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 .
Response to Amendment
The amendment filed 04/20/2026, has been entered.
Response to Arguments
Applicant's arguments, see Remarks pages 8-11, dated 04/20/2026, with respect to the 35 USC 101 rejection of claims 1, 3, and 7 have been fully considered but they are not persuasive.
In response to the applicant’s argument that the amended claims do not recite a mental process, the examiner respectfully disagrees. The examiner notes that, as amended, the claims recite both mental processes and mathematical concepts that are found within the abstract idea category. The examiner notes that the step of “generating” mesh models can be reasonably interpreted as either insignificant extra-solution activity as a data output or as a mental process. As a mental process, the human mind is capable of mentally putting pieces of a puzzle together, which is similar to mesh modeling in that you start with separate, identifiable units, require precise spatial and/or functional alignment to fit together, and the final result being a unified, coherent structure. Data extraction is also a mental process whereas the steps of denoising and resampling are mathematical processes used to further process the data (see 35 USC 101 rejection below).
In response to the applicant’s argument that the applicant’s claims are similar to those of Example 39 of the Subject Matter Eligibility Examples, the examiner respectfully disagrees. As mentioned above, the examiner finds that the steps are drawn to abstract ideas (both mental processes and mathematical concepts), which differentiate from the analysis of Example 39 in which there was no judicial exception present.
In response to the applicant’s argument that the claims are drawn into a practical application, the examiner respectfully disagrees. The applicant alleges the present application improves upon a technical field by providing a technical solution. However, it is important to note the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). The examiner maintains his stance that it does not appear that the units associated with the steps add meaningful limitation to the abstract idea, and, as claimed, could simply be performed as software on a computer.
In response to the applicant’s argument that the claims are not directed to insignificant data gathering or post-solution activity, the examiner respectfully disagrees. The examiner contends that the steps of “collecting”, “acquiring”, and, in one interpretation, “generating” are examples of necessary data gathering/output. See MPEP 2106.05(g).
In response to the applicant’s argument that the ordered combination amounts to significantly more than the abstract idea, the examiner respectfully disagrees. The steps mentioned in the arguments are carried out by various units, which as discussed above, are not defined in a way that disclose any particularity or distinguishable features from that of a computer program that can be run by a general computer.
Therefore, the 35 USC 101 rejection of claims 1, 3, and 7 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, 3, and 7 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, and to match locations of each vertex on a brain surface by re-sampling a grey matter surface and a white matter surface according to a preset reference template;
a brain thickness extraction unit to acquire brain thickness information by collecting and analyzing a distance between corresponding points of the re-sampled qrey matter surface mesh and the re-sampled 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 include a denoisinq unit confiqured to convert the brain thickness information into frequency domain and remove hiqh frequency components included in the brain thickness information, a dimensionality reduction unit configured to reduce a data dimension of the brain thickness information, and a hierarchical classifier training unit configured to classify the brain thickness information into groups of a hierarchical structure according to the diagnosis information, 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, the new brain thickness information through the hierarchical classifiers in a diagnosis model generated from the training data, and identify and notify a type of brain atrophy based on 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/agrammatic Variant 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) imagesd and to match locations of each vertex on a brain surface by re-samplinq a grey matter surface and a white matter surface according to a preset reference template;
a brain thickness extraction unit to acquire brain thickness information by collecting and analyzing a distance between corresponding points of the re-sampled grey matter surface mesh and the re-sampled 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 include a denoising unit configured to convert the brain thickness information into frequency domain and remove hiqh frequency components included in the brain thickness information, a dimensionality reduction unit confiqured to reduce a data dimension of the brain thickness information, and a hierarchical classifier traininq unit confiqured to classify the brain thickness information into qroups of a hierarchical structure accordinq to the diaqnosis information, acquire feature information for each qroup, and qenerate and train hierarchical classifiers based on the hierarchical structure of the qroups and the feature information for each qroup; and
a brain atrophy diagnosis unit to acquire new brain thickness information upon receiving a new input of an MRI image of a subject, the new brain thickness information through the hierarchical classifiers in a diagnosis model generated from the training data, and identify and notify a type of brain atrophy based on 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:
acquiring MRI images and diagnosis information of a plurality of patients with brain atrophy of Normal Cognition (NC), Alzheimer's Disease (AD), Behavior Variables Frontotemporal Dementia (bvFTD), Nonfluent/aqrammatic Variant Primary Progressive Aphasia (nfvPPA), and Semantic Variant Primary Progressive Aphasia (svPPA);
generating a grey matter surface mesh and a white matter surface mesh by mesh modeling of each MRI image, and achieving correspondence between the patients by re- samplinq according to a preset reference template;
extracting pairs of corresponding points of the re-sampled grey matter surface mesh and white matter surface mesh, and extracting brain thickness information of each patient by collecting and analyzinq distances between the corresponding points;
converting the brain thickness information into frequency domain and removing high frequency components included in the brain thickness information;
adding patient diagnosis information to the brain thickness information to generate multiple training data;
reducing a data dimension of the brain thickness information;
classifying the dimension-reduced brain thickness information into hierarchical groups according to the diagnosis information, extracting feature information for each group, and generating and training hierarchical classifiers for hierarchically diagnosing brain atrophy based on the hierarchical structure of the groups and the feature information for each group;
generating and storing a diagnosis model including the hierarchical classifiers;
when an MRI image of a subiect is newly acquired through MRI equipment, acquiring brain thickness information of the subiect by the same process of generating the grey matter surface mesh and the white matter surface mesh, re-sampling according to the preset reference template, extracting pairs of corresponding points, collecting and analyzinq the distances between the corresponding points, and converting the brain thickness information into frequency domain and removing high frequency components included in the brain thickness information;
hierarchically analyzing similarity between the brain thickness information of the subject and the hierarchical groups through the hierarchical classifiers in the diagnosis model; and
identifying and notifying a type of brain atrophy of the subiect based on the hierarchical analysis
Step 1:
The examiner finds claims 1 and 3 are drawn to machines and claim 7 is drawn to a method.
Step 2A Prong 1:
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.
“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, resampling, matching, analyzing, converting, reducing, classifying, training, identifying, and extracting recite mental processes capable of being performed in the human mind and/or mathematical concepts.
The examiner notes the steps of “collecting”, “storing”, “acquiring”, and “notifying” are being interpreted as insignificant extra-solution activities.
The steps of “generating” mesh models can be reasonably interpreted as either insignificant extra-solution activity as a data output or as a mental process. As a mental process, the human mind is capable of mentally putting pieces of a puzzle together, which is similar to mesh modeling in that you start with separate, identifiable units, require precise spatial and/or functional alignment to fit together, and the final result being a unified, coherent structure. The steps of “resampling” is an example of a mathematical concept, particularly a statistical technique that generates new samples from an existing dataset to estimate variability, validate models, or create synthetic data. The steps of “matching” locations of each vertex is a mental process capable of being performed in the human mind. For example, the human mind must match vertices of puzzle pieces to ensure the piece is in the correct position. The step of “analyzing” a distance is an example of a mental process capable of being performed in the human mind. For example, the human mind is capable of analyzing the length of a football field is 100 yards. The step of “converting” can be reasonably interpreted as either a mental process and/or a mathematical concept. As a mental process, the human mind is capable of converting raw data into images, ideas, etc. As a mathematical concept, data may be converted from one domain to another while preserving or transforming its meaning according to a defined rule. The step of “reducing” dimensionality can be reasonably interpreted as either a mental process and/or a mathematical concept. As a mental process, the human mind is capable of mentally reducing the image of a sphere (3D space) into a circle (2D space). As a mathematical concept, dimensionality reduction involves selecting a transformation (linear or nonlinear) that maps high-dimensional data into a lower-dimensional space while preserving meaningful structure. The step of “classifying” brain thickness information is an example of a mental process capable of being performed in the human mind. For example, the human mind. For example, the human mind is capable of classifying information into categories, such classifying an animal as a threat or non-threatening. The step of “training” a classifier is a mathematical concept of feeding a model/algorithm data to optimize/adjust the model/algorithms parameters to minimize the error between the model’s predicted outputs and the actual outputs. The step of “identifying” a type of brain atrophy is an example of a mental process capable of being performed in the human mind. For example, the human mind may conclude a person has dementia if they have a poor memory and are elderly. The step of “extracting” pairs of corresponding points is an example of a mental process capable of being performed in the human mind. For example, the human mind is capable of pattern mapping, where the human mind can mentally superimpose one data set onto another along aligned variables, such as mapping how many units of a product were sold in a month and mapping/drawing correlations to how much was spent on advertising within the same month.
The claimed steps of generating, resampling, matching, analyzing, converting, reducing, classifying, training, identifying, and extracting 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.
Step 2A Prong 2:
This judicial exception (abstract idea) in Claims 1, 3, and 7 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, resampling, matching, analyzing, converting, reducing, classifying, training, identifying, and extracting merely invoke a computer as a tool.
• The data-gathering step (collecting and acquiring) and the data-output step (generating data mesh) 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, resampling, matching, analyzing, converting, reducing, classifying, training, identifying, and extracting.
• 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, resampling, matching, analyzing, converting, reducing, classifying, training, identifying, and extracting. 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 additional elements are identified as follows: MRI equipment.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as MRI equipment would inherently obtain MRI images. The steps as claimed are carried out by various units that are not defined in a way that disclose any particularity or distinguishable features from that of a computer program that can be run by a general computer.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional generic computer components as those claimed. See option III. A. 2. in the Berkheimer memorandum.
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, 3, and 7 are alternatively rejected under 35 USC 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 per 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 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
/JUSTIN XU/Primary Examiner, Art Unit 3791
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