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 .
Status of Claims
This action is a non-final rejection
Claims 1-17 are pending
Claims 1-17 are rejected under 35 USC § 101
Priority
Acknowledgement is made of Applicant’s claim for a foreign priority date of 5-4-2023
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7-8-2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-17 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-17 they recite an abstract idea of diagnosing mild cognitive impairment, dementias and neurodegenerative diseases.
Independent Claim 1 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 1 recites a method for diagnostics of mild cognitive impairment, dementias and neurodegenerative diseases.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
A) Collecting brain imaging data with at least one type of imaging modality from a group of cognitively normal individuals and patients with confirmed cases of at least one diagnosis from a plurality of diseases;
B) Collecting laboratory data with at least one type of laboratory analysis from the group of cognitively normal individuals and patients;
C) Collecting functional data with at least one functional test from the group of cognitively normal individuals and patients;
D) Collecting diagnostics data of the majority of diagnostic procedures of steps A, B, and C from an examinee with unknown diagnosis;
wherein different types of diagnostics data comprise brain imaging data, laboratory data and functional data, and steps A, B, C and D are performed by medical staff;
E) Entering the diagnostic data ..;
F) Entering at least two types of diagnostic data into a .. system which produces .. regression models specific for each diagnosis and gets output values of the models;
G) Entering the output values as predictors and the diagnoses of the group of cognitively normal individuals and patients as targeted variables into the .. system which produces a classification ..dentifying at least one disease-specific .. regression model best-fitting to an individual case;
H) Assembling an .. model H from the disease-specific .. regression models and the classification ..
I) Deploying the ..model H ... to calculate probabilities of the diseases, output at least one diagnosis with the highest probability and the optimal therapeutic plan implementing at least one efficient treatment option;
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites the abstract idea of diagnosing mild cognitive impairment, dementias and neurodegenerative diseases. Alternatively it belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites the abstract idea of diagnosing mild cognitive impairment, dementias and neurodegenerative diseases (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). Claims 1, 29 and 30 recite:
a first computing device;
a machine learning system;
cross-modal regression models;
ensemble model;
classification module;
wherein the first computing device may be at least partially different from the machine learning system.
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two, claims 1, 29 and 30 recite:
a first computing device;
a machine learning system;
cross-modal regression models;
ensemble model;
classification module;
wherein the first computing device may be at least partially different from the machine learning system.
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, even when viewed as a whole the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The dependent claims recite additional limitations that further define the abstract idea of diagnosing mild cognitive impairment, dementias and neurodegenerative diseases. These claim limitations include:
Claim 2:
A) local and/or remote datasets comprising functional and/or laboratory and/or brain imaging data retrieved from diagnostic equipment for imaging of the brain, laboratory analysis of biological samples, and functional tests of human behavior;
B) at least one .. system ... for training a plurality of disease-specific ... regression models to predict at least one type of diagnostic data of part A from at least one other type of diagnostic data of part A;
C) at least one ... system ... for producing a classification ... to detect the correct clinical diagnosis and the optimal treatment from the output of disease-specific ... regression models;
D) at least ... storing a list of therapeutic plans comprising treatment options for each diagnosis and ... running an ... model which incorporates the disease-specific regression models of part B and the classification ...of part C to calculate probabilities of mild cognitive impairment, dementias and neurodegenerative disease, output at least one diagnosis with the highest probability and provide indications for a cognition-focused intervention from available treatment options;
wherein .. allocate the .. systems of parts B and C and it can work with the datasets of parts A.
Claim 3: wherein the brain imaging data are any one or a combination of voxel-based morphometry data, surface-based morphometry data, any other type of radiomics findings, brain-imaging data, angiography findings, metabolic imaging data, and blood oxygen level dependent images.
Claim 4: wherein the laboratory data are results in any one or a combination of biochemical, hormonal, immunologic, hematologic analyses, and any other type of biological data obtained from laboratory tests of human samples.
Claim 5: wherein the functional data are results in any one or a combination of cognitive, psychophysiological, neurophysiological tests and/or any other type of functional examination and assessment.
Claim 6: wherein disease-specific ... regression models predict diagnostic data of one type from the diagnostic data of at least one other type, and each model of the plurality of disease-specific .. regression models reflects a disease specific association among different diagnostic modalities: physiological findings in functional data, morphological features in brain imaging data and/or laboratory analysis findings in laboratory data.
Claim 7: wherein the plurality of diseases is any one or a combination of mild cognitive impairment, mild cognitive impairment, Alzheimer's disease, any type of non-Alzheimer's dementias, a neurodegenerative disease, and other diseases known to impair brain function.
Claims 8: wherein the classification .. differentiates the correct diagnosis either from healthy status or from a plurality of other diseases and provides indications for the correct treatment of the patient, wherein the classification .. is a ... classification algorithm which uses the errors of predicting one type of diagnostic data from at least one other type of diagnostic data to calculate probabilities for the diseases from the plurality of diseases.
Claim 9: wherein treatment is selected for the disease with the highest probability from the group of treatment options consisting of any one or a combination of cognitive treatment, active music therapy, neuroeducation, physical activity, physiotherapy, acupuncture, dietary or nutrition therapy, herbal medicines, immunotherapy, pharmacotherapy, and any other type of cognition-focused interventions.
Claim 10: wherein ... stores brain morphology data comprising voxel-based morphometry data, surface-based morphometry data, radiomics findings, brain-imaging data, angiography findings, metabolic imaging data, and blood oxygen level dependent images.
Claim 11: wherein the laboratory data stored ... are selected from the group consisting of biochemical, hormonal, immunologic, hematologic analytic data, and any other type of biological data obtained from laboratory tests of human samples.
Claim 12: wherein functional examinations are selected from the group comprising cognitive, psychophysiological, neurophysiological tests, any other type of functional examination and assessment and combinations thereof.
Claim 13: wherein the disease-specific .. models .. are .. regression models trained to predict one type of diagnostic data from at least one other type of diagnostic data of either cognitively normal individuals or patients with confirmed cases of a list of diagnoses.
Claim 14: wherein the list of diagnoses comprises mild cognitive impairment, Alzheimer's disease, any type of non-Alzheimer's dementias, a neurodegenerative disease, and other diseases known to impair brain function.
Claim 15: wherein the classification ..searches for at least one disease-specific ... regression model which optimally fits the individual data of an examinee with unknown diagnosis, calculates probabilities of the diagnoses of claim 14, outputs at least one diagnosis with the highest probability and selects at least one way of treating it from a list of treatment options.
Claim 16: wherein the list of treatment options in the ... system comprises any of the following: cognitive treatment, active music therapy, neuroeducation, physical activity, physiotherapy, acupuncture, dietary or nutrition therapy, herbal medicines, immunotherapy, pharmacotherapy, and any other type of cognition-focused interventions, and combinations thereof. The system stores information on the efficiency of treating diseases with various therapeutic options.
Claim 17: further comprising the step of implementing the at least one efficient treatment plan on the examinee with unknown diagnosis from the optimal therapeutic plan.
Step 2A Prong Two: (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception. The claims include:
Claim 2:
machine learning system comprising a memory and a processing unit for training a plurality of disease-specific cross-modal regression models;
machine learning system comprising a memory and a processing unit for producing a classification module;
disease-specific cross-modal regression models;
second computing device comprising a memory for storing;
a processing unit for running an ensemble model;
classification module;
second computing device is used to allocate the machine learning systems.
Claim 6: disease-specific cross-modal regression models.
Claim 8:
classification module differentiates the correct diagnosis;
classification module is a machine learning classification algorithm.
Claim 10: a computing device.
Claim 11: a computing device.
Claim 13: disease-specific cross-modal models in the computing device are machine learning regression models.
Claim 15: classification module searches for at least one disease-specific cross-modal regression model.
Claim 16: computer system.
Step B: (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, even when viewed as a whole the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 2:
machine learning system comprising a memory and a processing unit for training a plurality of disease-specific cross-modal regression models;
machine learning system comprising a memory and a processing unit for producing a classification module;
disease-specific cross-modal regression models;
second computing device comprising a memory for storing;
a processing unit for running an ensemble model;
classification module;
second computing device is used to allocate the machine learning systems.
Claim 6: disease-specific cross-modal regression models.
Claim 8:
classification module differentiates the correct diagnosis;
classification module is a machine learning classification algorithm.
Claim 10: a computing device.
Claim 11: a computing device.
Claim 13: disease-specific cross-modal models in the computing device are machine learning regression models.
Claim 15: classification module searches for at least one disease-specific cross-modal regression model.
Claim 16: computer system.
Subject Matter Free of Prior Art
Closest prior art:
The reference Collins, Tsokos , and Ito disclose as shown below:
Collins et al (US 2008/0101665 A1)- SYSTEMS AND METHODS OF CLINICAL STATE PREDICTION UTILIZING MEDICAL IMAGE DATA – teaches: a method for predicting a clinical state of a subject based on image data obtained from a Volume Of Interest in the subject. The method comprise the establishment of a predictive model that relates image features and the future evolution of a clinical state.
Tsokos (US 2023/0225668)- SYSTEMS, METHODS, AND MEDIA FOR PREDICTING A CONVERSION TIME OF MILD COGNITIVE IMPAIRMENT TO ALZHEIMER'S DISEASE IN PATIENTS– teaches: systems, methods, and media for predicting the conversion time of Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) in a patient are provided. In some embodiments, a system include a memory and a processor coupled to the memory. The processor is configured to: receive a plurality of risk factor indications and a plurality of interaction indications of a patient. Each interaction indication is an indication of interaction between two risk factor indications of the plurality of risk factor indications. The processor is further configured to obtain a trained machine learning model; apply the plurality of risk factor indications and the plurality of interaction indications to the trained machine learning model; and output a result based on the trained machine learning model
Ito (US 20250191768 A1) - COMBINATION OF BIOMARKERS, AND METHOD FOR DETECTING COGNITIVE DYSFUNCTION OR RISK THEREOF BY USING SAID COMBINATION - teaches: provides a cognitive impairment determination system comprising an information processing device that executes a determination step of determining presence or absence cognitive impairment or risk of cognitive impairment, based on amounts of the following biomarkers (a), (b), (c), (d), and (e) contained in a biological sample: (a) a biomarker consisting of an intact protein of Apolipoprotein A1 comprising an amino acid sequence represented by SEQ ID NO: 1, or a partial peptide thereof, (b) a biomarker consisting of an intact protein of Transthyretin comprising an amino acid sequence represented by SEQ ID NO: 2, or a partial peptide thereof, (c) a biomarker consisting of an intact protein of Complement C3 having an amino acid sequence represented by SEQ ID NO: 3, or a partial peptide thereof, (d) a biomarker Aβ1-40 consisting of a peptide having an amino acid sequence represented by SEQ ID NO: 4, and (e) a biomarker Aβ1-42 consisting of a peptide having an amino acid sequence represented by SEQ ID NO: 5.
The closest prior art of record noted below fails to expressly teach or suggest, either alone or in combination, the features found within the independent claim (Claim 1 recited here as representative). In particular, the closest prior art of record fails to expressly teach or suggest the combination of:
G) Entering the output values as predictors and the diagnoses of the group of cognitively normal individuals and patients as targeted variables into the machine learning system which produces a classification module identifying at least one disease-specific cross-modal regression model best-fitting to an individual case;
H) Assembling an ensemble model H from the disease-specific cross-modal regression models and the classification module;
I) Deploying the ensemble model H at the first computing device to calculate probabilities of the diseases, output at least one diagnosis with the highest probability and the optimal therapeutic plan implementing at least one efficient treatment option
Therefore, the claims of the instant application are novel and unobvious.
Foreign prior art and NPL search was conducted however no relevant prior art was found.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687