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 Applicants
This communication is in response to the Application filed on 7/17/2024.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/17/2024 has been considered by the examiner.
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 15-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 15-20 of U.S. Patent No. 12,059,284. Although the claims at issue are not identical, they are not patentably distinct from each other because they are anticipated. Claim 15 is shown below as an example, claims 16-20 are rejected/mapped similarly.
US Application No., 18/775,070
US Patent No., 12,059,284
15. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: obtain:
15. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: obtain:
first information relating to one or more first lung nodules identified in a first imaging of a chest of a patient,
first information relating to one or more first lung nodules identified in a first computed tomography (CT) scan of a chest of a patient,
second information relating to one or more second lung nodules identified in a second imaging of the chest of the patient,
second information relating to one or more second lung nodules identified in a second CT scan of the chest of the patient,
third information relating to an elapsed time between performance of the first imaging and the second imaging, and
third information relating to an elapsed time between performance of the first CT scan and the second CT scan, and
fourth information relating to at least one of an age of the patient, a gender of the patient, a smoking history of the patient, a family history of cancer associated with the patient, or an exposure of the patient to a carcinogen;
fourth information relating to at least one of an age of the patient, a gender of the patient, a smoking history of the patient, a family history of cancer associated with the patient, or an exposure of the patient to a carcinogen;
process the first information, the second information, the third information, and the fourth information with a machine learning model to determine a risk of lung cancer associated with the patient based on the third information and the fourth information and differences between the first information and the second information;
process the first information, the second information, the third information, and the fourth information with a machine learning model to determine a risk of lung cancer associated with the patient based on the third information and the fourth information and differences between the first information and the second information;
and perform one or more actions based on the risk of lung cancer that is determined.
and perform one or more actions based on the risk of lung cancer that is determined.
Claims 8-14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 8-14 of U.S. Patent No. 12,059,284 (hereafter, “’284 patent”) in view of US 2018/0068083 to Cohen et al. (hereafter, “Cohen”). All of the limitations of claim 8, as show in the table below is in the patent ‘284 except “third information relating to an elapsed time between performance of the first imaging and the second imaging, and fourth information relating to at least one of an age of the patient, a gender of the patient, a smoking history of the patient, a family history of cancer associated with the patient, or an exposure of the patient to a carcinogen”.
However, Cohen teaches both of these information, “third information relating to an elapsed time between performance of the first imaging and the second imaging, and fourth information relating to at least one of an age of the patient, a gender of the patient, a smoking history of the patient, a family history of cancer associated with the patient, or an exposure of the patient to a carcinogen” at paragraphs [0054, 0073-0074, 0078, 0197, 0202, etc. throughout the reference].
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify patent ‘284 to have elapsed time and one of the patient related criteria of Cohen’s reference. The suggestion/motivation for doing so would have been to analyze series of biomarkers over time (i.e., elapsed time) to determine rate of increase in velocity of the biomarker to determine a patient's risk of developing cancer stratified into high risk versus low risk, as suggested by Cohen at paragraph [0197]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Cohen with ‘284 patent to obtain the invention as specified in claim 8. Claims 9-14 are rejected similarly.
Claims 1-7 are similarly mapped to claims 1-7 of the ‘284 patent with few limitations being different than the ‘284 patent. However, Cohen, as pointed out in claim 8 above, meets claim 1 similarly.
US Application No., 18/775,070
US Patent No., 12,059,284
8. A device, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, to: obtain
8. A device, comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, to: obtain
first information relating to one or more first lung nodules identified in first imaging of a chest of a patient,
first information relating to one or more first lung nodules and one or more first disease conditions identified in first imaging of a chest of a patient and
second information relating to one or more second lung nodules identified in second imaging of the chest of the patient,
second information relating to one or more second lung nodules and one or more second disease conditions identified in second imaging of the chest of the patient;
third information relating to an elapsed time between performance of the first imaging and the second imaging, and
fourth information relating to at least one of an age of the patient, a gender of the patient, a smoking history of the patient, a family history of cancer associated with the patient, or an exposure of the patient to a carcinogen;
process the first information, the second information, the third information, and the fourth information to determine a risk of lung cancer associated with the patient based on the third information and the fourth information and differences between the first information and the second information; and,
process the first information and the second information with a machine learning model to determine a risk of lung cancer associated with the patient based on an elapsed time between performance of the first imaging and the second imaging and differences between the first information and the second information,
wherein the risk of lung cancer has an inverse correlation to the elapsed time and a direct correlation to the differences,
wherein the risk of lung cancer has an inverse correlation to the elapsed time and a direct correlation to the differences,
wherein the machine learning model has been trained according to lung nodule data relating to lung nodules of a plurality of subjects and disease condition data relating to disease conditions of the plurality of subjects,
wherein the machine learning model has been trained according to lung nodule data relating to lung nodules of a plurality of subjects and disease condition data relating to disease conditions of the plurality of subjects,
wherein the lung nodule data and the disease condition data for a subject, of the plurality of subjects, has been weighted based on a duration between a first time when the lung nodule data and the disease condition data was obtained for the subject and a second time when the subject died due to lung cancer; and
wherein the lung nodule data and the disease condition data for a subject, of the plurality of subjects, has been weighted based on a duration between a first time when the lung nodule data and the disease condition data was obtained for the subject and a second time when the subject died due to lung cancer; and
perform one or more actions based on the risk of lung cancer that is determined.
perform one or more actions based on the risk of lung cancer that is determined.
Allowable Subject Matter
Claims 1-20 are allowed over the cited art of record, except for the Double Patenting rejection.
The following is an examiner’s statement of reasons for allowance: the cited art of record by Hawkins, et al. discloses method to determine whether quantitative analyses (“radiomics”) of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer. The reference of Ardila, et al. discloses a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the-art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases. We conducted two reader studies. When prior computed tomography imaging was not available, our model outperformed all six radiologists with absolute reductions of 11% in false positives and 5% in false negatives. Where prior computed tomography imaging was available, the model performance was on-par with the same radiologists. This creates an opportunity to optimize the screening process via computer assistance and automation. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide. The cited art of record by Cohen et al. US2018/0068083 discloses non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients. The classifier may then be used to assesses the likelihood that a patient has cancer relative to a population by classify the patient into a category indicative of a likelihood of having cancer or into another category indicative of a likelihood of not having cancer.
However these and other cited art of record fails to teach, disclose or suggest limitations/features recited in claim 1 stating “providing, by the device, the first information, the second information, the third information, and the fourth information to a machine learning model; processing, by the device and using the machine learning model, the first information, the second information, the third information, and the fourth information to determine a risk of lung cancer associated with the patient based on the third information and the fourth information and differences between the first information and the second information, wherein the risk of lung cancer has an inverse correlation to the elapsed time and a direct correlation to the differences, wherein the machine learning model has been trained according to lung nodule data relating to lung nodules of a plurality of subjects and disease condition data relating to disease conditions of the plurality of subjects, wherein the lung nodule data and the disease condition data for a subject, of the plurality of subjects, has been weighted based on a duration between a first time when the lung nodule data and the disease condition data was obtained for the subject and a second time when the subject died due to lung cancer; and performing, by the device, one or more actions based on the risk of lung cancer that is determined” recited in claim 1; and corresponding features/limitations, in claims 8 and 15. Dependent claims would be allowed for the same reasons.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 7,711,404 discloses a system and method for lung cancer screening. The system includes a database including structured patient information for a patient population and a domain knowledge base including information about lung cancer; an individual patient record; and a processor for analyzing the patient record with data from the database to determine if a patient has indications of lung cancer. The method includes the steps of inputting patient-specific data into a patient record; performing at least one lung cancer screening procedure on a patient, wherein at least one result from the at least one procedure is inputted into the patient record in a structured format; and analyzing the patient record with a domain knowledge base to determine if the patient has indications of lung cancer.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEFALI D. GORADIA whose telephone number is (571)272-8958. The examiner can normally be reached Monday-Thursday 8AM-6PM, Friday 8AM-12PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SHEFALI D. GORADIA
Primary Patent Examiner
Art Unit 2676
/SHEFALI D GORADIA/Primary Patent Examiner, Art Unit 2676
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