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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim as a whole is directed to an algorithm which does not fall within at least one of the four categories of patent eligible subject matter.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1, 2, 4-6, 8, 10, 11, 14-17, and 20-23 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 5-12, 17-19, 23-25 of copending Application No. 18/838654 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the listed claims of instant application are anticipated by the claims of copending application as shown below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant claim 1 <= ‘654 claims 1, 10
Instant claim 2 <= ‘654 claims 1, 5, 10
Instant claim 4 <= ‘654 claims 1, 6, 10
Instant claim 5 <= ‘654 claims 1, 5, 6, 7, 8, 10
Instant claim 6 <= ‘654 claims 1, 9, 10
Instant claim 8 <= ‘654 claims 1, 10 (visible-light images inherent in surgical-microscope images)
Instant claim 10 <= ‘654 claims 1, 10, 11
Instant claim 11 <= ‘654 claims 1, 10, 11, 12
Instant claim 14 <= ‘654 claims 10, 17
Instant claim 15 <= ‘654 claims 10, 18
Instant claim 16 <= ‘654 claim 19
Instant claim 17 <= ‘654 claims 19, 23
Instant claim 20 <= ‘654 claim 23
Instant claim 21 <= ‘654 claims 19, 24
Instant claim 22 <= ‘654 claim 25
Instant claim 23 <= ‘654 claims 1, 17
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.
Claim(s) 1, 2, 4, 5, 7-18 and 20-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saur (U.S. PG-PUB NO. 2022/0405926) in view of Chen (U.S. PG-PUB NO. 2023/0260121).
-Regarding claim 1, Saur discloses a system for training a machine-learning algorithm (FIG. 5), the system comprising: one or more processors (processor 502, FIG. 5), and one or more storage devices (memory 504, FIG. 5), wherein the system is configured to: receive training data, the training data comprising microscope images from a surgical microscope obtained during a surgery, the microscope images showing tissue (providing 204 a plurality of first digital training images of tissue samples that were recorded under white light by means of a microsurgical optical system with a digital image recording unit, [0073]); adjust the machine-learning algorithm based on the training data to obtain a trained machine-learning algorithm (training 208 the combined machine learning system for forming the combined machine learning model for predicting the second digital image and the further representation, [0073]); and provide the trained machine-learning algorithm (the machine learning model 314 now belonging to the trained combined machine learning system 316 can be used productively in the prediction phase 304, [0080]).
Saur is silent to teaching that such that the trained machine-learning algorithm corrects marked sections of tissue in a microscope image. However, the claimed limitation is well known in the art as evidenced by Chen.
In the same field of endeavor, Chen teaches such that the trained machine-learning algorithm corrects marked sections of tissue in a microscope image ( system 10 may provide the operator options to update the suggested anatomical boundaries and/or labels, and the updated image may be added to the training set to update the classifier model, [0108]).
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 Saur with the teaching of Chen in order to correct falsely-marked sections yields a model that corrects marked sections.
-Regarding claim 2, the combination further discloses the training data further comprise at least one of annotations on the microscope images, or corrected microscope images corrected based on the annotations (Saur, Said second set of digital training images can contain additional indications about diseased tissue regions, [0079]), and wherein the annotations are indicative of at least one of: classes of sections of the tissue shown in the corresponding microscope images, and a correctness of the marked sections of the tissue shown in the corresponding microscope images (Chen, system 10 may allow the operator to label anatomical features, [0108]).
-Regarding claim 4, the combination further discloses the trained machine-learning algorithm is obtained based on supervised learning (Saur, supervised learning, [0032]).
-Regarding claim 5, the combination further discloses the supervised learning is based on at least one of classification or regression, wherein the classification is based on the annotations on the microscope images (Saur, neural classification networks, [0081]; metainformation about diseased tissue elements, [0073]), and wherein the regression is based on the corrected microscope images corrected based on the annotations.
-Regarding claim 7, the combination further discloses the microscope images comprise sets of corresponding images prior and after resection of the tissue (Chen, repeating one or more of steps (ii) or (iii) after the treatment procedure to identify one or more changes to the anatomical structure from the treatment procedure, [0063]).
-Regarding claim 8, the combination further discloses the microscope images comprise at least one of: visible light images, fluorescence light images, or combined visible light and fluorescence light images (Saur, fluorescence representation, [0072]).
-Regarding claim 9, the combination further discloses the marked sections of the tissue are obtained by fluorescence imaging of fluorescence markers in the tissue (Saur, fluorescence representation, [0072]).
-Regarding claim 10, the combination further discloses the training data further comprise: radiology images or scans of the tissue corresponding to the tissue shown in the microscope images (Chen, access information sources (e.g., MRI images and/or clinical/Artificial Intelligent database), [0095]).
-Regarding claim 11, the combination further discloses the radiology images are obtained from radiology scans and have a same field of view as the corresponding microscope images (Chen, MRI images, [0095]; Saur, areal alignment, [0076]).
-Regarding claim 12, the combination further discloses the machine-learning algorithm is based on at least one of the following parameters: a pixel colour in the microscope images, a pixel reflectance spectrum in the microscope images, a pixel glossiness in the microscope images, at least one measure in reflectance spectra of microscope images, and/or fluorescence intensity in the microscope images, or at least one variable derived from any of the parameters (Saur, 3-4 colour channels (i.e. pixel colour), [0071], excessively strong fluorescence signals (i.e., fluorescence intensity), [0077]; ); and wherein the adjusting the machine-learning algorithm is based on adjustment information for a weight of the at least one of the parameters or variables (Saur, neural classification networks, FIG. 4, [0081]).
-Regarding claim 13, the combination further discloses the training data is received from one or more databases, the one or more databases being provided with data from one or more applications for annotating on the microscope images obtained from different surgeries (Saur, storage system 604, [0087]; Chen, the boundary and/or labeling data may be saved to a training set of data, and the training set of data may be used to train a classifier model, [0108]).
-Regarding claim 14, Saur discloses a computer-implemented method for training of a machine-learning algorithm (FIG. 5), the method comprising: receiving training data, the training data comprising microscope images from a surgical microscope obtained during a surgery, the microscope images showing tissue (providing 204 a plurality of first digital training images of tissue samples that were recorded under white light by means of a microsurgical optical system with a digital image recording unit, [0073]); adjusting the machine-learning algorithm based on the training data to obtain a trained machine-learning algorithm (training 208 the combined machine learning system for forming the combined machine learning model for predicting the second digital image and the further representation, [0073]); and providing the trained machine learning algorithm (the machine learning model 314 now belonging to the trained combined machine learning system 316 can be used productively in the prediction phase 304, [0080]).
Saur is silent to teaching that such that the trained machine-learning algorithm corrects marked sections of tissue in a microscope image. However, the claimed limitation is well known in the art as evidenced by Chen.
In the same field of endeavor, Chen teaches such that the trained machine-learning algorithm corrects marked sections of tissue in a microscope image ( system 10 may provide the operator options to update the suggested anatomical boundaries and/or labels, and the updated image may be added to the training set to update the classifier model, [0108]).
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 Saur with the teaching of Chen in order to correct falsely-marked sections yields a model that corrects marked sections.
-Regarding claim 15, Saur discloses a trained machine-learning algorithm, trained by: receiving training data, the training data comprising microscope images from a surgical microscope obtained during a surgery, the microscope images showing tissue (providing 204 a plurality of first digital training images of tissue samples that were recorded under white light by means of a microsurgical optical system with a digital image recording unit, [0073]); and adjusting a machine learning a machine-learning algorithm based on the training data to obtain the trained machine-learning algorithm (training 208 the combined machine learning system for forming the combined machine learning model for predicting the second digital image and the further representation, [0073]).
Saur is silent to teaching that such that the trained machine-learning algorithm corrects marked sections of tissue in a microscope image. However, the claimed limitation is well known in the art as evidenced by Chen.
In the same field of endeavor, Chen teaches such that the trained machine-learning algorithm corrects marked sections of tissue in a microscope image ( system 10 may provide the operator options to update the suggested anatomical boundaries and/or labels, and the updated image may be added to the training set to update the classifier model, [0108]).
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 Saur with the teaching of Chen in order to correct falsely-marked sections yields a model that corrects marked sections.
-Regarding claim 16, Saur discloses a system for correcting a microscope image, the system (FIG. 5) comprising: one or more processors (processor 502, FIG. 5) and one or more storage devices (memory 504, FIG. 5), wherein the system is configured to: receive input data, the input data comprising a microscope image from a surgical microscope obtained during a surgery, the microscope image showing tissue (a microsurgical optical system 506 with a digital image recording unit—e.g. a surgical microscope with a camera—for providing a first digital image of a tissue sample that was recorded under white light, [0082]; fluorescence image 410, [0081]), applying a trained machine-learning algorithm (training 208 the combined machine learning system for forming the combined machine learning model for predicting the second digital image and the further representation, [0073]); and provide output data, the output data comprising the corrected microscope image (the machine learning model 314 now belonging to the trained combined machine learning system 316 can be used productively in the prediction phase 304, [0080]).
Saur is silent to teaching that including marked sections and correct the marked sections in order to obtain a corrected microscope image. However, the claimed limitation is well known in the art as evidenced by Chen.
In the same field of endeavor, Chen teaches including marked sections and correct the marked sections in order to obtain a corrected microscope image (update the suggested anatomical boundaries and/or labels, [0108]).
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 Saur with the teaching of Chen in order to correct falsely-marked sections yields a model that corrects marked sections.
-Regarding claim 17, the combination further discloses the input data is directly or indirectly received from an image sensor (Saur, white light recording 406 of the tissue 402 is recorded by a digital recording unit 404, [0081]).
-Regarding claim 18, the combination further discloses the input data is pre-processed raw data received from the image sensor (Saur, [0077]).
-Regarding claim 20, the combination further discloses a surgical microscope, an image sensor, and the system of claim 13 (Saur, microsurgical optical system 506, FIG. 4, 5, [0082]; digital recording unit 404, [0081]).
-Regarding claim 21, Saur discloses a computer-implemented method for correcting a microscope image (FIG. 5), the method comprising: receiving input data, the input data comprising: a microscope image from a surgical microscope obtained during a surgery, the microscope image showing tissue (a microsurgical optical system 506 with a digital image recording unit—e.g. a surgical microscope with a camera—for providing a first digital image of a tissue sample that was recorded under white light, [0082]; fluorescence image 410, [0081]), applying a trained machine-learning algorithm (training 208 the combined machine learning system for forming the combined machine learning model for predicting the second digital image and the further representation, [0073]); and providing output data, the output data comprising the corrected microscope image (the machine learning model 314 now belonging to the trained combined machine learning system 316 can be used productively in the prediction phase 304, [0080]).
Saur is silent to teaching that including marked sections and correct the marked sections in order to obtain a corrected microscope image. However, the claimed limitation is well known in the art as evidenced by Chen.
In the same field of endeavor, Chen teaches including marked sections and correct the marked sections in order to obtain a corrected microscope image (update the suggested anatomical boundaries and/or labels, [0108]).
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 Saur with the teaching of Chen in order to correct falsely-marked sections yields a model that corrects marked sections.
-Regarding claim 22, Saur discloses a method for providing an image to a user using a surgical microscope (FIG. 5), the method comprising: illuminating tissue of a patient, sections of the tissue being marked with fluorescence markers (a first digital image of a tissue sample that was recorded under white light (VIS—visible light source) by means of a microsurgical optical system, [0071]; excessively strong fluorescence signals, [0077]), capturing a microscope image of the tissue, the microscope image showing the tissue (a white light recording 406 of the tissue 402 is recorded by a digital recording unit 404, [0081]), applying a machine-learning algorithm (training 208 the combined machine learning system for forming the combined machine learning model for predicting the second digital image and the further representation, [0073]); and providing the corrected microscope image to the user of the surgical microscope (the machine learning model 314 now belonging to the trained combined machine learning system 316 can be used productively in the prediction phase 304, [0080]).
Saur is silent to teaching that including marked sections and correct the marked sections in order to obtain a corrected microscope image. However, the claimed limitation is well known in the art as evidenced by Chen.
In the same field of endeavor, Chen teaches including marked sections and correct the marked sections in order to obtain a corrected microscope image (update the suggested anatomical boundaries and/or labels, [0108]).
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 Saur with the teaching of Chen in order to correct falsely-marked sections yields a model that corrects marked sections.
-Regarding claim 23, the combination further discloses a non-transitory computer-readable medium having a program code stored thereon, the program code (Saur, memory 504, FIG. 5), when executed by a computer processor, causing performance of a method of claim 14 (Saur, processor 502, FIG. 5).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saur (U.S. PG-PUB NO. 2022/0405926) in view of Chen (U.S. PG-PUB NO. 2023/0260121) and further in view of Hemstreet (U.S. PATENT NO. 5741648).
-Regarding claim 3, the combination is silent to teaching that the corrected microscope images corrected based on the annotations are obtained by modifying intensity values in the microscope images based on the annotations. However, the claimed limitation is well known in the art as evidenced by Hemstreet.
In the same field of endeavor, Hemstreet teaches the corrected microscope images corrected based on the annotations are obtained by modifying intensity values in the microscope images based on the annotations (grey level (e.g., intensity values) image of the cell is then corrected on a pixel-by-pixel basis).
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 Hemstreet in order to obtain corrected images by modifying the intensity of falsely-marked pixels per the correctness annotations is a predictable use of a known intensity-correction technique.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saur (U.S. PG-PUB NO. 2022/0405926) in view of Chen (U.S. PG-PUB NO. 2023/0260121) and further in view of Khamene (U.S. PG-PUB NO. 2009/0161928).
-Regarding claim 6, the combination is silent to teaching that the trained machine-learning algorithm is obtained based on unsupervised learning. However, the claimed limitation is well known in the art as evidenced by Khamene.
In the same field of endeavor, Khamene teaches the trained machine-learning algorithm is obtained based on unsupervised learning (unsupervised classification of histological images of prostatic tissue using histological data obtained from NIR fluorescent co-staining of hematoxylin-and-eosin (H&E) images, [0023]).
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 Khamene in order to train the algorithm without labeled ground truth selects one of a finite set of known learning paradigms for a similar tissue-image classifier (obvious to try under KSR).
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PING Y HSIEH/ Primary Examiner, Art Unit 2664
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