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 .
Election/Restrictions
Applicant’s election without traverse of claim 11 in the reply filed on October 3, 2025 is acknowledged. Claims 1-10 have been non-elected. Claims 12-20 have been added. Claims 11-20 are pending and have been examined.
Priority
While Claims 11-13 are granted the priority date of the provisional application (63/462,877), Claims 14-20 are provided an effective filing date of April 26, 2024 as they reflect new matter that is not supported in the provisional application to which the application claims priority to.
Claim Objections
Claims 11-20 are objected to because of the following informalities:
Claim 11 recites: “comprising patient specific digital twin”. The claim should be modified to recite “comprising -a patient specific digital twin”.
Claims 12-20 are objected to by virtue of their dependency on claim 11.
Claim 12 recites “AI”. The first instance of AI must be expanded for clarity (ie. “artificial intelligence”).
Claim 13 is objected to by virtue of its dependency on claim 12.
Claim 13 recites “where once said digital twins are defined the digital patient pathway is then used”. Examiner suggests modifying the claim to read “where once the digital twins are defined[,] the…” for clarity.
Claim 17 recites “comprising utilize AI-driven processes”. The claim should be modified to recite “comprising utiliz[ing] AI-driven processes”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 11-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors, at the time the application was filed, had possession of the claimed invention.
Claims 11, 12, and 13 recite usage of digital twins. Claim 13 describes wherein the organization digital twin “is generated also through AI”. However, neither the claims nor Applicant specification explain what types of machine learning models the digital twins may be comprised of, the training process, or the model validation process. Applicant specification also fails to note if the digital twins are models previously developed and publicly available models that are being applied to the claimed invention. Thus, the patient specific and organizational digital twins do not have sufficient written description.
Claims 12-20 are rejected by virtue of their dependency on claim 11. Claim 13 is also further rejected by virtue of its dependency on claim 12.
Claim 15 recites “determ[ing] the feasibility and effectiveness of the proposed treatment options”. However, Applicant specification fails to detail what methods may be used to test the feasibility and effectiveness of a treatment solution.
Claims 15-20 are rejected by virtue of their dependency on claim 15.
Claim 17 recites “utilize AI-driven processes to organize the production of treatments proposed”. However, Applicant specification fails to describe what processes these are and how AI is used to augment to the processes.
Claims 18-20 are rejected by virtue of their dependency on claim 17.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation "the method". There is insufficient antecedent basis for this limitation in the claim. It is also indefinite as to what the method is. For the purpose of examination, Examiner will interpret the method to be “a method for generating customized treatments for patients” as suggested by [0007] of Applicant specification.
Claims 12-20 are rejected by virtue of their dependency on claim 11.
Claim 12 recites “wherein said digital twins are patient digital twins”. However, claim 11 specifies that one of the two digital twins is an organizational digital twin. It is indefinite as to if the organizational digital twin is a type of patient digital twin. The claim further recites “the digital patient pathway”. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 is rejected by virtue of its dependency on claim 12.
Claim 13 recites “uses available fabrication resources to stimulate, analyze and produce optimized solutions”. It is indefinite as to what resources are available and how they are used.
Claim 14 recites “generating customized treatments for patients by integrating two AI-driven data systems”, but does not describe which steps of the method each AI-driven data system performs or how they are integrated. This claim further recites “to create an algorithm based on this description”. It is indefinite as to what type of algorithm is being generated and what the description is.
Claims 15-20 are rejected by virtue of their dependency on claim 14.
Claim 20 recites “selected performance metrics”. There is insufficient antecedent basis for this claim as no selection step is performed. However, Examiner notes that Applicant specification also does not support a selection step.
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 11-20 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.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a machine
(claims 11-20). Accordingly, claims 11-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified system claim 11 as the claim that represents the claimed invention for analysis.
Claim 11:
A system comprising hardware and software connected to the internet to operate the method including digital twins in healthcare for patients comprising patient specific digital twin and an organizational digital twin.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity under managing personal behaviors of people. The claim elements are directed towards “the method”, which, according to [0007] of Applicant specification, refers to a method for generating customized treatments for patients. Generating treatments for patients is a human activity that could otherwise be performed by managing the personal behaviors of a healthcare provider.
Accordingly, the claim recites at least one abstract idea.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
hardware (1); software (11); patient specific digital twin (11,12,13); organizational digital twin (11,12,13); two AI-driven data systems (14); an algorithm (14); AI-driven processes (17)
Any computing devices that would be able to perform the method (hardware, software) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. Applicant specification provides no details on the type of hardware that may be used to implement the claimed steps. Thus, no specific, technical improvements are being made to computing devices as generic devices are simply being used to perform the abstract idea.
Digital twins (patient specific digital twin, organizational digital twin, two AI-driven data systems) are also taught at a high level of generality. [0028] of Applicant specification recites: “The digital twin is construct of data collected from various medical tests, DNA tests, imaging data (including X-ray/CT/MRI/PET scans, but already analyzed and data is segmented), patient’s health history, previous conditions, family health history, lifestyle, etc., and the most important - the patient’s complaints.” [0014] further recites: “Once defined the digital patient pathway is then used to drive the input for an organizational digital twin, which is generated also through AI and uses available fabrication resources to simulate, analyze and produce optimized solutions for the patient's case, including medication, surgery planning, instrumentation for the surgery, and prescriptions and predictions for further treatments and healing provided by said advanced healthcare systems.” No specific, technical improvements are being made to digital twin models as the method of creating a digital twin is not specified (type of models used, training steps, model validation steps). Thus, under the broadest reasonable interpretation, any generic type of artificial intelligence model may simply be applied to perform the abstract idea of generating customized treatment recommendations.
Artificial intelligence (algorithms, AI-driven processes) are also taught at a high level of generality. [0036] of Applicant specification recites: “Utilize advanced artificial intelligence algorithms to identify correlations between medical conditions and potential treatment options… Utilize advanced artificial intelligence algorithms to identify correlations between medical conditions and potential treatment options.” [0037] further recites: “Put another way an algorithm to encapsulate the above invention includes the steps of:” No specific, technical improvements are being made to the technology of artificial intelligence as any generic algorithm is applied to perform the abstract idea of identifying correlations between medical conditions and potential treatment options, which could otherwise be performed by medical providers or research scientists. Furthermore, the algorithm appears to simply be a number of steps to be performed, which could otherwise be followed and performed by humans without automated means.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claim 12: This claim recites wherein said digital twins are patient digital twins generated through AI and define the digital patient pathway based on data gathered from advanced healthcare systems through comprehensive diagnostic and treatment capability to harvest said data, including the patient's medical history; which teaches digital twins at a high level of generality, such that they are only applied to perform the abstract idea.
Claim 13: This claim recites where once said digital twins are defined the digital patient pathway is then used to drive the input for an organizational digital twin, which is generated also through AI and uses available fabrication resources to simulate, analyze and produce optimized solutions for the patient's case, including medication, surgery planning, instrumentation for the surgery, and prescriptions and predictions for further treatments and healing provided by said advanced healthcare systems, wherein the digital twins enable diagnosis and therapy selection, procedure planning and guidance to be tailored to the patient's needs and preferences and materialize suggested personalized treatment plans and devices, providing improved patient outcomes, costs reductions, and increased safety; which teaches digital twins at a high level of generality, such that they are only applied to perform the abstract idea.
Claim 14: This claim recites a method for generating customized treatments for patients by integrating two AI-driven data systems, comprising; to create an algorithm based on this description; including steps for: a) collecting various types of patient data including medical tests, genetic information, imaging results, health records, family medical history, lifestyle factors, and patient-reported symptoms; b) analyzing the collected data to create a comprehensive representation of the patient's health condition; c) Identifying correlations between the patient’s medical conditions and potential treatment options; and d) predicting future health risks and recommend treatment preventive measures or lifestyle modifications tailored to the individual patient; which teaches AI-driven data systems at a high level of generality, such that they are only applied to perform the abstract idea of generating customized treatments for patients.
Claim 15: This claim recites upon receiving the diagnosis and treatment recommendations, determine the feasibility and effectiveness of the proposed treatment options; which teaches an abstract idea of certain methods of organizing human activity as determining feasibility and effectiveness of treatments, which could otherwise be performed by medical providers.
Claim 16: This claim recites the system further comprising: a) optionally explore alternative treatment solutions based on the patient's specific health profile and constraints; which teaches an abstract idea of certain methods of organizing human activity as exploring alternative treatment solutions, which could otherwise be performed by medical providers.
Claim 17: This claim recites comprising utilize AI-driven processes to organize the production of treatments proposed; which teaches an abstract idea of certain methods of organizing human activity as organizing production, which could be performed by a production manager.
Claim 18: This claim recites the system further comprising creating patient-specific medications, orthopedic implants, surgical plans, or other tailored treatments; which teaches an abstract idea of certain methods of organizing human activity as creating treatments, which could be performed by pharmacists, mechanical engineers, or surgeons, respectively.
Claim 19: This claim recites the system further comprising implementing quality control measures to ensure the safety, efficacy, and compliance of the treatments with regulatory standards; which teaches an abstract idea of certain methods of organizing human activity as implementing quality control measures.
Claim 20: This claim recites the system further continuously monitoring and optimizing by feedback and selected performance metrics; which teaches iterative training at a high level of generality such that no improvements are made to the technology of machine learning.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims 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 reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do 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, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 12-20 additional limitations which amount to elements that have been recognized as activities in particular fields, claims 12-20, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 12-20, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). 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. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 11-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 11-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Peterson (US 20190005195).
Regarding claim 11, Peterson teaches a system comprising hardware and software connected to the internet to operate the method ([0033], “Various modules, units, engines, and/or systems shown in the attached figures may represent the hardware that operates based on software or hardwired instructions, the software that directs hardware to perform the operations, or a combination thereof.” [0148], “The Web-based portal or API may be accessible locally (e.g., in an office) and/or remotely (e.g., via the Internet and/or other private network or connection), for example”)
including digital twins in healthcare for patients comprising patient specific digital twin and an organizational digital twin ([0004], “The example processor is to configure the memory according to a patient digital twin of a first patient.” [0054], “an intelligent care ecosystem associated with the digital twin 130 is notified... Via the care ecosystem 1030, one or more algorithms can be run on, in, or with respect to the patient digital twin 130, for example. Execution of the algorithms via the intelligent care ecosystem 1030 using the digital twin 130 creates output(s) that can be synthesized to be provided to the digital twin 130 and/or other system, for example. In certain examples, an action plan (e.g., a patient care plan, etc.) can be created from the synthesized output.”). Examiner interprets the intelligent care ecosystem that runs algorithms to generate patient care plans to be equivalent to the organizational digital twin because both elements are derived from artificial intelligence and perform the same function of generating patient treatment plans ([0014] of Applicant specification, “an organizational digital twin, which is generated also through AI and uses available fabrication resources to simulate, analyze and produce optimized solutions for the patient's case, including medication, surgery planning, instrumentation for the surgery, and prescriptions and predictions for further treatments and healing provided by said advanced healthcare systems”).
Regarding claim 12, Peterson teaches the system of claim 11. Peterson further teaches wherein said digital twins are patient digital twins generated through AI ([0024], “FIG. 20 is a representation of an example deep learning neural network that can be used to implement the patient digital twin.”) and
define the digital patient pathway based on data gathered from advanced healthcare systems through comprehensive diagnostic and treatment capability to harvest said data, including the patient's medical history ([0048], “The patient digital twin 130 includes electronic medical record (EMR) 210 information, images 220, genetic data 230, laboratory results 240, demographic information 250, social history 260, etc. As shown in the example of FIG. 2, the patient digital twin 130 is fed from a plurality of data sources 210-260 to model the patient 110. Using the plurality of sources of patient 110 information, the patient digital twin 130 can be configured, trained, populated, etc., with patient medical data, exam records, patient and family history, lab test results, prescription information, friend and social network information, image data, genomics, clinical notes, sensor data, location data, etc.”). Examiner interprets generating the patient digital twin to encompass defining the digital patient pathway, as [0018] of Applicant specification recites “Digital patient pathway is created by collecting data from MRI scans, CT scans and other medical imaging devices, medical examination, and tests such as but not limited to the detailed below:”, which features medical data that is included in patient EMR information.
Regarding claim 13, Peterson teaches the system of claims 11 and 12. Peterson further teaches where once said digital twins are defined the digital patient pathway is then used to drive the input for an organizational digital twin ([0062], “The care ecosystem (e.g., care ecosystem 1030 of the example of FIG. 10) can include the care system 1020 and/or other system (e.g., an EMR, EHR, PHR, PHM, PACS, RIS, CVIS, LIS, HIS, etc.) associated with the digital twin 130, appointment, etc. Via the care ecosystem 1030, one or more algorithms can be run on, in, or with respect to the patient digital twin 130, for example. Execution of the algorithms via the intelligent care ecosystem 1030 using the digital twin 130 creates output(s) that can be synthesized to be provided to the digital twin 130 and/or other system, for example. In certain examples, an action plan (e.g., a patient care plan, etc.) can be created from the synthesized output.”),
which is generated also through AI and uses available fabrication resources to simulate, analyze and produce optimized solutions for the patient's case, including medication, surgery planning, instrumentation for the surgery, and prescriptions and predictions for further treatments and healing provided by said advanced healthcare systems ([0114], “Machine learning techniques, whether deep learning networks or other experiential/observational learning system, can be used to model information in the digital twin 130 and/or leverage the patient digital twin 130 to analyze and/or predict a patient 110 outcome, for example.” [0066], “identify issue(s), propose solution(s) (e.g., medication, diagnosis, treatment, etc.) with respect to the digital twin 130.” [0083], “A computer-assisted diagnosis (CAD) 1306 and recommended course of action (e.g., care plan, etc.) can be generated for the patient 110 and/or healthcare practitioner (e.g., care team, primary physician, surgeon, nurse, etc.) to follow. The course of action can be customized for that particular patient 110 given the patient digital twin 130.” [0102], “At 1706, analytics for a “smart” protocol are generated based on the patient's medical data (e.g., as reflected in the patient digital twin 130). For example, patient medical data from the digital twin 130 along with validation information from a care provider determines pre-operative testing requirements for safe patient surgical intervention (e.g., surgeon/anesthesia clearance that the patient 110 meets acceptable criteria for surgery and anesthesia, etc.). Such pre-operative testing requirements can be formulated as a “smart” protocol to be applied by or with respect to the patient digital twin 130, for example. A pre-operative (pre-op) smart protocol can identify lab(s), imaging, patient education, and/or other pre-op task(s) to be executed by the patient 110 and/or provider(s) prior to a procedure, for example.” [0095], “FIG. 11 to generate, receive, and incorporate post-operative feedback to the patient digital twin 130, electronic medical record, etc. At block 1602, discharge notifications/recommendations are generated. For example, medication information (e.g., prescription, dosage, etc.), exercise regime (e.g., stretches, cardio, weights, etc.), clinician follow-up, etc., can be generated for the patient 110.” [0109], “Instruments and/or other equipment used in procedures can be modeled, tracked, etc., with respect to the patient 110 and the patient's procedure via the digital twin 130, for example.”),
wherein the digital twins enable diagnosis and therapy selection, procedure planning and guidance to be tailored to the patient's needs and preferences ([0062], “healthcare software applications, medical big data, neural networks, other machine learning and/or artificial intelligence, etc., can be leveraged to diagnose, identify issue(s), propose solution(s) (e.g., medication, diagnosis, treatment, etc.) with respect to the digital twin 130.” [0062], “one or more algorithms can be run on, in, or with respect to the patient digital twin 130, for example. Execution of the algorithms via the intelligent care ecosystem 1030 using the digital twin 130 creates output(s) that can be synthesized to be provided to the digital twin 130 and/or other system, for example. In certain examples, an action plan (e.g., a patient care plan, etc.) can be created from the synthesized output.” [0095], “FIG. 11 to generate, receive, and incorporate post-operative feedback to the patient digital twin 130, electronic medical record, etc. At block 1602, discharge notifications/recommendations are generated.” [0097], “a next action is suggested in response to the feedback 1606-1614. For example, a recommendation for a follow-up appointment with the patient 110 can be suggested, a change in discharge instruction can be suggested, etc.”) and
materialize suggested personalized treatment plans and devices, providing improved patient outcomes, costs reductions, and increased safety ([0062], “one or more algorithms can be run on, in, or with respect to the patient digital twin 130, for example. Execution of the algorithms via the intelligent care ecosystem 1030 using the digital twin 130 creates output(s) that can be synthesized to be provided to the digital twin 130 and/or other system, for example. In certain examples, an action plan (e.g., a patient care plan, etc.) can be created from the synthesized output.” [0109], “Implants, such as knee, pacemaker, stent, etc., can be modeled for the benefit of the patient 110 and the provider via the digital twin 130, for example.” [0099], “using an evidence-based patient digital twin 130 for improved care planning and outcomes.” [0047], “the digital twin 130 can be tested inexpensively and efficiently in a plurality of ways while preserving patient 110 safety.”).
Regarding claim 14, Peterson teaches the system of claims 11 and 12. Peterson further teaches the system including a method for generating customized treatments for patients by integrating two AI-driven data systems, comprising; to create an algorithm based on this description; including steps for:
a) collecting various types of patient data including medical tests, genetic information, imaging results, health records, family medical history, lifestyle factors, and patient-reported symptoms ([0037], “Sensors connected to the physical object (e.g., the patient 110) can collect data and relay the collected data 120 to the digital twin 130 (e.g., via self-reporting, using a clinical or other health information system such as a picture archiving and communication system (PACS), radiology information system (RIS), electronic medical record system (EMR),...” [0048], “The patient digital twin 130 includes electronic medical record (EMR) 210 information, images 220, genetic data 230, laboratory results 240, demographic information 250, social history 260, etc.” [0048], “the patient digital twin 130 can be configured, trained, populated, etc., with patient medical data, exam records, patient and family history, lab test results, prescription information, friend and social network information, image data, genomics, clinical notes, sensor data, location data, etc.” [0105], “FIG. 6, information regarding behavioral choices 340 includes diet 610, exercise 620, alcohol 630, tobacco 640, drugs 650, sexual behavior 660, extreme sports 670, hygiene 680, etc... Behavioral choices 340 observed in and/or documented with respect to the patient 110 can be reflected in the patient's digital twin 130, and rules, consequences, and/or other outcomes of certain behaviors 610-680 can be modeled via the digital twin 130, for example.”), etc.”). Examiner notes that laboratory results are understood to be test results.
b) analyzing the collected data to create a comprehensive representation of the patient's health condition ([0068], “a medical event (e.g., surgery, image acquisition, real or virtual office visit, other procedure, etc.) is processed with respect to the patient digital twin 130. For example, image data, sensor data, observations, test results, etc., from a medical event is processed with respect to information and/or modeling of the patient digital twin 130. Image data can be processed to form image analysis, computer aided detection, image quality determination, etc.”);
c) Identifying correlations between the patient’s medical conditions and potential treatment options ([0162], “By analyzing a single large data set, correlations can be found in the data, and data quality can be evaluated.” [0040], “Rather than reading a report, a healthcare practitioner can view and simulate with the digital twin 130 to evaluate a condition, progression, possible treatment, etc., for the patient 110.”); and
d) predicting future health risks and recommend treatment preventive measures or lifestyle modifications tailored to the individual patient ([0040], “Using sensor data in combination with historical information, current and/or potential future conditions of the patient 110 can be identified, predicted, monitored, etc., using the digital twin 130... Using the digital twin 130, the patient's 110 physical behaviors can be simulated and visualized for diagnosis, treatment, monitoring, maintenance, etc.” [0095], “discharge notifications/recommendations are generated. For example, medication information (e.g., prescription, dosage, etc.), exercise regime (e.g., stretches, cardio, weights, etc.), clinician follow-up, etc., can be generated for the patient 110.” [0104], “Post-op data fed into the patient digital twin 130 helps to facilitate documented immediate post-op follow up as well as identify risks very early and have an ability to follow-up with the patient 110 before the first post-op visit (e.g., preventative measures, etc.).”).
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Peterson (US 20190005195) in view of Lemke (US 20090326336).
Regarding claim 15, Peterson teaches the system of claims 11 and 14. Peterson does not teach upon receiving the diagnosis and treatment recommendations, determine the feasibility and effectiveness of the proposed treatment options.
However, Lemke does teach upon receiving the diagnosis and treatment recommendations, determine the feasibility and effectiveness of the proposed treatment options ([0147], “One of the core functions throughout the TIMMS project is the creation and maintenance of the patient-model… Information obtained from the previously retrieved images would be used to determine feasibility of treatment based on anatomical features.” [0157], “Once all available data has been processed by TIMMS, the Adaptive Workflow Agent makes final revisions to the Executing Workflow, and the efficacy of the proposed treatment is confirmed through the Validation Engine (210G).”).
Peterson in view of Lemke are considered analogous to the claimed invention because they are in the field of generating treatments based on patient models. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Peterson with Lemke for the advantage of addressing “Inefficient, ineffective and redundant processes” (Lemke; [0006]).
Regarding claim 16, Peterson in view of Lemke teaches the system of claims 11, 14, and 15. Peterson further teaches the system further comprising: a) optionally exploring alternative treatment solutions based on the patient's specific health profile and constraints ([0095], “At block 1602, discharge notifications/recommendations are generated. For example, medication information (e.g., prescription, dosage, etc.), exercise regime (e.g., stretches, cardio, weights, etc.), clinician follow-up, etc., can be generated for the patient 110.” [0097], “For example, an electronic mail, instant message, and/or text message, etc., can be sent to a clinician associated with the participant who can respond to the feedback 1606-1614 (e.g., by checking the patient 110, updating a record, adjusting the discharge instructions, etc.). At block 1622, a follow-up and/or other recommendation is suggested. For example, a next action is suggested in response to the feedback 1606-1614. For example, a recommendation for a follow-up appointment with the patient 110 can be suggested, a change in discharge instruction can be suggested, etc.” [0106], “Post-op feedback can also help to improve the patient digital twin 130, as insight into how the patient 110 handles pain medication, physical therapy, and/or other procedure follow-up can help improve the accuracy of the patient digital twin 130 in modeling patient 110 behavior”). Under the broadest reasonable interpretation, Examiner interprets suggesting changes to the provided recommendation to encompass exploring additional or alternative treatment solutions. For example, suggesting an alternative physical therapy regiment if the patient is struggling with the current physical therapy regiment.
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Peterson (US 20190005195) in view of Lemke (US 20090326336) further in view of Cella (US 20220187847).
Regarding claim 17, Peterson in view of Lemke teaches the system of claims 11 and 14-16. Peterson in view of Lemke does not teach the system further comprising utilizing AI-driven processes to organize the production of treatments proposed.
However, Cella does teach the system further comprising utilizing AI-driven processes to organize the production of treatments proposed ([1996], “An artificial intelligence system (such as a robotic process automation system trained on a training set of expert medical devices or other data), to determine a recommended action, prototype, device, which in embodiments may involve production of a device and/or a component of a device. The additive manufacturing platform 10110 may, in some such embodiments, automatically determine (such as using an artificial intelligence system, such as robotic process automation trained on an expert data set) whether a medical device is readily available from a manufacturer (including a device that is currently in stock and/or on order) and/or whether an additive manufacturing system should produce the device, such as to meet an immediate patient need, to save costs, or the like.”).
Peterson in view of Lemke further in view of Cella are considered analogous to the claimed invention because they are in the field of machine learning in patient care. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Peterson in view of Lemke with Cella for the advantage of implementing additive manufacturing to “meet an immediate patient need, to save costs, or the like” (Cella; [1996]).
Regarding claim 18, Peterson in view of Lemke further in view of Cella teaches the system of claims 11 and 14-17. Peterson further teaches the system further comprising creating patient-specific medications, orthopedic implants, surgical plans, or other tailored treatments ([0095], “At block 1602, discharge notifications/recommendations are generated. For example, medication information (e.g., prescription, dosage, etc.), exercise regime (e.g., stretches, cardio, weights, etc.), clinician follow-up, etc., can be generated for the patient 110.” [0109], “Implants, such as knee, pacemaker, stent, etc., can be modeled for the benefit of the patient 110 and the provider via the digital twin 130, for example. Instruments and/or other equipment used in procedures can be modeled, tracked, etc., with respect to the patient 110 and the patient's procedure via the digital twin 130, for example.” [0103], “For example, as a data warehouse is populated, identified cohorts can be used to create “smart” pre-surgical protocols to standardize care plans and improve outcomes and perhaps decrease costs... One or more protocols can be selected (e.g., by provider, automatically via the patient digital twin 130, etc.) for the patient 110 based on procedure, patient type, other condition, etc.”).
Regarding claim 19, Peterson in view of Lemke further in view of Cella teaches the system of claims 11 and 14-18. Peterson in view of Lemke does not teach the system further comprising implementing quality control measures to ensure the safety, efficacy, and compliance of the treatments with regulatory standards.
However, Cella does teach the system further comprising implementing quality control measures to ensure the safety, efficacy, and compliance of the treatments with regulatory standards ([0315], “Yet further, a hybrid artificial intelligence system 3060 may provide two types of artificial intelligence to different applications, such as different demand management applications 824 (e.g., a sales management application and a demand prediction application) or different supply chain applications 812 (e.g., a logistics control application and a production quality control application).” [2148], “Examples of static risk analyses may include, but are not limited to, operational risks (e.g., product design risks, manufacturing risks, quality control risks, and/or the like) and/or regulatory/compliance risks.”). Examiner notes that a production quality control application for treatments created using additive manufacturing encompasses implementing quality control measures to ensure treatments are in compliance. Furthermore, the claim language indicates that ensuring the safety, efficacy, and compliance of the treatments with regulatory standards is an intended result of performing the functional step of implementing quality control measures, and thus is not patentably limiting (see MPEP 2111.04). As Cella teaches the functional step of the claim, it is considered to amount to the same benefit.
Peterson in view of Lemke further in view of Cella are considered analogous to the claimed invention because they are in the field of machine learning in patient care. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Peterson in view of Lemke with Cella for the advantage of ensuring “compliance with policy or regulations,” (Cella; [0276]).
Regarding claim 20, Peterson in view of Lemke further in view of Cella teaches the system of claims 11 and 14-19. Peterson further teaches the system further continuously monitoring and optimizing by feedback and selected performance metrics ([0059], “The patient digital twin 130 can be used to help drive a continuous loop of patient care such as shown in the example of FIG. 9.FIG. 9 illustrates an example process 900 for patient 110 monitoring using the digital twin 130.” [0069], “post-event feedback is generated, received, and incorporated to update the patient digital twin 130” [0072], “algorithms, score cards, patient-defined communication preferences, etc., can be used to evolve the patient digital twin 130 and provide feedback regarding performance indicators and predictions for the patient 110 and/or group of patients (e.g., with same condition, same provider, same location, other commonality, etc.).”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Methods For The Identification, Assessment, And Treatment Of Patients With Cancer Therapy (US 20080064055) teaches identification of predictive markers that can be used to determine whether patients with cancer are expected to demonstrate long term or short term survival times. In particular, the present invention is directed to the use of certain individual and/or combinations of predictive markers, wherein the expression of the predictive markers correlates with expected short term or long term survival. Thus, by examining the expression levels of individual predictive markers and/or predictive markers comprising a marker set, it is possible to determine predicted patient survival.
Surgery Digital Twin (US 20190087544) teaches providing a digital twin. An example apparatus includes a digital twin of a healthcare procedure. The example digital twin includes a data structure created from tasks defining the healthcare procedure and items to be used in the healthcare procedure to model the tasks and items associated with each task for query and simulation for a patient. The example digital twin is to at least: receive input regarding a first item at a location; compare the first item to the items associated with each task; and, when the first item matches an item associated with a task of the healthcare procedure, record the first item and approval for the healthcare procedure and update the digital twin based on the first item. When the first item does not match an item associated with a task, the example digital twin is to log the first item.
Methods And Systems For A Pharmacological Tracking And Representation Of Health Attributes Using Digital Twin (US 20200303047) teaches a computerized method for healthcare data management generally includes forming, using the healthcare data system computing device, a digital twin of the individual patient based on the health information related to the individual patient, the digital twin of the individual patient being a digital representation of at least one health state of the individual patient; forming, using the healthcare data system computing device, a digital twin of the population of patients based on the health information related to the population of patients, the digital twin of the population of patients being a digital representation of at least one health attribute of the population of patients; and presenting, at the healthcare data system computing device, to a user of the healthcare data system the digital twin of the patient and the digital twin of the population of patients.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684