DETAILED ACTION
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lee et al. (WO 2024080459).
Regarding claim 1, Lee (figure 1) discloses an electronic device 100A for predicting myopia regression, the electronic device comprising a memory, and a processor connected with the memory and configured to execute instructions included in the memory, wherein the processor collects first target data of a subject and second target data of the subject, extracts a first result value as output data for a first machine learning model 110 by using the first target data as input data for the first machine learning model, and determines whether there is a possibility of myopia regression of the subject based on the first result value (under TECH-SOLUTION, paragraph 3: In some embodiments, the machine learning processor is configured to extract a first feature from the numerical data, the deep learning processor is configured to extract a second feature from the image data, and the myopia regression prediction device is configured to extract the first feature from the image data. It may include a fusion processor configured to predict at least one of a probability of occurrence of myopic degeneration and whether or not myopic degeneration will occur from the feature and the second feature; under MODE-FOR-INVENTION, paragraph 16: The myopia regression prediction medical device 100A may include a machine learning processor 110, a deep learning processor 120, and a fusion processor 130. The machine learning processor 110 may extract the first feature (F10) from the numerical data (D10), and the deep learning processor 120 may extract the second feature (F20) from the image data (D20). Here, a feature means a number or an array of numbers).
Regarding claim 2, Lee further discloses wherein the processor extracts second result value as output data for a second machine learning model by using the first result value and the second target data as input data for the second machine learning model and determines whether there is the possibility of myopia regression of the subject based on the second result value (under TECH-SOLUTION, paragraph 3: In some embodiments, the machine learning processor is configured to extract a first feature from the numerical data, the deep learning processor is configured to extract a second feature from the image data, and the myopia regression prediction device is configured to extract the first feature from the image data. It may include a fusion processor configured to predict at least one of a probability of occurrence of myopic degeneration and whether or not myopic degeneration will occur from the feature and the second feature).
Regarding claim 3, Lee further discloses wherein the first target data is information about fundus photography of the subject, and wherein the second target data includes information about a central corneal thickness (under TECH-SOLUTION, paragraph 6: The method for predicting myopia regression according to embodiments of the present disclosure includes refractive power, corneal curvature, axial length, pupil size in photopic vision, pupil size in scotopic vision, corneal diameter, corneal thickness, corneal epithelial thickness, higher order aberration, visual acuity, intraocular pressure, gender, And it may include predicting at least one of the probability of occurrence of myopia degeneration and whether myopia degeneration will occur using a machine learning processor from numerical data including age).
Regarding claim 4, Lee further discloses wherein the processor collects first training data, processes a first training dataset based on the first training data, constructs the first machine learning model based on the first training dataset, and determines performance of the first machine learning model at a predetermined period, and wherein the first training data includes information about fundus photography for a plurality of subjects (under TECH-SOLUTION, paragraph 2: In some embodiments, the myopic regression prediction device includes fundus imaging images, corneal endothelial cell images, corneal morphology, and aberration analyzer (e.g., Keratron Scout) images to predict at least one of the probability of myopic regression occurring and whether myopic regression occurs. Deep learning processors and imaging data including optical coherence tomography images, OPD-SCAN III images, and computed corneal tomography (e.g., Pentacam AXL) images are further available).
Regarding claim 5, Lee further discloses wherein the processor collects second training data, processes a second training dataset based on the second training data, constructs a second machine learning model based on the second training dataset, and determines performance of the second machine learning model at a predetermined period, and wherein the second target data includes information about a central corneal thickness (under TECH-SOLUTION, paragraph 6: The method for predicting myopia regression according to embodiments of the present disclosure includes refractive power, corneal curvature, axial length, pupil size in photopic vision, pupil size in scotopic vision, corneal diameter, corneal thickness, corneal epithelial thickness, higher order aberration, visual acuity, intraocular pressure, gender, And it may include predicting at least one of the probability of occurrence of myopia degeneration and whether myopia degeneration will occur using a machine learning processor from numerical data including age).
Regarding claim 6, Lee (figure 1) discloses an operation method of an electronic device for predicting myopia regression, the operation method comprising collecting first target data of a subject and second target data of the subject, extracting a first result value as output data for a first machine learning model by using the first target data as input data for the first machine learning model, and determines whether there is a possibility of myopia regression of the subject based on the first result value (under TECH-SOLUTION, paragraph 3: In some embodiments, the machine learning processor is configured to extract a first feature from the numerical data, the deep learning processor is configured to extract a second feature from the image data, and the myopia regression prediction device is configured to extract the first feature from the image data. It may include a fusion processor configured to predict at least one of a probability of occurrence of myopic degeneration and whether or not myopic degeneration will occur from the feature and the second feature; under MODE-FOR-INVENTION, paragraph 16: The myopia regression prediction medical device 100A may include a machine learning processor 110, a deep learning processor 120, and a fusion processor 130. The machine learning processor 110 may extract the first feature (F10) from the numerical data (D10), and the deep learning processor 120 may extract the second feature (F20) from the image data (D20). Here, a feature means a number or an array of numbers).
Regarding claim 7, Lee further discloses the step of extracting a second result value as output data for a second machine learning model by using the first result value and the second target data as input data for the second machine learning model, and determining whether there is the possibility of myopia regression of the subject based on the second result value (under TECH-SOLUTION, paragraph 3: In some embodiments, the machine learning processor is configured to extract a first feature from the numerical data, the deep learning processor is configured to extract a second feature from the image data, and the myopia regression prediction device is configured to extract the first feature from the image data. It may include a fusion processor configured to predict at least one of a probability of occurrence of myopic degeneration and whether or not myopic degeneration will occur from the feature and the second feature).
Regarding claim 8, Lee further discloses wherein the first target data is information about fundus photography of the subject, and wherein the second target data includes information about a central corneal thickness (under TECH-SOLUTION, paragraph 6: The method for predicting myopia regression according to embodiments of the present disclosure includes refractive power, corneal curvature, axial length, pupil size in photopic vision, pupil size in scotopic vision, corneal diameter, corneal thickness, corneal epithelial thickness, higher order aberration, visual acuity, intraocular pressure, gender, And it may include predicting at least one of the probability of occurrence of myopia degeneration and whether myopia degeneration will occur using a machine learning processor from numerical data including age).
Regarding claim 9, Lee further discloses the steps of collecting first training data, processing a first training dataset based on the first training data, constructing the first machine learning model based on the first training dataset, and determining performance of the first machine learning model at a predetermined period, wherein the first training data includes information about fundus photography for a plurality of subjects (under TECH-SOLUTION, paragraph 2: In some embodiments, the myopic regression prediction device includes fundus imaging images, corneal endothelial cell images, corneal morphology, and aberration analyzer (e.g., Keratron Scout) images to predict at least one of the probability of myopic regression occurring and whether myopic regression occurs. Deep learning processors and imaging data including optical coherence tomography images, OPD-SCAN III images, and computed corneal tomography (e.g., Pentacam AXL) images are further available).
Regarding claim 10, Lee further discloses the step of collecting second training data; processing a second training dataset based on the second training data; constructing a second machine learning model based on the second training dataset; and determining performance of the second machine learning model at a predetermined period, wherein the second target data includes information about a central corneal thickness (under TECH-SOLUTION, paragraph 6: The method for predicting myopia regression according to embodiments of the present disclosure includes refractive power, corneal curvature, axial length, pupil size in photopic vision, pupil size in scotopic vision, corneal diameter, corneal thickness, corneal epithelial thickness, higher order aberration, visual acuity, intraocular pressure, gender, And it may include predicting at least one of the probability of occurrence of myopia degeneration and whether myopia degeneration will occur using a machine learning processor from numerical data including age).
Conclusion
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/JACK DINH/Primary Examiner, Art Unit 2872 5/29/26
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