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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/02/2025 has been entered.
Response to Arguments
Applicant's arguments, see Remarks pages 7-14, filed 12/02/2025, with respect to the 35 USC 101 rejection of claims 1 and 3-6 have been fully considered but they are not persuasive.
In response to the applicant’s argument that the 2019 Revised Patent Subject Matter Eligibility Guidance Example 39, which shows a system in which the “training” step is considered eligible subject matter at step 2A prong 1, is similar to the applicant’s claimed invention, the examiner respectfully disagrees. The examiner notes that the July 2024 Subject Matter Eligibility Examples, particularly example 47 claim 2, is more similar to the applicant’s claimed invention. For example, example 47 claim 2 and the applicant’s claims are drawn to a system that collects data, analyzes/processes data, uses the data to train models, and uses the models to output data. The examiner notes that in this example, the step of “training” was found to be a mental process capable of being performed in the human mind using observation, evaluation, judgment, and opinion.
In response to the applicant’s argument that the step of “training a neural network” does not fall within the category of mere data collection, the examiner notes that no such contention was made in the previous office action.
In response to the applicant’s argument that the step of “training a neural network” is not a mathematical concept, the examiner respectfully disagrees. The examiner notes that “training” a mathematical model inherently is inputting data into the model/algorithm to improve the accuracy and efficiency of said model/algorithm, which are by definition mathematical concepts. The examiner also notes that “training” a neural network can also be reasonably interpreted as a mental process, which is reflected in the 35 USC 101 rejection below.
In response to the applicant’s argument that the abstract idea is drawn to a practical application, the examiner respectfully disagrees. The examiner notes that in order to be drawn into a practical application, additional elements are required. As noted in the previous office action, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the units associated with the steps do not ad meaningful limitation to the abstract idea. The examiner noted in the previous office action that page 13 lines 9-11 of the applicant’s specification discloses that the claimed hierarchical diagnosis device with its units may be simply a software program. Computer programs per se are not directed to any of the statutory categories (see MPEP 2106.03). The examiner notes that new claims 7-14 also fail to provide additional elements.
Therefore, for the reasons provided above, the 35 USC 101 rejection of claims 1 and 2-6 is maintained.
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 and 3-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
A hierarchical diagnosis device of brain atrophy based on brain thickness information, comprising:
a brain structure modeling unit to generate a grey matter surface mesh and a white matter surface mesh by mesh modeling of multiple Magnetic Resonance Imaging (MRI) images;
a brain thickness extraction unit to acquire brain thickness information by collecting and analyzing a distance between corresponding points of the grey matter surface mesh and the white matter surface mesh;
a training data generation unit to generate and store multiple training data including the brain thickness information and diagnosis information when diagnosis model training is requested;
a diagnosis model training unit to classify and analyze the brain thickness information into groups of a hierarchical structure according to the diagnosis information to acquire feature information for each group, and generate and train hierarchical classifiers based on the hierarchical structure of the groups and the feature information for each group; and
a brain atrophy diagnosis unit to acquire new brain thickness information upon receiving a new input of an MRI image of a subject, hierarchically search for a group having highest similarity with the new thickness information through the hierarchical classifiers, and identify and notify a type of brain atrophy based on the results of the hierarchical search,
wherein the diagnosis model training unit is configured to:
generate and train the hierarchical classifiers by generating and training a first classifier for classifying the brain atrophy as normal cognition or dementia;
generate and train the hierarchical classifiers by generating and training a second classifier for classifying a result of the first classifier being dementia as Alzheimer's Disease (AD) or Frontotemporal Dementia (FTD);
generate and train the hierarchical classifiers by generating and training a third classifier for classifying a result of the second classifier being FTD as Behavior Variables FTD (bvFTD) or Primary Progressive Aphasia (PPA) in case of the Frontotemporal Dementia (FTD); and
generate and train the hierarchical classifiers by generating and training a fourth classifier for classifying a result of the third classifier being PPA as Nonfluent/agrammaticVariant PPA (nfvPPA) or Semantic Variant PPA (svPPA) in case of the Primary Progressive Aphasia (PPA).
Independent claim 3 recites:
A hierarchical diagnosis device of brain atrophy based on brain thickness information, comprising:
a brain structure modeling unit to generate a grey matter surface mesh and a white matter surface mesh by mesh modeling of multiple Magnetic Resonance Imaging (MRI) images;
a brain thickness extraction unit to acquire brain thickness information by collecting and analyzing a distance between corresponding points of the grey matter surface mesh and the white matter surface mesh;
a training data generation unit to generate and store multiple training data including the brain thickness information and diagnosis information when diagnosis model training is requested;
a diagnosis model training unit to classify and analyze the brain thickness information into groups of a hierarchical structure according to the diagnosis information to acquire feature information for each group, and generate and train hierarchical classifiers based on the hierarchical structure of the groups and the feature information for each group; and
a brain atrophy diagnosis unit to acquire new brain thickness information upon receiving a new input of an MRI image of a subject, hierarchically search for a group having highest similarity with the new thickness information through the hierarchical classifiers, and identify and notify a type of brain atrophy based on the results of the hierarchical search,
wherein the diagnosis model training unit is configured to:
generate and train the hierarchical classifiers by generating and training a first classifier for classifying the brain atrophy as normal cognition or dementia;
generate and train the hierarchical classifiers by generating and training a second classifier for classifying a result of the first classifier being dementia as Alzheimer's Disease (AD) or Frontotemporal Dementia (FTD);
generate and train the hierarchical classifiers by generating and training a third classifier for classifying a result of the second classifier being FTD as Behavior Variables FTD (bvFTD) or Nonfluent/agrammatic Variant PPA (nfvPPA);
generate and train the hierarchical classifiers by generating and training a fourth classifier for classifying a result of the third classifier being FTD as Behavior Variables FTD (bvFTD) or Semantic Variant PPA (svPPA) in case of the Frontotemporal Dementia (FTD); and
generate and train the hierarchical classifiers by generating and training a fifth classifier for classifying a result of the fourth classifier being FTD as Nonfluent/agrammatic Variant PPA (nfvPPA) or Semantic Variant PPA (svPPA).
Independent claim 7 recites:
A processor-implemented method, the method comprising:
generating training data based on brain thickness information from a plurality of MRI images of plural brains and respective diagnostic information for the each respective brain of the plural brains;
training a neural network on the training data to generate a trained model, wherein the training of the neural network comprises:
training a first classifier on first data being derived from the training data, the first data indicating a Normal Cognition (NC) or Dementia diagnosis;
training a second classifier on second data being derived from the training data, the second data indicating an Alzheimer's Disease (AD) or Frontotemporal Dementia (FTD) diagnosis;
training a third classifier on third data being derived from the training data, the third data indicating a Behavioral Variant Frontotemporal Dementia (bvFTD) or Primary Progressive Aphasia (PPA) diagnosis;
training a fourth classifier on fourth data being derived from the training data, the fourth data indicating a Nonfluent/Agrammatic Variant Primary Progressive Aphasia (nfvPPA) or Semantic Variant Primary Progressive Aphasia (svPPA) diagnosis; and
generating hierarchical groups based on the first data, the second data, the third data, and the fourth data;
extracting patient brain thickness information, based on a patient MRI brain scan;
assessing the patient MRI brain scan in the patient brain thickness information using the trained neural network by performing a similarity analysis between the patient brain thickness information and the hierarchical groups to identify a group with a highest similarity;
determining based on the assessing a diagnosis of one of Normal Cognition (NC) vs. Dementia, and progressing through Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), Behavioral Variant FTD (bvFTD), Primary Progressive Aphasia (PPA), Nonfluent/Agrammatic Variant PPA (nfvPPA), and Semantic Variant PPA (svPPA); and
generating an output report of the diagnosis based on similarity performed by the first classifier, the second classifier, the third classifier, and the fourth classifier.
Step 1:
Under one claim interpretation of independent claims 1 and 3, the device is embodied on a processor while claim 7 is drawn to a method. Even with this interpretation, the above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
Step 2A Prong 1:
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018).
The claimed steps of generating, collecting, analyzing, training, acquiring, classifying, extracting, assessing, and determining recite mental processes and/or mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The steps of “collecting…a distance…” in claims 1 and 3, “generating a surface mesh”, “generate and store multiple training data”, “generate and train hierarchical classifiers” in independent Claims 1, 3, and 7, and “acquire brain thickness information”, “acquire feature information”, and “acquire new brain thickness information” in independent Claims 1 and 3 are mental processes capable of being performed in the human mind and are mere data gathering steps that utilizes a computational device. For example, the human mind can collect, generate, and acquire a recipe from a cookbook to “acquire” knowledge and/or understanding on how to create a meal. The steps of “analyzing a distance” and “analyze the brain thickness information” in independent Claims 1 and 3, “assessing” the patient MRI brain scan in claim 7, and “determining” based on the assessing a diagnosis in claim 7 are mental processes capable of being performed in the human mind. For example, the human mind is capable of analyzing/assessing an input, such as visually observing a speed limit sign, to determine how fast to drive their vehicle. The step of “train hierarchical classifies” in claims 1, 3, and 7 can be reasonably interpreted as either a mental process and/or a mathematical concept. For example, the step of “training” a classifier/model/algorithm is a mathematical process to input data into a classifier/model/algorithm so that the classifier/model/algorithm can learn patterns and make predictions, thus improving the accuracy and efficiency of said classifier/model/algorithm. The step of “classify and analyze the brain thickness information” in claims 1 and 3 is a mental process in which an object is placed into a group based on a particular parameter. The step of “extracting” patient brain thickness information in claim 7 is an example of a mental process capable of being performed in the human mind. For example, the human mind is capable of “extracting” information from memory to perform actions, such as extracting memory of the proper form of how to throw a baseball.
The claimed steps of generating, collecting, analyzing, training, acquiring, classifying, extracting, assessing, and determining can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
“[T]he ‘mental processes’ abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” MPEP 2106.04(a)(2) III. The pending claims merely recite steps for estimation that include observations, evaluations, and judgments.
Examples of ineligible claims that recite mental processes include:
• a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind,
Electric Power Group, LLC v. Alstom, S.A.;
• claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind,
University of Utah Research Foundation v. Ambry Genetics Corp.
• a claim to collecting and comparing known information, which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
Regarding the dependent claims 4-6 and 8-14, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Step 2A Prong 2:
This judicial exception (abstract idea) in Claims 1 and 3-14 is not integrated into a practical application because:
• The abstract idea amounts to simply implementing the abstract idea on a computing device. For example, the recitations regarding the generic computing components for generating, collecting, analyzing, training, acquiring, and classifying merely invoke a computer as a tool.
• The data-gathering step (generating, collecting, and acquiring) and the data-output step do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
• There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computing device that is used as a tool for generating, collecting, analyzing, training, acquiring, and classifying.
• The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to estimate bio-information.
• The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computing device for generating, collecting, analyzing, training, acquiring, and classifying. The claims do not apply the obtained prediction to a particular machine. Rather, the data is merely output in a post-solution step.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the units associated with the steps do not add meaningful limitation to the abstract idea. A computer, processor, memory, or equivalent hardware is merely used as a tool for executing the abstract idea(s). The process claimed does not reflect an improvement in the functioning of the computer.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole 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.
Claims 1 and 3-14 are alternatively rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claims do not fall within at least one of the four categories of patent eligible subject matter. In particular, the specification discloses that the claimed hierarchical diagnosis device with its units may be simply a software program (page 13, lines 9-11 of the specification). Computer programs pe se are not directed to any of the statutory categories (see MPEP 2106.03: “Non-limiting examples of claims that are not directed to any of the statutory categories include…Products that do not have a physical or tangible form, such as information (often referred to as ‘data per se’) or a computer program per se (often referred to as ‘software per se’) when claimed as a product without any structural recitations”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN M HANEY whose telephone number is (571)272-0985. The examiner can normally be reached Monday through Friday, 0730-1630 ET.
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/JONATHAN M HANEY/Examiner, Art Unit 3791
/ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791