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 .
Election/Restrictions
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-5 (Invention I or Subcombination I), drawn to a system for characterizing the activities of one or more patients in a health care system, comprising: retrieving prescription drug data relating to the one or more patients; determining whether the prescription ordering patterns for the one or more patients; and indicating whether a subset of the ordering patterns is anomalous as compared with a stored ordering criterion, classified in G16H40/20 or G16H10/60.
II. Claims 6-20 (Invention II or Subcombination II), drawn to a computerized method for healthcare data management, comprising: receiving health data related to an individual patient and data related to a population of patients; determining whether said population of patients have one or more symptoms similar to said patient; and simulating, using a machine learning module, a future health state of the individual patient, classified in G16H50/50 or G16H50/70.
The inventions are independent or distinct, each from the other because:
Inventions I and II are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In the instant case, subcombination I has separate utility such as determining whether the prescription ordering patterns for the one or more patients; and indicating whether a subset of the ordering patterns is anomalous as compared with a stored ordering criterion. Subcombination II has separate utility such as determining whether said population of patients have one or more symptoms similar to said patient; and simulating a future health state of the individual patient. See MPEP § 806.05(d).
The examiner has required restriction between subcombinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a continuation or divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
(a) the inventions have acquired a separate status in the art in view of their recognized divergent subject matter;
(b) the inventions require a different field of search (for example, searching different classes/subclasses or electronic resources, or employing different search queries); and
(c) the prior art applicable to one invention would not likely be applicable to another invention.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
During a telephone conversation with Attorney Michael A. Schaldenbrand (Registration No. 47,923), Applicant's Attorney Representative, on Friday, July 10, 2026, a provisional election was made without traverse to prosecute Invention II, described in claims 6-20. Affirmation of this election must be made by Applicant in replying to this Office action. Claims 1-5, related to Inventions I as described above, are withdrawn from further consideration by the Examiner, pursuant to 37 CFR 1.142(b), as being drawn to non-elected inventions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 4, 2025 is in compliance with the provisions of 37 CFR 1.97, and has been considered by the examiner.
Claim Objections
Claims 9 and 16 are objected to because of the following informalities:
- Claim 9 recites a limitation directed to "wherein the machine learning simulation uses a digital twin of the patient". However, this limitation should read as "wherein the machine learning simulation uses the digital twin of the patient". Examiner suggests that Applicant amend this limitation to read the same, or make some other appropriate correction of course. For examination purposes, this phrase will be interpreted and read as, "wherein the machine learning simulation uses [a] the digital twin of the patient" (i.e., the machine learning simulation uses the same digital twin of the patient that was formed in independent claim 6).
Claim 16 is also objected to for depending from an objected claim. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 6-20 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 6 recites the limitation "the health information" in lines 2-3 There is insufficient antecedent basis for this limitation in the claim.
Claims 7-20 are rejected based on their dependency from claim 6.
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 6-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP § 2106 (hereinafter referred to as the “2019 Revised PEG”).
Step 1 of the 2019 Revised PEG
Following Step 1 of the 2019 Revised PEG, claims 6-20 are directed to a computerized method for healthcare data management, which is within one of the four statutory categories (i.e., a process). See MPEP § 2106.03.
Step 2A of the 2019 Revised PEG - Prong One
Following Prong One of Step 2A of the 2019 PEG, the claim limitations are to be analyzed to determine whether they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. See MPEP §2106.04. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: (1) Mathematical Concepts; (2) Certain Methods of Organizing Human Activity, and (3) Mental Processes. See MPEP § 2106.04(a).
Claims 6-20 are rejected under 35 U.S.C. § 101, because the claimed invention is directed to an abstract idea without significantly more. Representative independent claim 6 includes limitations that recite an abstract idea. Specifically, independent claim 6 recites the following limitations:
A computerized method for healthcare data management, the method comprising:
receiving health information from one or more healthcare communication sources, wherein the health information includes data includes related to an individual patient and data related to a population of patients;
forming a digital twin of said individual patient based on the health data related to said individual patient, wherein the digital twin of said individual patient is a digital representation of at least one health state of said individual patient;
forming a digital twin of said population of patients based on the health data related to said population of patients, wherein the digital twin of said population of patients is a digital representation of at least one health attribute of said population of patients;
determining whether said population of patients have one or more symptoms similar to said patient;
simulating, using a machine learning module, a future health state of the individual patient;
detecting a new health state of the individual patient based at least in part on new health data received; and
transmitting an alert to the at least one healthcare provider indicating a discrepancy between the simulated future health state and the new health state.
However, the Examiner submits that the foregoing underlined limitations constitute a process that, under its broadest reasonable interpretation, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See 2019 Revised PEG. The Certain Methods of Organizing Human Activity category covers concepts related to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (i.e., a method for forming digital twins for an individual patient and a population of patients; determining patients which have one or more similar symptoms; and detecting a new health state of the patient). See MPEP § 2106.04(a)(2)(II). That is, other than reciting some computer components and functions (the foregoing limitations in claim 6 which are not underlined), the context of claim 6 encompasses concepts directed to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (i.e., a method for forming digital twins for an individual patient and a population of patients; determining patients which have one or more similar symptoms; and detecting a new health state of the patient).
The aforementioned claim limitations described in claim 6 are analogous to claim limitations directed toward concepts of managing personal behavior or relationships or interactions between people, because they merely recite limitations for: (1) organizing healthcare data into digital twins (i.e., organizing the healthcare data into a digital representation of at least one health state of the patient, such as a digital representation of risk factors contributing to any suitable disease, syndrome, disorder, or health state of the patient – see paragraph [0288] of Applicant’s specification as filed on August 4, 2025); (2) determining patients which have one or more similar symptoms; and (3) detecting a new health state the patient based on receiving new health data (i.e., following rules or instructions for forming a digital representation of an individual patient and a digital representation of a population of patients, identifying patients who have similar symptoms, and detecting a new health state of the patient based on receiving new health data). If a claim limitation, under its broadest reasonable interpretation, covers the management of personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a)(II). Accordingly, claim 6 recites an abstract idea that falls within the Certain Methods of Organizing Human Activity category.
Furthermore, Examiner notes that dependent claims 7-20 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below. Examiner notes that: (1) dependent claims 13, 14, and 16-18 provide limitations that are deemed to be additional elements which require further analysis under Prong Two of Step 2A; and (2) dependent claims 7-12, 15, 19, and 20 do not provide any limitations that are deemed to be additional elements which require further analysis under Prong Two of Step 2A. For example, claims 7-12, 15, 19, and 20 merely recite additional steps for describing the type of data that is used to form the digital twins or further steps for comparing the data in the simulations of the digital twins (i.e., these steps are deemed to be following instructions or rules for forming and displaying the digital twins, because they recite more specific data that goes into forming the digital twins).
Step 2A of the 2019 Revised PEG - Prong Two
Regarding Prong Two of Step 2A of the 2019 Revised PEG, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted in the 2019 Revised PEG, it must be determined whether any additional elements in the claims are indicative of integrating the abstract idea 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 do not integrate a judicial exception into a “practical application.” See MPEP §§ 2106.05 (f)-(h).
In the present case, for independent claim 6, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A computerized method for healthcare data management, the method comprising:
receiving health information from one or more healthcare communication sources (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)), wherein the health information includes data includes related to an individual patient and data related to a population of patients;
forming a digital twin of said individual patient based on the health data related to said individual patient, wherein the digital twin of said individual patient is a digital representation of at least one health state of said individual patient;
forming a digital twin of said population of patients based on the health data related to said population of patients, wherein the digital twin of said population of patients is a digital representation of at least one health attribute of said population of patients;
determining whether said population of patients have one or more symptoms similar to said patient;
simulating, using a machine learning module, a future health state of the individual patient (the Examiner submits that this additional element amounts to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f); and the Examiner further submits that this additional element amounts to generally linking the abstract idea to a particular field of use or technological environment as noted below, see MPEP § 2106.05(h));
detecting a new health state of the individual patient based at least in part on new health data received; and
transmitting an alert to the at least one healthcare provider indicating a discrepancy between the simulated future health state and the new health state (the Examiner submits that these additional elements amount to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); the Examiner further submits that such steps are not unconventional as they merely consist of receiving data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)).
However, the recitation of these generic computer components and functions in , such that it amounts to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the abstract idea to a particular field of use or technological environment. See MPEP §§ 2106.05(f)-(h). For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
- The following is an example of a court decisions that demonstrates merely applying instructions by reciting the computer structure as a tool to implement the claimed limitations (e.g., see MPEP § 2106.05(f)):
- Requiring the use of software to tailor information and provide it to the user on a generic computer, e.g., see Intellectual Ventures I LLC v. Capital One Bank (USA) – similarly, the current invention requires software components (i.e., the healthcare communication sources and machine learning module) to perform the aforementioned abstract concepts of: (i) forming a digital representation of an individual patient and a digital representation of a population of patients; (ii) identifying patients who have similar symptoms; and (iii) detecting a new health state of the patient based on receiving new health data.
- The following is an example of an insignificant extra-solution activity (e.g., see MPEP § 2106.05(g)):
- Example of Mere Data Gathering/Mere Data Outputting:
- Obtaining information about transactions using the Internet to verify credit card transactions, e.g., see CyberSource v. Retail Decisions, Inc. – similarly, the steps directed to: “simulating a future health state of the individual patient” and “transmitting an alert to the at least one healthcare provider”, described in claim 6, are necessary data gathering/outputting steps in order to practice the invention (i.e., simulating a future health state and transmitting an alert, are necessary steps in order to display the data from the digital twins).
- The following is an example of generally linking use of a judicial exception to a particular technological environment or field of use (e.g., see MPEP § 2106.05(h)):
- Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, e.g., see FairWarning v. Iatric Sys. – similarly, the current invention specifies that the abstract idea of (i) forming a digital representation of an individual patient and a digital representation of a population of patients; (ii) identifying patients who have similar symptoms; and (iii) detecting a new health state of the patient based on receiving new health data, relates to simulations that are executed using a machine learning module (i.e., this requirement merely limits the claims to machine learning technologies).
Thus, the additional elements in independent claim 6 are not indicative of integrating the judicial exception into a practical application. Similarly, dependent claims 7-12, 15, 19, and 20 do not recite any additional elements outside of those identified as being directed to the abstract idea described above (or those additional elements which were already identified and analyzed in claim 6). Examiner notes that dependent claims 13, 14, and 16-18 recite the following additional elements in bold font below (with limitations deemed to be part of the above identified abstract idea identified in underlined font):
[as described in claim 13] simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by the timing of providing mediation to the individual patient (the Examiner submits that these additional elements amount to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f));
[as described in claim 14] simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by dosage level of mediation to the individual patient (the Examiner submits that these additional elements amount to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f));
[as described in claim 16] simulating application of best clinical practices for a desired clinical outcome on said individual patient via the digital twin of said individual patient (the Examiner submits that these additional elements amount to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f));
[as described in claim 17] receiving simulation instructions, the simulation instructions including one or more research experiments (adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); and the Examiner further submits that such steps are not unconventional as they merely consist of receiving or transmitting data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)); simulating the one or more research experiments, and results of best clinical practices on at least one of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients (the Examiner submits that these additional elements amount to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)); and
[as described in claim 18] receiving simulation instructions, the simulation instructions including one or more drug treatment regimens (the Examiner submits that these additional elements amount to adding insignificant extra-solution activity as noted below, see MPEP § 2106.05(g); and the Examiner further submits that such steps are not unconventional as they merely consist of receiving or transmitting data over a network, as evidenced by the Intellectual Ventures v. Symantec case, as noted below in the Step 2B Analysis Section, see MPEP § 2106.05(d)); simulating the one or more drug treatment regimens on one or both of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients (the Examiner submits that these additional elements amount to adding the words “apply it” (or an equivalent), or mere instructions to implement the abstract idea on a computer, see MPEP § 2106.05(f)).
As such, the additional elements in dependent claims 13, 14, and 16-18 are not indicative of integrating the judicial exception into a practical application. Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, unlike the claims that have been held as a whole to be directed to an improvement or otherwise directed to something more than the abstract idea, the additional elements in claims 6-20, when considered as a whole: (1) are not directed to improvements to the functioning of a computer, or to any other technology or technical field similar to the Enfish, LLC v. Microsoft Corp. case (see MPEP § 2106.05(a)); (2) do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see MPEP § 2106.04(d)(2)); (3) do not apply the judicial exception with, or by use of, a particular machine (see MPEP § 2106.05(b)); (4) do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)); nor do they (5) apply or use 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 whole is more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05(e) and MPEP § 2106.04(d)(2)). For these reasons, claims 6-20 as a whole do not integrate the above-noted at least one abstract idea into a practical application.
Step 2B of the 2019 Revised PEG
Regarding Step 2B of the 2019 Revised PEG, claims 6-20 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 abstract idea into a practical application, the additional elements of claims 6, 13, 14, and 16-18 amount to no more than: (1) adding the words “apply it” (or is the equivalent of) with the judicial exception; mere instructions to implement an abstract idea on a computer; or merely uses a computer as a tool to perform an abstract idea; (2) adding insignificant extra-solution activity to the judicial exception; and (3) generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.05(f)-(h). Further the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than limitations consistent with what the courts recognize, or those having ordinary skill in the art would recognize, to be well-understood, routine, and conventional computer components. See MPEP § 2106.05 (d).
Specifically, the Examiner submits that the additional elements of claims 6, 13, 14, and 16-18, as recited, the one or more healthcare communication sources; the machine learning module; and the steps of: “simulating, using a machine learning module, a future health state of the individual patient”; “transmitting an alert to the at least one healthcare provider indicating a discrepancy between the simulated future health state and the new health state”; “simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by the timing of providing mediation to the individual patient”; “simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by dosage level of mediation to the individual patient”; “simulating application of best clinical practices for a desired clinical outcome on said individual patient via the digital twin of said individual patient”; “simulating the one or more research experiments, and results of best clinical practices on at least one of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients”; and “simulating the one or more drug treatment regimens on one or both of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients”, are well-understood, routine, and conventional functions. See MPEP § 2106.05(d)(II). When viewed as a whole, claims 6-20 do not include additional limitations that are sufficient to amount to significantly more than the judicial exception because the claims recite processes that are routine and well-known in the art, and simply implementing the processes on a computer(s) is not enough to qualify as “significantly more.”
- In regard to the plurality of healthcare communication sources; machine learning module; and the steps directed to: “simulating, using a machine learning module, a future health state of the individual patient”; “simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by the timing of providing mediation to the individual patient”; “simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by dosage level of mediation to the individual patient”; “simulating application of best clinical practices for a desired clinical outcome on said individual patient via the digital twin of said individual patient”; “simulating the one or more research experiments, and results of best clinical practices on at least one of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients”; and “simulating the one or more drug treatment regimens on one or both of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients” – these additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than well-understood, routine, and conventional activities previously known in the industry, because:
- Applicant’s disclosure supports this assertion – for example, Applicant generally describes these devices as being embodied by generic computer devices, such as “any type of suitable computing device, such as a desktop computer, a tablet computer, a laptop computer, a wearable computing device such as eyewear, a watch or other piece of jewelry, or clothing that incorporates a computing device” and “may include a general-purpose computer and/or dedicated computing device or specific computing device” (see Applicant’s specification as filed on August 4, 2025, at paragraphs [0265] and [0353]); and “any suitable type of model, including neural networks, deep neural networks, recurrent neural networks, Hidden Markov Models, Bayesian models, regression models, and the like” (see Applicant’s specification as filed on August 4, 2025, at paragraph [0172]). By Applicant’s own admission, these devices are generic computer components and functions, such as a general purpose computer, which are old and well-known in the medical industry. Therefore, Applicant’s disclosure shows that the one or more healthcare communication sources; machine learning module; and the steps directed to: “simulating, using a machine learning module, a future health state of the individual patient”; “simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by the timing of providing mediation to the individual patient”; “simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by dosage level of mediation to the individual patient”; “simulating application of best clinical practices for a desired clinical outcome on said individual patient via the digital twin of said individual patient”; “simulating the one or more research experiments, and results of best clinical practices on at least one of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients”; and “simulating the one or more drug treatment regimens on one or both of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients”, are well-understood, routine, and conventional computer components which are old and well-known in the medical industry.
- The Examiner submits that these limitations amount to merely using a computer or other machinery as tools for performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f) and analysis of these limitations under Step 2A, Prong Two above).
- The Examiner submits that these limitations generally link the use of the judicial exception to a particular technological environment or field of use – for example, the limitation directed to “simulating, using a machine learning module, a future health state of the individual patient”, amounts to limiting the abstract idea to the field of machine learning (see MPEP § 2106.05(h) and analysis of these limitations under Step 2A, Prong Two above).
- Regarding the steps and features directed to: “transmitting an alert to the at least one healthcare provider indicating a discrepancy between the simulated future health state and the new health state”; “receiving simulation instructions, the simulation instructions including one or more research experiments”; and “receiving simulation instructions, the simulation instructions including one or more drug treatment regimens” - The following represents an example that courts have identified to be well-understood, routine, and conventional activities (e.g., see MPEP § 2106.05(d)):
- Receiving or transmitting data over a network, e.g., see Intellectual Ventures v. Symantec – the limitations directed to: “transmitting an alert to the at least one healthcare provider indicating a discrepancy between the simulated future health state and the new health state”; “receiving simulation instructions, the simulation instructions including one or more research experiments”; and “receiving simulation instructions, the simulation instructions including one or more drug treatment regimens”, are similarly deemed to be well-understood, routine, and conventional activity in the field of medical data processing systems and methods, because they also represent mere collection and transmission of data over a network (i.e., transmitting the alert and receiving the simulation instructions over a network).
Thus, taken alone, the additional elements of claims 6, 13, 14, and 16-18 do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, 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 functionality of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 6, 13, 14, and 16-18 are nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Additionally, dependent claims 7-12, 15, 19, and 20 (which depend on claim 6 due to their respective chains of dependency), do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Examiner notes that claims 7-12, 15, 19, and 20 do not include any additional elements beyond those identified as well-understood, routine, and conventional components as described above in the subject matter eligibility rejections of independent claim 6. Dependent claims 7-12, 15, 19, and 20 merely add limitations that further narrow the abstract idea described in independent claim 6. Therefore, claims 6-20 are nonetheless rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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.
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.
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.
Claims 6-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
- Peterson et al. (Pub. No. US 2019/0005195), in view of:
- T et al. (Pub. No. US 2019/0198169).
Regarding claim 6,
- Peterson et al. (Pub. No. US 2019/0005195) teaches:
- a computerized method for healthcare data management, the method comprising (Peterson, paragraph [0065] and FIG. 11; Paragraph [0065] teaches that Figure 11 illustrates a flow diagram of an example method 1100 to generate and update a patient digital twin.):
- receiving health data from one or more healthcare communication sources, wherein the health information includes data related to an individual patient and data related to a population of patients (Peterson, paragraphs [0065] and [0072]-[0075], FIGS. 11 and 12; Paragraphs [0065], [0072]-[0075], and [0130] generally teach obtaining various health data/information for patients from a plurality of sources, such as: (1) sensor data, patient 110 input, family and/or friend input, EMR records, lab results, image data, etc.; (2) EMR 210, images 220, genetics 230, laboratory results 240, demographics 250, social history 260, etc. (see paragraph [0072]); (3) and EMR and/or other medical records (e.g., EHR records, PHR records, etc.) for the patient 110 (see paragraph [0075]) (i.e., receiving health data from a plurality of healthcare communication sources, wherein the health information includes data related to an individual patient); and (4) that the health information may include information associated with health of one or more patients (i.e., receiving health information that includes data related to a population of patients) (see paragraph [0130]). Paragraph [0075] teaches that population health information can be provided to form the patient digital twin (i.e., receiving health information that includes data related to a population of patients).);
- forming a digital twin of said individual patient based on the health data related to said individual patient, wherein the digital twin of said individual patient is a digital representation of at least one health state of said individual patient (Peterson, paragraph [0065]; Paragraph [0065] generally teaches forming a digital twin from the various extracted health data/information, where the digital twin is a visual, digital representation of the patient (i.e., the digital twin is a representation of the health state of the individual patient).).;
- forming a digital twin of said population of patients based on the health data related to said population of patients, wherein the digital twin of said population of patients is a digital representation of at least one health attribute of said population of patients (Peterson, paragraphs [0045] and [0112]; Paragraph [0045] teaches the digital twin 130 can be a reference digital twin (e.g., a digital twin prototype, etc.) and/or a digital twin instance. The reference digital twin represents a prototypical or “gold standard” model of the patient 110 or of a particular type/category of patient 110 (i.e., the digital twin that is formed may be of a particular type/category of patient, which is interpreted to be the equivalent of “a digital twin of a population of patients”), while one or more reference digital twins represent particular patients 110 (i.e., forming a digital twin of a population of patients). Paragraph [0045] further teaches that multiple digital twin instances can be aggregated into a digital twin aggregate (e.g., to represent an accumulation or combination of multiple child patients sharing a common reference digital twin, etc. (i.e., forming a digital twin of a population of patients, where the digital twin of the population of patients is a digital representation of at least one health attribute of the population of patients). Paragraph [0112] also teaches that an ongoing cycle of feedback improves the digital twin for a patient population (i.e., forming a digital twin of a population of patients), and an understanding of “normal” or “standard” behavior/response, etc., provides better outcomes for patient and/or population health.);
…
- simulating, using a machine learning module, a future health state of the individual patient (Peterson, paragraphs [0042] and [0090]; Paragraph [0042] teaches that 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 (i.e., simulating a future health state of the patient based on the digital twin of the patient). Paragraph [0090] also teaches that at block 1404, machine learning (e.g., artificial intelligence application(s), engine(s), such as deep learning, neural network, etc.) access and review the patient digital twin 130 to generate a machine learning analysis. For example, the machine learning processor can execute a simulation using the digital twin 130 (i.e., the simulation to generate the future health state of the patient is performed using a machine learning module) and compare the results to known, expected, or reference results to determine their accuracy.);
- detecting a new health state of the individual patient based at least in part on new health data received (Peterson, paragraph [0070]; Paragraph [0070] teaches that the digital twin 130 can evolve over time based on available health data, machine-learning, human feedback, medical event processing, new or updated digital medical knowledge, and post-event feedback (i.e., updating the digital twin of the patient which detects a patient’s new health state on based on new health data received by the digital twin). The digital twin 130 provides an evolving model of the patient 110 that can learn and absorb information to reflect patient body systems and health information systems, rules, norms, best practices, etc. Using the patient digital twin 130, a healthcare practitioner may not need to consult with the patient 110. When a new piece of data comes in, the information is automatically analyzed and used to update the digital twin 130 (i.e., updating the digital twin of the patient which detects a patient’s new health state on based on new health data received by the digital twin) and provide one or more recommendations and/or further actions based on the twin 130 modeled interactions.); and
- transmitting an alert to the at least one healthcare provider indicating a discrepancy between the simulated future health state and the new health state (Peterson, paragraph [0107]; Paragraph [0107] teaches that the patient digital twin 130 can alert the provider to likely issues (i.e., transmitting an alert to the at least one healthcare provider) based on patient 110 information, reference/normal/standard information, and information from the provider regarding the circumstances of the patient’s operation, for example. While a post-surgery follow-up appointment may not be scheduled until a week after surgery, for example, the patient digital twin 130 (e.g., with or without patient 110 survey feedback, etc.) can identify a likely problem on day 3 (i.e., identifying a discrepancy between the simulated future health state and the new health state), for example, rather than waiting for the problem to worsen by day 7.).
- Peterson does not explicitly teach, however, in analogous art of medical systems and methods for simulating and evaluating patient data, T et al. (Pub. No. US 2019/0198169) teaches a method, comprising:
- determining whether said population of patients have one or more symptoms similar to said patient (T, paragraphs [0120] and [0128]; Paragraph [0128] teaches that the data analyzer 730 compares the patient health information to health information from previous patients and selects patients with similar health information (described in further detail in FIG. 18) (i.e., determining whether said population of patients have one or more similarities to said patient). Paragraph [0129] teaches that at block 1206, the data analyzer 730 generates a condition diagnosis. For example, the data analyzer compares the symptoms of the patient to the symptoms of past patients with similar health information. For example, if a patient presents symptoms of blurry vision, visual floaters, and scotomas, the data analyzer can find past patients, having those same symptoms (i.e., determining whether said population of patients have one or more symptoms similar to said patient), who were diagnosed with retinal vasculitis. Paragraph [0120] teaches that these features are beneficial for better analyzing past patient health information and identifying patient information concerning effects of a disease.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical systems and methods for simulating and evaluating patient data at the time of the effective filing date of the claimed invention to modify the computer implemented method for generating and updating patient digital twins taught by Peterson, to incorporate a step and feature directed to identifying other patients having similar symptoms to the patient in question, as taught by T, in order to better analyze past patient health information and identify patient information concerning effects of a disease. See T, paragraph [0120]; see also MPEP § 2143 G.
Regarding claim 7,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 7 depends on), as described above.
- Peterson further teaches a method, wherein:
- the machine learning simulation includes pharmaceutical data to simulate a future health state contingent upon the patient following a specified treatment plan (Peterson, paragraph [0042]; Paragraph [0042] teaches that a healthcare practitioner can view and simulate with the digital twin 130 to evaluate a condition, progression, possible treatment (i.e., the simulation includes simulating a future health state contingent upon the patient following a specified treatment plan), etc., for the patient 110.).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 8,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 8 depends on), as described above.
- T further teaches a method, wherein:
- the machine learning simulation includes treatment plan data to simulate a future health state contingent upon the patient receiving a stated medication (T, paragraphs [0038], [0093], and [0101]; Paragraph [0093] teaches that in some examples, the example patient 104 and/or the clinician 106 can interact with the data and the digital twin to examine the side effects and treatment success for the possible treatment plans (i.e., the simulation includes simulating a future health state contingent upon the patient following a specified treatment plan). Paragraph [0101] teaches that the example treatment plan selection includes scheduled medical operations, prescriptions prescribed (i.e., the future health state is dependent on the patient receiving a stated medication), medication dosages, etc. Paragraph [0038] teaches that this feature is beneficial for improving access to up-to-date information, which contributes to the clinician being better equipped to prescribe treatment plans that are best fits for the patient, and the patient can be informed of the success rates of the treatment plan and be mindful of the possible side effects and complications.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical systems and methods for simulating and evaluating patient data at the time of the effective filing date of the claimed invention to further modify the computer implemented method for generating and updating patient digital twins taught by Peterson, as modified in view of T, to incorporate a step and feature directed to enabling a patient and/or clinician to interact with digital twin to examine the side effects and success rates for possible treatment plans for a patient, as taught by T, in order to for improve access to up-to-date information, which: (1) contributes to the clinician being better equipped to prescribe treatment plans that are best fits for the patient; and (2) better informs the patient of the success rates, possible side effects, and complications of the treatment plan. See T, paragraph [0038]; see also MPEP § 2143 G.
Regarding claim 9,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 9 depends on), as described above.
- Peterson further teaches a method, wherein:
- the machine learning simulation uses a digital twin of the patient (Peterson, paragraph [0081]; Paragraph [0081] teaches that at block 1234, after information has been entered (blocks 1202, 1204, 1214, 1216) and verified (block 1226) to create the patient digital twin 130, the patient digital twin 130 can be leverage [sic] to create visualization(s) of patient 110 information. For example, the digital twin 130 can be used in simulation/emulation of the patient 110 (i.e., the machine learning simulation uses the digital twin of the patient) and conditions experienced and/or likely to be experienced by the patient 110.).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 10,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 10 depends on), as described above.
- Peterson further teaches a method, wherein:
- the machine learning simulation uses a plurality of digital twins of the patient (Peterson, paragraph [0055]; Paragraph [0055] teaches that each virtual space 250, 252, 254 can model a different digital twin instance and/or component of the digital twin 230 and/or each virtual space 250, 252, 254 can be used to perform a different analysis, simulation, etc., of the same digital twin 230 (i.e., the machine learning simulation uses a plurality of digital twins of the same patient). Using the multiple virtual spaces 250, 252, 254, the digital twin 230 can be tested inexpensively and efficiently in a plurality of ways while preserving patient 104 safety (i.e., the machine learning simulation uses a plurality of digital twins of the same patient).).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 11,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 11 depends on), as described above.
- Peterson further teaches a method, wherein:
- simulation of the new health state is based in part on a measured health state of a population of patients matched to the individual patient according to a criterion (Peterson, paragraphs [0075], [0083], and [0109]; Paragraph [0075] teaches that population health information, patient demographics, family and/or friend demographics, neighborhood information, access to care data, etc., can be provided to form the patient digital twin 130 (e.g., from an EMR, EHR, PHR, enterprise archive, etc.) (i.e., the health state is based on “population health information”, which is a measured health state of a population of patients). Paragraph [0083] teaches that the digital twin 130 and/or associated system (e.g., an EMR system, RIS/PACS system, etc.) can be programmed with rules and/or analytics 1304 to leverage the information, modeling, etc., provided by the digital twin 130 to make a decision, inform a decision, and/or otherwise drive a health outcome for the patient 110 (and/or a population including the patient 110, etc.) (i.e., the population of patients is matched to the individual patient). Further, paragraph [0109] teaches that in certain examples, data mining, modeling, prediction, other probabilities, etc., generated for the particular patient 110 via the patient digital twin 130 can be extrapolated (and anonymized) for an associated or similar population (e.g., via a PHMS, etc.) (i.e., the population of patients is matched to the individual patient). Thus, one patient/s 110 experience can help to improve health care experiences for a plurality of similar patients (e.g., by relation, geographic area, body type, condition, employment, race, gender, etc.) (i.e., the population of patients is matched to the individual patient according to a criterion).).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 12,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 12 depends on), as described above.
- Peterson further teaches a method, wherein:
- simulation of the new health state is based in part on a simulated health state of a population of patients matched to the individual patient according to a criterion (Peterson, paragraph [0109]; Paragraph [0109] teaches that in certain examples, data mining, modeling, prediction, other probabilities, etc., generated for the particular patient 110 via the patient digital twin 130 can be extrapolated (and anonymized) for an associated or similar population (e.g., via a PHMS, etc.) (i.e., the new health state is based on a simulated health state of a population of patients that is matched to the individual patient). Thus, one patient/s 110 experience can help to improve health care experiences for a plurality of similar patients (e.g., by relation, geographic area, body type, condition, employment, race, gender, etc.) (i.e., the population of patients is matched to the individual patient according to a criterion).).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 13,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 13 depends on), as described above.
- T further teaches a method, wherein:
- simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by the timing of providing mediation to the individual patient (T, paragraphs [0093]; Paragraph [0093] teaches that the analytical data and the digital twin can be interacted with and manipulated by the patient 104 and/or the clinician 106 of FIG. 1. In some examples, the patient can select different health factors for the machine learning engine 750 to prioritize (described in further detail in FIG. 18). In other examples, the patient can select a subset of treatment plans to evaluate or the patient can select sub-intervals of a treatment plan time to analyze (described in further detail in FIG. 19) (i.e., simulating the effects of one or more treatment options for the patient, where the treatment options vary by timing of providing the treatment to the patient). Paragraph [0101] teaches that the example treatment plan selection includes scheduled medical operations, prescriptions prescribed (i.e., the one or more treatment options are “one or more drug treatment options”), medication dosages, etc. Paragraph [0038] teaches that this feature is beneficial for improving access to up-to-date information, which contributes to the clinician being better equipped to prescribe treatment plans that are best fits for the patient, and the patient can be informed of the success rates of the treatment plan and be mindful of the possible side effects and complications.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical systems and methods for simulating and evaluating patient data at the time of the effective filing date of the claimed invention to further modify the computer implemented method for generating and updating patient digital twins taught by Peterson, as modified in view of T, to incorporate a step and feature directed to enabling a patient and/or clinician to interact with digital twin to examine the side effects and success rates for possible treatment plans for a patient, as taught by T, in order to for improve access to up-to-date information, which: (1) contributes to the clinician being better equipped to prescribe treatment plans that are best fits for the patient; and (2) better informs the patient of the success rates, possible side effects, and complications of the treatment plan. See T, paragraph [0038]; see also MPEP § 2143 G.
Regarding claim 14,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 14 depends on), as described above.
- T further teaches a method, wherein:
- simulating, using the machine learning module, effects of at least one of one or more drug treatment options of said individual patient, wherein the drug treatment options vary by dosage level of mediation to the individual patient (T, paragraphs [0093]; Paragraph [0093] teaches that the analytical data and the digital twin can be interacted with and manipulated by the patient 104 and/or the clinician 106 of FIG. 1. In some examples, the patient can select different health factors for the machine learning engine 750 to prioritize (described in further detail in FIG. 18). In other examples, the patient can select a subset of treatment plans to evaluate or the patient can select sub-intervals of a treatment plan time to analyze (described in further detail in FIG. 19) (i.e., simulating the effects of one or more treatment options for the patient). Paragraph [0101] teaches that the example treatment plan selection includes scheduled medical operations, prescriptions prescribed (i.e., the one or more treatment options are “one or more drug treatment options”), medication dosages (i.e., the one or more drug treatment options vary by dosage levels of the medication prescribed for the patient), etc. Paragraph [0038] teaches that this feature is beneficial for improving access to up-to-date information, which contributes to the clinician being better equipped to prescribe treatment plans that are best fits for the patient, and the patient can be informed of the success rates of the treatment plan and be mindful of the possible side effects and complications.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical systems and methods for simulating and evaluating patient data at the time of the effective filing date of the claimed invention to further modify the computer implemented method for generating and updating patient digital twins taught by Peterson, as modified in view of T, to incorporate a step and feature directed to enabling a patient and/or clinician to interact with digital twin to examine the side effects and success rates for possible treatment plans for a patient, as taught by T, in order to for improve access to up-to-date information, which: (1) contributes to the clinician being better equipped to prescribe treatment plans that are best fits for the patient; and (2) better informs the patient of the success rates, possible side effects, and complications of the treatment plan. See T, paragraph [0038]; see also MPEP § 2143 G.
Regarding claim 15,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 15 depends on), as described above.
- T further teaches a method, wherein:
- receiving healthcare study information including at least one of methodology and results of one or more healthcare studies (T, paragraphs [0056] and [0130]; Paragraph [0056] teaches that he patient digital twin 230 can be used to drive applied knowledge 310, access to care 320, costs 330, personal choices 340, social determinants/environment 350, etc. For example, the drive applied knowledge 310 can include information from sources including rules, guidelines, medical science, molecular science, medical journals, etc. (i.e., receiving results of at least one or more healthcare studies). Paragraph [0130] teaches that the data analytic algorithm server 752 can determine recommended treatment plans based on highest average success rates for a treatment, health trends for a geographic region, and information from academic journals and papers, etc. (i.e., receiving results of at least one or more healthcare studies).); and
- comparing, using the machine learning module, the healthcare study information to simulations of one or more said drug treatment options to determine at least one of reliability and consistency of the simulations of one or more said drug treatment options (T, paragraphs [0026], [0128], and [0130]; Paragraph [0128] teaches that at block 1204, the data analyzer 730 can access historical patient information including past patient health information and treatment success rates of past patients. For example, the data analyzer 730 compares the patient health information to health information from previous patients and selects patients with similar health information (described in further detail in FIG. 18) (i.e., comparing the healthcare study information to simulations of one or more drug treatment options). In some examples, the data analyzer 730 also accesses the diagnosis and treatment data (i.e., one or more drug treatment options). Paragraph [0130] teaches that at block 1208, the data analytic algorithm server 752 uses the patient health information, including symptoms, and past patient information, including diagnoses, to determine recommended treatment plans (i.e., determining the reliability and consistency of the one or more drug treatment options). For example, the data analytic algorithm server 752 can determine recommended treatment plans based on highest average success rates for a treatment (i.e., determining the reliability and consistency of the one or more drug treatment options), health trends for a geographic region, and information from academic journals and papers, etc. In some examples, the data analytic algorithm server 752 can limit the recommended treatment plans based on access to care 320 (i.e., determining the reliability and consistency of the one or more drug treatment options). Paragraph [0026] teaches that these features are beneficial for recommending at least one treatment plan, including determining success rates of the at least one treatment plan for a condition.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical systems and methods for simulating and evaluating patient data at the time of the effective filing date of the claimed invention to further modify the computer implemented method for generating and updating patient digital twins taught by Peterson, as modified in view of T, to incorporate steps and features directed to: (i) incorporating information from sources, such as medical journals and papers, into the patient digital twin; and (ii) comparing treatment options to determine the best treatment for a patient, as taught by T, in order to recommend at least one treatment plan, including determining success rates of the at least one treatment plan for a condition. See T, paragraph [0026]; see also MPEP § 2143 G.
Regarding claim 16,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 9 (which claim 16 depends on), as described above.
- Peterson further teaches a method, wherein:
- simulating application of best clinical practices for a desired clinical outcome on said individual patient via the digital twin of said individual patient (Peterson, paragraphs [0070] and [0089]; Paragraph [0070] teaches that the digital twin 130 can evolve over time based on available health data, machine-learning, human feedback, medical event processing, new or updated digital medical knowledge, and post-event feedback. The digital twin 130 provides an evolving model of the patient 110 that can learn and absorb information to reflect patient body systems and health information systems, rules, norms, best practices (i.e., applying the best clinical practices for a desired clinical outcome to the patient’s digital twin), etc. Paragraph [0089] also teaches that the care provider can execute a simulation using the digital twin 130 and compare the results to known, expected, or reference results to determine their accuracy. The care provider can compare modeled information from the patient digital twin 130 with known information for the patient 110 and/or reference/“gold standard” information for patients similar to the patient 110 (i.e., simulating application of the best clinical practices for a desired clinical outcome on the patient’s digital twin), for example.).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 17,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 17 depends on), as described above.
- Peterson further teaches a method, wherein:
- receiving simulation instructions, the simulation instructions including one or more research experiments (Peterson, paragraphs [0089] and [0103]; Paragraph [0089] teaches that at block 1402, a care provider accesses the patient digital twin 130 and reviews the patient digital twin 130 to generate a care provider analysis. For example, the care provider can execute a simulation using the digital twin 130 (i.e., receiving simulation instructions) and compare the results to known, expected, or reference results to determine their accuracy. Paragraph [0103] teaches that 1708, a library of smart protocols is created by cohort. 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. Provider users can build and select “evidence based” protocols from the library to help reduce unnecessary testing and assure required testing is completed, for example. 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. (i.e., the simulation instructions can be indicative of one or more research experiments).); and
- simulating the one or more research experiments, and results of best clinical practices on at least one of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients (Peterson, paragraph [0089]; Paragraph [0089] teaches that the care provider can execute a simulation using the digital twin 130 and compare the results to known, expected, or reference results to determine their accuracy. The care provider can compare modeled information from the patient digital twin 130 with known information for the patient 110 and/or reference/“gold standard” information for patients similar to the patient 110 (i.e., simulating results of the best clinical practices on the digital twin of the individual patient), for example. Paragraph [0108] teaches that digital twin 130 modeling, simulation, prediction, etc., information can be communicated to patient 110 and provider to improve adherence to pre- and post-op instructions and outcomes, for example (i.e., simulating the one or more research experiments and results of the best clinical practices for the patients using the digital twins).).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 18,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 6 (which claim 18 depends on), as described above.
- Peterson further teaches a method, further comprising:
- receiving simulation instructions, the simulation instructions including one or more drug treatment regimens (Peterson, paragraphs [0042] and [0089]; Paragraph [0089] teaches that at block 1402, a care provider accesses the patient digital twin 130 and reviews the patient digital twin 130 to generate a care provider analysis. For example, the care provider can execute a simulation using the digital twin 130 (i.e., receiving simulation instructions) and compare the results to known, expected, or reference results to determine their accuracy. Paragraph [0042] teaches that rather than reading a report, a healthcare practitioner can view and simulate with the digital twin 130 to evaluate a condition, progression, possible treatment (i.e., the simulation instructions can be indicative of one or more drug treatment regimens), etc., for the patient 110.); and
- simulating the one or more drug treatment regimens on one or both of said individual patient and a population of patients using at least one of the digital twin of said individual patient and the digital twin of said population of patients (Peterson, paragraph [0089]; Paragraph [0089] teaches that the care provider can execute a simulation using the digital twin 130 and compare the results to known, expected, or reference results to determine their accuracy. The care provider can compare modeled information from the patient digital twin 130 with known information for the patient 110 and/or reference/“gold standard” information for patients similar to the patient 110 (i.e., simulating the drug treatment regimens on the patient digital twin), for example.).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 19,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 18 (which claim 19 depends on), as described above.
- Peterson further teaches a method, wherein:
- simulation of said individual patient and/or said population of patients is performed according to simulation instructions received from one or more of healthcare workers (Peterson, paragraph [0089]; Paragraph [0089] teaches at block 1402, a care provider accesses the patient digital twin 130 and reviews the patient digital twin 130 to generate a care provider analysis (i.e., simulation instructions of the patient digital twin are received from one or more healthcare workers).).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Regarding claim 20,
- The combination of: Peterson, as modified in view of: T, teaches the limitations of claim 18 (which claim 20 depends on), as described above.
- Peterson further teaches a method, wherein:
- simulation of said individual patient and/or said population of patients is performed according to simulation instructions formed by the machine learning module (Peterson, paragraph [0090]; Paragraph [0090] teaches at block 1404, machine learning (e.g., artificial intelligence application(s), engine(s), such as deep learning, neural network, etc.) access and review the patient digital twin 130 to generate a machine learning analysis (i.e., simulation instructions of the patient digital twin are formed and received from the machine learning module).).
The motivation and rationale for modifying the computer implemented method for generating and updating patient digital twins taught by Peterson, in view of T, described in the obviousness rejections of claim 6 above similarly apply to this obviousness rejection, and are incorporated herein by reference.
Conclusion
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/N.A.A./Examiner, Art Unit 3686
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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