DETAILED ACTION
Claims 1, 3-9, & 11-17 are currently pending and have been examined.
This action is in response to the amendment filed on 10/13/2025.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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, 3-9, & 11-17 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 1, 3-8 are directed to a system (i.e., a machine); Claims 9 & 11-17 are directed to a method (i.e., a process). Accordingly, claims 1, 3-9, & 11-17 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, 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).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
1. A prospective classification device for predicting dementia comprising:
at least one processor configured to predict a risk of a patient with mild cognitive impairment being converted to a dementia patient by executing a prospective classification program recorded in memory,
wherein the at least one processor is configured to:
convert features of diagnostic brain imaging data of the patient with mild cognitive impairment obtained at the time of diagnosis into features of prognostic brain imaging data corresponding to a prognostic time after the time of diagnosis by a prospective classification model; and
convert a diagnostic brain image data matrix obtained at the time of diagnosis of the patient with mild cognitive impairment to generate a projection data matrix by a projection matrix of the prospective classification model;
smooth the projection data matrix to adapt to a manifold of a prognostic brain image data matrix to generate a prospective data matrix by a brain graph matrix of the prospective classification model;
predict the risk of the patient with mild cognitive impairment being converted to the dementia patient by calculating a dementia conversion risk score indicating a probability of the mild cognitive impairment being converted to dementia, the dementia conversion risk score being calculated by applying a coefficient vector of the prospective classification model to the prospective data matrix: and output the calculated dementia conversion risk score,
wherein the prospective classification model is a model trained to transform features of diagnostic brain imaging data acquired for patients suffering from mild cognitive impairment at a first time point to prognostic brain imaging data acquired at a second time point for the patients after the first time point.
The Examiner submits that the foregoing underlined limitations constitute “a mathematical process” because the underlined limitations, given their broadest reasonable interpretation in light of the specification, recite various steps using mathematical methods including converting features from imaging data; predicting risk using a classification model; calculating a risk score using the classification model; and training a model using feature data. The specification supports this conclusion by describing how these steps are performed via applying machine learning algorithms which are mathematical calculations.
Accordingly, independent claim 1 and analogous independent claim 9 recite at least one abstract idea.
Furthermore, dependent claims 3-8 & 11-17 further narrow the abstract idea described in the independent claims. Claims 3 & 11 recites generating the brain graph matrix, Claims 4 & 12 recites converting brain image data into a matrix, calculating the matrix, generating divergence functions, calculating a risk score, and optimizing the matrix based on the divergence and cross-entropy loss, Claims 5-6 & 13-14 recites calculating gradient functions and optimizing the matrix, Claims 7 & 15 recites generating a divergence function, Claims 8 & 16 recites generating objective functions.. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 1 and analogous independent claim 9, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: 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 abstract idea into a practical application. As noted at MPEP §2106.04(II)(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 do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
1. A prospective classification device for predicting dementia comprising:
at least one processor configured to predict a risk of a patient with mild cognitive impairment being converted to a dementia patient by executing a prospective classification program recorded in memory,
wherein the at least one processor is configured to:
convert features of diagnostic brain imaging data of the patient with mild cognitive impairment obtained at the time of diagnosis into features of prognostic brain imaging data corresponding to a prognostic time after the time of diagnosis by a prospective classification model; and
convert a diagnostic brain image data matrix obtained at the time of diagnosis of the patient with mild cognitive impairment to generate a projection data matrix by a projection matrix of the prospective classification model;
smooth the projection data matrix to adapt to a manifold of a prognostic brain image data matrix to generate a prospective data matrix by a brain graph matrix of the prospective classification model;
predict the risk of the patient with mild cognitive impairment being converted to the dementia patient by calculating a dementia conversion risk score indicating a probability of the mild cognitive impairment being converted to dementia, the dementia conversion risk score being calculated by applying a coefficient vector of the prospective classification model to the prospective data matrix: and output the calculated dementia conversion risk score,
wherein the prospective classification model is a model trained to transform features of diagnostic brain imaging data acquired for patients suffering from mild cognitive impairment at a first time point to prognostic brain imaging data acquired at a second time point for the patients after the first time point.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the processor, the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations 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 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 to effect a particular treatment or prophylaxis for a disease or medical condition, implement/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 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(II)(A)(2).
For these reasons, representative independent claim 1 and analogous independent claim 9 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recite at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 17: These claims recite a non-transitory computer readable medium; these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 1 does 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.
As discussed above, regarding the additional limitations of the processor, the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
The dependent claims also 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 the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1, 3-9, & 11-17 are ineligible under 35 USC §101.
Prior Art Rejection
All of the cited references fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims. In particular, the cited prior art of record fails to expressly teach or suggest the combination of: a prospective classification device wherein the at least one processor is configured to:
convert a diagnostic brain image data matrix obtained at the time of diagnosis of the patient with mild cognitive impairment to generate a projection data matrix by a projection matrix of the trained prospective classification model; smooth the projection data matrix to adapt to a manifold of prognostic brain image data matrix to generate a prospective data matrix by a brain graph matrix of the trained prospective classification model; and predict the risk the patient with mild cognitive impairment being converted to a dementia patient by calculating a dementia conversion risk score indicating a probability that mild cognitive impairment being converted to dementia, the dementia conversion risk score being calculated by applying a coefficient vector of the prospective classification model to the prospective data matrix.
The most relevant prior art of record includes:
Lure (US20240065609) teaches a system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis.
Li (US20240065609) teaches to a system generating predicted brain images based on a current brain image and estimating dementia risk based on predicted brain images and the related devices. The method comprises: receiving a first brain image; encoding the first brain image to generate a latent vector; and decoding the latent vector and one or more conditional features to generate the predicted brain image.
Raj (US20160300352) teaches to analyzing a medical image of a subject's brain is provided in which an image of the subject's brain is parcellated by a computing device, to obtain an initial disease state. A diffusion kernel is then applied to the subject's initial disease state by the computing device to obtain an output vector. The diffusion kernel may be obtained from the subject's connectivity matrix. Based on the output vector, future changes to the subject's brain are predicted.
Response to Arguments
Applicant’s arguments on pages 8-9 regarding claims 1, 3-9, & 11-17 being rejected under 35 USC § 101 have been fully considered but they are not persuasive. Applicant claims that:
The claimed invention contains limitations that integrate the claims into a practical application.
The Examiner, however, asserts that the limitations identified by the Applicant has been identified as part of the abstract idea. The limitations as discussed in the 101 rejection, above, given their broadest reasonable interpretation in light of the specification, recite various steps using mathematical methods including converting features from imaging data; predicting risk using a classification model; calculating a risk score using the classification model; and training a model using feature data. The specification supports this conclusion by describing how these steps are performed via applying machine learning algorithms which are mathematical calculations. The additional limitation of the processor fails to integrate the judicial exception into a practical application.
Applicant’s arguments on page 9 regarding claims 1, 3-9, & 11-17 being rejected under 35 USC § 103 have been fully considered and are persuasive. The 103 rejection has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mork (US20220319675A1) teaches systems, methods, and mechanisms for analyzing patients by using latent space representations of genetic data to generate patient images, and grouping patients based on the patient images
Raj (US20160300352) teaches to analyzing a medical image of a subject's brain is provided in which an image of the subject's brain is parcellated by a computing device, to obtain an initial disease state. A diffusion kernel is then applied to the subject's initial disease state by the computing device to obtain an output vector. The diffusion kernel may be obtained from the subject's connectivity matrix. Based on the output vector, future changes to the subject's brain are predicted.
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 Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-7949. 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.
/Jonathan Ng/ Primary Examiner, Art Unit 3619