DETAILED ACTION
Notice to Applicant
This communication is in response to the application submitted February 28, 2025. Claims 1, 3 – 7, 9 – 10, 13, 16 – 19, and 21 – 22 are amended. Claims 12, 15, and 20 are cancelled. The present application is the U.S. National Phase application under 35 U.S.C. §371 of International Application No. PCT/EP2023/074101, filed on September 4, 2023, which claims the benefit of EP Application Serial No. 22193836.8, filed September 5, 2022. Claims 1 – 11, 13 – 19, and 21 – 23 are pending examination.
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 .
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.
Regarding claims 7 and 19, the phrase "for example" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Claims 7 and 19 recite “e.g. indicative of a result of the comparison”, where “e.g.” is the Latin phrase of “For Example”.
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 – 11, 13 – 19, and 21 – 23 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.
Step One
Claims 1 – 11, 13 – 19, and 21 – 23 are drawn to a system and method, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1 and 13 recite providing guidance to a dental practitioner in respect of a dental treatment, the method comprising: receive input variables comprising at least: a type of treatment required by the patient, selected from a pre-defined set of possible treatment types; generate an output prediction dataset comprising a predicted measure of treatment pain for each of a series of steps comprised by the input type of treatment; and access the prediction dataset; generate guidance information indicative of the predicted treatment pain for at least a subset of the series of treatment steps; wherein each visit entry includes at least an indication of: a type of treatment administered; and a measure indicative of treatment pain for each of a series of steps comprised by the treatment.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, by providing clinical decision support for pain prediction for dental procedures. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because dentists can adapt their treatment plan to the needs of a specific patient (paragraph 8 of the published specification). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “computer-implemented”, “prediction algorithm”, “trained machine learning algorithm”, “control a user interface to generate at least one user-perceptible output indicative of the guidance information”, “wherein the prediction algorithm comprises a machine learning algorithm pre-trained using a training database comprising respective data entries for each of a plurality of prior visits by a plurality of different patients to a set of different dental treatment providers”
Claim 2: “prediction algorithm”
Claim 3: “user interface”
Claim 4: “database storing records”, “prediction algorithm”
Claim 5: “training database”
Claim 9: “signal sensing apparatus”
Claim 10: “receiving data indicative of motion and/or force patterns of a dental tool during at least one step of the treatment”, “applying a treatment style analyzer algorithm”
Claim 11: “prediction algorithm”, “treatment style analyzer algorithm”
Claim 13: “processing unit”, “input/output”, “one or more processors”, “prediction algorithm”, “input variables”, “guidance algorithm”, “user interface”, “wherein the prediction algorithm comprises a machine learning algorithm pre-trained using a training database comprising respective data entries for each of a plurality of prior visits by a plurality of different patients to a set of different dental treatment providers”
Claim 14: “processing unit”, “prediction algorithm”
Claim 16: “processing unit”, “one or more processors”, “database storing records”, “prediction algorithm”
Claim 17: “processing unit”, “training database”
Claim 18: “processing unit”
Claim 19: “processing unit”, “one or more processors”
Claim 21: “processing unit”, “signal sensing apparatus”
Claim 22: “processing unit”, “one or more processors”, “treatment style analyzer algorithm”
Claim 23: “processing unit”, “one or more processors”, “prediction algorithm”, “treatment style analyzer algorithm”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, 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.
Hence, the 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. Accordingly, 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, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0085] The processing unit 32 could be part of a computing device of the dental practitioner, for example a mobile computing device, for example a smartphone or tablet. It could be part of a dental device in other examples. It could be part of a cloud-based server in some examples. It could be part of a computing workstation in some examples.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2 – 11, 14 – 19, and 21 – 23 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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.
Claim(s) 1 – 3, 5 – 6, 13 – 14, and 17 – 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lach et al., herein after Lach (U.S. Publication Number 2022/0223286 A1) in view of Steinberg-Koch et al., herein after Steinberg-Koch (U.S. Publication Number 2022/0223293 A1).
Claim 1 (Currently amended). Lach teaches a computer-implemented method (paragraph 44 discloses a computer, processor, computer network, or computer server) for use in providing guidance to a dental practitioner in respect of a dental treatment, the method comprising:
running a prediction algorithm, the prediction algorithm comprising at least one trained machine learning algorithm (paragraph 45 discloses relating the patient and caregiver data to the cancer or non-cancer pain events data or cancer-related or other disease-related symptom events data of patient, where the relation may be accomplished with predictive pain algorithms. This data can then inform and train personalized models that find relations between behavioral, environmental, physiological, and contextual factors and pain events and inform real-time notifications for early intervention; paragraph 103 discloses predictive models and effective targeted interventions), wherein the prediction algorithm is configured to:
receive input variables comprising at least: a type of treatment required by the patient, selected from a pre-defined set of possible treatment types (paragraph 83 discloses a real-time personalized intervention may comprise, but not limited to, at least one or more of any combination of the following: providing guidance of treatment for the patient and/or caregiver; predicting occurrence of cancer or noncancer pain events and/or magnitude of cancer or non-cancer pain events of patient; or predicting cancer-related or other disease-related symptom events and/or magnitude of cancer related or other disease-related symptoms of patient; Claim 4);
running a guidance algorithm (paragraph 84 discloses providing guidance of treatment for the patient and/or caregiver includes, but is not limited to, at least one or more of any combination of the following: providing guidance regarding dosing and timing of medication for the patient; providing guidance of pain management for the patient; providing non-pharmacological treatment for the patient; or providing behavioral, environmental or contextual modifications for the patient and/or caregiver; claim 4), the guidance algorithm configured to:
generate guidance information indicative of the predicted treatment pain for at least a subset of the series of treatment steps (paragraph 84 discloses providing guidance regarding dosing and timing of medication for the patient, providing guidance of pain management for the patient, providing non-pharmacological treatment for the patient, or providing behavioral, environmental or contextual modifications for the patient and/or caregiver);
wherein each visit entry includes at least an indication of:
a type of treatment administered (paragraph 84 discloses providing guidance regarding dosing and timing of medication for the patient; providing guidance of pain management for the patient; providing non-pharmacological treatment for the patient; or providing behavioral, environmental or contextual modifications for the patient and/or caregiver);
a measure indicative of treatment pain for each of a series of steps comprised by the treatment (paragraph 84 discloses providing guidance regarding dosing and timing of medication for the patient; providing guidance of pain management for the patient; providing non-pharmacological treatment for the patient; or providing behavioral, environmental or contextual modifications for the patient and/or caregiver; paragraph 110 discloses an event will be classified as a pain event when it is marked by either the patient or caregiver using one hour pre- and one hour post-pain event is classified as part of that pain event, and measures showing predictive promise for an individual will then be combined into a multivariable predictive algorithm for each patient to describe the most predictive measurements and look for similarities across patients).
Lach fails to explicitly teach the following limitations met by Steinberg-Koch as cited:
generate an output prediction dataset comprising a predicted measure of treatment pain for each of a series of steps comprised by the input type of treatment (Figure 5; paragraph 172 discloses the system provides initial guidelines for intervention selection among a group of available treatment options, and based on prior training of the algorithm for optimal outcomes. Such intervention may be based on novel therapies developed by third parties, which are expected to be developed over time. Thus, the system may be updated on a regular basis to incorporate the current standard of treatment for CD); and
access the prediction dataset (paragraph 97 discloses using a method previously trained by a machine learning routine including access to novel treatments, providing an assumed optimum treatment for long term management of the autoimmune disease);
control a user interface to generate at least one user-perceptible output indicative of the guidance information (paragraph 120 discloses a user interface that provides to a human operator at least one of recommended interventions, referrals to specialists, schedule of follow up testing, and ranked list of treatment recommendations);
wherein the prediction algorithm comprises a machine learning algorithm pre- trained using a training database (paragraph 109 discloses training the intervention recommendation model may be performed using at least one of artificial intelligence, machine learning, deep learning, natural language processing, reinforcement learning, and big data analytics techniques. The intervention recommendation model may be a form of artificial intelligence algorithm trained using supervised learning. Alternatively, the intervention recommendation model may be trained via supervised learning from the success or effectiveness of interventions and treatments in the database comprising records of health related data of a large population; paragraph 119 discloses the artificial intelligence algorithms may comprise a machine learning algorithm or a deep learning algorithm) comprising respective data entries for each of a plurality of prior visits by a plurality of different patients to a set of different dental treatment providers (Figure 7; paragraph 31 discloses providing at least one classifier that has been trained on a large population dataset to diagnose at least one specific autoimmune disease such as CD, these classifiers are trained and tested on a large set of sample subjects, a collection of symptoms, concurrent diagnoses, or other parameters; paragraph 41 discloses generating training data for a machine learning/deep learning diagnosis algorithm from a large database of historical medical data of a general population), and
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Lach to further include methods enabling prediction, screening, early diagnosis, and recommended intervention or treatment selection of autoimmune conditions using artificial intelligence operating in conjunction with large medical datasets as disclosed by Steinberg-Koch.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Lach in this way to assist healthcare providers identify autoimmune related illnesses in undiagnosed subjects as early as possible and select the best treatment for these patients by utilizing an AI-based decision support platform which analyzes subjects' data from multiple sources such as EMR, EHR, claims data, sensor data, or health application data, and calculates a risk factor (probability) for having autoimmune related disease (Steinberg-Kochs: paragraph 35).
Claim 2 (Original). Lach and Steinberg-Koch teach the method of claim 1. Lach teaches a method wherein the prediction algorithm is configured to receive input variables which further include: a treatment style to be implemented by the dental practitioner, selected from a pre-defined set of possible treatment styles (paragraph 45 discloses relating the patient and caregiver data to the cancer or non-cancer pain events data or cancer-related or other disease-related symptom events data of patient, where the relation may be accomplished with predictive pain algorithms. This data can then inform and train personalized models that find relations between behavioral, environmental, physiological, and contextual factors and pain events and inform real-time notifications for early intervention; paragraph 83 discloses a real-time personalized intervention may comprise, but not limited to, at least one or more of any combination of the following: providing guidance of treatment for the patient and/or caregiver; predicting occurrence of cancer or noncancer pain events and/or magnitude of cancer or non-cancer pain events of patient; or predicting cancer-related or other disease-related symptom events and/or magnitude of cancer related or other disease-related symptoms of patient; Claim 4).
System claim 14 repeats the subject matter of claim 2. As the underlying processes of claim 14 has been shown to be fully disclosed by the teachings of Lach and Steinberg-Koch in the above rejections of claim 2; as such, these limitations (claim 14) are rejected for the same reasons given above for claim 2 and incorporated herein.
Claim 3 (Currently amended). Lach and Steinberg-Koch teach the method of claim 1.
Lach fails to explicitly teach the following limitations met by Steinberg-Koch as cited:
further comprising controlling the user interface to generate an ordered sequence of user perceptible outputs, each indicative of the predicted treatment pain for one of the series of treatment steps (paragraph 120 discloses a user interface that provides to a human operator at least one of recommended interventions, referrals to specialists, schedule of follow up testing, and ranked list of treatment recommendations).
The motivation to combine the teachings of Lach and Steinberg-Koch is discussed in the rejection of claim 1, and incorporated herein.
Claim 5 (Currently amended). Lach and Steinberg-Koch teach the method of claim 2. Lach teaches a method wherein each visit entry of the training database further includes an indication of a treatment style, where the treatment style is one of the said pre-defined set of treatment styles (paragraph 45 discloses predictive pain algorithms which can inform and train personalized models that find relations between behavioral, environmental, physiological, and contextual factors and pain events and inform real-time notifications for early intervention).
System claim 17 repeats the subject matter of claim 5. As the underlying processes of claim 17 has been shown to be fully disclosed by the teachings of Lach and Steinberg-Koch in the above rejections of claim 5; as such, these limitations (claim 17) are rejected for the same reasons given above for claim 5 and incorporated herein.
Claim 6 (Currently amended). Lach and Steinberg-Koch teach the method of claim 1. Lach teaches a method wherein, within each visit entry, the measure indicative of treatment pain for each of the series of steps corresponds to a total measured pain amount, the total measured pain amount corresponding to a product of a measured pain response level and a time for which the pain response level was experienced, for each of one or more pain response episodes of each treatment step (paragraph 9 discloses when a patient or caregiver records a pain event on their respective smartwatch, BESI-C provides a comprehensive 'snap shot' of exactly what is occurring at and around the time of the event where the data can then inform and train personalized models that find relations between behavioral, environmental, physiological, and contextual factors and pain events and inform real-time notifications for early intervention; paragraph 110 discloses focusing analysis on severity of pain events (those marked as 5 and with corresponding moderate/high levels of distress) and frequency of pain events (number of marked pain events in a specified time period, regardless of severity/distress level) and the number of pain events will be compared between patients using mixed effects);
wherein, within each visit entry, the measure indicative of treatment pain for each of the series of steps comprises a maximum or peak pain level experienced during the respective treatment step (paragraph 110 discloses focusing analysis on severity of pain events (those marked as 5 and with corresponding moderate/high levels of distress) and frequency of pain events (number of marked pain events in a specified time period, regardless of severity/distress level); and/or
wherein, within each visit entry, the measure indicative of treatment pain for each of the series of steps comprises a measure of a maximum rate of change of pain level experienced during each of the treatment steps.
System claim 18 repeats the subject matter of claim 6. As the underlying processes of claim 18 has been shown to be fully disclosed by the teachings of Lach and Steinberg-Koch in the above rejections of claim 6; as such, these limitations (claim 18) are rejected for the same reasons given above for claim 6 and incorporated herein.
Claim 13 (Currently amended). Lach teaches a processing unit (paragraph 44 discloses a computer, processor, computer network, or computer server), comprising:
an input/output (paragraph 71 discloses an input/output interface); and
one or more processors (paragraph 44 discloses a computer, processor, computer network, or computer server) adapted to:
run a prediction algorithm, the prediction algorithm (paragraph 45 discloses relating the patient and caregiver data to the cancer or non-cancer pain events data or cancer-related or other disease-related symptom events data of patient, where the relation may be accomplished with predictive pain algorithms. This data can then inform and train personalized models that find relations between behavioral, environmental, physiological, and contextual factors and pain events and inform real-time notifications for early intervention; paragraph 103 discloses predictive models and effective targeted interventions) configured to:
receive input variables comprising at least a type of treatment required by the patient, selected from a pre-defined set of possible treatment types (paragraph 83 discloses a real-time personalized intervention may comprise, but not limited to, at least one or more of any combination of the following: providing guidance of treatment for the patient and/or caregiver; predicting occurrence of cancer or noncancer pain events and/or magnitude of cancer or non-cancer pain events of patient; or predicting cancer-related or other disease-related symptom events and/or magnitude of cancer related or other disease-related symptoms of patient; Claim 4), and
run a guidance algorithm (paragraph 84 discloses providing guidance regarding dosing and timing of medication for the patient, providing guidance of pain management for the patient, providing non-pharmacological treatment for the patient, or providing behavioral, environmental or contextual modifications for the patient and/or caregiver) configured to:
generate guidance information indicative of the predicted treatment pain for at least a subset of the series of treatment steps (paragraph 84 discloses providing guidance regarding dosing and timing of medication for the patient, providing guidance of pain management for the patient, providing non-pharmacological treatment for the patient, or providing behavioral, environmental or contextual modifications for the patient and/or caregiver);
wherein each visit entry includes at least an indication of:
a type of treatment administered (paragraph 84 discloses providing guidance regarding dosing and timing of medication for the patient; providing guidance of pain management for the patient; providing non-pharmacological treatment for the patient; or providing behavioral, environmental or contextual modifications for the patient and/or caregiver); and
a measure indicative of treatment pain for each of a series of steps comprising the treatment (paragraph 84 discloses providing guidance regarding dosing and timing of medication for the patient; providing guidance of pain management for the patient; providing non-pharmacological treatment for the patient; or providing behavioral, environmental or contextual modifications for the patient and/or caregiver; paragraph 110 discloses an event will be classified as a pain event when it is marked by either the patient or caregiver using one hour pre- and one hour post-pain event is classified as part of that pain event, and measures showing predictive promise for an individual will then be combined into a multivariable predictive algorithm for each patient to describe the most predictive measurements and look for similarities across patients).
Lach fails to explicitly teach the following limitations met by Steinberg-Koch as cited:
generate an output prediction dataset comprising a predicted measure of treatment pain for each of a series of steps comprised by the input type of treatment (Figure 5; paragraph 172 discloses the system provides initial guidelines for intervention selection among a group of available treatment options, and based on prior training of the algorithm for optimal outcomes. Such intervention may be based on novel therapies developed by third parties, which are expected to be developed over time. Thus, the system may be updated on a regular basis to incorporate the current standard of treatment for CD); and
access the prediction dataset (paragraph 97 discloses using a method previously trained by a machine learning routine including access to novel treatments, providing an assumed optimum treatment for long term management of the autoimmune disease);
control a user interface to generate at least one user-perceptible output indicative of the guidance information (paragraph 120 discloses a user interface that provides to a human operator at least one of recommended interventions, referrals to specialists, schedule of follow up testing, and ranked list of treatment recommendations);
wherein the prediction algorithm comprises a machine learning algorithm pre- trained using a training database (paragraph 109 discloses training the intervention recommendation model may be performed using at least one of artificial intelligence, machine learning, deep learning, natural language processing, reinforcement learning, and big data analytics techniques. The intervention recommendation model may be a form of artificial intelligence algorithm trained using supervised learning. Alternatively, the intervention recommendation model may be trained via supervised learning from the success or effectiveness of interventions and treatments in the database comprising records of health related data of a large population; paragraph 119 discloses the artificial intelligence algorithms may comprise a machine learning algorithm or a deep learning algorithm) comprising respective data entries for each of a plurality of prior visits by a plurality of different patients to a set of different dental treatment providers (Figure 7; paragraph 31 discloses providing at least one classifier that has been trained on a large population dataset to diagnose at least one specific autoimmune disease such as CD, these classifiers are trained and tested on a large set of sample subjects, a collection of symptoms, concurrent diagnoses, or other parameters; paragraph 41 discloses generating training data for a machine learning/deep learning diagnosis algorithm from a large database of historical medical data of a general population).
The motivation to combine the teachings of Lach and Steinberg-Koch is discussed in the rejection of claim 1, and incorporated herein.
Claim(s) 4, 10 – 11, 16, and 22 – 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lach et al., herein after Lach (U.S. Publication Number 2022/0223286 A1) in view of Steinberg-Koch et al., herein after Steinberg-Koch (U.S. Publication Number 2022/0223293 A1) further in view of Kitching et al., herein after Kitching (U.S. Publication Number 2008/0306724 A1).
Claim 4 (Currently amended). Lach and Steinberg-Koch teach the method of claim 1.
Lach and Steinberg-Koch fail to explicitly teach the following limitations met by Kitching as cited:
further comprising: accessing a scheduling database storing records indicative of scheduled treatment sessions, each record including at least an indication of a type of treatment to be performed (paragraph 7 discloses planned treatment phases can include customized treatment guidelines tailored to the particular treatment plan generated for the patient being treated, the guidelines being useful in helping to more effectively manage delivery and treatment of the patient according to the treatment plan: paragraph 52 discloses a treatment plan will typically include a series of one or more appointments, guidelines will typically include one or more recommended patient/practitioner appointments that may include suggested timing for the appointments);
receiving information indicative of a treatment style to be implemented by the dental practitioner (paragraph 10 discloses generating a case difficulty assessment based on information received, generating a treatment plan for a patient, providing customized set(s) of treatment guidelines, and tracking progression of the patient's teeth along a treatment path or according to the treatment plan; paragraph 28 discloses customized treatment guidelines and appointment planning tools, and treatment progress monitoring and tracking techniques), and optionally wherein the treatment style to be implemented is also comprised as part of each data record in the scheduling database;
generating the input variables for the prediction algorithm based on said accessing and said receiving steps (paragraph 48 discloses in the computerized visual guide interface system, there are provided one or more databases which have stored therein an index of statistical information, computational algorithms, patient conditions, and associated treatment information based upon, for example, desired one or more treatment goals –Kitching discloses computational algorithms, but does not explicitly a prediction algorithm however a prediction algorithm is disclosed by Lach (see paragraph 103 of Lach “predictive models”) .
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Lach and Steinberg-Koch to further include systems and methods of managing planning and delivery of an orthodontic treatment using planning tools, treatment guidelines, instructions and appointment planning tools customized to the individual patient being treated, as well as tools and methods for tracking delivery and patient progression through an orthodontic treatment plan as disclosed by Kitching.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Lach and Steinberg-Koch in this way to provide a treatment planning and management systems that can provide earlier and better off track determinations, together with other enhanced planning and management tools for facilitating orthodontic practice among a wide range of practitioners, including those with limited experience in orthodontics as well as experienced practitioners desiring more guidance particularly for complex cases (Kitching: paragraph 6).
System claim 16 repeats the subject matter of claim 4. As the underlying processes of claim 16 has been shown to be fully disclosed by the teachings of Lach, Steinberg-Koch, and Kitching in the above rejections of claim 4; as such, these limitations (claim 16) are rejected for the same reasons given above for claim 4 and incorporated herein.
Claim 10 (Currently amended). Lach and Steinberg-Koch teach the method of claim 1.
Lach and Steinberg-Koch fail to explicitly teach the following limitations met by Kitching as cited:
further comprising:
receiving data indicative of motion and/or force patterns of a dental tool during at least one step of the treatment (paragraph 45 discloses each appliance configuration corresponds to a planned successive arrangement of the teeth, and represents a step along the treatment path for the patient. The steps are defined and calculated so that each discrete position can follow by straight-line tooth movement or simple rotation from the tooth positions achieved by the preceding discrete step and so that the amount of repositioning required at each step involves an orthodontically acceptable amount of force on the patient's dentition); and
applying a treatment style analyzer algorithm configured to estimate based on the motion and/or force patterns a treatment style associated therewith (paragraph 45 discloses the tooth paths and associated tooth position data are used to calculate clinically acceptable appliance configurations (or successive changes in appliance configuration) that will move the teeth on the defined treatment path in the steps specified).
The motivation to combine the teachings of Lach, Steinberg-Koch, and Kitching is discussed in the rejection of claim 4, and incorporated herein.
System claim 22 repeats the subject matter of claim 10. As the underlying processes of claim 22 has been shown to be fully disclosed by the teachings of Lach, Steinberg-Koch, and Kitching in the above rejections of claim 10; as such, these limitations (claim 22) are rejected for the same reasons given above for claim 10 and incorporated herein.
Claim 11 (Original). Lach, Steinberg-Koch, and Kitching teach the method of claim 10.
Lach and Steinberg-Koch fail to explicitly teach the following limitations met by Kitching as cited:
further comprising re-running the prediction algorithm using the treatment style estimated by the treatment style analyzer algorithm to generate a new prediction of the measure of treatment pain for the at least one step (paragraph 84 discloses that once a determination is made that the patient's actual arrangement of teeth deviates from a planned arrangement and that the patient's teeth are not progressing as expected/planned, a change or correction in the course of treatment can be selected, for example, by generating an interim or modified treatment plan); and
generating a user-perceptible output indicative of the new prediction (paragraph 83 discloses peripheral devices typically include a storage subsystem (memory subsystem and file storage subsystem), a set of user interface input and output devices, and an interface to outside networks).
The motivation to combine the teachings of Lach, Steinberg-Koch, and Kitching is discussed in the rejection of claim 4, and incorporated herein.
System claim 23 repeats the subject matter of claim 11. As the underlying processes of claim 23 has been shown to be fully disclosed by the teachings of Lach, Steinberg-Koch, and Kitching in the above rejections of claim 11; as such, these limitations (claim 23) are rejected for the same reasons given above for claim 11 and incorporated herein.
Claim(s) 7 – 9, 19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lach et al., herein after Lach (U.S. Publication Number 2022/0223286 A1) in view of Steinberg-Koch et al., herein after Steinberg-Koch (U.S. Publication Number 2022/0223293 A1) further in view of Jain (U.S. Publication Number 2015/0025334 A1).
Claim 7 (Currently amended). Lach and Steinberg-Koch teach the method of claim 1. Lach discloses a method further comprising: obtaining a measure indicative of estimated actual treatment pain for at least a subset of the steps of the treatment ((paragraph 45 discloses relating the patient and caregiver data to the cancer or non-cancer pain events data or cancer-related or other disease-related symptom events data of patient, where the relation may be accomplished with predictive pain algorithms. This data can then inform and train personalized models that find relations between behavioral, environmental, physiological, and contextual factors and pain events and inform real-time notifications for early intervention; paragraph 103 discloses predictive models and effective targeted interventions).
Lach and Steinberg-Koch fail to explicitly teach the following limitations met by Jain as cited:
comparing for each of the at least subset of steps the estimated actual treatment pain and the predicted treatment pain (paragraph 48 discloses the diagnostic module measures the intensity of pain experienced by the user on a pre-determined scale using the plurality of one or more bio-markers);
generating feedback information for the dental practitioner based on the comparison, e.g. indicative of a result of the comparison (paragraph 66 discloses the pain monitoring application may generate tabular report of the pain profile corresponding to results obtained from the diagnostic module).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Lach and Steinberg-Koch to further include a method and system for stimulating and monitoring intensity of pain experienced by one or more users is provided, including measuring the intensity of pain experienced by the one or more users on a pre-determined scale and augmented chart or physician's personal assessment using a plurality of one or more bio-markers as disclosed by Jain.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Lach and Steinberg-Koch in this way to provide a treatment planning system and method including monitoring the pain of patients and enabling the tailoring of treatments accordingly (Jain: paragraph 7).
System claim 19 repeats the subject matter of claim 7. As the underlying processes of claim 19 has been shown to be fully disclosed by the teachings of Lach, Steinberg-Koch, and Jain in the above rejections of claim 7; as such, these limitations (claim 19) are rejected for the same reasons given above for claim 7 and incorporated herein.
Claim 8 (Original). Lach, Steinberg-Koch, and Jain teach the method of claim 7. Lach teaches a method wherein the measure indicative of estimated actual treatment pain for each of said at least subset of steps of the treatment is received in real time during performance of each of the subset of the steps of the treatment by the dental practitioner (paragraph 45 discloses relating the patient and caregiver data to the cancer or non-cancer pain events data or cancer-related or other disease-related symptom events data of patient, where the relation may be accomplished with predictive pain algorithms. This data can then inform and train personalized models that find relations between behavioral, environmental, physiological, and contextual factors and pain events and inform real-time notifications for early intervention; paragraph 103 discloses predictive models and effective targeted interventions).
wherein the comparison and feedback generation are generated in real time with receipt of the estimated actual treatment pain for each step (paragraph 63 discloses allowing a participant user (e.g., healthcare provider or clinician) to configure an account, report out, download ordering information, such as a menu or survey, and interact with a health system to partake in the monitoring and delivering in-situ real-time personalized intervention for a patient).
Claim 9 (Currently amended). Lach, Steinberg-Koch, and Jain teach the method of claim 7. Lach discloses wherein the obtaining a measure indicative of estimated actual treatment pain for at least a subset of the steps of the treatment comprises:
receiving, during each of the at least subset of steps of the treatment, a biological signal for the subject from a biological signal sensing apparatus coupled to the subject (paragraph 43 discloses in-situ sensor and devices are provided to obtain various environmental data, behavioral data, physiological data, and contextual data of each of the patient and caregiver; paragraph 103 discloses a smart health system that lives in peoples' homes through embedded sensors and a smartwatch and can collect rich, in-depth, data that facilitates personalized system learning, predictive models and effective targeted interventions identifying physiological response events in the biological signal).
Lach fails to explicitly teach the following limitations met by Steinberg-Koch as cited:
determining the measure indicative of estimated actual treatment pain for each said step of the treatment based on the physiological response events identified for that step (paragraph 35 discloses following the subject's medical data throughout therapy and classifying the individual response to each treatment; paragraph 168 discloses the training inputs for the model are examples generated from the population medical record database using medical guidelines, and by collecting patients' response to specific treatments and scoring them accordingly).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Lach to further include methods enabling prediction, screening, early diagnosis, and recommended intervention or treatment selection of autoimmune conditions using artificial intelligence operating in conjunction with large medical datasets as disclosed by Steinberg-Koch.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Lach in this way to assist healthcare providers identify autoimmune related illnesses in undiagnosed subjects as early as possible and select the best treatment for these patients by utilizing an AI-based decision support platform which analyzes subjects' data from multiple sources such as EMR, EHR, claims data, sensor data, or health application data, and calculates a risk factor (probability) for having autoimmune related disease (Steinberg-Kochs: paragraph 35).
System claim 21 repeats the subject matter of claim 9. As the underlying processes of claim 21 has been shown to be fully disclosed by the teachings of Lach, Steinberg-Koch, and Jain in the above rejections of claim 9; as such, these limitations (claim 21) are rejected for the same reasons given above for claim 9 and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure includes:
Yadalam et al. (P. K. Yadalam et al., "Machine Learning Predicts Patient Tangible Outcomes After Dental Implant Surgery," in IEEE Access, vol. 10, pp. 131481-131488, 2022, doi: 10.1109/ACCESS.2022.3228793) discloses predicting postoperative discomfort using an AI-based multi-linear regression model to determine the degree of pain and discomfort experienced during a surgical procedure and how it varies from one patient to another
Farook et al. (Farook TH, Jamayet NB, Abdullah JY, Alam MK. Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review. Pain Res Manag. 2021 Apr 24;2021:6659133. doi: 10.1155/2021/6659133. PMID: 33986900; PMCID: PMC8093041) discloses the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTINE K RAPILLO whose telephone number is (571)270-3325. The examiner can normally be reached Monday - Friday 7:30 - 4 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
KRISTINE K. RAPILLO
Examiner
Art Unit 3626
/KRISTINE K RAPILLO/Examiner, Art Unit 3682
Prosecution Insights