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 .
Status of Claims
This action is in response to the application filed 7/29/2024.
Claims 1-20 are currently pending and have been examined.
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, 11, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 18, and 25 of U.S. Patent No. 12051508. Although the claims at issue are not identical, they are not patentably distinct from each other because both claimed inventions train a machine learning model using training data associated with a plurality of patients to predict treatment responses indicative of patient survival rate based on a change in volume of a lesion of a patient, wherein the training data is indicative of at least one of diagnostic imaging scans at baselines, follow-up intervals, or temporary changes in lesion volume and providing the pre-treatment image of the target lesions of a target patient to the machine learning model to generate a treatment response.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are drawn to a method and a device which are statutory categories of invention (Step 1: YES).
Independent claims 1, 11, and 20 recite training, using training data associated with a plurality of patients to predict biologic therapy treatment responses indicative of a patient survival rate based on a change in volume of a lesion of a patient, wherein the training data is indicative of at least one of unique diagnostic imaging scans at baselines, follow-up intervals, or temporary changes in lesion volume; providing a pre-treatment image of one or more target lesions of a target patient to generate a biologic therapy treatment response; and generating, based on the biologic therapy treatment response, a recommended treatment plan indicating a pharmaceutical product to treat the one or more target lesions of the target patient.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between a clinical user, as reflected in the specification, which states that “With respect to post-processing, a variety of techniques may be used to post-process individual model predictions to obtain the predictions accuracy and explainability required by clinical end users. Examples of post processing methods used may include, but are not limited to:… In some clinical scenarios, the clinical requirement is to predict treatment response at the patient level (i.e. Will this patient benefit from given therapy overall, considering that some lesions may respond while others will continue to progress?).” (see: specification paragraphs 47-49). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “a need [that] exists for improved methods and systems for managing informed consent data for human specimen research” (see: specification page 5). This problem is addressed by outputting the patient’s treatment plan to the clinical user “[The output 400 may also include therapy information 406. The therapy information 406 may indicate which types of immunotherapy, chemotherapy, and/or targeted therapy are recommended to use in treatment. The output 400 may further include patient profile 408 that includes information associated with the patient receiving treatment.” (see: specification paragraph 59). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “processing device”, “AI model”, “treatment analysis system”, “memory”, “non transitory computer-readable storage medium” are recited at a high level of generality (e.g., that the training, providing and displaying is performed using a generic machine learning model implemented on generic computing components with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 5 and
Paragraph 19, where “Figure 1 is a diagram showing a machine learning system 100 for use with embodiments of the present disclosure. Although specific components are disclosed in machine learning system 100, it should be appreciated that such components are examples. That is, embodiments of the present invention are well suited to having various other components or variations of the components recited in machine learning system 100. It is appreciated that the components in machine learning system 100 may operate with other components than those presented, and that not all of the components of machine learning system 100 may be required to achieve the goals of machine learning system 100. and/or resources for the web application.”
Paragraph 20, where “In one embodiment, system 100 includes server 101, network 106, and client device 150. Server 100 may include various components, which may allow for predicting responses to PD-1 checkpoint blockades ( and other immunotherapy treatments) using deep learning analysis and of imaging and clinical data on a server device or client device. Each component may perform different functions, operations, actions, processes, methods, etc., for a web application and/or may provide different services, functionalities,
Paragraph 26, where “In embodiments, other types of machine learning models may be used instead of or in conjunction with the at least one deep learning model. In some embodiments, a large set of predefined imaging and clinical features is generated, followed by a feature selection algorithm (e.g. minimum redundancy maximum relevance (MRMR) or least absolute shrinkage and selection operator (LASSO)), and fitted using machine learning methods (e.g. gradient boosted decision trees, random decision forests, or support vector machines) to produce a predictive model.”
Paragraph 29, where “Examples of patient-level model include, but are not limited to, artificial neural network, random forest model, support vector machine, and logistic regression model. In another embodiment, a single machine learning model may be used that considers multiple lesions at once. “
Paragraph 30, where “The at least one deep learning model may include any suitable variety of machine learning models including, but not limited to, a convolutional neural network. In one embodiment, the models are trained on same data, using different hyper-parameters and optimization techniques. In another embodiment, the models are trained on different data, using different techniques, have different objectives, etc., the results of which may be aggregated in a variety of ways.”
Paragraph 62, where “The exemplary computer system 500 includes a processing device 502, a main memory 504 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), a static memory 506 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 518, which communicate with each other via a bus 530. Any of the signals provided over various buses described herein may be time multiplexed with other signals and provided over one or more common buses. Additionally, the interconnection between circuit components or blocks may be shown as buses or as single signal lines. Each of the buses may alternatively be one or more single signal lines and each of the single signal lines may alternatively be buses.”
Paragraph 63, where “Processing device 502 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device may be complex instruction set computing (CISC) microprocessor, reduced instruction set computer (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processing device 502 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 502 is configured to execute processing logic 526, which may be one example of system 100 shown in Figure 1, for performing the operations and steps discussed herein.”
Paragraph 64, where “The data storage device 518 may include a machine-readable storage medium 528, on which is stored one or more set of instructions 522 (e.g., software) embodying any one or more of the methodologies of functions described herein, including instructions to cause the processing device 502 to execute system 100. The instructions 522 may also reside, completely or at least partially, within the main memory 504 or within the processing device 502 during execution thereof by the computer system 500; the main memory 504 and the processing device 502 also constituting machinereadable storage media. The instructions 522 may further be transmitted or received over a network 520 via the network interface device 508.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 2-10 and 12-18 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 2-10 and 12-18 recite receiving healthcare data and calculating prediction scores on the generically recited machine learning model and generically recited computing components as shown in the parent claims above.
Claims 4 and 14 further recite “a convolutional neural network” which is recited at a high level of generality (e.g., that the associating and correlating are performed using generic computer components with instructions are executed to perform the claimed limitations) as shown in the specification paragraph 30. Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Allowable Subject Matter
Claims 1-20 are allowable over prior art as they overcome the prior art references of Yip (US 2020/0258223 A1), Lou (US 2020/0069973 A1), Tuli (US 2020/0114004 A1), Madabuhushi (US 10950351 B2), Zhou (US 2017/0263023 A1) and Kaigala (US 2019/0286790 A1) of its parent application. Specifically the claim limitation of training an AI model “using training data associated with a plurality of patients to predict biologic therapy treatment responses indicative of a patient survival rate based on a change in volume of a lesion of a patient, wherein the training data is indicative of at least one of unique diagnostic imaging scans at baselines, follow-up intervals, or temporary changes in lesion volume” in combination of the other claim limitations overcome the prior art. A new prior art search was conducted and found the prior art of Hasey (WO 2009/103156 A1) which is similar in that it teaches treatment responses based on tumor data, however it did not teach using machine learning models to calculate these responses.
Conclusion
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/KIMBERLY A. SASS/Examiner, Art Unit 3686