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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/18/2025 and 10/17/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 112(b)
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 10 and 13-20 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.
The term “relatively high relevance” in claim 10 is a relative term which renders the claim indefinite. The term “relatively high” 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. This term makes the “relevance” indefinite as anything could be considered as relatively high.
Claim 13 recites the limitation “the machine-learning algorithm” in line 11. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation “the plurality of dental study subjects” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claims 14-16 and 18-20 are rejected based on their dependency on claims 13 and 17 respectively.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-12, 13-16, and 17-20 are drawn to methods, each of which is within the four statutory categories. Claims 1-20 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a computer-implemented method comprising:
1) (a) receiving, for each dental user subject of a plurality of dental user subjects, survey result information respectively relating to an executed survey by each dental user subject;
2) (b) selecting a) at least one machine-learning model based at least on the survey result information;
3) (c) inputting, into the at least one machine-learning model, the received survey result information;
4) (d) determining, by the at least one machine-learning model, at least one dental condition score for each dental user subject based, at least in part, on the survey result information; and
5) (e) producing the at least one dental condition score for each dental user patient in at least one of: a visually interpretable form, or b) an electronic form,
c) wherein at least steps (a)-(e) are performed by one or more processing devices.
Claim 1 recites, in part, performing the steps of 1) (a) receiving, for each dental user subject of a plurality of dental user subjects, survey result information respectively relating to an executed survey by each dental user subject, 2) (b) selecting at least one model based at least on the survey result information, 3) (c) inputting, into the at least one model, the received survey result information, 4) (d) determining, by the at least one model, at least one dental condition score for each dental user subject based, at least in part, on the survey result information, and 5) (e) producing the at least one dental condition score for each dental user patient in at least one of: a visually interpretable form, or a form. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how one can score a dental situation for a patient.
Claim 13 recites a computer-implemented method, comprising:
6) (a) receiving, for each dental study subject of a plurality of dental study subjects, one or more dental treatment codes respectively relating to a dental history of each dental study subject;
7) (b) receiving, for each dental study subject of the plurality of dental study subjects, study survey result information respectively relating to an executed study survey by each dental study subject;
8) (c) selecting a) at least one machine learning model based on at least one of: the one or more dental treatment codes, or the study survey result information;
9) (d) associating, by the at least one machine learning model, the one or more dental treatment codes and the survey result information;
10) (e) calibrating the at least one machine-learning algorithm based, at least in part, on the associated one or more dental treatment codes and the received study survey result information;
11) (f) determining, by the at least one machine-learning model, a calibration dental condition score; and
12) (g) producing the at least one calibration dental condition score for each dental study subject in at least one of: a visually interpretable form, or b) an electronic form,
c) wherein at least steps (a)-(g) are performed by one or more processing devices.
Claim 13 recites, in part, performing the steps of 6) (a) receiving, for each dental study subject of a plurality of dental study subjects, one or more dental treatment codes respectively relating to a dental history of each dental study subject, 7) (b) receiving, for each dental study subject of the plurality of dental study subjects, study survey result information respectively relating to an executed study survey by each dental study subject, 8) (c) selecting at least one model based on at least one of: the one or more dental treatment codes, or the study survey result information, 9) (d) associating, by the at least one model, the one or more dental treatment codes and the survey result information, 10) (e) calibrating the at least one algorithm (essentially putting data into a model) based, at least in part, on the associated one or more dental treatment codes and the received study survey result information, 11) (f) determining, by the at least one model, a calibration dental condition score, and 12) (g) producing the at least one calibration dental condition score for each dental study subject in at least one of: a visually interpretable form, or a form. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how one can score a dental situation for a patient.
Claim 17 recites a computer-implemented method, comprising:
13) (a) receiving, for each dental study subject of the plurality of dental study subjects, study survey result information respectively relating to an executed study survey by each dental study subject;
14) (b) receiving, for one or more dental study subject of the plurality of dental study subjects, one or more dental records comprising dental records for treatment of dental caries and/or periodontitis;
15) (c) selecting a) at least one machine learning model based on at least one of: the study survey result information, or the one or more dental records;
16) (d) associating, by the at least one machine learning model, the one or more dental records and the survey result information;
17) (e) calibrating the at least one machine-learning algorithm based, at least in part, on the associated one or more dental records and the received study survey result information;
18) (f) determining, by the at least one machine-learning model, a calibration dental condition score; and
19) (g) producing the at least one calibration dental condition score for each dental study subject in at least one of: a visually interpretable form, or b) an electronic form,
c) wherein at least steps (a)-(g) are performed by one or more processing devices.
Claim 17 recites, in part, performing the steps of 13) (a) receiving, for each dental study subject of the plurality of dental study subjects, study survey result information respectively relating to an executed study survey by each dental study subject, 14) (b) receiving, for one or more dental study subject of the plurality of dental study subjects, one or more dental records comprising dental records for treatment of dental caries and/or periodontitis, 15) (c) selecting at least one model based on at least one of: the study survey result information, or the one or more dental records, 16) (d) associating, by the at least one model, the one or more dental records and the survey result information, 17) (e) calibrating the at least one algorithm (essentially putting data into a model) based, at least in part, on the associated one or more dental records and the received study survey result information, 18) (f) determining, by the at least one model, a calibration dental condition score, and 19) (g) producing the at least one calibration dental condition score for each dental study subject in at least one of: a visually interpretable form, or a form. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how one can score a dental situation for a patient.
Depending claims 2-12, 14-16, and 18-20 include all of the limitations of claims 1, 13, and 17, and therefore likewise incorporate the above described abstract idea. Depending claims 2-4, 7-8, 10-12, 14-16, and 18-20 add additional, functional steps to the claims, and these additional limitations only further serve to limit the abstract idea. The limitations of depending claims 5-6 and 9 further specify elements from the claims from which they depend on without adding any additional steps. Additionally, claims 2, 7, 12, 16, and 20 also add additional elements to the claims, and these will be evaluated below. Thus, depending claims 2-12, 14-16, and 18-20 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 13, and 17 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) at least one machine learning model, b) an electronic form, c) wherein at least steps (a)-(g) are performed by one or more processing devices, d) an electronic interface device (from claim 2), e) a first machine-learning model (from claims 7, 16, and 20), f) a second machine-learning model (from claims 7, 16, and 20), g) a third machine-learning model (from claim 12, 16, and 20), and h) a fourth machine-learning model (from claim 12, 16, and 20) to perform the claimed steps.
The a) at least one machine learning model, b) electronic form, c) wherein at least steps (a)-(g) are performed by one or more processing devices, e) a first machine-learning model, f) a second machine-learning model, g) a third machine-learning model, and h) a fourth machine-learning model in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions such as determining data from a set of data) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraphs [0178] – [0179] where there is a range of generic and special purpose components which could be used for these components, see MPEP 2106.05(f)).
Additionally, the d) electronic interface device in these steps adds insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, see MPEP 2106.05(g).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination 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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) at least one machine learning model, b) an electronic form, c) wherein at least steps (a)-(g) are performed by one or more processing devices, d) an electronic interface device, e) a first machine-learning model, f) a second machine-learning model, g) a third machine-learning model, and h) a fourth machine-learning model to perform the claimed steps amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity) and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain method steps of organizing human activity. Specifically, MPEP 2106.05(d) and MPEP 2106.05(f) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); and
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The current invention generates a dental condition score utilizing a) at least one machine learning model, b) an electronic form, c) wherein at least steps (a)-(g) are performed by one or more processing devices, e) a first machine-learning model, f) a second machine-learning model, g) a third machine-learning model, and h) a fourth machine-learning model, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Furthermore, the d) electronic interface device in these steps add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives survey data from the electronic interface device, and transmits the data to a system over a network, for example the Internet.
Mere instructions to apply an exception using generic computer components or insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-20 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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 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 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by W.O. 2024/020318 to Sharifi.
As per claim 1, Sharifi teaches a computer-implemented method comprising:
--(a) receiving, for each dental user subject of a plurality of dental user subjects, survey result information respectively relating to an executed survey by each dental user subject; (see: paragraph [0039] where there is reception of answers to question by patients/subjects)
--(b) selecting at least one machine-learning model based at least on the survey result information; (see: paragraph [0064] where there is retrieval of the predictor model (at least one machine learning model) based on the historical data (survey result data). Also see: paragraph [0048] where the predictor models are machine learning models)
--(c) inputting, into the at least one machine-learning model, the received survey result information; (see: paragraph [0045] where there is inputting of this data into at least one predictor model. Also see: paragraph [0048] where the predictor models are machine learning models)
--(d) determining, by the at least one machine-learning model, at least one dental condition score for each dental user subject based, at least in part, on the survey result information; (see: 158 of FIG. 1C, 428 of FIG. 4B, claim 1, and paragraph [0068] where there is prediction of an outcome involving calculating a risk score using the model based at least in part on the answers to the questions by the patient) and
--(e) producing the at least one dental condition score for each dental user patient in at least one of: a visually interpretable form, or an electronic form, (see: 430 of FIG. 4B and paragraph [0068] where there is providing of this predicted outcome. The providing of this score is done so in both a visually interpretable form with an outcome and an electronic form as the computer is outputting this to the individual)
--wherein at least steps (a)-(e) are performed by one or more processing devices (see: paragraph [0050] where there is a processor which is used to perform the steps above).
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 2-6 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/020318 to Sharifi in view of U.S. 2014/00114680 to Mills et al.
As per claim 2, Sharifi teaches the method of claim 1, see discussion of claim 1. Sharifi further teaches wherein the receiving the survey result information further comprises:
--(a-i) presenting, to each dental user subject, a survey comprising a plurality of questions via an electronic interface device; (see: paragraph [0040] where there is presenting of a survey comprising questions to a patient for answering using a mobile application on a mobile device (electronic interface device)) and
--(a-3) receiving, via the electronic interface device, a dental user subject selected answer choice for the at least some of the plurality of questions (see: paragraph [0040] where there is communication of the answers from the patients to the application server).
Sharifi may not further, specifically teach:
--(a-2) presenting, to each dental user subject, a predetermined selection of answer choices for at least some of the plurality of questions.
Mills et al. teaches:
--(a-2) presenting, to each dental user subject, a predetermined selection of answer choices for at least some of the plurality of questions (see: paragraphs [0057] and [0064] where there is selection of answers and the answers can have selection boxes).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to present, to each dental user subject, a predetermined selection of answer choices for at least some of the plurality of questions as taught by Mills et al. in the method as taught by Sharifi with the motivation(s) of providing an improved assessment (see: paragraph [0044] of Mills et al.).
As per claim 3, Sharifi and Mills et al. in combination teaches the method of claim 2, see discussion of claim 2. Sharifi further teaches:
--(c-2) inputting, into the at least one machine-learning model, each value respectively corresponding to the dental user subject selected answer choice for each of the at least some of the plurality of questions (see: paragraph [0048] where there is a learning phase using the inputted information which would include the answers to the questions).
Mills et al. further teaches wherein the inputting the received survey result information further comprises:
--(c-i) converting the dental user subject selected answer choice for each of the at least some of the plurality of questions to a respective numerical value; (see: paragraph [0012] where there is a transformation of answers to a score) and
--value as a numerical value (see: paragraph [0012] where there is a numerical value of a score).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 2, and incorporated herein.
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the numerical value as taught by Mills et al. for the value as disclosed by Sharifi since each individual element and its function are shown in the prior art, with the difference being the substitution of the elements. In the present case, Sharifi already uses answers to train the model and one can replace how those answers are represented and obtain predictable results of training a model using distinguishing values. Thus, one of ordinary skill in the art could have substituted the one known element for the other to produce a predictable result (MPEP 2143).
As per claim 4, Sharifi and Mills et al. in combination teaches the method of claim 3, see discussion of claim 3. Mills et al. further teaches:
--(c-3) determining, by the one or more processing devices, at least one reply comment for each dental user subject selected answer choice for the at least some of the plurality of questions; (see: paragraphs [0057] and [0068] where there is a determination of a progress indicator (reply comment) and this is based on the selected answer) and
--(c-4) producing one or more of the at least one reply comments in at least one of: a visually interpretable form, or an electronic form (see: paragraphs [0057] and [0068] where there is display of the progress indicator (reply comment) in a visually interpretable and electronic form).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 2, and incorporated herein.
As per claim 5, Sharifi and Mills et al. in combination teaches the method of claim 4, see discussion of claim 4. Mills et al. further teaches wherein the at least one reply comment for each dental user subject selected answer choice is based on a predetermined correspondence between the at least one reply comment and the respective dental user subject selected answer choice (see: paragraphs [0057] and [0068] where the reply comment (progress indicator) is based on a predetermined correspondence (goes up upon the completion of a question) between the progress indicator and the answer).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 2, and incorporated herein.
As per claim 6, Sharifi and Mills et al. in combination teaches the method of claim 4, see discussion of claim 4. Mills et al. further teaches wherein the at least one reply comment for each dental user subject selected answer choice provides the dental user subject with at least one of: constructive non-medical feedback corresponding to the dental user subject selected answer choice, or a non-medical affirmation corresponding to the dental user subject selected answer choice, wherein at least one of: the constructive non-medical feedback corresponding to the dental user subject selected answer choice, or the non-medical affirmation corresponding to the dental user subject selected answer choice comprises at least one of: a text message, an alpha-numeric message, or one or more symbols (see: paragraphs [0057] and [0068] where the reply comment is a non-medical feedback which is in the form of an indicator/bar/symbol).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 2, and incorporated herein.
As per claim 11, Sharifi teaches the method of claim 1, see discussion of claim 1. Sharifi may not further, specifically teach:
--(h) determining, by the one or more processing devices, at least one score comment corresponding to the at least one dental condition score for each dental user patient, the at least one score comment comprising at least one of: constructive non-medical feedback corresponding to the at least one dental condition score, or a non-medical affirmation corresponding to the at least one dental condition score; and
--(i) producing the at least one score comment in at least one of: a visually interpretable form, or an electronic form, wherein at least one of: the constructive non-medical feedback corresponding to the at least one dental condition score, or the non-medical affirmation corresponding to the at least one dental condition score, comprises at least one of: a text message, an alpha-numeric message, or one or more symbols; and
--wherein the at least one score comment comprises one or more of: a lifestyle suggestion, one or more learning materials, one or more dental care products, or one or more coaching suggestions.
Mills et al. teaches:
--(h) determining, by the one or more processing devices, at least one score comment corresponding to the at least one dental condition score for each dental user patient, the at least one score comment comprising at least one of: constructive non-medical feedback corresponding to the at least one dental condition score, or a non-medical affirmation corresponding to the at least one dental condition score; (see: paragraph [0012] where there is a determination of a score comment/feedback based on the numerical health score. The feedback here is commentary here which can be a constructive comment regarding happiness as explained in paragraph [0073]) and
--(i) producing the at least one score comment in at least one of: a visually interpretable form, or an electronic form, wherein at least one of: the constructive non-medical feedback corresponding to the at least one dental condition score, or the non-medical affirmation corresponding to the at least one dental condition score, comprises at least one of: a text message, an alpha-numeric message, or one or more symbols; (see: paragraph [0012] where the score comment/feedback is being displayed on a display. The feedback here is commentary here which can be a constructive comment regarding happiness as explained in paragraph [0073]) and
--wherein the at least one score comment comprises one or more of: a lifestyle suggestion, one or more learning materials, one or more dental care products, or one or more coaching suggestions (see: paragraph [0073] wherein the score comment/feedback is a coaching suggestion/commentary).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to (h) determine, by the one or more processing devices, at least one score comment corresponding to the at least one dental condition score for each dental user patient, the at least one score comment comprising at least one of: constructive non-medical feedback corresponding to the at least one dental condition score, or a non-medical affirmation corresponding to the at least one dental condition score and (i) produce the at least one score comment in at least one of: a visually interpretable form, or an electronic form, wherein at least one of: the constructive non-medical feedback corresponding to the at least one dental condition score, or the non-medical affirmation corresponding to the at least one dental condition score, comprises at least one of: a text message, an alpha-numeric message, or one or more symbols, and have wherein the at least one score comment comprises one or more of: a lifestyle suggestion, one or more learning materials, one or more dental care products, or one or more coaching suggestions as taught by Mills et al. in the method as taught by Sharifi with the motivation(s) of providing an improved assessment (see: paragraph [0044] of Mills et al.).
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/020318 to Sharifi in view of U.S. 2025/0149189 to Hubenov et al.
As per claim 7, Sharifi teaches the method of claim 1, see discussion of claim 1. Sharifi may not further, specifically teach wherein the selecting at least one machine-learning model further comprises:
--(b-1) selecting a first machine-learning model based at least on the survey result information, the first machine-learning model corresponding to gum disease analysis; and
--(b-2) selecting a second machine-learning model based at least on the survey result information, the second machine-learning model corresponding to teeth condition analysis.
Hubenov et al. teaches:
--wherein the selecting at least one machine-learning model further comprises:
--(b-1) selecting a first machine-learning model based at least on the survey result information, (see: paragraphs [0006] and [0044] where there is selection of models which would include a first learning model) the first machine-learning model corresponding to gum disease analysis; (see: paragraph [00070] where the models can correspond to gum recession (gum disease)) and
--(b-2) selecting a second machine-learning model based at least on the survey result information, (see: paragraphs [0006] and [0044] where there is selection of models which would include a second learning model) the second machine-learning model corresponding to teeth condition analysis (see: paragraph [00070] where the models can correspond to tooth conditions such as wear).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the selecting at least one machine-learning model further comprises: (b-1) selecting a first machine-learning model based at least on the survey result information, the first machine-learning model corresponding to gum disease analysis and (b-2) selecting a second machine-learning model based at least on the survey result information, the second machine-learning model corresponding to teeth condition analysis as taught by Hubenov et al. in the method as taught by Sharifi with the motivation(s) of assessing a patient’s condition (see: paragraph [0013] of Hubenov et al.).
As per claim 8, Sharifi and Hubenov et al. in combination teaches the method of claim 7, see discussion of claim 7. Hubenov et al. further teaches wherein the determining the at least one dental condition score for each dental user subject further comprises:
--(d-1) determining, by the first machine-learning model, a first dental condition score corresponding to gum disease for each dental user subject based, at least in part, on the survey result information; (see: paragraphs [0047] and [0070] where there is a determination of dental conditions using a model, where a first condition score of gum recession can be determined) and
--(d-2) determining, by the second machine-learning model, a second dental condition score corresponding to teeth condition for each dental user subject based, at least in part, on the survey result information (see: paragraphs [0047] and [0070] where there is a determination of dental conditions using a model, where a second condition score of teeth condition can be determined).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 7, and incorporated herein.
As per claim 9, Sharifi and Hubenov et al. in combination teaches the method of claim 8, see discussion of claim 8. Hubenov et al. further teaches wherein at least one of:
--the first dental condition score is at least one of: a low or bad gum health score, a medium or average gum health score, or a high or good gum health score, and the second dental condition score is at least one of: a low or bad teeth health score, a medium or average teeth health score, or a high or good teeth health score;
--the first dental condition score is at least one of: a binary gum health score, a numerical gum health score, and/or a relative gum health score, and the second dental condition score is at least one of: a binary teeth health score, a numerical teeth health score, and/or a relative teeth health score; or
--the first dental condition score is at least one of: a binary gum risk score, a numerical gum risk score, and/or a relative gum risk score, and the second dental condition score is at least one of: a binary teeth risk score, a numerical teeth risk score, and/or a relative teeth risk score (see: paragraphs [0038] and [0047] where there is a determination of conditions of the dental situation including a first binary score of the gum health (good or not) and a second score of a teeth condition (has the condition or not)).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 7, and incorporated herein.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/020318 to Sharifi in view of U.S. Patent No. 12,144,656 to Ljung et al.
As per claim 10, Sharifi teaches the method of claim 1, see discussion of claim 1. Sharifi may not further, specifically teach:
--(f) determining, by the at least one machine-learning model, one or more parameters having a relatively high relevance to the at least one dental condition score for each dental user patient based, at least in part, on the survey result information; and
--(g) producing at least some of the one or more parameters for the at least one dental condition score for each dental user patient in at least one of: a visually interpretable form, or an electronic form.
Ljung et al. teaches:
--(f) determining, by the at least one machine-learning model, one or more parameters having a relatively high relevance to the at least one dental condition score for each dental user patient based, at least in part, on the survey result information; (see: claim 1 where there is determination of parameters with a relevance score. The score being dental related was already taught in the base reference) and
--(g) producing at least some of the one or more parameters for the at least one dental condition score for each dental user patient in at least one of: a visually interpretable form, or an electronic form (see: claim 1 where there is displaying of parameters based on their score in an electronic form).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to (f) determine, by the at least one machine-learning model, one or more parameters having a relatively high relevance to the at least one dental condition score for each dental user patient based, at least in part, on the survey result information and (g) producing at least some of the one or more parameters for the at least one dental condition score for each dental user patient in at least one of: a visually interpretable form, or an electronic form as taught by Ljung et al. in the method as taught by Sharifi with the motivation(s) of improving user experience when monitoring a patient (see: column 1, lines 48-49 of Ljung et al.).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/020318 to Sharifi in view of U.S. 2017/0175172 to Apte et al.
As per claim 12, Sharifi teaches the method of claim 1, see discussion of claim 1. Sharifi may not further, specifically teach wherein the selecting at least one machine-learning model further comprises:
--(b-3) selecting a third machine-learning model based at least on the survey result information, the third machine-learning model corresponding to breath odor analysis; and
--(b-4) selecting a fourth machine-learning model based at least on the survey result information, the fourth machine-learning model corresponding to at least one of: dentition/gum sensitivity analysis, enamel erosion analysis, dry mouth analysis, and/or mouth aging analysis.
Apte et al. teaches:
--wherein the selecting at least one machine-learning model further comprises:
--(b-3) selecting a third machine-learning model based at least on the survey result information, (see: paragraph [0059] where there is selection of models which includes a third model) the third machine-learning model corresponding to breath odor analysis; (see: paragraph [0019] where there is determination of halitosis (a bad breath analysis)) and
--(b-4) selecting a fourth machine-learning model based at least on the survey result information, (see: paragraph [0059] where there is selection of models which includes a fourth model) the fourth machine-learning model corresponding to at least one of: dentition/gum sensitivity analysis, enamel erosion analysis, dry mouth analysis, and/or mouth aging analysis (see: paragraph [0019] where there is determination of mouth conditions which is a mouth aging analysis).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to wherein the selecting at least one machine-learning model further comprises: (b-3) selecting a third machine-learning model based at least on the survey result information, the third machine-learning model corresponding to breath odor analysis and (b-4) selecting a fourth machine-learning model based at least on the survey result information, the fourth machine-learning model corresponding to at least one of: dentition/gum sensitivity analysis, enamel erosion analysis, dry mouth analysis, and/or mouth aging analysis as taught by Apte et al. in the method as taught by Sharifi with the motivation(s) of improving processing speed in characterization (see: paragraph [0022] of Apte et al.).
Claims 13, 15, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/020318 to Sharifi in view of U.S. 2019/0172555 to Apte et al., hereafter Apte 1.
As per claim 13, Sharifi teaches a computer-implemented method, comprising:
--(b) receiving, for each dental study subject of the plurality of dental study subjects, study survey result information respectively relating to an executed study survey by each dental study subject; (see: paragraph [0039] where there is reception of answers to question by patients/subjects)
--(c) selecting at least one machine learning model based on at least one of: the one or more dental treatment codes, or the study survey result information; (see: paragraph [0064] where there is retrieval of the predictor model (at least one machine learning model) based on the historical data (survey result data). Also see: paragraph [0048] where the predictor models are machine learning models)
--(f) determining, by the at least one machine-learning model, a calibration dental condition score; (see: 158 of FIG. 1C, 428 of FIG. 4B, claim 1, and paragraph [0068] where there is prediction of an outcome involving calculating a risk score using the model based at least in part on the answers to the questions by the patient) and
--(g) producing the at least one calibration dental condition score for each dental study subject in at least one of: a visually interpretable form, or an electronic form, (see: 430 of FIG. 4B and paragraph [0068] where there is providing of this predicted outcome. The providing of this score is done so in both a visually interpretable form with an outcome and an electronic form as the computer is outputting this to the individual)
--wherein at least steps (a)-(g) are performed by one or more processing devices (see: paragraph [0050] where there is a processor which is used to perform the steps above).
Sharifi may not further, specifically teach:
1) --(a) receiving, for each dental study subject of a plurality of dental study subjects, one or more dental treatment codes respectively relating to a dental history of each dental study subject;
2) --(d) associating, by the at least one machine learning model, the one or more dental treatment codes and the survey result information; and
3) --(e) calibrating the at least one machine-learning algorithm based, at least in part, on the associated one or more dental treatment codes and the received study survey result information.
Apte 1 teaches:
1) --(a) receiving, for each dental study subject of a plurality of dental study subjects, one or more dental treatment codes respectively relating to a dental history of each dental study subject; (see: paragraph [0211] where there is reception of dental code information in the form of supplementary information)
2) --(d) associating, by the at least one machine learning model, the one or more dental treatment codes and the survey result information; (see: paragraph [0211] where there is association of code data with survey data) and
3) --(e) calibrating the at least one machine-learning algorithm based, at least in part, on the associated one or more dental treatment codes and the received study survey result information (see: paragraphs [0211] and [0223] where there is calibration of a model via training an algorithm with this data).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) (a) receive, for each dental study subject of a plurality of dental study subjects, one or more dental treatment codes respectively relating to a dental history of each dental study subject, 2) (d) associate, by the at least one machine learning model, the one or more dental treatment codes and the survey result information, and 3) (e) calibrate the at least one machine-learning algorithm based, at least in part, on the associated one or more dental treatment codes and the received study survey result information as taught by Apte 1 in the method as taught by Sharifi with the motivation(s) of improving state of oral health (see: paragraph [0023] of Apte 1).
As per claim 15, Sharifi and Apte 1 in combination teaches the method of claim 13, see discussion of claim 13. Apte 1 further teaches wherein the associating the one or more dental treatment codes and the survey result information further comprises:
--(d-1) associating the one or more dental treatment codes respectively relating to a dental history of a respective dental study subject with the study survey result information relating to the executed study survey by the same respective dental study subject (see: paragraph [0211] where there is associating of patient data. The patient data including survey-derived data and code data).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 13, and incorporated herein.
As per claim 17, Sharifi teaches a computer-implemented method, comprising:
--(a) receiving, for each dental study subject of the plurality of dental study subjects, study survey result information respectively relating to an executed study survey by each dental study subject; (see: paragraph [0039] where there is reception of answers to question by patients/subjects)
--(c) selecting at least one machine learning model based on at least one of: the study survey result information, or the one or more dental records; (see: paragraph [0064] where there is retrieval of the predictor model (at least one machine learning model) based on the historical data (survey result data). Also see: paragraph [0048] where the predictor models are machine learning models)
--(f) determining, by the at least one machine-learning model, a calibration dental condition score; (see: 158 of FIG. 1C, 428 of FIG. 4B, claim 1, and paragraph [0068] where there is prediction of an outcome involving calculating a risk score using the model based at least in part on the answers to the questions by the patient) and
--(g) producing the at least one calibration dental condition score for each dental study subject in at least one of: a visually interpretable form, or an electronic form, (see: 430 of FIG. 4B and paragraph [0068] where there is providing of this predicted outcome. The providing of this score is done so in both a visually interpretable form with an outcome and an electronic form as the computer is outputting this to the individual)
--wherein at least steps (a)-(g) are performed by one or more processing devices (see: paragraph [0050] where there is a processor which is used to perform the steps above).
Sharifi may not further, specifically teach:
1) --(b) receiving, for one or more dental study subject of the plurality of dental study subjects, one or more dental records comprising dental records for treatment of dental caries and/or periodontitis;
2) --(d) associating, by the at least one machine learning model, the one or more dental records and the survey result information; and
3) --(e) calibrating the at least one machine-learning algorithm based, at least in part, on the associated one or more dental records and the received study survey result information.
Apte 1 teaches:
1) --(b) receiving, for one or more dental study subject of the plurality of dental study subjects, one or more dental records comprising dental records for treatment of dental caries and/or periodontitis; (see: paragraph [0211] where there is reception of dental code information/dental records including a periodontal evaluation in the form of supplementary information)
2) --(d) associating, by the at least one machine learning model, the one or more dental records and the survey result information; (see: paragraph [0211] where there is association of code data with survey data) and
3) --(e) calibrating the at least one machine-learning algorithm based, at least in part, on the associated one or more dental records and the received study survey result information (see: paragraphs [0211] and [0223] where there is calibration of a model via training an algorithm with this data).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) (b) receive, for one or more dental study subject of the plurality of dental study subjects, one or more dental records comprising dental records for treatment of dental caries and/or periodontitis, 2) (d) associate, by the at least one machine learning model, the one or more dental records and the survey result information, and 3) (e) calibrate the at least one machine-learning algorithm based, at least in part, on the associated one or more dental records and the received study survey result information as taught by Apte 1 in the method as taught by Sharifi with the motivation(s) of improving state of oral health (see: paragraph [0023] of Apte 1).
As per claim 19, claim 19 is similar to claim 15 and is therefore rejected in a similar manner.
Claims 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/020318 to Sharifi in view of U.S. 2019/0172555 to Apte et al., hereafter Apte 1 as applied to claims 13 and 17, and further in view of U.S. 2024/0172943 to Sorensen et al.
As per claim 14, Sharifi and Apte 1 in combination teaches the method of claim 13, see discussion of claim 13. The combination may not further, specially teach wherein the calibrating the at least one machine-learning model further comprises at least one of:
--(e-1) iteratively updating one or more parameters of the at least one machine-learning algorithm to minimize at least one of: an objective function, or a loss function, of the at least one machine-learning algorithm; or
--(e-2) adjusting the one or more parameters of the at least one machine-learning model to reduce a deviation between the calibration dental condition score and the one or more dental treatment codes.
Sorensen et al. teaches:
--(e-1) iteratively updating one or more parameters of the at least one machine-learning algorithm to minimize at least one of: an objective function, or a loss function, of the at least one machine-learning algorithm; (see: paragraph [0098] where there is such an iterative process) or
--(e-2) adjusting the one or more parameters of the at least one machine-learning model to reduce a deviation between the calibration dental condition score and the one or more dental treatment codes (see: paragraphs [0297] and [0302] where the model may be adjustable and parameters are being updated).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to (e-1) iteratively update one or more parameters of the at least one machine-learning algorithm to minimize at least one of: an objective function, or a loss function, of the at least one machine-learning algorithm or (e-2) adjust the one or more parameters of the at least one machine-learning model to reduce a deviation between the calibration dental condition score and the one or more dental treatment codes as taught by Sorensen et al. in the method as taught by Sharifi and Apte 1 in combination with the motivation(s) of detecting mouth, health conditions (see: paragraph [0004] of Sorensen et al.).
As per claim 18, claim 18 is similar to claim 14 and is therefore rejected in a similar manner.
Claims 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/020318 to Sharifi in view of U.S. 2019/0172555 to Apte et al., hereafter Apte 1 as applied to claims 13 and 17, and further in view of U.S. 2025/0149189 to Hubenov et al. and in view of U.S. 2017/0175172 to Apte et al.
As per claim 16, Sharifi and Apte 1 in combination teaches the method of claim 13, see discussion of claim 13. Apte 1 further teaches:
--dental treatment codes as case details (see: paragraph [0211] where there is a determination of a correspondence/ between a code and a model).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 13, and incorporated herein.
The combination may not further, specifically teach wherein the selecting the at least one machine learning model further comprises:
--(c-1) determining a correspondence between the one or more case details and at least one of: a first machine-learning algorithm model corresponding to gum disease analysis, or a second machine-learning model corresponding to teeth condition analysis; and
--(c-2) selecting at least one of: the first machine-learning model, or the second machine-learning model, based on the determined correspondence;
--(c-3) determining a correspondence between the one or more case details and at least one of: a third machine-learning algorithm model corresponding to breath odor analysis, or a fourth machine-learning model corresponding to at least one of: dentition/gum sensitivity analysis, enamel erosion analysis, dry mouth analysis, and/or mouth aging analysis; and
--(c-4) selecting at least one of: the third machine-learning model, or the fourth machine-learning model, based on the determined correspondence.
Hubenov et al. teaches:
--(c-1) determining a correspondence between the one or more case details and at least one of: a first machine-learning algorithm model corresponding to gum disease analysis, (see: paragraph [0070] where the models can correspond to gum recession (gum disease)) or a second machine-learning model corresponding to teeth condition analysis; (see: paragraph [0070] where the models can correspond to tooth conditions such as wear. Also see: paragraph [0006] where there is a selection of models based on data, thus there is a determination of correspondence between the model and the case details present)
--(c-2) selecting at least one of: the first machine-learning model, or the second machine-learning model, based on the determined correspondence; (see: paragraphs [0006] and [0044] where there is selection of models which would include a first learning model) and
--(c-3) determining a correspondence between the one or more case details and at least one of: a model; (see: paragraph [0006] where there is a selection of models based on data, thus there is a determination of correspondence between the model and the case details present) and
--(c-4) selecting at least one of: the third machine-learning model, or the fourth machine-learning model, based on the determined correspondence (see: paragraphs [0006] and [0044] where there is selection of models which would include a first learning model).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to (c-1) determine a correspondence between the one or more case details and at least one of: a first machine-learning algorithm model corresponding to gum disease analysis, or a second machine-learning model corresponding to teeth condition analysis, (c-2) select at least one of: the first machine-learning model, or the second machine-learning model, based on the determined correspondence, and (c-3) determine a correspondence between the one or more case details and at least one of: a model, and (c-4) select at least one of: the third machine-learning model, or the fourth machine-learning model, based on the determined correspondence as taught by Hubenov et al. in the method as taught by Sharifi and Apte 1 in combination with the motivation(s) of assessing a patient’s condition (see: paragraph [0013] of Hubenov et al.).
Apte et al. teaches:
--(c-3) a third machine-learning algorithm model corresponding to breath odor analysis, (see: paragraph [0019] where there is determination of halitosis (a bad breath analysis)) or a fourth machine-learning model corresponding to at least one of: dentition/gum sensitivity analysis, enamel erosion analysis, dry mouth analysis, and/or mouth aging analysis (see: paragraph [0019] where there is determination of mouth conditions which is a mouth aging analysis).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute (c-3) a third machine-learning algorithm model corresponding to breath odor analysis, or a fourth machine-learning model corresponding to at least one of: dentition/gum sensitivity analysis, enamel erosion analysis, dry mouth analysis, and/or mouth aging analysis as taught by Apte et al. for the models as disclosed by Sharifi, Apte 1, and Hubenov et al. in combination, since each individual element and its function are shown in the prior art, with the difference being the substitution of the elements. In the present case, the combination of Sharifi, Apte 1, and Hubenov et al. teaches of using a model to assess mouth conditions thus one can substitute some mouth condition models for other mouth condition models to obtain predictable results of assessing mouth conditions. Thus, one of ordinary skill in the art could have substituted the one known element for the other to produce a predictable result (MPEP 2143).
As per claim 20, claim 20 is similar to claim 16 and is therefore rejected in a similar manner.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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