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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6, 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song (WO 2022/166399 A1) in view of Huang (U.S. PATENT NO. 7905599).
-Regarding claim 1, Song discloses a system for operating an ensemble model targeting a multiplicity of inputs to provide a single unified classification (the bimodal fundus disease auxiliary diagnosis system consists of two symmetrical branches, wherein the feature extraction module for processing color fundus images is denoted as Model-F, and the feature extraction module for processing OCT images is denoted as Model-F O, Step 102, FIG. 2), the system comprising: a first fusion network trained on an image (the color fundus image is extracted by the first feature extraction module; Perform feature extraction to obtain a first feature vector, Step 102, FIG. 2); a second fusion network trained (the bimodal fundus disease auxiliary diagnosis system consists of two symmetrical branches, Step 102 ); and an ensemble feature layer comprising a channel-level summation of a plurality of features extracted from the first fusion network and a plurality of features extracted from the second fusion network (the feature-based weight assignment strategy obtains the feature vector Fadd by adding Ff and FO according to the weight: Fadd =aFf +(1-a)FO , where a is a hyperparameter and 0<a<1, Step 103).
Song is silent to teaching that optic nerve head (ONH) dataset; on a macular image dataset. However, the claimed limitation is well known in the art as evidenced by Huang.
In the same field of endeavor, Huang teaches optic nerve head (ONH) dataset (Song, The ppNFL thickness map and optic disc shape are constructed by ONH scan, col. 5; FIG. 3); on a macular image dataset (the parameters are generated through two scan patterns. The mGCC thickness map is constructed by mGCC scan, col. 5; FIG. 2).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Song with the teaching of Huang in order to improve accuracy.
-Regarding claim 2, the combination further discloses the first fusion network comprises a first local classifier; the second fusion network comprises a second local classifier; and the system further comprises an ensemble classifier (Song, Figure 2(c), the weight assignment strategy based on the classification results first inputs Ff and FO into the fully connected layer to obtain, Step 10; After that, the final output is obtained through the fully connected layer, Step 3).
-Regarding claim 3, the combination further discloses the first local classifier comprises a first plurality of features from the first fusion network; and the second local classifier comprises a second plurality of features from the second fusion network (Song, Let Ff be the feature vector extracted from the color fundus image by Model-F (the upper rectangular block in Figure 2), Step 102; Among them, Wf, WO are the parameters of the fully connected layer applied to the color fundus image and the OCT image, respectively, Step 103).
-Regarding claim 4, the combination further discloses the ensemble classifier comprises a third plurality of aggregated features comprising at least one feature from the first plurality of features and at least one feature from the second plurality of features (Song, the feature-based weight assignment strategy obtains the feature vector Fadd by adding Ff and FO according to the weight: Fadd =aFf +(1-a)FO , where a is a hyperparameter and 0<a<1, Step 103; where Wadd is the parameter of the fully connected layer, Step 103).
-Regarding claim 6, the combination further discloses the first fusion network comprises a first feature extraction component, a first feature fusion component, and a first feature reconstruction component; and the second fusion network comprises a second feature extraction component, a second feature fusion component, and a second feature reconstruction component (Song, the color fundus image is extracted by the first feature extraction module. Perform feature extraction to obtain a first feature vector, and perform feature extraction on the OCT image through a second feature extraction module to obtain a second feature vector, Step 102; the bimodal fundus disease auxiliary diagnosis system consists of two symmetrical branches, Step 102).
-Regarding claim 8, the combination further discloses the first feature fusion component comprises a first intermediate node that integrates extracted image features from a first plurality of feature extraction channels; and the second feature fusion component comprises a second intermediate node that integrates extracted image features from a second plurality of feature extraction channels (Huang, the order of combination may be reversed, i.e., superior, inferior and overall parameter may be combined first for each anatomy region followed by combining the parameters across the 3 anatomic regions. In this way, steps 1 and 2 will result in a set of reduced global parameters, col. 6; Song, In order to facilitate subsequent feature fusion operations, the sizes of the feature vectors Ff and FO need to be unified, Step 102).
-Regarding claim 9, the combination further discloses the first feature extraction component comprises a first plurality of residual blocks; and the second feature extraction component comprises a second plurality of residual blocks (Song, the mainstream feature extraction modules in computer vision can be used in bimodal fundus disease auxiliary diagnosis systems, such as VGGNet, GoogleNet, ResNet, etc, Step 102; Model-F and Model-O can be the same feature extraction module (homogeneous) or different feature extraction modules (heterogeneous), Step 102).
Claim(s) 5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Song (WO 2022/166399 A1) in view of Huang (U.S. PATENT NO. 7905599) and further in view of Pan (U.S. PATENT NO. 12383189).
-Regarding claim 5, the combination further discloses the ensemble classifier is configured to return a classification through a majority voting method taking at least one input, respectively, from each of the first plurality of features, the second plurality of features, and the third plurality of aggregated features (Song, The weight assignment strategy based on the classification result is a weighted voting method, Step 103; They respectively give the prediction of the classification result and obtain the final result through weighted voting, Step 103).
The combination is silent to teaching that a binary classification indicating either AD-dementia or No-dementia. However, the claimed limitation is well known in the art as evidenced by Pan.
In the same field of endeavor, Pan teaches a binary classification indicating either AD-dementia or No-dementia (Two values in the vector correspond to a probability of a current input sMRI image belonging to an Alzheimer's disease patient (class AD) and a probability of a current input sMRI image belonging to a health care person (HC class), col. 5).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of the combination with the teaching of Pan in order to improve data analysis technology of MRI imaging.
-Regarding claim 7, the combination further discloses the first feature extraction component comprises a first plurality of feature maps, each respective feature map in the first plurality of feature maps having a unique convolution kernel weighting; and the second feature extraction component comprises a second plurality of feature maps, each respective feature map in the second plurality of feature maps having a unique convolution kernel weighting (Song, the mainstream feature extraction modules in computer vision can be used in bimodal fundus disease auxiliary diagnosis systems, such as VGGNet, GoogleNet, ResNet, etc, Step 102; Pan, Due to a computational method of one-dimensional convolution, each weight of the one-dimensional convolution corresponds to each of the base classifiers respectively, col. 5).
Allowable Subject Matter
Claims 10-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 13-20 are allowed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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/PING Y HSIEH/ Primary Examiner, Art Unit 2664
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