DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of Applicant's claim for priority to the following application(s):
* PCT/US2023/070210 filed on 14 July 2023
* 63368784 filed on 19 July 2022
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on the following date(s) is/are entered and considered by Examiner:
* 01 May 2025
* 08 January 2025
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
A computer-implemented system for assigning a dental patient to a risk category, the system comprising:
one or more computing devices storing
a dental risk model;
dental intervention predictors comprising one or more of tooth location, diet, snacking pattern, brushing habits, existing fillings, existing devices, fluoride use, age, eating disorders, dry mouth, heartburn, and acid reflux; and
one or more computing devices executing instructions to
receive patient specific data for a patient and a population dataset for a patient population, wherein the patient specific data comprises data selected from the group comprising medical claims data and pharmacy claims data, wherein population data comprises data selected from the group comprising demographic data, geographic data, and financial data;
analyze the population data to identify a subset of the population dataset having one or more of the dental risk model triggers present in the patient specific data;
process the patient specific data and the subset of the population dataset using an algorithm selected from the group comprising variable selection, principle component analysis, and clustering;
extract features from the patient specific data by temporal feature extraction;
generate a predicted dental intervention;
provide a plurality of training conditions to a computing device wherein the training conditions comprise tooth location, diet, snacking pattern, brushing habits, existing fillings, existing devices, fluoride use, age, eating disorders, dry mouth, heartburn, and acid reflux;
develop the dental intervention predictive model using a modeling technique selected from the group comprising decision tree, logistic regression, artificial neural networks, and ensemble;
provide the patient specific data and the population dataset to the computing device that comprises the dental intervention predictive model;
receive a calculated risk score from the computing device that comprises the dental intervention predictive model, wherein the dental risk score represents the likelihood that the patient will require a dental procedure within a predetermined time period, and wherein the risk score is determined at least in part based on the presence or absence of each of the dental predictors in the patient specific data for the patient;
sort the received calculated risk score into one of a plurality of groups according to a severity of risk determined by the calculated risk score; and
assign a program or intervention for the patient, wherein the assignment is determined based on the calculated risk score.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mathematical concepts” because the modeling techniques of at least decision tree and logistic regression are directed towards mathematical relationships, i.e. mathematical relationships, mathematical formulas or equations, mathematical calculations. MPEP § 2106.04(a)(2)(I)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the steps of diagnosing patient’s dental risk are traditionally performed by a human dentist, i.e. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a computer recited with a high level of generality in a post hoc manner to implement the abstract idea, the highlighted steps may be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
The different categories of abstract ideas are being considered together as one single abstract idea. MPEP 2106.04(II)(B)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 5-6 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
one or more computing devices storing
a dental risk model;
dental intervention predictors comprising one or more of tooth location, diet, snacking pattern, brushing habits, existing fillings, existing devices, fluoride use, age, eating disorders, dry mouth, heartburn, and acid reflux; and
one or more computing devices executing instructions to
receive patient specific data for a patient and a population dataset for a patient population, wherein the patient specific data comprises data selected from the group comprising medical claims data and pharmacy claims data, wherein population data comprises data selected from the group comprising demographic data, geographic data, and financial data;
provide a plurality of training conditions to a computing device wherein the training conditions comprise tooth location, diet, snacking pattern, brushing habits, existing fillings, existing devices, fluoride use, age, eating disorders, dry mouth, heartburn, and acid reflux;
provide the patient specific data and the population dataset to the computing device that comprises the dental intervention predictive model;
receive a calculated risk score from the computing device that comprises the dental intervention predictive model, wherein the dental risk score represents the likelihood that the patient will require a dental procedure within a predetermined time period, and wherein the risk score is determined at least in part based on the presence or absence of each of the dental predictors in the patient specific data for the patient.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the computing device, the Specification as originally filed in parent application 63368784 on 19 July 2022 (hereafter referred to as “the Provisional Specification”) discloses a generic computer (page 10-11 paragraph 0044), and amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
Regarding the steps of storing data on a computer and a computer sending and receiving data, these limitations add(s) insignificant extra-solution activity to the abstract idea (mere data gathering, selecting a particular data source or type of data to be manipulated, insignificant application). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) 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, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does 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 amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
Regarding the step of storing data on a computer, this amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv))).
Regarding the step of a computer sending and receiving data, this amount(s) to element(s) that have been recognized as WURC activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
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.
The claim is not patent eligible.
Claim(s) 7-20 recite(s) substantially similar limitations as those of claim(s) 1-6 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
Subject Matter Free of Prior Art
Claim(s) 1-20 distinguish(es) over the prior art for the following reasons.
The following is a statement of reasons for the subject matter free of prior art:
Claim 1: the primary reason for the indication of subject matter free of prior art is the inclusion of the following limitations in the combination (particular emphasis added) as recited in the abstract concept and not found in the closest available prior art of record:
dental intervention predictors comprising one or more of tooth location, diet, snacking pattern, brushing habits, existing fillings, existing devices, fluoride use, age, eating disorders, dry mouth, heartburn, and acid reflux; and
provide a plurality of training conditions to a computing device wherein the training conditions comprise tooth location, diet, snacking pattern, brushing habits, existing fillings, existing devices, fluoride use, age, eating disorders, dry mouth, heartburn, and acid reflux;
develop the dental intervention predictive model using a modeling technique selected from the group comprising decision tree, logistic regression, artificial neural networks, and ensemble;
provide the patient specific data and the population dataset to the computing device that comprises the dental intervention predictive model;
receive a calculated risk score from the computing device that comprises the dental intervention predictive model, wherein the dental risk score represents the likelihood that the patient will require a dental procedure within a predetermined time period, and wherein the risk score is determined at least in part based on the presence or absence of each of the dental predictors in the patient specific data for the patient;
sort the received calculated risk score into one of a plurality of groups according to a severity of risk determined by the calculated risk score; and
assign a program or intervention for the patient, wherein the assignment is determined based on the calculated risk score.
The closest available prior art of record are as follows:
Kearney (20210365736) discloses processing data related to a tooth to determine a best course diagnosis and treatment (Figure 28 and respective disclosure therefor), but does not fairly disclose or suggest the presence or absence of each of the dental predictors, in the combination as recited, being used to score the patient’s risk.
Sachdeva (20200066391) discloses processing data related to a tooth to determine a best course diagnosis and treatment (Figure 4A and respective disclosure therefor), but does not fairly disclose or suggest the presence or absence of each of the dental predictors, in the combination as recited, being used to score the patient’s risk.
Based on the evidence presented above, none of the closest available prior art of record fairly discloses or suggests the claimed invention. For this reason, claim 1 would be found to be subject matter free of prior art.
Claim(s) 2-6: this/these claim(s) would also be found to be subject matter free of prior art for at least the same rationale as applied to parent claim 1 above, and incorporated herein.
Claim(s) 7-20: this/these claim(s) would also be found to be subject matter free of prior art for substantially similar rationale as applied to claim(s) 1-6 above, and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bates (20170323064) discloses processing patient data to generate a diagnosis (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Cheung (20180314795) discloses using genomic information to diagnose a patient (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.N.N./ Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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