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 .
Election/Restrictions
Applicant’s election without traverse of Specie 3 (Figs. 12-14) in the reply filed on July 31, 2026 is acknowledged. Claims 4 and 5 are withdrawn.
Information Disclosure Statement
The information disclosure statement filed November 19, 2025 fails to comply with the provisions of 37 CFR 1.98(a)(4) because it lacks the appropriate size fee assertion. It has been placed in the application file, but the information referred to therein has not been considered as to the merits.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3 and 6-10 (all pending claims) are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 10 recite “the probability,” but this lacks sufficient antecedent basis because the prior recitation recited a plurality of different probabilities. MPEP 2173.05(e).
Claims 8 and 9 recite “wherein the generating a plurality of patches, after generating the patch.” However, this language is unclear because according to parent claim 7, generating the singular patch is part of the claimed generating a plurality of patches. Thus, there is a conflict as to whether this step is part of the generating a plurality of patches, or if it is after the patches are generated. If the intent is that this is after a particular patch, the antecedent basis should be amended.
Dependent claims are likewise rejected.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-3 and 6-10 (all pending claims) are rejected under 35 U.S.C. 103 as being unpatentable over Armand TP, Bhattacharjee S, Kim HC. Overview of the potentials of multiple instance learning in cancer diagnosis: Applications, challenges, and future directions. In2024 26th International Conference on Advanced Communications Technology (ICACT) 2024 Feb 4 (pp. 419-425). IEEE. (“Armand”) in view of Roka S, Diwakar M. Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance. Journal of Electronic Imaging. 2023 May 1;32(3):033015-1 – 033015-23. (“Roka”).
1. A method of performing a cancer metastasis tissue determination apparatus operated by a processor, the method comprising:
obtaining a pathology image including tissue to be determined as cancer; (Armand, Fig. 1B)
generating a plurality of patches by dividing the pathology image into a preset size; (Armand, Fig. 1B)
determining a probability that each of the plurality of patches includes tumor tissue by inputting the plurality of patches into a first neural network model trained to distinguish whether the pathology image includes tumor tissue; (Armand, p. 420 right side, “MIL algorithms are usually divided into two main parts: An instance-level classifier and a bag-level classifier. An instance level classifier provides information about the individual instances within each bag.”)
selecting a patch to be observed from among the plurality of patches based on the probability; and (Armand, p. 422 right side, “A Kernel Density Estimation (KDE) was used to select the most positive instance in the bag”)
determining whether the pathology image includes tumor tissue or a location of the tumor tissue by inputting the selected patch into a second neural network model trained based on multiple patches to determine whether a specific patch contains tumor tissue. (Armand, p. 424 left side, “Integrating multiple instance learning (MIL) with deep learning techniques holds great potential for advancing cancer diagnostic accuracy.”)
The citation to Armand, p. 424, is in a section titled “Future Directions and Emerging Trends.”
However, Roka teaches the implementation of the technique described by Armand.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Roka to the teachings of Armand such that Roka’s autoencoder is as the deep learning integrated with Armand’s MIL (Armand, p. 424, left side) for the purpose of implementation details and improving detection of abnormality (Roka, abstract). One of ordinary skill in the art would appreciate that Roka’s work is applicable to other types of images, see, e.g., Roka, p. 18, “our approach can be used with any type of video anomaly dataset no matter whether they are captured in constrained or unconstrained conditions.”
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
2. The method of claim 1, wherein the first neural network model is composed of a neural network with a multiple instance learning (MIL) structure and trained based on training data labeled with a single BAG class that only specifies whether the pathology image includes an instance corresponding to tumor tissue, and outputs a probability that input data includes the instance. (Armand, Fig. 1B)
3. The method of claim 2, wherein the selecting a patch to be observed comprises:
classifying patches determined to have the probability greater than a certain threshold; and arranging the classified patches in order of high probability. (Armand, p. 422 right side, “A Kernel Density Estimation (KDE) was used to select the most positive instance in the bag” Choosing the most positive instance results in the most positive being classified in order of high probability.)
4-5. (Withdrawn)
6. The method of claim 2, wherein the second neural network model is composed of a neural network with an autoencoder structure including two encoders and one decoder trained based on different training data and trained to encode and decode input data (Roka, Fig. 2.)
based on training data of a pathology image including only normal tissue and restore the input data, and (Roka, p. 9, “DSDAE is trained using only the clips of the normal video where it tries to reconstruct the frame Xi” The combination with Armand teaches the claimed pathology image and tissue.)
determines that the pathology image includes tumor tissue if standard deviation for difference values of respective restoration errors by the two encoders is greater than or equal to a preset value when receiving the selected patch and performing encoding and decoding. (Roka, abstract, “Only those pixels whose reconstruction error is greater than the threshold will be considered abnormal pixels.”)
7. The method of claim 1, wherein the generating a plurality of patches comprises:
determining a border of tissue included in the pathology image;
removing data of an external area of the border of the tissue; and
generating a patch by dividing an internal area of the border of the tissue into a preset size.
8. The method of claim 7, wherein the generating a plurality of patches, after generating the patch, comprises: when a tissue area included in the patch is 30 % or more and 50 % or less of the patch, making the tissue area included in the patch symmetrical left-right or up-down within the patch. (Armand, , Fig. 1B:
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Because the tissue shown in the patch is more than 50%, this limitation is not invoked. MPEP 2111.04(II))
9. The method of claim 7, wherein the generating a plurality of patches, after generating the patch, comprises: when a tissue area included in the patch is less than 30 % of the patch, copying the tissue area included in the patch and pasting the tissue area into a blank area. (Armand, , Fig. 1B:
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Because the tissue shown in the patch is more than 50%, this limitation is not invoked. MPEP 2111.04(II))
Claim 10 is rejected as per claim 1. Roka, p. 10, bottom teaches the claimed hardware.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220180512 A1 – title, “Method for predicting disease based on medical image,” autoencoder at [0079]
US 20230169652 A1 – abstract, “Systems and methods for chest condition determination can leverage one or more machine-learned models to process radiograph data to determine risk data (e.g., a preliminary diagnosis).”
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/DAVID ORANGE/Primary Examiner, Art Unit 2663
