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 .
Notice to Applicant
Receipt of Applicant’s Claims filed August 25, 2026 is acknowledged.
Response to Amendment
Claims 1-2, 10, and 12-14 have been amended. Claims 4-9 have not been modified. Claims 3 and 11 have been cancelled. Claims 1-2, 4-10, and 12-14 are pending and are provided to be examined upon their merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 13, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to Remarks filed on August 25, 2026 have been considered but are not fully persuasive. Response has been provided below.
Applicant argues 35 U.S.C. §101 Rejection, starting pg. 9 of Remarks:
Regarding 1, Applicant argues that the claims are directed to a specific technological improvement to a problem in training predictive AI models by introducing a specific, objective biometric data-filtering mechanism. Examiner respectfully disagrees.
Providing a filtering mechanism does not improve the functioning of the machine learning itself, as filtering data prior to training the machine learning model is a pre-solution activity to the primary process of training and using a generic machine learning model. See MPEP 2106.05(g), which recites: “The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.” Providing a filtration process only serves to limit the type of data that is gathered prior to the claimed process of performing multiple disease diagnosis.
Additionally, filtering itself is considered abstract. See MPEP 2106.04(a)(2)IIC, which recites: “Other examples of managing personal behavior recited in a claim include: i. filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis);”.
The instant claims are analogous to claim 2 of Example 47, which found the processing of data to be used for training to be abstract while the computer used to perform the function was determined to be merely to tool to perform the abstract idea, such that the computer amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Although Applicant argues that a human cannot mentally process raw, multi-channel brainwave data at distinct chronological intervals, calculate numerical validity values, and utilize the result to adjust training input, there is no evidence of such complexity in the particular claim language that would indicate that the tasks performed within the abstract idea could not practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (see MPEP § 2106.04(a)(2)(III)(A) citing SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019)).
For example, pg. 12 of Applicant specification recites:
“In addition, the results of comparing brainwave data (brainwave data) of the pure-ADD and the pure-LBD showed that an alpha peak frequency of a pure-LBD group was lower than that of the pure-ADD, and power of a delta frequency and a theta frequency of the pure-LBD group tends to be stronger than that of the pure-ADD.
Here, the alpha peak has individual differences for each person. However, in the case of normal people, the alpha peak is usually formed above 10 Hz and tends to slow down as cognitive impairment occurs. In addition, delta and theta are areas where power increases during sleep, but in the case of normal people without cognitive impairment, the delta and theta waves do not occur significantly. Therefore, it can be seen that the cognitive impairment of the pure-LBD group progressed more than that of the pure-ADD.”
The above passage merely describes comparing frequencies at the scale of 10 Hz, which is one cycle per second. One of ordinary skill in the art would be fully capable of determining frequencies based on obtained brainwave data and performing further analysis.
However, training of the machine learning model is considered to be an additional element and is analyzed accordingly. Specifically, Pg. 17 recites: “The neural network may be trained by at least one method of supervised learning, unsupervised learning, and semi supervised learning.” No specific, technical improvements are being made to the technology of machine learning as a variety of generic training methods may be applied to improve the machine learning model’s ability to perform the abstract idea of performing multiple disease diagnosis.
Regarding 2, Applicant argues that the claims provide significantly more than the judicial exception by reciting a specific, unconventional sequence of operations including correlating distinct temporal measurements of brainwave activity around a localize event to computationally filter the very dataset used to train the machine learning model. Examiner respectfully disagrees.
The consideration under Step 2B is if the additional elements, alone or in combination, (computing device, diagnostic models, training of a diagnostic model) are well-understood, routine and conventional in the field – the novelty of the abstract ideas of correlating data and filtering datasets are not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea of performing multiple disease diagnosis using a general purpose computer, machine learning model, and machine learning training method. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Applicant argues 35 U.S.C. §102 and 103 Rejections, starting pg. 11 of Remarks:
Applicant argues that the claim amendments overcome the prior 102 rejection and Ciupa in view of De Bruin further in view of Simpraga is insufficient to teach the prior subject matter of claim 3 that has been incorporated into amended claim 1. Examiner acknowledges Applicant amendments and withdraws the 102 rejection. However, Examiner respectfully disagrees that the claim amendments overcome the 103 rejection.
Applicant specifically argues that the cited references do not disclose the structure of selectively determining whether to use the “first brainwave data before medication” itself as training data based on a “validity value calculated by comparison with the second brainwave data after medication”. Examiner notes that the claim only recites: “training a diagnostic model, which diagnoses whether the patient has the specific brain disease, among the plurality of diagnostic models using the classified valid first brainwave data as the training data”.
In the prior rejection, this claim limitation was taught by the combination of Ciupa in view of De Bruin.
De Bruin reciting:
[0072], “FIG. 5, only reliable and valid data are added to the estimation/prediction models.”
[0083], “The present invention provides an adaptive and intelligent feedback system to assess predictive accuracy and improve performance and reliability through the addition of new pre-treatment neuro-psycho-biological data and post-treatment outcome information as this new data becomes available over time. As discussed earlier, to prevent degradation of training data validity, only data passing validity screens can be entered to increase the size of the training data set.”
Ciupa reciting:
[0085], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.”
[0070], “The processed signals can also be used by a training system 406 for training classifier 408.”
[0123], “Upon getting the new set of data, the "medical digital expert system" uses the collection of old and new sets of data to generate better results (171). And this procedure iterates for as long as the digital expert system and the user can handle.”
As claimed, the training data is defined as “classified valid first brainwave data”. Under the broadest reasonable interpretation, the valid data of De Bruin, which encompasses all pre-treatment biological data that passes a validity screen teaches a valid first brainwave data. Ciupa is combined to teach wherein processed signal data can be used for training and training data is classified as a training classifier requires classification of the data, which in combination with the valid data of De Bruin encompasses a “classified valid first brainwave data”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-10, and 12-14 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a system and a method (1-2, 3-10, and 12-14). Accordingly, claims 1-2, 3-10, and 12-14 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method claim 1 and method claim 10 as the claims that represents a claimed invention for analysis; method claim 1 being similar to device claim 13 and product claim 14.
Claim 1:
A digital phenotyping method for drug response classification and prediction, which is performed by a computing device, the method comprising:
acquiring biometric data of a patient; and
performing a multiple disease diagnosis on the patient by analyzing the acquired biometric data using a disease diagnostic model,
wherein the disease diagnostic model includes a plurality of diagnostic models that independently perform diagnoses of each of multiple distinct diseases based on the acquired biometric data,
wherein the acquiring comprises:
acquiring first brainwave data for a patient with a specific brain disease at a first time point, which is a time point before the patient with the specific brain disease takes a target drug;
acquiring second brainwave data for the patient with the specific brain disease at a second time point, which is a time point after the patient with the specific brain disease takes the target drug;
comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value;
classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value; and
training a diagnostic model, which diagnoses whether the patient has the specific brain disease, among the plurality of diagnostic models using the classified valid first brainwave data as the training data.
These above limitations, not in bold, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activities. The claim elements are directed towards “acquiring biometric data of a patient”, “performing a multiple disease diagnosis on the patient by analyzing the acquired biometric data”, “acquiring first brainwave data…”, “acquiring second brainwave data…”, “comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value”, and “classifying the acquired first brainwave data”. Diagnosing a patient condition falls under the abstract concept of managing personal behaviors of people, as it is a human activity typically performed by medical care providers for their patients.
These claims further recite: mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer performing an abstract idea can also fall under mental processes. These encompass “acquiring biometric data of a patient”, “performing a multiple disease diagnosis on the patient by analyzing the acquired biometric data”, “acquiring first brainwave data…”, “acquiring second brainwave data…”, “comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value”, and “classifying the acquired first brainwave data”.
Accordingly, the claim recites an abstract idea.
Claims 13 and 14 are abstract for similar reasons.
Claim 10:
A digital phenotyping method for drug response classification and prediction, which is performed by a computing device, the method comprising:
acquiring first biometric data of a patient at a first time point which is a time point before the patient takes a target drug;
acquiring second biometric data of the patient at a second time point which is later than the first time point and is a time point after the patient takes the target drug; and
analyzing the acquired first biometric data using a disease diagnostic model to perform a multiple disease diagnosis on the patient,
wherein the disease diagnostic model includes a plurality of diagnostic models that independently perform diagnoses of each of multiple distinct diseases based on the acquired biometric data,
wherein the acquiring comprises:
acquiring first brainwave data for a patient with a specific brain disease at a first time point, which is a time point before the patient with the specific brain disease takes a target drug;
acquiring second brainwave data for the patient with the specific brain disease at a second time point, which is a time point after the patient with the specific brain disease takes the target drug;
comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value;
classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value; and
training a diagnostic model, which diagnoses whether the patient has the specific brain disease, among the plurality of diagnostic models using the classified valid first brainwave data as the training data.
These above limitations, not in bold, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activities. The claim elements are directed towards “acquiring first biometric data of a patient”, “acquiring second biometric data of the patient”, “analyzing the acquired first biometric data”, “performing a multiple disease diagnosis on the patient by analyzing the acquired biometric data”, “acquiring first brainwave data…”, “acquiring second brainwave data…”, “comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value”, and “classifying the acquired first brainwave data”. Diagnosing a patient condition falls under the abstract concept of managing personal behaviors of people, as it is a human activity typically performed by medical care providers for their patients.
These claims further recite: mental processes. The claims recite elements, underlined above, that can be performed in the mind of a person, with pen and paper, or using a generic computer. See also MPEP 2106.04(a)(2) III C that teaches generic computer performing an abstract idea can also fall under mental processes. These encompass “acquiring first biometric data of a patient”, “acquiring second biometric data of the patient”, “analyzing the acquired first biometric data”, “performing a multiple disease diagnosis on the patient by analyzing the acquired biometric data”, “acquiring first brainwave data…”, “acquiring second brainwave data…”, “comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value”, and “classifying the acquired first brainwave data”.
Accordingly, the claim recites an abstract idea.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the Claims:
Computing device (1,10,14); disease diagnostic model (1-4,7-14); training diagnostic models (1-2,10,12-14); processor (13); network interface (13); memory (13); computer program (13-14); non-transitory computer readable storage medium (14)
Any computing systems that would be able to perform the method (computing device, processor) and associated elements (memory, computer program, non-transitory computer readable storage medium) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. Pg. 11 of Applicant specification recites: “In this specification, a computer means all kinds of hardware devices including at least one processor and can be understood as including a software component which is operated in the corresponding hardware device according to the embodiment. For example, the computer may be understood as a meaning including any of smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device, but is not limited thereto.” Pg. 20 further recites: “The processor 110 controls an overall operation of each component of the computing device 100. The processor 110 may include a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), a graphic processing unit (GPU), or any type of processor well known in the art of the present invention.” Pg. 21 further recites: “The memory 120 stores various data, commands, and/or information. The memory 120 may load the computer program 151 from the storage 150 to execute methods/operations according to various embodiments of the present invention. When the computer program 151 is loaded into the memory 120, the processor 110 may perform the method/operation by executing one or more instructions constituting the computer program 151. The memory 120 may be implemented as a volatile memory such as a RAM, but the technical scope of the present disclosure is not limited thereto.” No specific, technical improvements are being made to the technology of computing devices as any generic computing components may be applied to perform the abstract idea of performing multiple disease diagnosis.
Machine learning models and their training are also taught at a high level of generality. Pg. 16 recites: “In various embodiments, the disease diagnostic model may be a deep learning model. The deep learning model (e.g., a deep neural network (DNN)) may refer to a disease diagnostic model including a plurality of hidden layers in addition to an input layer and an output layer. It is possible to identify latent structures of data by using the DNN. That is, it is possible to identify the latent structures (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.) of a photo, text, video, sound, or music. The DNN may include a convolutional neural network (CNN), a recurrent neural network (RNN), an auto encoder, a generative adversarial network (GAN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a Q network, a U network, a Siamese network, etc., but is not limited thereto.” Pg. 17 further recites: “The neural network may be trained by at least one method of supervised learning, unsupervised learning, and semi supervised learning.” No specific, technical improvements are being made to the technology of machine learning as a wide variety of generic machine learning models with generic training methods may be applied to perform the abstract idea of performing multiple disease diagnosis.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above -noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 2: This claim recites the method further comprising: acquiring a plurality of pieces of brainwave data for each of multiple patients as biometric data of the multiple patients having different types of brain diseases; classifying the plurality of pieces of acquired brainwave data based on a type of brain disease; and generating the plurality of diagnostic models that individually diagnose whether to have different types of brain diseases by training different diagnostic models using the plurality of pieces of classified brainwave data as training data; which teaches an abstract idea of acquiring and classifying pieces of acquired brainwave data, as identifying and classifying medical data is regularly performed by neurologists. This claim further teaches training machine learning models at a high level of generality, such that there are no specific, technical improvements to the functioning of machine learning.
Claim 4: This claim recites wherein the plurality of diagnostic models include a first diagnostic model for diagnosing whether a first disease is present and a second diagnostic model for diagnosing whether a second disease related to the first disease is present, and the performing of the multiple disease diagnosis includes: calculating a first probability value, which is a possibility that the patient has the first disease, by analyzing the acquired biometric data through the first diagnostic model, when a request for diagnosis of the first disease for the patient is acquired from a user, and when the calculated first probability value is greater than or equal to a reference probability value, calculating a second probability value, which is a possibility that the patient has the second disease, by analyzing the acquired biometric data through the second diagnostic model; and performing multiple diagnoses of whether the first disease is present and whether the second disease is present based on the calculated first probability value and the calculated second probability value; which serves to further limit the abstract idea of performing the multiple disease diagnosis. This claim teaches an abstract idea of mathematical concepts such as calculating probability values and comparing them to a threshold.
Claim 5: This claim recites wherein the performing of the multiple diagnoses includes: determining that the patient has only the first disease when the calculated second probability value is less than the reference probability value; and determining that the patient has the first disease and the second disease when the calculated second probability value is greater than or equal to the reference probability value; which serves to further limit the abstract idea of performing the multiple disease diagnosis. This claim teaches an abstract idea of mathematical concepts as calculating probability values and calculating differences between values.
Claim 6: This claim recites wherein the determining of that the patient has the first disease and the second disease includes: determining a dominant between the first disease and the second disease based on a result of comparing magnitudes of the calculated first probability value and the calculated second probability value and a difference between the calculated first probability value and the calculated second probability value; determining that the patient has the first disease mixed with symptoms of the second disease based on the determined dominant, when the calculated first probability value is greater than the calculated second probability value and the difference between the calculated first probability value and the calculated second probability value is greater than or equal to a preset difference value; determining that the patient has both the first disease and the second disease based on the determined dominant when a magnitude of the difference between the calculated first probability value and the calculated second probability value is less than the preset difference value; and determining that the patient has the second disease mixed with symptoms of the first disease based on the determined dominant, when the calculated second probability value is greater than the calculated first probability value and a difference between the calculated second probability value and the calculated first probability value is greater than or equal to the preset difference value; which serves to further limit the abstract idea of performing the multiple disease diagnosis. This claim teaches an abstract idea of mathematical concepts as calculating probability values and calculating the difference between each other in comparison to a threshold.
Claim 7: This claim recites wherein the performing of the multiple disease diagnosis includes: calculating a probability value corresponding to the possibility that the patient has each of the multiple distinct diseases by inputting the acquired biometric data to each of the plurality of diagnostic models; and selecting at least one disease of which a calculated probability value is greater than or equal to a reference probability value from among the multiple distinct diseases, and determining that the patient is a patient having at least one of the selected diseases as a result of the multiple disease diagnosis of the patient; which serves to further limit the abstract idea of performing the multiple disease diagnosis. This claim teaches an abstract idea of mathematical concepts such as calculating probability values and comparing them to a threshold.
Claim 8: This claim recites wherein the performing of the multiple disease diagnosis includes: selecting at least one second disease having a correlation with the first disease based on a plurality of predefined correlations between diseases when acquiring a first disease diagnostic request for the patient from a user; and calculating a first probability value that is a possibility of having the first disease and one or more second probability values that is a possibility of having the selected one or more second diseases by analyzing the acquired biometric data through one diagnostic model that performs a diagnosis of the first disease among the plurality of diagnostic models and one or more diagnostic models that perform a diagnosis of the selected one or more second diseases; which serves to further limit the abstract idea of performing the multiple disease diagnosis. This claim teaches an abstract idea of mathematical concepts as calculating probability values.
Claim 9: This claim recites wherein the performing of the multiple disease diagnosis includes: calculating a plurality of probability values, which are possibilities of having each of the multiple distinct diseases, by analyzing the acquired biometric data through the plurality of diagnostic models; grouping the plurality of calculated probability values according to correlations based on a plurality of predefined correlations between diseases; and performing the multiple disease diagnosis on the patient based on a comparison result of magnitudes of each of the plurality of grouped probability values and a reference probability value, a comparison result of a magnitude between the plurality of grouped probability values, and a difference between the plurality of grouped probability values; which serves to further limit the abstract idea of performing the multiple disease diagnosis. This claim teaches an abstract idea of mathematical concepts such as calculating probability values and comparing them to a threshold.
Claim 11: This claim recites the method further comprising: acquiring first brainwave data measured at the first time point and second brainwave data measured at the second time point for each of the multiple patients as biometric data of multiple patients having different types of brain diseases; classifying the acquired first brainwave data based on a type of brain disease; and generating a plurality of diagnostic models that individually diagnose whether different types of brain diseases are present by training different diagnostic models using the classified first brainwave data as training data; which teaches an abstract idea of acquiring and classifying pieces of acquired brainwave data, as identifying and classifying medical data is regularly performed by neurologists. This claim further teaches training machine learning models at a high level of generality, such that there are no specific, technical improvements to the functioning of machine learning.
Claim 12: This claim recites the method further comprising: calculating a validity value through a comparison between the acquired first brainwave data and second brainwave data; classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value; and regenerating the plurality of diagnostic models using the valid first brainwave data as the training data; which teaches an abstract idea of acquiring and classifying pieces of acquired brainwave data, as identifying and classifying medical data is regularly performed by neurologists. This claim further teaches iterative training machine learning models at a high level of generality, such that there are no specific, technical improvements to the functioning of machine learning. This claim teaches an abstract idea of mathematical concepts as calculating a validity value, which is a calculation of the difference between two frequencies.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of 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 generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2, 4-9, and 12, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 2, 4-9, and 12, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2, 4-9, and 12, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). 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.
Therefore, whether taken individually or as an ordered combination, claims 1-2, 4-10, and 12-14 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ciupa (US 20210241908) in view of De Bruin (US 20140279746) further in view of Simpraga (Simpraga; Sonja, EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease, 18 Jul 2017, Scientific Reports, 7:5775).
Regarding claim 1, Ciupa teaches a digital phenotyping method for drug response classification and prediction, which is performed by a computing device ([0106], “Diagnosis and Classification: It is important to correctly diagnose and classify the patient” [0117], “A beginning and end point of the predicted progress of the patient are determined with regard to Parkinson's disease. The path between these points is a result of treatment/therapy and the natural progression of Parkinson's.” [0118], “Treatment(s) are preferably selected in stage 1516. Medications may help a patient to manage problems with walking, movement, and tremor.” [0009], “a module may comprise computer instructions—which can be a set of instructions, an application, software—which are operable on a computational device (e.g., a processor) to cause the computational device to conduct and/or achieve one or more specific functionality.”), the method comprising:
acquiring biometric data of a patient ([0037], “An EEG (electroencephalography) sensor 118, typically implemented as a plurality of such sensors, preferably collects EEG signals.” [0038], “Additional sensor 120 can collect biological signals about the user and/or may collect additional information to assist the depth sensor 104. Non-limiting examples of biological signals include a heartrate sensor, an oxygen saturation sensor, infrared pulse sensor, optical pulse sensor, an EKG or EMG sensor, or a combination thereof.”); and
performing a multiple disease diagnosis on the patient by analyzing the acquired biometric data using a disease diagnostic model, wherein the disease diagnostic model includes a plurality of diagnostic models that independently perform diagnoses of each of multiple distinct diseases based on the acquired biometric data ([0047], “After calibration, user data is preferably collected by receiving sensor data during actions performed by the user in 156. The sensor data is analyzed in 158, and the diagnosis is determined in 160.” [0443], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.”).
Ciupa does not teach wherein the acquiring of the plurality of pieces of brainwave data includes: acquiring first brainwave data for a patient with a specific brain disease at a first time point, which is a time point before the patient with the specific brain disease takes a target drug; and acquiring second brainwave data for the patient with the specific brain disease at a second time point, which is a time point after the patient with the specific brain disease takes the target drug, and the generating of the plurality of diagnostic models includes: comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value; classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value; and training a diagnostic model, which diagnoses whether the patient has the specific brain disease, among the plurality of generated diagnostic models using the classified valid first brainwave data as the training data.
However, Ciupa in view of De Bruin does teach wherein the acquiring of the plurality of pieces of brainwave data includes:
acquiring first brainwave data for a patient with a specific brain disease at a first time point, which is a time point before the patient with the specific brain disease takes a target drug (De Bruin, [0045], “in experiments conducted during the development of the invention (these experiments being discussed in detail hereinafter), pre-treatment EEG signals and clinical attributes are collected. The EEG data includes the signals collected by many sensors placed on the scalp, which are then pre-processed to obtain some meaningful raw features that might be relevant to predict the treatment efficacy.”); and
acquiring second brainwave data for the patient with the specific brain disease at a second time point, which is a time point after the patient with the specific brain disease takes the target drug (De Bruin, [0074], “there are many potential "indicators" of patient response to treatment in the case of psychiatric illnesses and disorders. These include various features obtained from the EEG” [0206], “when using anti-depressant medication therapy, for example, in a normal routine, after an optional psychotropic drug washout period (to remove the potential contaminating effects of these drugs on the EEG signal) the data acquisition step would begin… Depression and anxiety severity could be measured at baseline and at two-week intervals during treatment with antidepressant medication.”), and
the generating of the plurality of diagnostic models includes:
training a diagnostic model, which diagnoses whether the patient has the specific brain disease, among the plurality of generated diagnostic models using the classified valid first brainwave data as the training data (De Bruin, [0072], “FIG. 5, only reliable and valid data are added to the estimation/prediction models.” [0083], “The present invention provides an adaptive and intelligent feedback system to assess predictive accuracy and improve performance and reliability through the addition of new pre-treatment neuro-psycho-biological data and post-treatment outcome information as this new data becomes available over time. As discussed earlier, to prevent degradation of training data validity, only data passing validity screens can be entered to increase the size of the training data set.” Ciupa, [0085], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.” [0070], “The processed signals can also be used by a training system 406 for training classifier 408.” [0123], “Upon getting the new set of data, the "medical digital expert system" uses the collection of old and new sets of data to generate better results (171). And this procedure iterates for as long as the digital expert system and the user can handle.”).
Ciupa in view of De Bruin are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ciupa with De Bruin for the advantage of “improv[ing] the performance of the classification/recognition algorithm by enhancing the computational methods and system for treatment-response-prediction.” (De Bruin; [0072]).
Ciupa in view of De Bruin does not teach comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value;
classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value.
However, Simpraga does teach comparing the acquired first brainwave data with the acquired second brainwave data to calculate a validity value (pg. 1, “The mAChR index also discriminated healthy elderly from patients with Alzheimer’s disease (AD)” Fig. 1, “Spectral and temporal correlation biomarkers exhibit sensitivity to scopolamine administration. (a) EEG of a subject in the baseline (blue) and scopolamine (red) condition. (b) Grand average normalized power spectra indicate large effects of scopolamine, most notably a reduction of power in the alpha and beta bands, and an increase of delta and theta power. (c) Oscillation dynamics were studied by extracting the amplitude envelope from band-pass filtered data (e.g., the alpha band, black) using the Hilbert transform (blue, red) and a median-amplitude threshold to determine the onset and offset of a burst.” Pg. 7, “To examine the validity of scopolamine as a model of AD pathophysiology, we applied the mAChR index to healthy elderly controls and patients with AD. We also derived an AD index to test whether scopolamine-induced EEG changes resemble those of AD. Applying the mAChR index to AD patients and controls we observed that it indeed showed an effect (Fig. 5c);”). Examiner notes that pg. 12 of Applicant specification notes that the validity value may be a comparison between alpha peak frequency values of the first and second brainwaves. Thus, one of ordinary skill in the art would recognize that comparing alpha bands of EEG for testing effectiveness of an Alzheimer’s drug, as taught by Simpraga is functionally analogous.
classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value ([0019], “Elastic net logistic regression algorithm was used for developing two integrated indices: 1) The mAChR index, which is classifying whether an EEG was recorded during the baseline or when scopolamine has been administrated; 2) The AD index, which is classifying whether an EEG was recorded from a healthy elderly or an AD patient.” Fig. 5, “(b) AD index separates healthy elderly from Alzheimer’s disease patients with high precision.” Pg. , “Applying the mAChR index to AD patients and controls we observed that it indeed showed an effect (Fig. 5c); however, it discriminated less accurately than the AD index and with a shift in the classification threshold.”). Examiner notes that utilizing a classification threshold to classify whether EEGs correspond to healthy or Alzheimer’s patients encompasses classifying brainwave data into when the calculated value meets a threshold for disease.
Ciupa in view of De Bruin further in view of Simpraga are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ciupa with Simpraga for the advantage of “integrating multiple EEG biomarkers [that] can enhance the accuracy of identifying disease or drug interventions.” (Simpraga; pg. 1).
Regarding claim 2, Ciupa in view of De Bruin further in view of Simpraga teaches the method of claim 1. Ciupa teaches the method further comprising: acquiring a plurality of pieces of brainwave data for each of multiple patients as biometric data of the multiple patients having different types of brain diseases; classifying the plurality of pieces of acquired brainwave data based on a type of brain disease; and generating the plurality of diagnostic models that individually diagnose whether to have different types of brain diseases by training different diagnostic models using the plurality of pieces of classified brainwave data as training data.
However, Ciupa in view of De Bruin does teach the method further comprising:
acquiring a plurality of pieces of brainwave data for each of multiple patients as biometric data of the multiple patients having different types of brain diseases (De Bruin, [0221], “for each patient, we have several epochs of data... There are 6 EEG data collections for each patient (3 EO plus 3 EC, if available), and our final treatment-response prediction result for each patient is based on averaging the corresponding points in the feature space before a decision is made.”);
classifying the plurality of pieces of acquired brainwave data based on a type of brain disease (De Bruin, [0118], “In the above example a two-class diagnosis scenario was also investigated using pre-treatment EEG to differentiate normal (healthy) subjects from patients who suffer from either MDD or Schizophrenia.” [0047], “The method of the present invention combines the information from as many indicators/attributes as possible into a machine learning process which classifies the predicted patient diagnosis and/or response to a set of given treatments.” [0199], “Ranking techniques are useful when the problem is to classify the input data into numerous classes.”); and
generating the plurality of diagnostic models that individually diagnose whether to have different types of brain diseases by training different diagnostic models using the plurality of pieces of classified brainwave data as training data (De Bruin, [0032], “Table 4 shows the result when the 42 simple relevant features (selected based on mutual information) are used to construct the medical diagnosis model.” Ciupa, [0443], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.”).
Ciupa in view of De Bruin are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ciupa with De Bruin for the advantage of “improv[ing] the performance of the classification/recognition algorithm by enhancing the computational methods and system for treatment-response-prediction.” (De Bruin; [0072]).
Regarding claims 13 and 14, these claims are rejected for the same reasons as claim 1. Ciupa further teaches a processor ([0007], “selected steps of methods of at least some embodiments of the disclosure can be described as being performed by a processor, such as a computing platform for executing a plurality of instructions.”);
a network interface ([0010], “a “computer network,””);
a memory (claim 1, “a computational device comprising a processor and memory”); and
a computer program loaded into the memory and executed by the processor ([0007], “number of software instructions being executed by a computer (e.g., a processor of the computer) using an operating system” [0008], “Software (e.g., an application, computer instructions) which is configured to perform (or cause to be performed) specific functionality may also be referred to as a “module” for performing that functionality, and also may be referred to a “processor” for performing such functionality.”); and
a computer-readable recording medium, on which a computer program which is combined with a computing device to execute a digital phenotyping method (claim 3, “said memory stores instructions for enabling said processor to process”).
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Ciupa (US 20210241908) in view of De Bruin (US 20140279746) further in view of Simpraga (Simpraga; Sonja, EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease, 18 Jul 2017, Scientific Reports, 7:5775) and Ay (US 20200129237).
Regarding claim 4, Ciupa in view of De Bruin further in view of Simpraga teaches the method of claim 1. Ciupa further teaches wherein the plurality of diagnostic models include a first diagnostic model for diagnosing whether a first disease is present and a second diagnostic model for diagnosing whether a second disease related to the first disease is present ([0085], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.”). Examiner interprets models that correspond to different neural diseases to encompass at least a first and second model for disease diagnosis wherein the second disease is related to the first disease, as both diseases would be related to each other if they were both neural in nature.
Ciupa in view of De Bruin further in view of Simpraga does not teach wherein the performing of the multiple disease diagnosis includes: calculating a first probability value, which is a possibility that the patient has the first disease, by analyzing the acquired biometric data through the first diagnostic model, when a request for diagnosis of the first disease for the patient is acquired from a user, and when the calculated first probability value is greater than or equal to a reference probability value, calculating a second probability value, which is a possibility that the patient has the second disease, by analyzing the acquired biometric data through the second diagnostic model; and performing multiple diagnoses of whether the first disease is present and whether the second disease is present based on the calculated first probability value and the calculated second probability value
However, Ay does teach wherein the performing of the multiple disease diagnosis includes:
calculating a first probability value, which is a possibility that the patient has the first disease, by analyzing the acquired biometric data through the first diagnostic model, when a request for diagnosis of the first disease for the patient is acquired from a user ([0064], “The diagnosis of the first body condition can thereby function as an indicator of the current presence or likely future presence of a second body condition, where diagnosis of the first body condition can be a decision trigger to evaluate and/or monitor the user for the second body condition such that upon detection of the first body condition, the system can run a diagnostic test to evaluate for the presence or absence of the second body condition.” [0063], “The device can be a user-specific device specifically designed to engage with the user to fit (e.g., conform with) the user's unique body size, body shape, and/or body dimensions. The user-specific device can be designed to conform to the user's body. The user-specific device can be adaptable to conform to the user's body as the user grows, as the user's body condition changes, as a new condition (e.g., another condition different from a previously diagnosed condition) is diagnosed, or any or all of the three.”). Examiner notes that a user would not wear a device if they did not want a diagnosis. Thus, choosing to wear the device for diagnostic purposes encompasses a request for diagnosis.
and when the calculated first probability value is greater than or equal to a reference probability value, calculating a second probability value, which is a possibility that the patient has the second disease, by analyzing the acquired biometric data through the second diagnostic model ([0064], “The diagnosis of the first body condition can thereby function as an indicator of the current presence or likely future presence of a second body condition, where diagnosis of the first body condition can be a decision trigger to evaluate and/or monitor the user for the second body condition such that upon detection of the first body condition, the system can run a diagnostic test to evaluate for the presence or absence of the second body condition.”); and
performing multiple diagnoses of whether the first disease is present and whether the second disease is present based on the calculated first probability value and the calculated second probability value ([0190], “the scaled score can indicate the probability of having the diagnosed condition, where a score of 0 indicates with 99% or 100% certainty that the patient does not have a body condition, and where a score of 100 indicates with 99% or 100% certainty that the patient does have a body condition.”)
Ciupa in view of De Bruin further in view of Simpraga and Ay are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ciupa in view of De Bruin further in view of Simpraga with Ay for the advantage of “indicate that the target object 103T may have the secondary condition (…) upon a determination that the target object 103T has the primary condition.” (Ay; [0246]).
Regarding claim 5, Ciupa in view of De Bruin further in view of Simpraga and Ay teaches the method of claims 1 and 4. Ciupa in view of De Bruin further in view of Simpraga does not teach wherein the performing of the multiple diagnoses includes: determining that the patient has only the first disease when the calculated second probability value is less than the reference probability value; and determining that the patient has the first disease and the second disease when the calculated second probability value is greater than or equal to the reference probability value.
However, Ay does teach wherein the performing of the multiple diagnoses includes:
determining that the patient has only the first disease when the calculated second probability value is less than the reference probability value; and determining that the patient has the first disease and the second disease when the calculated second probability value is greater than or equal to the reference probability value ([0064], “The diagnosis of the first body condition can thereby function as an indicator of the current presence or likely future presence of a second body condition, where diagnosis of the first body condition can be a decision trigger to evaluate and/or monitor the user for the second body condition such that upon detection of the first body condition, the system can run a diagnostic test to evaluate for the presence or absence of the second body condition.” [0190], “the scaled score can indicate the probability of having the diagnosed condition, where a score of 0 indicates with 99% or 100% certainty that the patient does not have a body condition, and where a score of 100 indicates with 99% or 100% certainty that the patient does have a body condition.”). Examiner notes that it would be obvious to one of ordinary skill in the art that the results of a test to evaluate the presence or absence of a second condition after diagnosis of a first would determine whether the patient has only the first disease or both.
Ciupa in view of De Bruin further in view of Simpraga and Ay are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ciupa in view of De Bruin further in view of Simpraga with Ay for the advantage of “indicate that the target object 103T may have the secondary condition (…) upon a determination that the target object 103T has the primary condition.” (Ay; [0246]).
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ciupa (US 20210241908) in view of De Bruin (US 20140279746) further in view of Simpraga (Simpraga; Sonja, EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease, 18 Jul 2017, Scientific Reports, 7:5775) and Choi (US 20230162359).
Regarding claim 7, Ciupa in view of De Bruin further in view of Simpraga teaches the method of claim 1. Ciupa in view of De Bruin further in view of Simpraga does not teach wherein the performing of the multiple disease diagnosis includes: calculating a probability value corresponding to the possibility that the patient has each of the multiple distinct diseases by inputting the acquired biometric data to each of the plurality of diagnostic models; and selecting at least one disease of which a calculated probability value is greater than or equal to a reference probability value from among the multiple distinct diseases, and determining that the patient is a patient having at least one of the selected diseases as a result of the multiple disease diagnosis of the patient.
However, Cipa in view of Choi does teach wherein the performing of the multiple disease diagnosis includes:
calculating a probability value corresponding to the possibility that the patient has each of the multiple distinct diseases by inputting the acquired biometric data to each of the plurality of diagnostic models (Ciupa, [0047], “After calibration, user data is preferably collected by receiving sensor data during actions performed by the user in 156. The sensor data is analyzed in 158, and the diagnosis is determined in 160.” [0443], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.” Choi, [0292], “When a diagnosis assistance neural network model provided in the form of a classifier is used, a predicted label may be determined in consideration of whether an output probability value (or predicted score) exceeds a threshold value.”); and
selecting at least one disease of which a calculated probability value is greater than or equal to a reference probability value from among the multiple distinct diseases, and determining that the patient is a patient having at least one of the selected diseases as a result of the multiple disease diagnosis of the patient (Choi, [0379], “the first diagnostic module may obtain a first diagnosis assistance information related to the presence of an eye disease of a subject by using a first neural network model that predicts the presence of an eye disease of the subject, and the second diagnostic module may obtain a second diagnosis assistance information related to the presence of a systemic disease of a subject by using a second neural network model that predicts the presence of a systemic disease of the subject.” [0392], “The CAM images may be output when a predetermined condition is satisfied. For example, in any one of the case in which a first diagnosis assistance information indicates that the subject is abnormal in relation to a first characteristic or the case in which a second diagnosis assistance information indicates that the subject is abnormal in relation to a second characteristic, a CAM image obtained from a diagnosis assistance neural network model, from which diagnosis assistance information indicating that the subject is abnormal has been output, may be output.”). Examiner interprets outputting information based on a subject/patient being abnormal/having a diagnosis to encompass a selection and subsequent determination.
Ciupa in view of De Bruin further in view of Simpraga and Choi are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ciupa in view of De Bruin further in view of Simpraga with Choi for the advantage of producing “a probability output related to a target disease” (Choi; [0682]).
Regarding claim 8, Ciupa in view of De Bruin further in view of Simpraga teaches the method of claim 1. Ciupa in view of De Bruin further in view of Simpraga does not teach wherein the performing of the multiple disease diagnosis includes: selecting at least one second disease having a correlation with the first disease based on a plurality of predefined correlations between diseases when acquiring a first disease diagnostic request for the patient from a user; and calculating a first probability value that is a possibility of having the first disease and one or more second probability values that is a possibility of having the selected one or more second diseases by analyzing the acquired biometric data through one diagnostic model that performs a diagnosis of the first disease among the plurality of diagnostic models and one or more diagnostic models that perform a diagnosis of the selected one or more second diseases.
However, Ciupa in view of Choi does teach the performing of the multiple disease diagnosis includes:
selecting at least one second disease having a correlation with the first disease based on a plurality of predefined correlations between diseases when acquiring a first disease diagnostic request for the patient from a user (Choi, [0134], “The diagnostic server 4000 may store the first diagnosis assistance neural network model that obtains the first diagnosis assistance information and the second diagnosis assistance neural network model that obtains the second diagnosis assistance information, obtain diagnosis assistance information in response to a request for obtaining diagnosis assistance information from the first client device 3000a” [0508], “the first diagnosis assistance information and the second diagnosis assistance information may correlate with each other”). Examiner interprets user submission of second diagnosis assistance information to encompass a selection.
and calculating a first probability value that is a possibility of having the first disease and one or more second probability values that is a possibility of having the selected one or more second diseases by analyzing the acquired biometric data through one diagnostic model that performs a diagnosis of the first disease among the plurality of diagnostic models and one or more diagnostic models that perform a diagnosis of the selected one or more second diseases (Choi, [0379], “the first diagnostic module may obtain a first diagnosis assistance information related to the presence of an eye disease of a subject by using a first neural network model that predicts the presence of an eye disease of the subject, and the second diagnostic module may obtain a second diagnosis assistance information related to the presence of a systemic disease of a subject by using a second neural network model that predicts the presence of a systemic disease of the subject. Ciupa, [0047], “After calibration, user data is preferably collected by receiving sensor data during actions performed by the user in 156. The sensor data is analyzed in 158, and the diagnosis is determined in 160.” [0443], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.”).
Ciupa in view of De Bruin further in view of Simpraga and Choi are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ciupa in view of De Bruin further in view of Simpraga with Choi for the advantage of producing “a probability output related to a target disease” (Choi; [0682]).
Claim 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over De Bruin (US 20140279746) in view of Ciupa (US 20210241908) further in view of Simpraga (Simpraga; Sonja, EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease, 18 Jul 2017, Scientific Reports, 7:5775).
Regarding claim 10, De Bruin teaches a digital phenotyping method for drug response classification and prediction, which is performed by a computing device ([0132], “a system or digital or computerized or intelligent or automatic medium having encoded instructions and methods for causing a computer, or digital system/equipment to perform any or all of the methods of present invention.” [0045], “The status can include predicted response to a treatment, a diagnosis, an indication of disease progression, a susceptibility to an illness, or other items of medical interest.”), the method comprising:
acquiring first biometric data of a patient at a first time point which is a time point before the patient takes a target drug ([0045], “in experiments conducted during the development of the invention (these experiments being discussed in detail hereinafter), pre-treatment EEG signals and clinical attributes are collected. The EEG data includes the signals collected by many sensors placed on the scalp, which are then pre-processed to obtain some meaningful raw features that might be relevant to predict the treatment efficacy.”);
acquiring second biometric data of the patient at a second time point which is later than the first time point and is a time point after the patient takes the target drug ([0074], “there are many potential "indicators" of patient response to treatment in the case of psychiatric illnesses and disorders. These include various features obtained from the EEG” [0206], “when using anti-depressant medication therapy, for example, in a normal routine, after an optional psychotropic drug washout period (to remove the potential contaminating effects of these drugs on the EEG signal) the data acquisition step would begin… Depression and anxiety severity could be measured at baseline and at two-week intervals during treatment with antidepressant medication.”);
Wherein the acquiring comprises:
acquiring first brainwave data measured at the first time point and second brainwave data measured at the second time point for each of the multiple patients as biometric data of multiple patients having different types of brain diseases ([0221], “for each patient, we have several epochs of data... There are 6 EEG data collections for each patient (3 EO plus 3 EC, if available), and our final treatment-response prediction result for each patient is based on averaging the corresponding points in the feature space before a decision is made.”);
classifying the acquired first brainwave data based on a type of brain disease ([0118], “In the above example a two-class diagnosis scenario was also investigated using pre-treatment EEG to differentiate normal (healthy) subjects from patients who suffer from either MDD or Schizophrenia.” [0047], “The method of the present invention combines the information from as many indicators/attributes as possible into a machine learning process which classifies the predicted patient diagnosis and/or response to a set of given treatments.” [0199], “Ranking techniques are useful when the problem is to classify the input data into numerous classes.”).
De Bruin does not teach analyzing the acquired first biometric data using a disease diagnostic model to perform a multiple disease diagnosis on the patient, wherein the disease diagnostic model includes a plurality of diagnostic models that independently perform diagnoses of each of multiple distinct diseases based on the acquired biometric data; and generating a plurality of diagnostic models that individually diagnose whether different types of brain diseases are present by training different diagnostic models using the classified first brainwave data as training data.
However, Ciupa does teach analyzing the acquired first biometric data using a disease diagnostic model to perform a multiple disease diagnosis on the patient, wherein the disease diagnostic model includes a plurality of diagnostic models that independently perform diagnoses of each of multiple distinct diseases based on the acquired biometric data ([0047], “After calibration, user data is preferably collected by receiving sensor data during actions performed by the user in 156. The sensor data is analyzed in 158, and the diagnosis is determined in 160.” [0443], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.”).
However, the combination of De Bruin in view of Ciupa does teach generating a plurality of diagnostic models that individually diagnose whether to have different types of brain diseases by training different diagnostic models using the plurality of pieces of classified brainwave data as training data (De Bruin, [0032], “Table 4 shows the result when the 42 simple relevant features (selected based on mutual information) are used to construct the medical diagnosis model.” Ciupa, [0443], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.”).
De Bruin in view of Ciupa are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin with Ciupa for the advantage of providing “a plurality of different models each relating to a single neural disease, injury or condition” (Ciupa; [0085]).
De Bruin in view of Ciupa are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin with Ciupa for the advantage of providing “a plurality of different models each relating to a single neural disease, injury or condition” (Ciupa; [0085]).
Regarding claim 12, De Bruin in view of Ciupa teaches the method of claims 10. De Bruin does not teach the method further comprising: calculating a validity value through a comparison between the acquired first brainwave data and second brainwave data; classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value; and regenerating the plurality of diagnostic models using the valid first brainwave data as the training data.
However, De Bruin in view of Ciupa does teach regenerating the plurality of diagnostic models using the valid first brainwave data as the training data (De Bruin, [0072], “FIG. 5, only reliable and valid data are added to the estimation/prediction models.” [0083], “The present invention provides an adaptive and intelligent feedback system to assess predictive accuracy and improve performance and reliability through the addition of new pre-treatment neuro-psycho-biological data and post-treatment outcome information as this new data becomes available over time. As discussed earlier, to prevent degradation of training data validity, only data passing validity screens can be entered to increase the size of the training data set.” Ciupa, [0085], “The patient data is then preferably fed into a neural digital disease model 904 which preferably relates to a plurality of different diseases, but optionally features a plurality of different models each relating to a single neural disease, injury or condition.” [0070], “The processed signals can also be used by a training system 406 for training classifier 408.” [0123], “Upon getting the new set of data, the "medical digital expert system" uses the collection of old and new sets of data to generate better results (171). And this procedure iterates for as long as the digital expert system and the user can handle.”).
De Bruin in view of Ciupa are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin with Ciupa for the advantage of “revis[ing] and improv[ing the system] by gradual learning and adaptive training as new data becomes available” (Ciupa; [0088]).
De Bruin in view of Ciupa does not teach calculating a validity value through a comparison between the acquired first brainwave data and second brainwave data; classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value.
Simpraga does teach calculating a validity value through a comparison between the acquired first brainwave data and second brainwave data; classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value (pg. 1, “The mAChR index also discriminated healthy elderly from patients with Alzheimer’s disease (AD)” Fig. 1, “Spectral and temporal correlation biomarkers exhibit sensitivity to scopolamine administration. (a) EEG of a subject in the baseline (blue) and scopolamine (red) condition. (b) Grand average normalized power spectra indicate large effects of scopolamine, most notably a reduction of power in the alpha and beta bands, and an increase of delta and theta power. (c) Oscillation dynamics were studied by extracting the amplitude envelope from band-pass filtered data (e.g., the alpha band, black) using the Hilbert transform (blue, red) and a median-amplitude threshold to determine the onset and offset of a burst.” Pg. 7, “To examine the validity of scopolamine as a model of AD pathophysiology, we applied the mAChR index to healthy elderly controls and patients with AD. We also derived an AD index to test whether scopolamine-induced EEG changes resemble those of AD. Applying the mAChR index to AD patients and controls we observed that it indeed showed an effect (Fig. 5c);”). Examiner notes that pg. 12 of Applicant specification notes that the validity value may be a comparison between alpha peak frequency values of the first and second brainwaves. Thus, one of ordinary skill in the art would recognize that comparing alpha bands of EEG for testing effectiveness of an Alzheimer’s drug, as taught by Simpraga is functionally analogous.
classifying the acquired first brainwave data into valid first brainwave data when the calculated validity value is greater than or equal to a preset validity value ([0019], “Elastic net logistic regression algorithm was used for developing two integrated indices: 1) The mAChR index, which is classifying whether an EEG was recorded during the baseline or when scopolamine has been administrated; 2) The AD index, which is classifying whether an EEG was recorded from a healthy elderly or an AD patient.” Fig. 5, “(b) AD index separates healthy elderly from Alzheimer’s disease patients with high precision.” Pg. , “Applying the mAChR index to AD patients and controls we observed that it indeed showed an effect (Fig. 5c); however, it discriminated less accurately than the AD index and with a shift in the classification threshold.”). Examiner notes that utilizing a classification threshold to classify whether EEGs correspond to healthy or Alzheimer’s patients encompasses classifying brainwave data into when the calculated value meets a threshold for disease.
De Bruin in view of Ciupa further in view of Simpraga are considered analogous to the claimed invention because they are in the field of machine learning in healthcare. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified De Bruin in view of Ciupa with Simpraga for the advantage of “integrating multiple EEG biomarkers [that] can enhance the accuracy of identifying disease or drug interventions.” (Simpraga; pg. 1).
Regarding claim 6 , this claim was searched and considered but does not result in a prior art rejection at this time.
Regarding claim 9, although the individual limitations of the claim are known, it would not be obvious to one of ordinary skill in the art to combine the multitude of references to result in the claimed subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to whose telephone number is 571-272-3931. The examiner can normally be reached.
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, Shahid Merchant can be reached on (571)270-1360. 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.
/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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