of interest and the extracted relevant region into the prediction model to calculate the predicted value of the parameter (Sorensen teaches “determining, via one or more machine-learning algorithms, a first differential periodontal pocket depth value for the first assessment location based, at least in part, on the first image, the second image, the first set of one or more first data channels, and/or the second set of one or more first data channels” in para. [0136]. Here, the first image represents the region of interest, the second image represents the region of interest, and the first differential periodontal pocket depth value represents the predicted value of the parameter); and
calculating the state index value using the calculated predicted value of the parameter (Sorensen teaches calculating the predicted value of the parameter (first differential periodontal pocket depth value or first periodontal pocket depth value range) which is then used to determine the state index value (as the state index value is equivalent to the predicted value) wherein the process may further include “providing an indication of the first differential periodontal pocket depth value for the first assessment location in a visually interpretable format via the digital representation of at least a part of the oral cavity on the display device” as shown in para. [0137]; Here, the term “calculate” can be interpreted broadly, as the processor as taught by Sorensen determines which value(s) (i.e. “the first periodontal pocket depth value, or the first periodontal pocket depth value range for the first assessment location” as shown in para. [0130]) to use to represent the gingival condition. The chosen value(s) is interpreted as the calculated state index value).
Regarding claim 5, Sorensen and Tsuji teach the medium according to claim 2, comprising:
a prediction model that is trained to output the predicted value of the parameter in response to an input of a feature value related to the tooth of interest (Sorensen teaches “cross-sectionally, ML algorithm analysis may predict/estimate the pocket depth and/or gingivitis from the outer shape of the soft tissue, perhaps together with the gingival color, the shape and position of the gingival margin (e.g., in relation to the CEJ-cement enamel junction, among other topological information)” in para. [0080]. Sorensen additionally teaches that “the topological data channel 912b may comprise at least one of a facet normal information such as the Nx, Ny, and/or Nz components. One or more, or each, of these snapshots and the corresponding data channels may be input to the one or more neural networks/ML algorithms described herein to obtain (e.g., optimal) detection of pocket depth measurements and/or gingivitis assessments” as shown in para. [0095], wherein the input is a tooth of interest and the color data channel(s) and topological data channel(s) are interpreted as equivalent to the claimed feature value, and the gingivitis assessment includes determining the MGI (state index value) as shown in para. [0088]. The ML algorithms/neural network(s) here are interpreted as the claimed model), wherein the calculation of the state index value includes:
calculating the feature value from the region of interest (Sorensen additionally teaches determining topological data channels [and color data channels] (feature value) for a probing site/assessment location of a single tooth as shown in para. [0095], wherein “the CNNs may be set up such that they are compatible with the 2D images/snapshots that may be generated from the virtual probing sites/assessment locations from the 3D digital data/model” as shown in para. [0096]. The assessment location is interpreted as equivalent to the region of interest which includes a first tooth (para. [0138]) (tooth of interest));
inputting the calculated feature value into the prediction model to calculate the predicted value of the parameter (Sorensen teaches “one or more, or each, of these snapshots and the corresponding data channels may be input to the one or more neural networks/ML algorithms described herein to obtain (e.g., optimal) detection of pocket depth measurements and/or gingivitis assessments” as shown in para. [0095], wherein the input is a tooth of interest and the topological data channel(s) is interpreted as equivalent to the claimed feature value, and the gingivitis assessment includes determining the predicted value(s) of the parameters (“ the CNNs/models produce scores according to the MGI scale (e.g., a MGI scale, a gingival index (GI) scale, and/or another index scale related to a gingival condition)” as shown in para. [0098]) as shown in para. [0088]); and
calculating the state index value using the calculated predicted value of the parameter (Sorensen teaches “the ML algorithm analysis may provide measurements/estimates for an MGI values (e.g., a MGI values, a gingival index (GI) values, and/or another index value related to a gingival condition) and/or pocket depth measurements for the one or more sites/assessment locations” as shown in para. [0090]. Here, the term “calculate” can be interpreted broadly, as the processor as taught by Sorensen determines which value(s) (of the index values and/or pocket depth measurement value) to use to represent the gingival condition. The chosen value(s) is interpreted as the calculated state index value).
Regarding claim 6, Sorensen and Tsuji teach the medium according to claim 1, comprising:
an index value calculation model that is trained to output the state index value in response to an input of a region of interest (Sorensen teaches “the ML algorithm analysis may provide measurements/estimates for an MGI values (e.g., a MGI values, a gingival index (GI) values, and/or another index value related to a gingival condition) and/or pocket depth measurements for the one or more sites/assessment locations 60” in para. [0090], wherein the site/assessment location is interpreted as the input region of interest) and, wherein the region of interest is a three-dimensional region that is included in the intraoral three-dimensional data and that includes all or a part of a tooth of interest (Sorensen teaches “the scan data may comprise at least a three-dimensional (3D) representation of the oral cavity” in para. [0142], and “generat[ing] a first image from the scan data. The first image may include at least the first assessment location” in para. [0139]. As shown above, the first assessment location is interpreted as equivalent to the region of interest which includes a first tooth (para. [0138]) (tooth of interest)) and periodontium of the tooth of interest (Tsuji teaches specifically analyzing the periodontium of teeth of interest as shown in para. [0014], wherein Tsuji teaches “using a plurality of cross-sectional images provided by x-ray CT images of teeth and periodontal tissues to detect a contour of each tooth in each of the cross-sectional images”), the tooth of interest being a tooth regarding which the state index value is to be calculated (Sorensen teaches “a first assessment location proximate to a first tooth of the one or more teeth” in para. [0138], wherein “a first modified gingival index (MGI) value for the first assessment location” in para. [0140] is generated. Here, the first MGI is interpreted as equivalent to the state index value), and
wherein the calculation of the state index value includes inputting the region of interest into the index value calculation model to calculate the state index value (Sorensen teaches “via one or more machine-learning algorithms, a first modified gingival index (MGI) value [state index value] for the first assessment location [region of interest] based, at least in part, on the first image and/or the one or more first data channels” in para. [0140]. Here the ML algorithm(s) (which include models) are interpreted as equivalent to the index value calculation model and are used to generate the MGI (state index value)).
Similar motivation as applied to claim 1 can be applied here to claim 6.
Regarding claim 7, Sorensen and Tsuji teach the medium according to claim 1, comprising:
an index value calculation model that is trained to output the state index value in response to an input of a region of interest and a relevant region (Sorensen teaches “the ML algorithm analysis may provide measurements/estimates for an MGI values (e.g., a MGI values, a gingival index (GI) values, and/or another index value related to a gingival condition) and/or pocket depth measurements for the one or more sites/assessment locations 60” in para. [0090]. Here, the site/assessment location is interpreted as equivalent to the region of interest and the MGI/pocket depth measurement is interpreted as equivalent to the state index value. Sorensen additionally teaches “The processor may be configured to indicate a second assessment location proximate to the first tooth, based at least in part, on the scan data” as shown in para. [0150]. Here, the second assessment location is interpreted as equivalent to the claimed relevant region. Sorensen further teaches generating a second image including “at least the second assessment location and/or one or more second data channels” as shown in para. [0151]. Additionally, Sorensen teaches “determining, via one or more machine-learning algorithms, a first differential periodontal pocket depth value for the first assessment location based, at least in part, on the first image, the second image, the first set of one or more first data channels, and/or the second set of one or more first data channels” in para. [0136]. Here, the first image represents the region of interest, the second image represents the region of interest, and the first differential periodontal pocket depth value represents the state index value),
wherein the region of interest is a three-dimensional region that is included in the intraoral three-dimensional data (Sorensen teaches “the scan data may comprise at least a three-dimensional (3D) representation of the oral cavity” in para. [0142], and “generat[ing] a first image from the scan data. The first image may include at least the first assessment location” in para. [0139]. As shown above, the first assessment location is interpreted as equivalent to the region of interest which includes a first tooth (para. [0138]) (tooth of interest)) and that includes all or a part of a tooth of interest and periodontium of the tooth of interest (Tsuji teaches specifically analyzing the periodontium of teeth of interest as shown in para. [0014], wherein Tsuji teaches “using a plurality of cross-sectional images provided by x-ray CT images of teeth and periodontal tissues to detect a contour of each tooth in each of the cross-sectional images”), the tooth of interest being a tooth regarding which the state index value is to be calculated (Sorensen teaches “a first assessment location proximate to a first tooth of the one or more teeth” in para. [0138], wherein “a first modified gingival index (MGI) value for the first assessment location” in para. [0140] is generated. Here, the first MGI is interpreted as equivalent to the state index value),
wherein the relevant region is a three-dimensional region that is included in the intraoral three-dimensional data and that is relevant to the region of interest (Sorensen teaches “the processor may be configured to indicate a second assessment location proximate to the first tooth, based at least in part, on the scan data” as shown in para. [0150]. Here, the second assessment location is interpreted as equivalent to the claimed relevant region. Sorensen further teaches generating a second image including “at least the second assessment location and/or one or more second data channels” as shown in para. [0151]. Since the second assessment location is determined based on the scan data, and the scan data is three-dimensional as shown in para. [0142], it is inherent that the relevant region is a three-dimensional region that is included in the intraoral 3D data), and
wherein the calculation of the state index value includes:
extracting the relevant region corresponding to the region of interest from the intraoral three-dimensional data (Sorensen teaches “the processor may be configured to indicate a second assessment location proximate to the first tooth, based at least in part, on the scan data” as shown in para. [0150]. Here, the second assessment location is interpreted as equivalent to the claimed extracted relevant region. Sorensen further teaches generating a second image including “at least the second assessment location and/or one or more second data channels” as shown in para. [0151]); and
inputting the region of interest and the extracted relevant region into the index value calculation model to calculate the state index value (Sorensen teaches “determining, via one or more machine-learning algorithms, a first differential periodontal pocket depth value for the first assessment location based, at least in part, on the first image, the second image, the first set of one or more first data channels, and/or the second set of one or more first data channels” in para. [0136]. Here, the first image represents the region of interest, the second image represents the region of interest, and the first differential periodontal pocket depth value represents the state index value).
Similar motivation as applied to claim 1 can be applied here to claim 7.
Regarding claim 8, Sorensen and Tsuji teach the medium according to claim 1, comprising:
an index value calculation model that is trained to output the state index value in response to an input of a feature value related to a tooth of interest regarding which the state index value is to be calculated (Sorensen teaches “Cross-sectionally, ML algorithm analysis may predict/estimate the pocket depth and/or gingivitis from the outer shape of the soft tissue, perhaps together with the gingival color, the shape and position of the gingival margin (e.g., in relation to the CEJ-cement enamel junction, among other topological information)” in para. [0080]. Sorensen additionally teaches “The topological data channel 912b may comprise at least one of a facet normal information such as the Nx, Ny, and/or Nz components. One or more, or each, of these snapshots and the corresponding data channels may be input to the one or more neural networks/ML algorithms described herein to obtain (e.g., optimal) detection of pocket depth measurements and/or gingivitis assessments” as shown in para. [0095], wherein the input is a tooth of interest and the topological data channel(s) is interpreted as equivalent to the claimed feature value, and the gingivitis assessment includes determining the MGI (state index value) as shown in para. [0088]. The ML algorithms/neural network(s) here are interpreted as the claimed model), wherein the calculation of the state index value includes:
calculating the feature value from a region of interest (Sorensen additionally teaches determining topological data channels (feature value) for a probing site/assessment location of a single tooth as shown in para. [0095], wherein “the CNNs may be set up such that they are compatible with the 2D images/snapshots that may be generated from the virtual probing sites/assessment locations from the 3D digital data/model” as shown in para. [0096]. The assessment location is interpreted as equivalent to the region of interest which includes a first tooth (para. [0138]) (tooth of interest)), the region of interest being a three-dimensional region that is included in the intraoral three-dimensional and that includes all or a part of the tooth of interest (Sorensen teaches “the scan data may comprise at least a three-dimensional (3D) representation of the oral cavity” in para. [0142], and “generat[ing] a first image from the scan data. The first image may include at least the first assessment location” in para. [0139]. As shown above, the first assessment location is interpreted as equivalent to the region of interest which includes a first tooth (para. [0138]) (tooth of interest)) and periodontium of the tooth of interest (Tsuji teaches specifically analyzing the periodontium of teeth of interest as shown in para. [0014], wherein Tsuji teaches “using a plurality of cross-sectional images provided by x-ray CT images of teeth and periodontal tissues to detect a contour of each tooth in each of the cross-sectional images”); and
inputting the calculated feature value into the index value calculation model to calculate the state index value (Sorensen teaches “One or more, or each, of these snapshots and the corresponding data channels may be input to the one or more neural networks/ML algorithms described herein to obtain (e.g., optimal) detection of pocket depth measurements and/or gingivitis assessments” as shown in para. [0095], wherein the topological data channel(s) is interpreted as equivalent to the claimed feature value, and the gingivitis assessment includes determining the MGI (state index value) as shown in para. [0088]. The ML algorithms/neural network(s) here are interpreted as the claimed model).
Similar motivation as applied to claim 1 can be applied here to claim 8.
Regarding claim 9, Sorensen and Tsuji teach the medium according to claim 4,
wherein the relevant region is a three-dimensional region proximal to the region of interest (Sorensen teaches “the processor may be configured to indicate a second assessment location proximate to the first tooth, based at least in part, on the scan data” as shown in para. [0150]. Here, the second assessment location is interpreted as equivalent to the claimed relevant region. Since the second assessment location is determined based on the scan data, and the scan data is three-dimensional as shown in para. [0142], it is inherent that the relevant region is a three-dimensional region that is included in the intraoral 3D data),
a three-dimensional region located at a position symmetrical to the region of interest via a tooth centerline of the tooth of interest as a reference, or
a three-dimensional region located at a position symmetrical to the region of interest via an oral midline as a reference.
Note: Only one limitation need be found in the prior art due to the “or” language in the claim above.
Regarding claim 10, Sorensen and Tsuji teach the medium according to claim 5, wherein the feature value related to the tooth of interest represents
a shape of the tooth of interest,
a size of the tooth of interest,
a tone of the tooth of interest,
smoothness of the tooth of interest,
a distance between the tooth of interest and a tooth adjacent to the tooth of interest,
presence or absence of exposure of a cemento-enamel junction in the tooth of interest,
a color of gingiva around the tooth of interest (Sorensen teaches “the use of a 2D snapshot/images obtained from the 3D scan data may provide color data/information” wherein “ the color data/information may be useful for gingivitis and/or pocket depth detection, as the changes in color of the gingiva may indicate gingival conditions and/or periodontal pocket issues” as shown in para. [0083]-[0084]. Here, the color data/topological data make up the data channels wherein “the first image may include at least the first assessment location at the first time index and/or a first set of one or more first data channels” as shown in para. [0122]. See also para. [0149]),
a change in color density of the gingiva depending on a position,
a shape of the gingiva,
a surface smoothness of the gingiva,
a distance between the surface of the gingiva and the surface of the tooth of interest,
a surface area of the gingiva,
a volume of the gingiva,
a distance between gingival alveolar mucosal border of the gingiva and gingival margin,
a shape of gingival papilla of the gingiva,
a surface area of the gingival papilla of the gingiva,
a volume of the gingival papilla of the gingiva, or
a gingival papilla height of the gingiva.
Note: Only one limitation need be found in the prior art due to the “or” language in the claim above.
Regarding claim 11, Sorensen and Tsuji teach the medium according to claim 1, comprising using
(path 1) the gingival state index value (see claim 1),
(path 2) the alveolar bone resorption index value, or
(path 3) both thereof
(path 1.1) for determining presence or absence of a periodontal-related disease (Sorensen teaches calculating a the periodontal pocket depth and/or MGI (equivalent to the claimed gingival state index value) wherein “there may be at least two types of assessments made for gingivitis and/or periodontal pocket depth/pocket depth—cross-sectional and/or longitudinal. For example, cross-sectional assessment may include the detection and/or diagnosis of disease at one particularly point in time” as shown in para. [0076]),
(path 1.2) for determining a state of the periodontal-related disease, or
(path 1.3) determining whether to recommend a visit to dentist.
Please note, only one of paths 1-3 need be found in the prior art due to the “or” language. Similarly, only one of paths 1.1-1.3 need be found in the prior art due to the “or” language.
Regarding claim 12, Sorensen teaches an index value calculation apparatus (Sorensen, see para. [0132] and para. [0138]) comprising:
at least one memory that is configured to store instructions (Sorensen teaches a memory #420 in FIG. 4); and
at least one processor that is configured to execute the instructions (Sorensen teaches “the processor 410 can be capable of processing instructions stored in the memory 420 and/or on the storage device 430” in para. [0107]) to:
acquire intraoral three-dimensional data that represents a tooth of a subject person and (Sorensen teaches “ the scan data may comprise at least a three-dimensional (3D) representation of the oral cavity” in para. [0142]); and
calculate
(path 1) a gingival state index value of the subject person (Sorensen teaches “For gingivitis assessment, the output may be an estimated modified gingival index (MGI) (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) score rather than, or in addition to, a pocket depth measurement” in para. [0088]. Here, these output index(es) are interpreted as equivalent to the claimed gingival state index value of the subject person),
(path 2) an alveolar bone resorption index value of the subject person, or
(path 3) both thereof
as a state index value of the subject person by using the intraoral three-dimensional data (Sorensen teaches “after training of the ML algorithms for pocket depth measurement and/or gingivitis indication, one or more diagnostic techniques may include receiving IOS 3D scan data. The 3D scan data may be tooth, gum/gingiva, and one or more virtual probing sites/assessment locations,” wherein “the one or more ML algorithms may return analysis estimates of pocket depth measurements for one or more of the sites/assessment locations” [or return analysis estimates of the index values as defined in para. [0088]] as shown in para. [0086]. The state index value is equivalent to the gingival state index value as defined in the above claim limitation),
(path 1.1) wherein the gingival state index value of the subject person is an index value related to a state of a gingival of the subject person (Sorensen teaches “the gingivitis ML algorithm analysis may function very much like the pocket depth measurement ML algorithm analysis described above. For gingivitis assessment, the output may be an estimated modified gingival index (MGI) (e.g., a MGI value, a gingival index (GI) value, and/or another index value related to a gingival condition) score rather than, or in addition to, a pocket depth measurement” in para. [0088]. Here, the gingival state index value is an index value which represents a gingivitis assessment (state of a gingival)),
(path 2.1) wherein the alveolar bone resorption index value of the subject person is an index value related to resorption of an alveolar bone of the subject person, and
wherein the state index value is an index value related to a state of teeth of the subject person (Sorensen teaches calculating the periodontal pocket depth and/or MGI (equivalent to the claimed gingival state index value and state index value) wherein “there may be at least two types of assessments made for gingivitis and/or periodontal pocket depth/pocket depth—cross-sectional and/or longitudinal. For example, cross-sectional assessment may include the detection and/or diagnosis of disease at one particularly point in time” as shown in para. [0076]).
Please note that, due to the “or” language in the above claim, only one path and its sub-parts need be found in the prior art. As such, path 1 has been chosen as shown in the mapping above in claim 12.
Sorensen fails to teach that the intraoral three-dimensional data specifically captures the periodontium.
However, Tsuji teaches that the intraoral three-dimensional data specifically captures the periodontium (Tsuji teaches “the measurement using intraoral radiography or panoramic x-ray imaging is a method where the bone level and the external shape of the alveolar bone 35 are grasped from an x-ray of the (three-dimensional) tooth 40 and periodontal tissue to make a measurement” as shown in para. [0006]).
Sorensen and Tsuji are both considered to be analogous to the claimed invention because they are in the same field of analyzing the state of periodontal disease of an oral cavity through image analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sorensen to incorporate the teachings of Tsuji and include “that the intraoral three-dimensional data specifically captures the periodontium”. The motivation for doing so would have been to “collect an indicator indicating the progression of periodontal disease of a patient by x-ray CT without causing pain to the patient and causing a dentist to spend time and effort, achieve a highly accurate calculation for obtaining an objective indicator, and improve the accuracy of automatic detection measurement”, as suggested by Tsuji in para. [0030]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sorensen with Tsuji to obtain the invention specified in claim 12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamaguchi et al. (U.S. Publication No. 2020/0375570 A1) teaches “a testing method for determining an oral indicator (gingival health state) by which an oral indicator can be determined” through image analysis
Togawa et al. (U.S. Publication No. 2021/0164028 A1) teaches “estimating a periodontal pocket inflammation area and comprehensively estimating the degree of inflammation of periodontal tissue” through image analysis
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/Kyla Guan-Ping Tiao Allen/ /JOHN VILLECCO/
Examiner, Art Unit 2661 Supervisory Patent Examiner, Art Unit 2661
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