DETAILED ACTION
Claims 1 & 3-8 are currently pending and have been examined.
This action is in response to the amendment filed on 10/12/2025.
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 & 3-8 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 method (i.e., a process). Accordingly, claims 1 & 3-8 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 learning method for Alzheimer prediction model based on domain adaptation performed by at least one processor, the method comprising:
acquiring, by the at least one processor, a target dataset of a target domain associated with a first brain,
acquiring, by the at least one processor, a source dataset of a source domain associated with a second brain, wherein the source dataset includes a label indicating diagnostic information about the second brain,
operating, by the at least one processor, domain adaptation on the target dataset based on the source dataset to obtain a transformed target dataset and transformed target data; and
training, by the at least one processor, a machine learning model using the transformed target dataset and the source dataset as learning data,
wherein the machine learning model is trained to output data containing information related to a possibility of conversion from mild cognitive impairment to Alzheimer’s disease as brain-related data is input
wherein an objective function of the machine learning model is constructed based on a difference between the transformed target dataset and the source dataset,
wherein the objective function is based on an adaptation matrix, the adaptation matrix is configured to reduce a difference between a first average vector obtained from the target dataset and a second average vector obtained from the source dataset,
the first average vector corresponds to a center of a data distribution according to a label of the target dataset, and
the second average vector corresponds to a center of a data distribution according to the label of the source dataset.
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 operating domain adaptation on a dataset; training a machine learning model using the dataset; and constructing a trained machine learning model using the dataset. 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 recite at least one abstract idea.
Furthermore, dependent claims 3-8 further narrow the abstract idea described in the independent claims. Claims 3-8 recites generating an objective function using an adaptation matrix; determining averaged vectors; performing an Alzheimer prediction model using the domain adaptation; and correlation matrices. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 1, 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 learning method for Alzheimer prediction model based on domain adaptation performed by at least one processor, the method comprising:
acquiring, by the at least one processor, a target dataset of a target domain associated with a first brain,
acquiring, by the at least one processor, a source dataset of a source domain associated with a second brain, wherein the source dataset includes a label indicating diagnostic information about the second brain,
operating, by the at least one processor, domain adaptation on the target dataset based on the source dataset to obtain a transformed target dataset and transformed target data; and
training, by the at least one processor, a machine learning model using the transformed target dataset and the source dataset as learning data,
wherein the machine learning model is trained to output data containing information related to a possibility of conversion from mild cognitive impairment to Alzheimer’s disease as brain-related data is input
wherein an objective function of the machine learning model is constructed based on a difference between the transformed target dataset and the source dataset,
wherein the objective function is based on an adaptation matrix, the adaptation matrix is configured to reduce a difference between a first average vector obtained from the target dataset and a second average vector obtained from the source dataset,
the first average vector corresponds to a center of a data distribution according to a label of the target dataset, and
the second average vector corresponds to a center of a data distribution according to the label of the source dataset.
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 a 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)).
Regarding the additional limitation of acquiring various datasets, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) and is conventional as it merely consists of transmitting data over a network (see MPEP § 2106.05(d)(II)).
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 does not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recite 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 a 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)).
Regarding the additional limitation of acquiring various datasets, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) and is conventional as it merely consists of transmitting data over a network (see MPEP § 2106.05(d)(II)).
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 & 3-8 are ineligible under 35 USC §101.
Prior Art Rejection
The closest prior art of record, including Venkataramani (US20200118043), Wachinger (“Domain adaptation for Alzheimer's disease diagnostics”), & Lisi (US20110301431), fail to expressly teach the independent claims including wherein the objective function is based on an adaptation matrix, the adaptation matrix is configured to reduce the difference between a first average vector obtained from the target dataset and a second average vector obtained from the source dataset, the first average vector corresponds to the center of the data distribution according to the label of the target dataset, and the domain adaptation-based Alzheimer's prediction model learning method performed by at least one processor, wherein the second average vector corresponds to the center of the data distribution according to the label of the source dataset; wherein the objective function is based on an adaptation matrix, the adaptation matrix reduces the difference between a first correlation matrix obtained from the target dataset and a second correlation matrix obtained from the source dataset.
Response to Arguments
Applicant’s arguments on pages 5-6 regarding claims 1 & 3-8 being rejected under 35 USC § 101 have been fully considered but they are not persuasive. Applicant claims that:
The machine learning model trained to output a possibility of conversion of a mild cognitive impairment to Alzheimer’s is an additional element that integrates the claims into a practical application.
The Examiner, however, asserts that the machine learning model, given their broadest reasonable interpretation in light of the specification, recite various steps using mathematical methods including operating domain adaptation on a dataset; training a machine learning model using the dataset; and constructing a trained machine learning model using the dataset. The specification supports this conclusion by describing how these steps are performed via applying machine learning algorithms which are mathematical calculations. The additional limitations of the processor and acquiring various datasets do not integrate the above-noted at least one abstract idea into a practical application.
Applicant’s arguments on page 6 regarding claims 1 & 3-8 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.
Liu (US20210407086) teaches to source domain image and the target domain image are aligned in the output space, so that the trained image segmentation model can reduce, in the output space, a difference between the source domain image and the target domain image, and reduce an error in segmentation of a target domain by the trained image segmentation model, to further enable a segmentation result of the target domain image to be more accurate.
Doretto (US 20230281970) teaches to system and methods for object segmentation include providing a pre-trained neural network model to segment object instances based on a first set of images and a first loss function.
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