DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the filed on 11/15/2024.
Claims 1-20 currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statement filed on 4/8/2025 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Priority
Applicant’s claim for the benefit of prior-filed applications (provisional application 63/599,471, filed 11/15/2023) under 35 U.S.C. 110(e) or under 35 U.S.C. 120, 121, or 365(c), or under 35 U.S.C. 119(a)-(d) or (f) is acknowledged.
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.
Human Interactions Organized
Applicant discloses (Applicant’s Specification, [0004]-[0005]) that conventional cancer diagnosis and treatment models suffer from several drawbacks. For example, clinicians are currently struggling to understand a clinically homogeneous group of patients that do not present with symptoms or conditions sufficient to allow the clinician to adequately diagnose the patient's risk profile based upon clinical criteria So a need exists to organize these human interactions by/through stratifying patient cancer risk using computational oncology and molecular data using the steps of “receiving molecular data, processing molecular data, generating matched treatment strategies,” etc. Applicant’s system/method/computer readable medium is therefore a certain method of organizing the human activities as described and disclosed by Applicant.
Rejection
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim(s) 1, 15 and 19 is/are directed to the abstract idea of “stratifying patient cancer risk using computational oncology and molecular data,” etc. (Applicant’s Specification, Abstract, paragraph(s) [0002]), etc., as explained in detail below, and thus grouped as a certain method of organizing human interactions. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea. Accordingly, claims 1-20 recite an abstract idea.
Step 2A Prong 1 – The Judicial Exception
The claim(s) recite(s) in part, system/method/computer readable medium for performing the steps of “receiving molecular data, processing molecular data, generating matched treatment strategies,” etc., that is “stratifying patient cancer risk using computational oncology and molecular data,” etc. which is a method of managing personal behavior or relationships or interactions between people (social activities, teaching, following rules, instructions) and thus grouped as a certain method of organizing human interactions. Accordingly, claims 1-20 recite an abstract idea.
Step 2A Prong 2 – Integration of the Judicial Exception into a Practical Application
This judicial exception is not integrated into a practical application because the generically recited additional computer elements (i.e. molecular risk prediction computing device, processor, NIC, memory, client computing device, network, sequencer, input device, output device (Applicant’s Specification [0044]-[0050], [0067]-[0069] and [0072]), etc.) to perform steps of “receiving molecular data, processing molecular data, generating matched treatment strategies,” etc. do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and this is nothing more than an attempt to generally link the product of nature to a particular technological environment. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limit on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Claim(s) 1-20 recites storing data steps, retrieving data steps, providing data steps, output steps (Bilski v. Kappos, 561 U.S. 593, 610-12 (2010), Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Can., 771 F.Supp.2d 1054, 1066 (E.D. Mo. 2011), aff’d, 687 F.3d at 1266), and/or transmitting data step (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014), Apple, Inc. v. Ameranth, Inc., 842 F.3d 1299, 1241-42 (Fed. Cir. 2016)) that is/are insignificant extra-solution activity. Extra-solution activity limitations are insufficient to transform judicially excepted subject matter into a patent-eligible application (MPEP §2106.05(g)).
Step 2B – Search for an Inventive Concept/Significantly More
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (i.e. molecular risk prediction computing device, processor, NIC, memory, client computing device, network, sequencer, input device, output device, etc.) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept (Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)). Accordingly, the claims are not patent eligible.
Individually and in Combination
The additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The additional elements amount to no more than generic computer components that serve to merely link the abstract idea to a particular technological environment (i.e. molecular risk prediction computing device, processor, NIC, memory, client computing device, network, sequencer, input device, output device, etc.). At paragraph(s) [0044]-[0050], [0067]-[0069] and [0072], Applicant’s specification describes generic computer hardware for implementing the above described functions including “molecular risk prediction computing device, processor, NIC, memory, client computing device, network, sequencer, input device, output device,” etc. to perform the functions of “receiving molecular data, processing molecular data, generating matched treatment strategies,” etc. The recited “molecular risk prediction computing device, processor, NIC, memory, client computing device, network, sequencer, input device, output device,” etc. does/do not add meaningful limitations to the idea of beyond generally linking the system to a particular technological environment, that is, implementation via computers. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, or improves any other technology, or improves a technical field, or provides a technical improvement to a technical problem. Their collective functions merely provide generic computer implementation. Therefore, claims 1-20 do not amount to significantly more than the underlying abstract idea of “an idea of itself” (Alice).
Dependent Claims
Dependent claim(s) 2-14, 16-18 and 20 include(s) all the limitations of the parent claims and are directed to the same abstract idea as discussed above and incorporated herein.
Although dependent claims 2-14, 16-18 and 20 add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. Dependent claims 2-14, 16-18 and 20 merely describe physical structures to implement the abstract idea. These information and physical characteristics do not change the fundamental analogy to the abstract idea grouping of certain method of organizing human interactions, and when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as independent claim(s) 1, 15 and 19.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hafez et al. 419 (US 2021/0343419), in view of (US Y).
CLAIM 1
As per claim 1, Hafez et al. 419 disclose:
a computer-implemented method (Hafez et al. 419, [0010] method) for stratifying patient cancer risk using molecular data, comprising:
receiving, via one or more processors, molecular data corresponding to a patient (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18);
processing, via one or more processors, the molecular data using a machine learning model to determine the patient’s molecular data risk (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18),
wherein the machine learning model is trained using a patient training dataset and/or a reference training data set (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18),
and correct its training data, and wherein the machine learning model includes a survival model (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18); and
generating a matched treatment strategy corresponding to the patient based upon the patient’s molecular data risk (Hafez et al. 419, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12, Figure 19B, Figure 23).
Hafez et al. 419 fail to expressly disclose:
wherein the machine learning model uses univariate gene selection, RNA bias correction and multivariate gene selection to filter.
However, Hafez et al. 419 teach:
wherein the machine learning model uses univariate gene selection, RNA bias correction and multivariate gene selection to filter (Hafez et al. 419, Figure 4 400, [0050], [0058]).
One of ordinary skill in the art before the effective filing date would have found it obvious to include “wherein the machine learning model uses univariate gene selection, RNA bias correction and multivariate gene selection to filter,” etc. as taught by Hafez et al. 419 within the method as taught by the Hafez et al. 419 as Hafez et al. 419 teach “gene copy number analysis,” an epigenome module, etc. and the addition of the known techniques of univariate gene selection, RNA bias correction and multivariate gene selection would have been obvious with the motivation of providing univariate and multivariate analysis (Hafez et al. 419, Figure 4 400, [0050], [0058]).
CLAIM 2
As per claim 2, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 1 and further disclose the limitations of:
wherein the survival model is a Cox Proportional Hazards model (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
The obviousness of combining the teachings of Hafez et al. 419 with the method as taught by Hafez et al. 419 is discussed in the rejection of claim X, and incorporated herein.
CLAIM 3
As per claim 3, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 1 and further disclose the limitations of:
wherein the molecular data corresponding to the patient includes RNA seq data (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIM 4
As per claim 4, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 1 and further disclose the limitations of:
wherein the cancer is endometrial cancer, and wherein the machine learning model was trained on a cohort of patient data selected using a greedy algorithm (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
The obviousness of combining the teachings of Hafez et al. 419 with the method as taught by Hafez et al. 419 is discussed in the rejection of claim X, and incorporated herein.
CLAIM 5
As per claim 5, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 4 and further disclose the limitations of:
wherein the greedy algorithm includes: identifying patients with uterine subtype and primary site endometrium or uterus; identifying time to progression eligible patients; identifying patients with sarcoma cancers; identifying patients having serous tissue cancers and squamous tissue cancers; and identifying patients having sarcoma cancers, serous tissue cancers and squamous tissue cancers (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
The obviousness of combining the teachings of Hafez et al. 419 with the method as taught by Hafez et al. 419 is discussed in the rejection of claim X, and incorporated herein.
CLAIM 6
As per claim 6, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 1 and further disclose the limitations of:
wherein the patient has a pre-existing clinical risk group assignment of at least one of the following: low clinical risk, low-intermediate clinical risk, high-intermediate clinical risk or high clinical risk (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIM 7
As per claim 7, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 6 and further disclose the limitations of:
wherein generating the treatment strategy corresponding to the patient based upon the patient’s molecular data risk includes generating the treatment strategy based upon both of (i) the pre-existing clinical risk group; and (ii) the patient’s molecular risk (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIM 8
As per claim 8, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 7 and further disclose the limitations of:
wherein the pre-existing clinical risk group is high-intermediate, the patient’s molecular risk is high, and the treatment strategy is at least one of systemic therapy or external beam radiation therapy (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIM 9
As per claim 9, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 7 and further disclose the limitations of:
wherein the pre-existing clinical risk group is high-intermediate, the patient’s molecular risk is low, and the matched treatment strategy is observation (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIM 10
As per claim 10, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 7 and further disclose the limitations of:
wherein the pre-existing clinical risk group is low-intermediate, the patient’s molecular risk is high, and the matched treatment strategy is at least one of brachytherapy, external beam radiation therapy or systemic therapy (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIM 11
As per claim 11, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 7 and further disclose the limitations of:
wherein the pre-existing clinical risk group is high, the patient’s molecular risk is low, and the matched treatment strategy is observation (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIM 12
As per claim 12, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 7 and further disclose the limitations of:
wherein the pre-existing clinical risk group is high, the patient’s molecular risk is low, and the matched treatment strategy is observation (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
[motivation]
CLAIM 13
As per claim 13, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 7 and further disclose the limitations of:
wherein the pre-existing clinical risk group is high, the patient’s molecular risk is high, and the matched treatment strategy is at least one of systemic therapy or external beam radiation therapy (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIM 14
As per claim 14, Hafez et al. 419 and Hafez et al. 419
teach the method of claim 1 and further disclose the limitations of:
wherein the matched treatment strategy includes at least one of systemic therapy, external beam radiation therapy, brachytherapy or observation (Hafez et al. 419, Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 16, Figure 17, Figure 18).
CLAIMS 15-18
As per claims 15-18, claims 15-18 are directed to a system. Claims 15-18 recite the same or similar limitations as those addressed above for claims 1-14. Claims 15-18 are therefore rejected for the same reasons set forth above for claims 1-14.
CLAIMS 19-20
As per claims 19-20, claims 19-20 are directed to a computer readable medium. Claims 19-20 recite the same or similar limitations as those addressed above for claims 1-14. Claims 19-20 are therefore rejected for the same reasons set forth above for claims 1-14.
Prior Art
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO-892 and include:
Hafez et al. 906 (US 2021/0319906) disclose systems and methods for predicting metastasis of a cancer in a subject. A plurality of data elements for the subject's cancer is obtained, including sequence features comprising relative abundance values for gene expression in a cancer biopsy of the subject, optional personal characteristics about the subject, and optional clinical features related to the stage, histopathological grade, diagnosis, symptom, comorbidity, and/or treatment of the cancer in the subject, and/or a temporal element associated therewith.
Hafez et al. 736 et al. (US 2022/0148736) disclose systems and methods for predicting metastasis of a cancer in a subject. A plurality of data elements for the subject's cancer is obtained, including sequence features comprising relative abundance values for gene expression in a cancer biopsy of the subject, optional personal characteristics about the subject, and optional clinical features related to the stage, histopathological grade, diagnosis, symptom, comorbidity, and/or treatment of the cancer in the subject, and/or a temporal element associated therewith.
Amiri Souri et al. 2021 (Reference U) identify a 70-gene signature for assessing clinical risk which is shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. A Cancer Grade Model was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumor risk.
Cheong et al. 2022 (Reference V) use a machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets.
Conclusion
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/C. P. C./
Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683
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