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 .
Response to Arguments
Applicant’s arguments, see page 11, filed 7/8/26, with respect to claim 13 and the drawings have been fully considered and are persuasive. The objections have been withdrawn.
Applicant’s arguments, see page 11, filed 7/8/26, with respect to claim 14 have been fully considered and are persuasive. The 35 U.S.C. 101 rejection has been withdrawn.
Applicant's arguments filed 7/8/26 have been fully considered but they are not persuasive.
Regarding applicant’s argument for claim 1, on pages 12 and 13, that Shaw does not disclose inputting image data extracted from the set of whole slide images corresponding to the selected classification results into the second trained function, examiner disagrees. Examiner notes that the first student function becomes a “trained” function upon processing the pseudo-labels and unlabeled data and is then the “pre-trained” student which is then fed the labeled dataset. The labeled dataset and the unlabeled dataset come from the same overall dataset (as taught on page 2, item 2.2), therefore the image data input to the trained function (i.e. the labeled dataset) “corresponds” to the selected classification results as all of the whole slides are from the same original dataset. Therefore the argument is overcome and the previous rejection remains.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
1) Claim(s) 1, 3-7, 11, 12 and 17-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Teacher-Student chain for efficient semi-supervised histology image classification” by Shaw et al (see IDS filed 7/8/24).
2) Regarding claim 1, Shaw teaches a computer-implemented method for providing a trained function configured to provide a classification result for a whole slide image depicting tissue according to a plurality of tissue types, the method comprising: obtaining a set of whole slide images depicting tissue of unknown tissue types (page 2, section 2.2; WSIs are utilized); providing a first trained function configured to provide a first classification result for at least one whole slide image of the set of whole slide images depicting tissue according to a plurality of tissue types; generating intermediate classification results by inputting the set of whole slide images into the first trained function (page 3, figure 1; teacher model is trained on labeled data and then classifies unlabeled data); selecting classification results from the intermediate classification results (page 3, figure 1; highest probability data is selected); providing a second trained function configured to provide a second classification result for the at least one whole slide image of the set of whole slide images; inputting image data extracted from the set of whole slide images corresponding to the selected classification results into the second trained function to generate predictive classification results (page 3, figure 1; student is trained on the selected pseudo labeled data wherein labeled data is then input to the pretrained student, and since both labeled and unlabeled data come from the same set the labeled input data “corresponds” to the set as a whole); adapting the second trained function based on a comparison of the selected classification results with the predictive classification results (page 3, figure 1; student is fined tuned, tuning a model involves comparisons); and providing the adapted trained function as the trained function (page 3; trained functions are utilized for classification as shown).
3) Regarding claim 3, Shaw teaches the method of claim 1, further comprising: obtaining a comparative set of whole slide images of known tissue type, wherein the inputting the image data inputs image data extracted from the comparative set into the second trained function to generate further predictive classification results, and the adapting adapts the second trained function based on a comparison of the further predictive classification results with the known tissue type (figure 1; labeled data [i.e. known tissue comparative set] fine tunes the student data as shown).
4) Regarding claim 4, Shaw teaches the method of claim 3, wherein the known tissue type relates to healthy tissue (figure 2; “normal” tissue [analogous to healthy] can be one of the labeled patches).
5) Regarding claim 5, Shaw teaches the method of claim 1, wherein the generating the intermediate classification results includes obtaining certainty measures for the intermediate classification results, and the selecting selects the selected classification results based on the certainty measures (figure 1; page 4, section 3.2; only the highest probability classifications are fed to the student model).
6) Regarding claim 6, Shaw teaches the method of claim 1, wherein the generating the intermediate classification results further includes obtaining certainty measures for the intermediate classification results, and the adapting adapts the second trained function based on the certainty measures (figure 1; student is trained based on the “pseudo-labeled” data [i.e. highest probability data]).
7) Regarding claim 7, Shaw teaches the method of claim 5, wherein the obtaining the certainty measures comprises: providing a trained certainty calculation module configured to attribute the certainty measures to the intermediate classification results (page 4, section 3.2; pseudo-labeled set can be filtered [i.e. a model applied] is the ways described).
8) Regarding claim 11, Shaw teaches the method of claim 1, further comprising: respectively defining a plurality of tiles in the whole slide images of the set of whole slide images, wherein the generating the intermediate classification results generates an intermediate classification result for each of at least a part of the tiles, and the inputting image data inputs image data of the tiles corresponding to the selected classification results into the second trained function (page 2, section 2.2; figure 2; data can be handled in units of patches [i.e. subsets of the whole slide image] with classifications of patches [figure 2] being fed to the student model).
9) Regarding claim 12, Shaw teaches a computer-implemented method for providing a classification result for a whole slide image depicting tissue according to a plurality of tissue types, the method comprising: providing the trained function of claim 1; obtaining a whole slide image to be classified; applying the trained function to the whole slide image to be classified to obtain a classification result corresponding to the whole slide image to be classified; and providing the obtained classification result (page 2, section 2.2; whole slide images are classified in units of patches).
10) Claims 17-19 are taught in the same manner as described in the rejections of claims 5-7 above, respectively.
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.
11) Claim(s) 2 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shaw as applied to claim 1 above, and further in view of U.S. patent application publication 2023/0351732 by Story et al.
12) Regarding claim 2, Shaw does not specifically teach the method of claim 1, further comprising: replacing, before the providing the trained function, the first trained function with the adapted trained function such that the adapted trained function becomes the first trained function, and the second trained function with the first trained function such that the first trained function becomes the second trained function; and repeating, the generating the intermediate classification results, the selecting the selected classification results, the inputting the image data, and the adapting the second trained function.
Story teaches the method of claim 1, further comprising: replacing, before the providing the trained function, the first trained function with the adapted trained function such that the adapted trained function becomes the first trained function, and the second trained function with the first trained function such that the first trained function becomes the second trained function; and repeating, the generating the intermediate classification results, the selecting the selected classification results, the inputting the image data, and the adapting the second trained function (paragraph 419; student and teacher can swap places for training).
Shaw and Story are combinable because they are both from the neural network training field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Shaw with Story to add swapping teacher and student for training. The motivation for doing so would have been to improve the teacher model (paragraph 419). Therefore it would have been obvious to combine Shaw with Story to obtain the invention of claim 2.
13) Claim 16 is taught in the same manner as described in the rejection of claim 3 above.
14) Claim(s) 8, 9, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Shaw as applied to claim 1 above, and further in view of U.S. patent application publication 2024/0330652 by Dolezal et al.
15) Regarding claim 8, Shaw does not specifically teach the method of claim 7, wherein the certainty calculation module comprises a Bayesian model.
Dolezal teaches the method of claim 7, wherein the certainty calculation module comprises a Bayesian model (paragraphs 25 and 28; Bayesian model can be used to determine uncertainty in classification).
Shaw and Dolezal are combinable because they are both from the neural network classification field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Shaw with Dolezal to add a Bayesian model. The motivation for doing so would have been for a “reliable” estimation of uncertainty (paragraph 28). Therefore it would have been obvious to combine Shaw with Dolezal to obtain the invention of claim 8.
16) Regarding claim 9, Dolezal (as combined with Shaw in the rejection of claim 8 above) teaches the method of claim 7, wherein the certainty calculation module is configured to perform at least one of: a specialized spatial segmentation loss algorithm, a feature pyramid pooling algorithm, a Monte Carlo Dropout algorithm, a Monte Carlo Depth algorithm, or a Deep Ensemble algorithm (paragraph 25; Monte Carlo dropout and deep ensemble are disclosed).
17) Claim 13, is taught in the same manner as described in the rejection of claim 1 above, with the exception of: an interface unit configured to receive a set of whole slide images depicting tissue of unknown tissue types; and a computing unit.
Shaw does not specifically teach an interface unit and a computing unit.
Dolezal teaches an interface unit configured to receive a set of whole slide images depicting tissue of unknown tissue types; and a computing unit (figure 16; interface and processing unit is disclosed for neural network training).
Shaw and Dolezal are combinable because they are both from the neural network classification field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Shaw with Dolezal to add an interface unit and a computing unit. The motivation for doing so would have been for a “reliable” estimation of uncertainty (paragraph 28). Therefore it would have been obvious to combine Shaw with Dolezal to obtain the invention of claim 13.
18) Claim 15 is taught in the same manner as described in the rejection of claim 13 above.
19) Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Shaw as applied to claim 1 above, and further in view of U.S. patent application publication 2021/0327061 by Clymer et al.
Shaw does not specifically teach the method of claim 7, wherein the certainty calculation module is configured to perform a novelty detection algorithm to detect at least one of input parameters of the first trained function or output parameters of the first trained function which are underrepresented in the training of the first trained function, and the obtaining the certainty measures includes computing the certainty measures based on the detection results of the novelty detection algorithm.
Clymer teaches the method of claim 7, wherein the certainty calculation module is configured to perform a novelty detection algorithm to detect at least one of input parameters of the first trained function or output parameters of the first trained function which are underrepresented in the training of the first trained function, and the obtaining the certainty measures includes computing the certainty measures based on the detection results of the novelty detection algorithm (paragraphs 45 and 46; underrepresented data can be augmented for improved classification).
Shaw and Clymer are combinable because they are both from the neural network classification field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Shaw with Clymer to add boosting underrepresented data. The motivation for doing so would have been to account for class imbalance in a training stage (paragraph 45). Therefore it would have been obvious to combine Shaw with Dolezal to obtain the invention of claim 10.
Allowable Subject Matter
Claim 20 is 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.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN O DULANEY whose telephone number is (571)272-2874. The examiner can normally be reached Mon-Fri 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abderrahim Merouan can be reached at (571)270-5254. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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BENJAMIN O. DULANEY
Primary Examiner
Art Unit 2676
/BENJAMIN O DULANEY/ Primary Examiner, Art Unit 2683
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