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 .
Applicant’s response to the Non-Final Office Action dated 10/31/2025, filed with the office on 02/02/2026, has been entered and made of record.
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 11/10/2025 has been reviewed and the listed references have been considered.
Terminal Disclaimer
The terminal disclaimer filed on 12/29/2025 has been accepted and placed in the records.
Status of Claims
Claims 1 and 5-11 are pending. Claims 2-4 are cancelled.
Response to Amendments
In light of Applicant’s amendments, the objections of record with respect to the specification is withdrawn.
In light of Applicant’s submission of the terminal disclaimer, the nonstatutory double patenting rejection has been withdrawn
In light of the Applicant’s amendments of claims 4 and 7, the 112(b) rejections of record for indefiniteness has been withdrawn.
Response to Arguments
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. On page 10 of Applicant’s reply, Applicant merely states “The cited references, however, do no disclose or suggest…” The claims as amended contain claim language from previously rejected claims 2, 3, and 4. Therefore, Applicant’s arguments are not found to be persuasive and updated analyses have been presented below.
Consequently, THIS ACTION IS MADE FINAL.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (WO2022032998A1 – translation of Espacenet) in view of Suzuki et al. (EP3637320A1).
Regarding claim 1, Wang teaches “A training device (Wang paragraph [0143] “the electronic device 500”) comprising: a memory configured to store instructions (Wang paragraph [0145] "The memory 504 is configured to store various types of data to support operations in the electronic device 500"); and
one or more processors configured to execute the instructions to (Wang paragraph [0145] "The processing component 502 generally controls the overall operation of the electronic device 500"):
generate a plurality of partial images smaller than an input image from the input image (Wang Figure 2A, 2B, 2C, 2D and paragraph [0046] "Among the obtained multiple partial images, the second partial image includes more content than the first partial image");
generate training data by acquiring a plurality of training partial images from the plurality of partial images based on the feature space (Wang paragraph [0124] "segmentation module 406, configured to perform segmentation processing on the image to be processed based on the feature maps corresponding to the local images of the one or more scales to obtain a segmentation result");
perform prediction for all or a part of the plurality of partial images included in the input image (Wang paragraph [0124] "segmentation module 406, configured to perform segmentation processing on the image to be processed based on the feature maps corresponding to the local images of the one or more scales to obtain a segmentation result")
wherein the one or more processors are further configured to execute the instructions (Wang paragraph [0145] "The processing component 502 generally controls the overall operation of the electronic device 500") to:
determine selection probabilities of the plurality of spatial areas (Wang paragraph [0068] "probability map, the probability of each pixel point represents the probability that the pixel point is located in a specific area (for example, when the probability is greater than or equal to a probability threshold (for example, 50%), the pixel point can be considered to be located in a specific area)"), and acquires the plurality of training partial images from the plurality of partial images corresponding to a spatial area selected according to the selection probabilities (Wang paragraph [0040] "the image to be processed is segmented based on the feature map corresponding to the local image at one or more scales to obtain a segmentation result"); and
updates the selection probabilities (Wang paragraph [0078] "when the first network loss is less than or equal to a preset threshold, or converges to a preset interval, the first training condition is met") of the plurality of spatial areas based on predicted values for all or a part of the partial images included in the input image such that, reliability of a predicted value for the plurality of partial images increases, a selection probability of the spatial area corresponding to the plurality of partial images increases (Suzuki paragraph [0118] "machine learning apparatus 100 compares, against a predetermined threshold, the maximum prediction confidence measure amongst the prediction confidence measures of the classes, and then adopts the region proposal whose maximum prediction confidence measure exceeds the predetermined threshold").”
However, Wang does not explicitly teach “generat[ing] a feature space to which feature values of the plurality of partial images are mapped, for each input image”, “repeatedly train a prediction model for predicting a probability that a predetermined feature is included in the training partial images using the generated training data;”, “using a trained prediction model, plurality of partial images in the feature space,”, “divide the feature space into a plurality of spatial areas”, “predicted values for all or a part of the partial images included in the input image such that, reliability of a predicted value for the plurality of partial images increases, a selection probability of the spatial area corresponding to the plurality of partial images increases.”
Suzuki teaches “generate a feature space to which feature values of the plurality of partial images are mapped, for each input image (Suzuki Figure 8 and paragraph [0094] "One feature value is calculated based on each sub-image included in the training dataset for the feature model training, and corresponds to a single point in the feature space 60. To generate the feature space 60, the machine learning apparatus 100 inputs the sub-images included in the training dataset one by one to the feature model, and obtains a feature value corresponding to each sub-image from the feature model")”,
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Suzuki Figure 8
“repeatedly train a prediction model (Suzuki paragraph [0096] "Once the feature model and the feature space are generated, the machine learning apparatus 100 trains the detection model on a training dataset prepared for the detection model training") for predicting a probability that a predetermined feature is included in the training partial images using the generated training data (Suzuki paragraph [0097] "The machine learning apparatus 100 inputs sub-images of the training dataset one by one to the detection model, and obtains a plurality of region proposals and prediction confidence measures for each of the region proposals");”,
“using a trained prediction model (Suzuki paragraph [0097] "The machine learning apparatus 100 inputs sub-images of the training dataset one by one to the detection model, and obtains a plurality of region proposals and prediction confidence measures for each of the region proposals"), plurality of partial images in the feature space (Suzuki paragraph [0097] "The machine learning apparatus 100 updates the synaptic weights in the detection model to reduce the error. The machine learning apparatus 100 repeats this process to train the detection model"),”,
“divide the feature space into a plurality of spatial areas (Suzuki Figure 8 and paragraph [0095] "The feature space 60 includes a cluster 61 of feature values calculated for sub-images belonging to a class C1; a cluster 62 of feature values calculated for sub-images belonging to a class C2; and a cluster 63 of feature values calculated for sub-images belonging to a class C3");”
“predicted values for all or a part of the partial images included in the input image (Suzuki paragraph [0118] "machine learning apparatus 100 compares, against a predetermined threshold, the maximum prediction confidence measure amongst the prediction confidence measures of the classes, and then adopts the region proposal whose maximum prediction confidence measure exceeds the predetermined threshold") such that, reliability of a predicted value for the plurality of partial images increases, a selection probability of the spatial area corresponding to the plurality of partial images increases (Suzuki paragraph [0118] "machine learning apparatus 100 compares, against a predetermined threshold, the maximum prediction confidence measure amongst the prediction confidence measures of the classes, and then adopts the region proposal whose maximum prediction confidence measure exceeds the predetermined threshold").”
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine an
the training model of segmented an input image into a plurality of smaller images as taught by Wang use training device and method of creating a feature space, and training data set as taught by Suzuki.
The suggestion/motivation for doing so would have been “in the case of detecting particular cellular tissues from a medical image and assessing the state of each cellular tissue as e.g. positive or negative, there is only a small difference in shape and pattern between tissue cells in different states, and also there is a small number of states to be distinguished. In this case, there remains a problem that a trained detection model tends to generate misclassification due to location shifts. Misclassification due to a location shift is a consequence of cropping, from a target image, a region with an object slightly off-center and classifying the object into a wrong class, while accurate cropping would lead to accurate classification of the object. Therefore, if a generated detection model has insufficient accuracy in identifying region detection locations, the detection model gives poor class-based classification accuracy on images other than a training dataset used for machine learning” as disclosed by Suzuki paragraph 10-11. Therefore, it would have been obvious to combine the disclosure of Wang with
the Suzuki disclosure to obtain the invention as specified in claim 1 as there is a
reasonable expectation of success and/or because doing so merely combines prior art
elements according to known methods to yield predictable results.
Regarding claim 5, the combination of Wang and Suzuki teaches “The training device according to claim 1the one or more processors are further configured to execute the instructions to: divide the feature space into the plurality of spatial areas by mapping the feature values of the plurality of partial images to the feature space and clustering distribution of the feature values (Suzuki Figure 8 and paragraph [0095] "The feature space 60 includes a cluster 61 of feature values calculated for sub-images belonging to a class C1; a cluster 62 of feature values calculated for sub-images belonging to a class C2; and a cluster 63 of feature values calculated for sub-images belonging to a class C3").”
The proposed combination as well as the motivation for combining Wang and Suzuki references presented in the rejection of claim 1, applies to claim 5. Finally the device recited in claim 5 is met by Wang and Suzuki.
Regarding claim 6, the combination of Wang and Suzuki teaches “The training device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: (Suzuki paragraph [0117] "The sub-images are input one by one to the detection model, which then outputs the location of each region proposal and a prediction confidence measure vector associated with the region proposal").”
The proposed combination as well as the motivation for combining Wang and Suzuki references presented in the rejection of claim 1, applies to claim 6. Finally the device recited in claim 6 is met by Wang and Suzuki.
Claim 9 recites a method with steps corresponding to the device elements
recited in claim 1. Therefore, the recited steps of this claim are mapped to the
proposed combination in the same manner as the corresponding elements of device
claim 1. Additionally, the rationale and motivation to combine the Wang and Suzuki
references, presented in rejection of claim 1 apply to this claim.
Claim 10 recites a computer readable medium including computer executable instructions corresponding to the elements of the device recited in claim 1. Therefore, the recited instructions of the computer readable medium of claim 10 are mapped to the proposed combination in the same manner as the corresponding elements of the device claim 1. Additionally, the rationale and motivation to combine Wang and Suzuki presented in rejection of claim 1, apply to this claim.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wang and Suzuki, in view of Song et al. (JP2020533725A – translation of Espacenet).
Regarding claim 7, the combination of Wang and Suzuki teaches “The training device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: select partial images in which a greater than a threshold (Suzuki paragraph [0118] "machine learning apparatus 100 compares, against a predetermined threshold, the maximum prediction confidence measure amongst the prediction confidence measures of the classes, and then adopts the region proposal whose maximum prediction confidence measure exceeds the predetermined threshold"), from among the plurality of partial images generated from the input image (Wang Figure 2A and paragraph [0047] "The first partial image x1 of the reference size contains finer local detail features (e.g., detail features of the coronary vessels themselves), and the second partial images x2 and x3 contain more global distribution features (e.g., the distribution of coronary vessels and the connection between them and other vessels), for example, the connection between the target in the first partial image x1 and other regions in the second partial image x2 or x3 (e.g., the connection between the coronary vessels in the first partial image x1 and the vessels in other regions in the second partial image x2 or x3)"), and maps the selected partial images on the feature space (Suzuki Figure 8 and paragraph [0094] "One feature value is calculated based on each sub-image included in the training dataset for the feature model training, and corresponds to a single point in the feature space 60. To generate the feature space 60, the machine learning apparatus 100 inputs the sub-images included in the training dataset one by one to the feature model, and obtains a feature value corresponding to each sub-image from the feature model").”
However, the combination of Wang and Suzuki does teach “a proportion of tumor cells”
Song teaches “proportion of tumor cells (Song [0003] paragraph "identifying regions of interest in digital images, for example, identifying foreground objects from background scenes, or identifying cancer cells in digital histopathology images")”.
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine an
the training model of segmented an input image into a plurality of smaller images as taught by Wang and Suzuki to process images containing cancerous cells as taught by Song.
The suggestion/motivation for doing so would have been to address the problem of “determinations are technically problematic in that they are often unreliable, expensive, time consuming, and generally require verification by multiple pathologists to minimize the possibility of an erroneous determination” as disclosed by Song paragraph 6. Therefore, it would have been obvious to combine the disclosure of Wang and Suzuki with the Song disclosure to obtain the invention as specified in claim 7 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claims 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wang and Suzuki, in view of Klimov et al. (US 2022/0058801 A1).
Regarding claim 8, the combination of Wang and Suzuki teaches “A prediction device comprising: memory storing instructions (Wang paragraph [0145] "The memory 504 is configured to store various types of data to support operations in the electronic device 500"); and one or more processors configured to execute the instructions to (Wang paragraph [0145] "The processing component 502 generally controls the overall operation of the electronic device 500"): (Wang paragraph [0124] "segmentation module 406, configured to perform segmentation processing on the image to be processed based on the feature maps corresponding to the local images of the one or more scales to obtain a segmentation result");
(Suzuki paragraph [0097] "The machine learning apparatus 100 inputs sub-images of the training dataset one by one to the detection model, and obtains a plurality of region proposals and prediction confidence measures for each of the region proposals")”.
The combination of Wang and Suzuki does not teach “
However, Klimov teaches “integrate prediction results for all the partial images and output a prediction score indicating a probability that the predetermined feature is included in the input image” (For example: Klimov Figure 1 and paragraph [0040] "At 114, the patch probabilities may be combined to produce a whole slide annotation 114").”
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Klimov Figure 1
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine an
the training model of segmented an input image into a plurality of smaller images as taught by Wang and Suzuki to include combining the results of the partial images into a final output as taught by Klimov.
The suggestion/motivation for doing so would have been that there is a need in medical imaging to improve predication capabilities using whole slide image analysis as stated the need to “provide improved capability to predict the risk of recurrence of ductal carcinoma in situ (DCIS) conditions using whole slide image analysis based on machine learning techniques” as disclosed by Klimov paragraph 5. Therefore, it would have been obvious to combine the disclosure of Wang and Suzuki with the Klimov disclosure to obtain the invention as specified in claim 8 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Regarding claim 11, the combination of Wang, Suzuki, and Klimov teaches “The training device according to claim 1, wherein the one or more processors are further configured to execute the instructions to: train the prediction model, by deep learning, to predict the probability that at least one of tumor, stroma and duct is included in a pathological tissue image of a patient (Klimov paragraph [0040] "At 110, the extracted features may be input into a random forest, which may output 112 a probability of each patch belonging to a specific category (malignant duct, immune rich stroma, non-immune rich stroma, non-cancerous duct, and blood
vessel)").”
The proposed combination as well as the motivation for combining Wang, Suzuki, and Klimov references presented in the rejection of claim 8, applies to claim 11 Finally the device recited in claim 11 is met by Wang, Suzuki, and Klimov.
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.
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/JASPREET KAUR/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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