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 .
Notice to Applicant
This communication is in response to application filed 12/17/2024. It is noted that application is a continuation of 18/349,584 filed 07/10/2023 (US Patent No. 12171542) which is a continuation of 17/510,055 filed 10/25/2021 (US Patent No. 11696701) which is a continuation of 15/509,953 filed 03/09/2017 (US Patent No. 11154212) which is a 371 of PCT/US2015/049656 filed 09/11/2015 which claims priority to Provisional Application No. 62/049,011 filed 09/11/2014. Claims 1-17 are pending.
Information Disclosure Statement
Information Disclosure Statement dated 1/17/2025 has been acknowledged and considered.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-17 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 11,154,212; claims 1-19 of U.S. Patent No. 11,696,701; claims 1-23 of U.S. Patent No. 12,171,542 and . Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are directed to estimating quantitative histological features of a tissue and is an obvious variant of the claims in the previous patents.
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.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-10 are drawn to a method for classifying a medical image, which is within the four statutory categories (i.e. process).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
1.A method for classifying a medical image as corresponding to a particular contrast enhancement based on underlying histological features, the method comprising:
providing a plurality of medical images of a subject to a computer system, wherein the plurality of medical images was acquired using at least one medical imaging system;
providing a trained model to the computer system, wherein the trained model has been trained using machine learning to differentiate between different sources of contrast enhancement based on a histological feature of interest; and
classifying the plurality of medical images as indicating one of a first source of contrast enhancement or a second source of contrast enhancement in the subject by applying the trained model to the plurality of medical images using the computer system.
These recited underlined limitations fall within the "Certain Methods of Organizing Human Activities" grouping of abstract ideas as it relates to certain methods of organizing human activity – managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II). The limitations of providing a plurality of medical images; providing a trained model; and classifying the images as drafted and detailed above, are steps that, under its broadest reasonable interpretation, recites steps for organizing human interactions. The claimed invention is directed to providing images and a model and classifying the images according to the model which is a concept relating to tracking or filtering information. Tracking information or filtering content has been found to be an abstract idea and a method of organizing human behavior. See MPEP 2106.04(a)(2)(II)(C). This is a method of organizing medical image data thus falling into one category of abstract idea. That is other than reciting “computer system” and “machine learning” language, nothing in the claim element precludes the steps from describing concepts related to receiving and organizing image data between people. If a claim limitation, under its broadest reasonable interpretation, covers concepts related to interpersonal and intrapersonal activities then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
1.A method for classifying a medical image as corresponding to a particular contrast enhancement based on underlying histological features, the method comprising:
providing a plurality of medical images of a subject to a computer system, wherein the plurality of medical images was acquired using at least one medical imaging system;
providing a trained model to the computer system, wherein the trained model has been trained using machine learning to differentiate between different sources of contrast enhancement based on a histological feature of interest; and
classifying the plurality of medical images as indicating one of a first source of contrast enhancement or a second source of contrast enhancement in the subject by applying the trained model to the plurality of medical images using the computer system.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
The additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f).
the recitations performing the functions by the computer system amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraph [0065] of the present Specification.
the recitation of using machine learning to differentiate between different sources of contrast enhancement based on a histological feature of interest recites only the idea of a solution or outcome (i.e. claim fails to recite details of how a solution to a problem is accomplished).
in order to transform a judicial exception into a patent-eligible application, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’".
generally link the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h)– for example, the recitation of computers and medical images merely limits the abstract idea the environment of medical imaging and histopathology.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Independent claim 1 does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally linking the abstract idea to a particular technological environment or field of use and the same analysis applies with regards to whether they amount to “significantly more.” Therefore, the additional elements do not add significantly more to the at least one abstract idea.
The following dependent claims further the define the abstract idea or are also directed to an abstract idea itself:
Dependent claims 2-5, and 7 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract).
In relation to claim 6, this claim specifies estimating values and classifying the images which is a certain method of organizing human activity, under its broadest reasonable interpretation, covers interactions between people or managing personal behavior or relationships
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 10: These claims specify images from: a magnetic resonance imaging (MRI) system, an x-ray computed tomography (CT) system, an ultrasound imaging system, an optical imaging system, or a positron emission tomography (PET) system. which thus does no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the at least one abstract idea is performed (see MPEP § 2106.05(e)).
Claims 8, 9: These claims recite a neural network and support vector machine which thus amount to mere instructions to apply an exception by invoking the computer as a tool OR reciting the idea of a solution (i.e. claim fails to recite details of how a solution to a problem is accomplished) or outcome (see MPEP § 2106.05(f)).
The dependent claims further do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-10 are ineligible under 35 USC §101.
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.
Claim 1-8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Brasch (6009342) in view of Saidi (7761240).
As per claim 1, Brasch teaches the method for classifying a medical image as corresponding to a particular contrast enhancement based on underlying histological features, the method comprising:
providing a plurality of medical images of a subject to a computer system, wherein the plurality of medical images was acquired using at least one medical imaging system (Brasch; Col. 2, line 65 to Col. 3, line 5; Col. 8, lines 49-55);
providing a trained model to the computer system, wherein the trained model has been trained to differentiate between different sources of contrast enhancement based on a histological feature of interest (Brasch; Col. 3, lines 7-10 microvascular permeability of tumors as measured by MCMI correlates with the determination of malignancy or non-malignancy and the S-B-R histological grading of malignant tumors); and
classifying the plurality of medical images as indicating one of a first source of contrast enhancement or a second source of contrast enhancement in the subject applying the trained model to the plurality of medical images using the computer system. (Brasch; Col. 15, lines 5-11 Qualitatively, benign tumors were noted to enhance uniformly throughout following administration of MMCM, consistent with the absence of necrosis on histology. All malignant tumors showed a Strong enhancement of the periphery or rim – “benign” vs “malignant” reads on classification).
Brasch does not expressly teach using machine learning as the type of trained model. However, the use of a machine learning model in the image classification arts was old and well-known at the time of the claimed invention as evidenced by Saidi. In particular, Saidi Col. 5, lines 40-55 teaches training a neural network or other learning machine with image-level morphometric data from a plurality of tissue images with known classification. Brasch explicitly correlates imaging-derived physiological parameters with histology, and presents the subjectivity of manual classification. Saidi teaches machine-learning automations for using image features to classify cancer tissue. Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the machine learning of the Saidi for the manual classification means of Brasch. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As per claim 2, Brasch teaches the method of claim 1, wherein the first source of contrast enhancement is a treatment effect in the subject (Brasch; Col. 16, lines 13-17 Furthermore, previous MRI tumor studies have shown the tumor rim to be the region most representative of viable tumor tissue (Van Dijke, Supra) and the most responsive to chemotherapy).
As per claim 3, Brasch teaches the method of claim 2, wherein the treatment effect comprises a radiation treatment effect (Brasch; Col. 16, lines 13-17 Furthermore, previous MRI tumor studies have shown the tumor rim to be the region most representative of viable tumor tissue (Van Dijke, Supra) and the most responsive to …radiotherapy).
As per claim 4, Brasch teaches the method of claim 2, wherein the treatment effect comprises a chemotherapy treatment effect (Brasch; Col. 16, lines 13-17 Furthermore, previous MRI tumor studies have shown the tumor rim to be the region most representative of viable tumor tissue (Van Dijke, Supra) and the most responsive to chemotherapy).
As per claim 5, Brasch teaches the method of claim 2, wherein the second source of contrast enhancement is a viable tumor in the subject (Brasch; Col. 15, lines 7-10 All malignant tumors showed a Strong enhancement of the periphery or rim)
As per claim 6, Brasch teaches the method of claim 1, wherein classifying the plurality of medical images comprises:
estimating values for the histological feature of interest from the plurality of medical images by applying the trained model to the plurality of medical images using the computer system; and classifying the plurality of medical images based on the estimated values for the histological feature of interest (Brasch; Col. 15, lines 25-32 Tumors were graded for level of malignancy using the S-B-R method (Le Doussal, supra).).
As per claim 7, Brasch teaches the method of claim 1, wherein the histological feature of interest is percentage of necrosis (Brasch; Col. 16, lines 8-15 kinetic analysis of tumor enhancement responses was limited to the tumor rim; the tumor rim is typically the most vascularized and least necrotic region and is less subject to elevated interstitial pressure).
As per claim 8, Brasch in view of Saidi teaches the method of claim 1, wherein the trained model is based on a neural network (Saidi Col. 5, lines 40-55 teaches training a neural network or other learning machine with image-level morphometric data from a plurality of tissue images with known classification).
As per claim 10, Brasch teaches the method of claim 1, wherein the plurality of medical images comprises medical images acquired with at least one of a magnetic resonance imaging (MRI) system, an x-ray computed tomography (CT) system, an ultrasound imaging system, an optical imaging system, or a positron emission tomography (PET) system (Brasch; Col. 8, lines 48-55).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Brasch (6009342) in view of Saidi (7761240) in further view of Official Notice.
As per claim 9, Brasch in view of Saidi does not expressly teach the method of claim 1, wherein the trained model is based on a support vector machine. Saidi Col. 5, lines 40-55 teaches training a neural network or other learning machine with image-level morphometric data from a plurality of tissue images with known classification. It would have been obvious to one of ordinary skill in the art to substitute a “support vector machine” as an “other learning machine” application, as broadly claimed in view of Official Notice. Examiner takes Official Notice that support vector machines were well-known in the art at the time of the claimed invention one of ordinary skill in the art would be motivated to apply a “support vector machine” to most accurately analyze the medical images.
Subject Matter free from Prior Art
As per claims 11-17, the closest prior art of record does not expressly teach:
providing to a computer system, a plurality of medical images, wherein the plurality of medical images comprises at least one medical image of each of a plurality of different tissue samples, wherein the plurality of different tissue samples contain both contrast-enhancing tumor and contrast-enhancing treatment effects;
providing to the computer system, quantitative histological feature values determined from the plurality of different tissue samples; forming training data with the computer system, wherein the training data comprise an image contrast matrix formed from the plurality of medical images and a histological feature matrix formed from the quantitative histological feature values;
training a model on the training data using the computer system, wherein the model is trained on the training data using machine learning to differentiate between different sources of contrast enhancement based on a histological feature of interest; and
storing the trained model with the computer system.
No final decision on patentability has been made in light of pending rejections
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
Lohse (CA002817161), the closest foreign reference of record, teaches a visualizing targets in histological samples useful in medical diagnostics.
Kabli (Kabli, Samira; He, Shuning; Herman P; Hurlstone, Adam; Jagalsk, Ewa Snaar; et al. In vivo magnetic resonance imaging to detect malignant melanoma in adult zebrafish. Zebrafish 7.2: 143(6). Mary Ann Liebert, Inc. (June 2010)), the closest Non Patent Literature of record teaches characterizing tumor anatomy and establishing parameters for in vivo MRI of zebrafish melanomas.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH GIANG MICHELLE LE whose telephone number is (571)272-8207. The examiner can normally be reached Mon- Fri 8:30am - 5:30pm PST.
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LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/Primary Examiner, Art Unit 3686 12/12/2025