DETAILED ACTION
Notice to Applicant
This communication is in response to the application submitted October 3, 2024. The present application claims priority to EP Patent Application number 23201116.3, filed on October 02, 2023. Claims 1, 3 – 9, and 11 – 13 are amended. Claims 1 – 13 are presented for examination.
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 Objections
Claim 12 is objected to because of the following informalities: typographical error/omission. Claim 12 recites “a memory storing an application program configured to perform, when executed by the processor, an operation, the operation”. Appropriate correction is required.
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 – 13 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.
Step One
Claims 1 – 13 are drawn to a method, system, and non-transitory computer-readable storage medium, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1, 12, and 13 recite selecting a patient for a therapy, the method comprising: receiving a request for the issuance of a convertible bond, wherein the convertible bond comprises a patient's right to receive a therapy for a disease if conversion criteria are met, wherein the request comprises initial patient data, determining whether issue criteria for issuance of the convertible bond to the patient are met based on the initial patient data, in the event that the issue criteria are met: determining the conditions under which the convertible bond is issued based on the initial patient data and/or based on data on convertible bonds already issued to other patients and/or based on data from other patients who suffered from the disease, and outputting the conditions under which the convertible bond is issued.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that the present invention “relate[s] to selection of patients for therapy” (paragraph 1 of the published specification). 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 to provide “Personalized medicine [which] makes it possible to take a "one size fits all" approach to diagnostics, drug therapy, and prevention and turn it into an individualized approach” (paragraph 5 of the published specification). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “computer-implemented
Claim 9: “trained machine learning model”
Claim 10: “trained machine learning model is configured and was trained to determine a probability value based on the initial patient data, wherein the trained machine learning model was trained on training data, wherein the training data”
Claim 12: “System”, “processor”, “a memory storing an application program configured to perform, when executed by the processor, an operation, the operation”
Claim 13: “A non-transitory computer readable storage medium having stored thereon software instructions that, when executed by a processor of a computer system, cause the computer system to execute the following steps”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these 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.
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).
Step 2B
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 components 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 not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
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 published specification supports this conclusion as follows:
[0201] A "computer system" is a system for electronic data processing that processes data by means of programmable calculation rules. Such a system usually comprises a "computer", that unit which comprises a processor for carrying out logical operations, and also peripherals.
[0203] Computer systems of today are frequently divided into desktop PCs, portable PCs, laptops, notebooks, netbooks and tablet PCs and so-called handhelds (e.g, smartphone); all these systems can be utilized for carrying out the invention.
[0205] The term "computer" should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, personal computers, servers, embedded cores, computing system, communication devices, processors ( e.g., digital signal processor (DSP)), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing devices.
[0210] The processing unit (20) may be a number of processors, a multi-core processor or some other type of processor. depending on the particular implementation. For example, it may be a central processing unit (CPU), a field programmable gate array (FPGA), a graphics processing unit (GPU) and/or a tensor processing unit (TPU). Further, the processing unit (20) may be implemented using a number of heterogeneous processor systems in which a main processor is present with one or more secondary processors on a single chip. As another illustrative example, the processing unit (20) may be a symmetric multi-processor system containing multiple processors of the same type. In yet another example, the processing unit (20) may be embodied as or otherwise include one or more ASICs. FPGAs or the like. Thus, although the processing unit (20) may be capable of executing a computer program to perform one or more functions, the processing unit (20) of various examples may be capable of performing one or more functions without the aid of a computer program. In either instance the processing unit (20) may be appropriately programmed to perform functions or operations according to example implementations of the present disclosure.
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 routine, 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 claim(s) 3, 5, 10, 12, 17, and 19 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 limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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(s) 1 – 5, 7, 11 – 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inwald et al., herein after Inwald (U.S. Publication Number 2021/0193332 A1) in view of Elton et al., herein after Elton (WO 2012/122127 A3).
Claim 1 (Currently amended). Inwald teaches a computer-implemented method for selecting a patient for a therapy, the method comprising:
receiving a request for the issuance of a convertible bond (paragraph 55 discloses the database can further be in electronic communication with an insurance company's databases and have access to a particular patient's insurance plan. Once a particular dose of the cancer therapy/drug combination has been determined, the logic engine can further analyze the feasibility of the patient taking the particular combination of cancer therapies/drugs according to what their insurance will cover),
wherein the request comprises initial patient data (paragraph 28 discloses various data is collected about the patient),
determining whether issue criteria for issuance of the convertible bond to the patient are met based on the initial patient data (Figure 2; paragraph 27 discloses at a patient encounter, it is determined if there is any existing fit history or visit data, if not, the patient data can be collected. A pre-query is performed to identify any required data points to perform an analysis. If the required data points are not present, then they are gathered or collected),
outputting the conditions under which the convertible bond is issued (Figures 4, 6, and 7; paragraph 11 discloses adjusting treatment of a cancer patient, by a patient inputting data about nutrition, medication, lifestyle, symptoms, and user defined metrics in an application, integrating data from outside devices and outside databases including updated patient scans, performing an analysis on the data, outputting a result from the data to medical professionals, and the medical professionals adjusting the treatment of the patient based on the data; paragraph 61 discloses the application includes an input module for inputting variables from a user in electronic communication with an output variable module, an analysis module that analyzes data from the input variables and output variables, and an output module for presenting results to the user).
Inwald fails to explicitly teach the following limitations met by Elton as cited:
wherein the convertible bond comprises a patient's right to receive a therapy for a disease if conversion criteria are met (paragraph 12 discloses the patient data comprises one or more genetic profiles and the medical histories of the one or more patients; paragraph 24 discloses the financial data comprises costs associated with the care of the patient; paragraph 65 discloses modules that allow for comprehensive real-time analytics involving collecting and using clinical, genetic financial data to enhance patient care, cost, safety and efficiency, and data are examined on a variety of levels including the ability to interpret clinical data at the point of decision making; paragraph 69 discloses module allows for the healthcare provider or insurer to determine the costs associated with a particular treatment and potentially more cost-efficient treatments for a particular disease, where the system can integrate the financial information (i.e., financial data) into the data set);
in the event that the issue criteria are met: determining the conditions under which the convertible bond is issued based on the initial patient data and/or based on data on convertible bonds already issued to other patients and/or based on data from other patients who suffered from the disease (paragraph 69 discloses module allows for the healthcare provider or insurer to determine the costs associated with a particular treatment and potentially more cost-efficient treatments for a particular disease, where the system can integrate the financial information (i.e., financial data) into the data set).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Inwald to further include personalized medical management system for the assignment of therapeutic pathways to members of a network of oncology practices as disclosed by Elton.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Inwald in this way to provide a system that stores the disparate information using uniform semantics so that information is accessible to all members of a network (i.e., clinicians, patients, insurers, practitioners, and researchers). In addition, there remains a need to provide members of a network calculated treatment pathways based on information stored in the system. Such pathways can vastly decrease the cost of treating patients, while increasing the quality of life of the patients and efficiency of treatment. (Elton: paragraph 6).
System claims 12 – 13 repeat the subject matter of claim 1. As the underlying processes of claims 12 – 13 have been shown to be fully disclosed by the teachings of Inwald and Elton in the above rejections of claim 1; as such, these limitations (12 – 13) are rejected for the same reasons given above for claim 1 and incorporated herein.
Claim 2 (Original). Inwald and Elton teach the method of claim 1. Inwald teaches a method further comprising:
receiving a request for conversion of the convertible bond, the request comprising subsequent patient data (paragraph 35 discloses requesting supplemental data),
determining whether the conversion criteria are met (paragraph 55 discloses the database can further be in electronic communication with an insurance company's databases and have access to a particular patient's insurance plan. Once a particular dose of the cancer therapy/drug combination has been determined, the logic engine can further analyze the feasibility of the patient taking the particular combination of cancer therapies/drugs according to what their insurance will cover),
in case the conversion conditions are met: notifying the patient and/or a physician that the patient will receive therapy (paragraph 96 discloses the application can also include any suitable alarms or notifications that can remind users to input data into the input module or output variable module at certain times of the day or daily).
Claim 3 (Currently Amended). Inwald and Elton teach the method of claim 1. Inwald teaches a method wherein the therapy is an individualized therapy adapted to the individual needs of the patient (paragraph 106 discloses the platform takes into account the patient’s clinical and physical conditions thereby personalizing the patient’s prescriptions).
Claim 4 (Currently Amended). Inwald and Elton teach the method of claim 1. Inwald teaches a method wherein the therapy is or comprises gene therapy, cell therapy and/or a therapy for treating a rare disease (paragraph 109 discloses cellular therapy).
Claim 5 (Currently Amended). Inwald and Elton teach the method of claim 1. Inwald teaches a method wherein determining whether the issue criteria are met includes: determining whether the patient already has the disease, and/or determining whether one or more of patient's dependents have or had the disease (paragraph 31 discloses collecting genetic components for generating ethnicity and demographics features for multidimensional nearest neighbor calculations).
Claim 7 (Currently Amended). Inwald and Elton teach the method of claim 1. Inwald teaches a method wherein determining whether the conversion criteria are met includes: determining if the patient has the disease, and/or determining whether a conversion period has expired (paragraph 28 discloses various data is collected about the patient including symptoms, diagnoses, and proposed cancer therapies, drugs, and/or treatments that the patient has been prescribed to take by a doctor or other medical professional).
Claim 11 (Currently Amended). Inwald and Elton teach the method of claim 1. Inwald teaches a method wherein initial patient data includes one or more of the following: patient's age, sex, body size, body weight, body mass index, blood group, blood values, blood pressure values, resting heart rate, heart rate variability, an electrocardiogram, sugar concentration in urine and/or blood and/or other values from urine/blood tests, body temperature, impedance, lifestyle information about the life of the patient, including consumption of alcohol, smoking, and/or exercise and/or the patient's diet, medical intervention parameters including regular medication, occasional medication, and/or other previous or current medical interventions and/or other information about the patient's previous and/or current treatments and/or reported health conditions, results of genetic testing on the patient and/or other results of other laboratory tests, one or more medical images including x-rays and/or magnetic resonance imaging images and/or computed tomography images and/or ultrasound images and/or positron emission tomography images and/or combinations thereof (paragraph 28 discloses various data is collected about the patient including blood and urine samples).
Claim(s) 6 and 8 – 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inwald et al., herein after Inwald (U.S. Publication Number 2021/0193332 A1) in view of Elton et al., herein after Elton (WO 2012/122127 A3) further in view of Das et al., herein after Das (U.S. Publication Number 2022/0084662 A1).
Claim 6 (Currently Amended). Inwald and Elton teach the method of claim 1.
Inwald and Elton fail to explicitly teach the following limitations met by Das as cited:
wherein determining whether the issue criteria are met includes: determining a probability value, the probability value indicating a probability that the patient will develop the disease at all and/or within a pre-defined time period (paragraph 5 discloses a probability that the patient will require clinical intervention within a predetermined time period; paragraph 49 discloses automatic advance notice to a caregiver that a patient is likely to require clinical intervention within a predetermined time period. That time period can be any time period established and provided as input by the caregiver, or it can be established in the model when the probability or likelihood that the patient needs clinical intervention reaches a predetermined threshold).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Inwald and Elton to further include using machine learning techniques to increase the statistical power of a clinical trial analysis by automatically identifying and selecting candidate patients who meet the clinical trial inclusion criteria and who are statistically likely to meet at least one of the one or more clinical trial endpoints as disclosed by Das.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Inwald and Elton in this way to personalize medicine and therapeutic intervention points as there is a clear need for better communication regarding the patient's health condition to medical practitioners (Das: paragraph 2).
Claim 8 (Currently Amended). Inwald and Elton teach the method of claim 1.
Inwald and Elton fail to explicitly teach the following limitations met by Das as cited:
wherein determining whether the conversion criteria are met includes: determining a probability value, the probability value indicating a probability that the patient will develop the disease at all and/or within a pre-defined time period (paragraph 5 discloses a probability that the patient will require clinical intervention within a predetermined time period; paragraph 49 discloses automatic advance notice to a caregiver that a patient is likely to require clinical intervention within a predetermined time period. That time period can be any time period established and provided as input by the caregiver, or it can be established in the model when the probability or likelihood that the patient needs clinical intervention reaches a predetermined threshold).
The motivation to combine the teachings of Inwald, Elton, and Das is discussed in the rejection of claim 6, and incorporated herein.
Claim 9 (Currently Amended). Inwald, Elton, and Das teach the method of claim 6.
Inwald and Elton fail to explicitly teach the following limitations met by Das as cited:
wherein the probability value is determined using a trained machine learning model (paragraph 5 discloses a probability that the patient will require clinical intervention within a predetermined time period and the training data includes the health record data obtained from the plurality of patients).
The motivation to combine the teachings of Inwald, Elton, and Das is discussed in the rejection of claim 6, and incorporated herein.
Claim 10 (Original). Inwald, Elton, and Das teach the method of claim 9.
Inwald and Elton fail to explicitly teach the following limitations met by Das as cited:
wherein the trained machine learning model is configured and was trained to determine a probability value based on the initial patient data, wherein the trained machine learning model was trained on training data (paragraph 5 discloses the training data includes the health record data obtained from the plurality of patients),
wherein the training data comprised, for each reference patient of a plurality of reference patients,
health data of the reference patient as input data (paragraph 5 discloses the training data includes the health record data obtained from the plurality of patients), and
(ii) a disease information as target data, the disease information indicating whether and/or when the reference patient had the disease (paragraph 6 discloses diagnosis information, which indicates whether the patient had/has the disease).
The motivation to combine the teachings of Inwald, Elton, and Das is discussed in the rejection of claim 6, and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lenga et al. (Lenga P, Papakonstantinou V, Kiening K, Unterberg AW, Ishak B. Outcomes of cervical spinal stenosis surgery in patients aged ≥ 65 years based on insurance status: a single-center cohort study from a tertiary center in Germany. Acta Neurochir (Wien). 2023 Oct;165(10):3089-3096. doi: 10.1007/s00701-023-05700-9. Epub 2023 Jul 6. PMID: 37410186; PMCID: PMC10541335) discloses outcomes of cervical spinal stenosis surgery in patients aged ≥65 years based on insurance status: a single-center cohort study from a tertiary center in Germany).
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KRISTINE K. RAPILLO
Examiner
Art Unit 3626
/KRISTINE K RAPILLO/Examiner, Art Unit 3682