Patent Application 18312434 - MEDICAL AND HEALTHCARE SERVICE PLATFORMS AND

Title: MEDICAL AND HEALTHCARE SERVICE PLATFORMS AND USES THEREOF
Application Information

Invention Title: MEDICAL AND HEALTHCARE SERVICE PLATFORMS AND USES THEREOF
Application Number: 18312434
Submission Date: 2025-04-07T00:00:00.000Z
Effective Filing Date: 2023-05-04T00:00:00.000Z
Filing Date: 2023-05-04T00:00:00.000Z
National Class: 705
National Sub-Class: 002000
Examiner Employee Number: 93574
Art Unit: 3684
Tech Center: 3600
Rejection Summary

102 Rejections: 0
103 Rejections: 2
Cited Patents

The following patents were cited in the rejection:
Office Action Text



    DETAILED ACTION
Notices to Applicant
This communication is a non-final rejection. Claims 1-20, as filed 05/04/2023, are currently pending and have been considered below.
Priority is generally acknowledged to foreign application CN 202210482706.0 filed 05/05/2022.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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. 

Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.


Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.  The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.  

Step 1
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:


1. A medical and healthcare service platform, wherein the medical and healthcare service platform is supported by a digital data currency system and provides medical and healthcare data processing, analyzing, and predicting based on a digital human system by integrating participating parties comprising individual persons, researchers, healthcare providers, and regulatory and public sectors, the medical and healthcare service platform (additional element – applying the idea using computers as tools) comprising:
a digital human replica system that constructs a digital human replica to provide virtual representation, modeling, and visualization services based on present and past medical and health data of physical persons (abstract idea – organizing human activity and mental processes; “digital” is an additional element that merely uses computers as tools to perform the abstract idea); 
a digital human simuli system that constructs a digital human simuli to provide virtual simulation and modeling of future health and physiological evolution of a physical person based on the present and past medical and health data (abstract idea – organizing human activity and mental processes; “digital”,  as used in this limitation and others throughout the claims, is an additional element that merely uses computers as tools to perform the abstract idea);
a digital human agent system that represents virtual medical and health service professionals with specialties and functions, wherein the virtual medical and healthcare service professionals are formed based on professional knowledge and capabilities, specialties, and experiences of physical medical and healthcare professionals and characteristics and specialties of non-medical and healthcare professionals or practitioners (abstract idea – organizing human activity and mental processes); and 
a digital human data acquisition system that collects biometric identification and medical- related data (additional element – insignificant extra-solution activity, namely data-gathering), 
wherein the digital data currency system awards data sharing and contributions in a full ecosystem of data generation comprising data processing, data cleaning and denoising, data encryption and anonymization, data labeling and calibration, and data analytics, data contributions related to medical and healthcare services; and services provided by medical and healthcare professionals from clinical practices, drug companies from laboratories or clinical trial data, and academic researchers from research work  (abstract idea – organizing human activity and mental processes), 
wherein the digital human replica system receives input data from the human data acquisition system for both a target physical person and other persons with similar biomedical, social- demographical, occupational, and lifestyle characteristics (additional element – insignificant extra-solution activity, namely data-gathering) for building, calibrating, and customizing the digital human simuli system for the target physical person to simulate the growing and aging, disease events, injury events, and their reactions to medicines and treatment plans (abstract idea – organizing human activity and mental processes; intended use of the data), 
wherein the digital human agent system creates a special digital human replica of actions, treatment plans, and decision makings of medical and healthcare professionals, the digital human agent system configured to execute simulated medical, care, and health services as an intervention based on simulation in a digital human simuli model of the digital human simuli system (abstract idea – organizing human activity and mental processes), 
wherein the digital human stimuli and the digital human agent system are integrated to perform model optimization to select and determine an optimal treatment or support plan to achieve an optimal health and medical outcome (abstract idea – organizing human activity and mental processes), and 
wherein the digital human digital data currency system awards digital currency for data contribution by the participating parties who interface with the digital human data acquisition system (abstract idea – organizing human activity and mental processes) and
wherein the data contribution results in improvement of performance of the digital human simuli system and the digital human agent system (abstract idea – organizing human activity and mental processes).

2. The medical and healthcare service platform of claim 1, wherein the digital human replica system provides a representation of physical human bodies, and wherein the representation comprises one or more of: 3D contour body model, multi-dimensional anatomical model, multi- dimensional data feature tensor, and spatiotemporal transformation of health and disease state (additional element – generally linking to a technical field or applying the idea with computers).

3. The medical and healthcare service platform of claim 2, wherein the representation further comprises one or more of medical diagnosis and treatment; pharmaceutical use state quantity; diet, living and healthcare habits state quantity; environmental impact state quantity; virtual detection monitoring and observation modeling; and a process of psychophysiological changes for a full life cycle (abstract idea – organizing human activity and mental processes).

4. The medical and healthcare service platform of claim 1, wherein the digital human replica system comprises a full retrospective system generated based on spatiotemporal data, and wherein the spatiotemporal data comprises: retrospective of life cycle, retrospective of life course and event process, or retrospective of disease and psychophysiological social environment impact events (abstract idea – organizing human activity and mental processes).

5. The medical and healthcare service platform of claim 1, wherein the digital human replica system provides virtual representation, modeling, and visualization service for both participant users and non-participant users of the medical and healthcare service platform, (additional element – insignificant extra-solution activity, namely, data output. Alternatively, this data output amounts to using the computer as a tool to perform the abstract ideas) wherein the non- participant users have similar biomedical, social-demographical, occupational, and lifestyle characteristics to the participant users, and wherein the digital human replica system uses the characteristics of the non-participant users to infer and interpolate missing data of the participant users (abstract idea – organizing human activity and mental processes).

6. The medical and healthcare service platform of claim 5, wherein the digital human replica system is configured to: identify the participant users using customizable identity and biometric information, or receive feedback on physical human health or medical processes (abstract idea – organizing human activity and mental processes).

7. The medical and healthcare service platform of claim 5, wherein the non-participant users are identified by de-identified information created by: age groups, gender, or biometric group characteristics (abstract idea – organizing human activity and mental processes).

8. The medical and healthcare service platform of claim 1, wherein the digital human simuli system comprises an organ or part simulation subsystem that simulates a change process by using organs or parts of a constructed digital human replica (abstract idea – organizing human activity and mental processes).

9. The medical and healthcare service platform of claim 1, wherein the digital human simuli system comprises a simulation process dynamic visualization subsystem for dynamically visualizing changed data (additional element – insignificant extra-solution activity, namely, data output. Alternatively, this data output amounts to using the computer as a tool to perform the abstract ideas).

10. The medical and healthcare service platform of claim 1, wherein the digital human agent system, through one or more deep neural network models, performs training and learning based on input data comprising: real-world doctors' treatment and prescription strategies, nursing and service strategies for nurses, therapists, research, experimentation, or development strategies for researchers, regulations and services for government agencies, wherein the digital human agent system generates digital human simuli models based on the input data, and wherein the digital human simuli models interact with a baseline digital human replica model (abstract idea – organizing human activity and mental processes. Alternatively, the training and learning amounts to mathematical concepts).

11. The medical and healthcare service platform of claim 10, wherein the digital human agent system creates deep neural network models of:(a) doctors, nurses, or therapists to treat the digital human simuli and provide virtual instructions and services to physical persons on their healthcare and disease treatment process; (b) researchers and experimental groups to conduct virtual tests and surveys on the digital human replica and the digital human simuli to conduct research and development of new medical devices, drugs, treatment plans, or general health and behavioral studies; (c) health insurance providers that interact with the digital human replica about coverage and billing costs, insurance claims for ongoing treatments and services, and different treatment plans and insurance coverage and billing options; (d) governing bodies, regulators, and digital security agencies to oversee, monitor and enforce critical health, medical, privacy protection, data security policy and regulations, freedom of information and disinformation suppression in virtual worlds;(e) public health workers and platform users to provide related government services comprising medical material distribution, medical forms, and insurance claims; or (f) professionals who supervise scientific research and production services, and self- diagnosis services and volunteers who provide health diagnosis and treatment services to family members and family communities, wherein the digital human agent system undertakes medical treatment and care services, virtual simulation and modeling application (abstract idea – organizing human activity and mental processes. Alternatively, the neural network models amount to mathematical concepts).

12. The medical and healthcare service platform of claim 1, wherein the present and past medical and health data comprises: real time data, events data, and historical data and paper records (abstract idea – organizing human activity and mental processes).

13. The medical and healthcare service platform of claim 1, wherein the data acquisition system comprises an interface interacting with users and contributors of the medical and healthcare service platform to facilitate biomedical, healthcare, data inputs and labeling, and wherein the interface comprises one or more of: an interface for regular individual participants, an interface for doctors and hospitals, an interface for companies and R&D groups, and an interface to interact with datasets and data feeds provided by public agencies (additional element – insignificant extra-solution activity, namely data-gathering).

14. The medical and healthcare service platform of claim 13, wherein the interface comprises: a wearable body health sensor interface, a digital human body multimodal data interface, or a digital human body medical record conversion interface (additional element – insignificant extra-solution activity, namely data-gathering).

15. The medical and healthcare service platform of claim 14, wherein the medical and health data are acquired from: wearable sensors, medical records, or lab tests (additional element – insignificant extra-solution activity, namely data-gathering).

16. The medical and healthcare service platform of claim 1, wherein the data acquisition system is configured to: clean collected data; perform desensitization processing on collected data; perform data privacy protection processing on the collected data; or visualize the collected data (abstract idea – organizing human activity and mental processes).

17. The medical and healthcare service platform of claim 1, wherein the medical and healthcare service platform constructs a spatial-temporal virtual world that simulates external factors related to human health, disease, conditions, and wherein the external factors comprise one or more of: social activity, travel behavior, family environment, hospital, occupation, workplace, transportation, community, and city (abstract idea – organizing human activity and mental processes).

18. The medical and healthcare service platform of claim 17, comprising an auxiliary and interactive system interfacing between the virtual world of digital humans and real-world personal healthcare and government public health management to perform one or more of: auxiliary diagnosis and treatment, health care, nursing care, public health management, and smart device and robot interaction, wherein the auxiliary diagnosis and treatment, health care, and nursing care comprises: real- world medical diagnosis and treatment; emergency responses; diagnosis and treatment of rare diseases; offline personal health care; family daily care, emotional care, or psychological counseling; real-world offline community health management services; or medical resource retrieval, distribution and sharing, equalization and fairness services, and wherein the public health management: conducts real-world government regulation, control disease, or infectious disease control; conducts national health education; prevents quack doctors or false medical information; or prevents and reduces major injury events with high accident rate and low rescue success rate (abstract idea – organizing human activity and mental processes).

19. The medical and healthcare service platform of claim 1, comprising a hybrid data warehouse system for storing the medical and health data, wherein the hybrid data warehouse system comprises: a centralized data warehouse and a federated data warehouse, wherein the centralized data warehouse has an upload interface for users to upload their healthcare and medical data directly to the medical and healthcare service platform, and wherein the federated data warehouse has an application interface for the participating parties to share converted aggregate data and models from protected data sources within each party (additional element – insignificant extra-solution activity, namely data-gathering).

20. The medical and healthcare service platform of claim 1, wherein the digital data currency system awards digital currency for one or more of: contributors based on entire data contribution and generation life cycles, contributions to early detection, tracking, and prevention of infectious diseases, success rate, and positive user feedback for healthcare worker agents, and successful applications and adoptions for research and discovery results from research and discovery agents (abstract idea – organizing human activity and mental processes).

Step 2A Prong One
The broadest reasonable interpretation of these steps includes certain methods of organizing human activity because the italicized portions are analogous to steps a care team could perform when planning and executing care for a patient. For example, but for the digital language, constructing human replicas and simuli (i.e., simulations) are thoughts that a care manager could have when planning which specialist a patient should see or which procedure should be performed first. These steps are also mental processes because they could practicably be performed in the mind of the care manager. Additionally, other steps such as representing health services professionals giving their simulated functions could be performed by the care manager calling the professionals and asking hypothetical questions about the patient’s care. Rewarding people with data currency system awards is analogous to paying claims or giving favors for helpful professionals. Other than reciting generic computer terms like “digital”, nothing in the claims precludes many of the italicized portions from practically being performed in the mind.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims as set forth above. 

Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, generic computing language such as “digital” amounts to invoking computers as a tool to perform the abstract idea, see applicant’s application as published paragraph [0177], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, collecting biometric data and similar limitations in claims 13-15 and 19 amount to mere data gathering and selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
generally link the abstract idea to a particular technological environment or field of use such as representing the human replica with modeling techniques in claim 2, see MPEP 2106.05(h))
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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.

Step 2B
The claim(s) does/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 of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. For example, the biometric and other data gathering amount to receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or 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 with the additional elements in the independent claims. 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.



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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1, 4-7, and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ata (USP App. Pub. No. 2022/0076841) in view of Jarrell (USP App. Pub. No. 2008/0015418) and Li (Li, W., Wu, Wj., Wang, Hm. et al. Crowd intelligence in AI 2.0 era. Frontiers Inf Technol Electronic Eng 18, 15–43 (2017). https://doi.org/10.1631/FITEE.1601859).
Regarding claim 1, Ata discloses: A medical and healthcare service platform, wherein the medical and healthcare service platform is supported by a digital data currency system and provides medical and healthcare data processing, analyzing, and predicting based on a digital human system by integrating participating parties comprising individual persons, researchers, healthcare providers, and regulatory and public sectors, the medical and healthcare service platform comprising:
--a digital human replica system that constructs a digital human replica to provide virtual representation, modeling, and visualization services based on present and past medical and health data of physical persons (“From the base model and the health attributes, a patient model (also referred to as a “digital twin”) may be generated. The patient model may incorporate some or all of the base model and a representation of the health attributes into the health dependency graph,” [0006]; “Example embodiments, described herein, provide a patient model, also referred to as a Human Digital Twin (HDT), for such solutions. A digital twin is commonly defined as a software model of a physical asset, system or process designed to detect, prevent, predict, and optimize through real time analytics. HDT extends the concept of digital twin to cover the mathematical replication of human health process dynamics,” [0044]); 
--a digital human simuli system that constructs a digital human simuli to provide virtual simulation and modeling of future health and physiological evolution of a physical person based on the present and past medical and health data (“Based on those findings, a report may be generated for the patient 105 (235). The report may indicate the identified risks, and may also provide a diagnosis of the patient 105 and one or more remedies/interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome,” [0060]);

--a digital human data acquisition system that collects biometric identification and medical- related data (Health attributes 106 in FIG. 1; 210 in FIG. 2), 
--wherein the digital human replica system receives input data from the human data acquisition system for both a target physical person and other persons with similar biomedical, social- demographical, occupational, and lifestyle characteristics for building, calibrating, and customizing the digital human simuli system for the target physical person to simulate the growing and aging, disease events, injury events, and their reactions to medicines and treatment plans (“In order to characterize a model of the patient model beyond its static description, additional information about the subject patient model and its components is collected and incorporated into the model as a definition of the dynamic complexity of the patient model. Inputs of this stage include the static complexity definition produced in stage (605), as well as information regarding how the static complexity changes over time. This information can be obtained through analysis of historical data about the patient model, epidemiological data (e.g., data derived from a given population that relates health attributes and incidences of various changes exhibited by the population), historical simulation data, data about comparable patient models, the patient's health attributes, and/or other sources,” [0085]), 
--wherein the digital human agent system creates a special digital human replica of actions, treatment plans, and decision makings of medical and healthcare professionals, the digital human agent system configured to execute simulated medical, care, and health services as an intervention based on simulation in a digital human simuli model of the digital human simuli system (“After building the emulator in phase two 2810, in phase three 2815 (risk discovery), modified scenarios are run to identify possible risks. By modifying the parameters of each scenario within the emulator, one by one, by group or by domain, to represent possible changes, one may extrapolate each time the point at which the patient model will hit a singularity and use the corresponding information to diagnose the case. The emulator supports risk categorization based on the severity of impact, the class of mitigation, and many other characteristics that support decision making such as urgency, and the complexity and/or cost of implementation of mitigating actions,” [0124]), 
--wherein the digital human stimuli and the digital human agent system are integrated to perform model optimization to select and determine an optimal treatment or support plan to achieve an optimal health and medical outcome (“The pairing human perception and decision-making capabilities of supervised machine learning with the modeling methods presented herein provides an optimal synthesis for the intelligent treatment of disease (see FIG. 31),” [0248]; ), and 
wherein the data contribution results in improvement of performance of the digital human simuli system and the digital human agent system (“the patient model is modeled and emulated under varying conditions, and the results of the emulation are analyzed and quantified, providing for solutions for improving the patient model,” [0076]).

	Ata does not expressly disclose but Jarrell teaches:
--a digital human agent system that represents virtual medical and health service professionals with specialties and functions, wherein the virtual medical and healthcare service professionals are formed based on professional knowledge and capabilities, specialties, and experiences of physical medical and healthcare professionals and characteristics and specialties of non-medical and healthcare professionals or practitioners (“The virtual tutor agent 152 provides responses from a virtual tutor to a trainee based on a pedagogical model (a set of pedagogical rules as described above, also called a mentor model) and a pedagogical logic processor and one or more tutor personalities, representing different medical specialties or differing opinions within the same specialty, as described in more detail below,” [0050]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Ata’s health modeling with Jarrell’s specialist agents because this would “improv[e] manageability of content and logic and minimiz[e] logic conflict.” See Jarrell [0068].

Ata and Jarrell do not expressly disclose but Li teaches:
--wherein the digital data currency system awards data sharing and contributions in a full ecosystem of data generation comprising data processing, data cleaning and denoising, data encryption and anonymization, data labeling and calibration, and data analytics, data contributions related to medical and healthcare services; and services provided by medical and healthcare professionals from clinical practices, drug companies from laboratories or clinical trial data, and academic researchers from research work  (“crowdsourcing has been widely used for data cleaning…medical drug development, taxonomy construction, topic discovery, social network analysis, and even software design,” pages 17-18), 
-- wherein the digital human digital data currency system awards digital currency for data contribution by the participating parties who interface with the digital human data acquisition system (“These developers are motivated to contribute innovative designs for both reputation and payment by the micro-payment mechanism of the App Store,” page 24).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Ata and Jarrell’s health modeling using specialist agents with Li’s payments for crowdsourced data cleaning because this would incentivize workers to participate in data processing. See Li page 21: “They found that monetary compensation and control of working conditions (e.g., working duration, payment rate, and the allocated tasks) are the primary factors for joining these systems.”

Regarding claim 4, Ata further discloses: wherein the digital human replica system comprises a full retrospective system generated based on spatiotemporal data, and wherein the spatiotemporal data comprises: retrospective of life cycle, retrospective of life course and event process, or retrospective of disease and psychophysiological social environment impact events (“The health attributes may include a range of data… medical records detailing the patient's medical history,” [0006]; “Ultimately, the value of human digital twin, AI, deep learning and neural computation technologies in medicine will only be realized if they can quickly deliver to users something complementary and more complete than the current knowledge domain,” [0206]).
Regarding claim 5, Ata further discloses: wherein the digital human replica system provides virtual representation, modeling, and visualization service for both participant users and non-participant users of the medical and healthcare service platform, wherein the non-participant users have similar biomedical, social-demographical, occupational, and lifestyle characteristics to the participant users, and wherein the digital human replica system uses the characteristics of the non-participant users to infer and interpolate missing data of the participant users (“In order to characterize a model of the patient model beyond its static description, additional information about the subject patient model and its components is collected and incorporated into the model as a definition of the dynamic complexity of the patient model. Inputs of this stage include the static complexity definition produced in stage (605), as well as information regarding how the static complexity changes over time. This information can be obtained through analysis of historical data about the patient model, epidemiological data (e.g., data derived from a given population that relates health attributes and incidences of various changes exhibited by the population), historical simulation data, data about comparable patient models, the patient's health attributes, and/or other sources. The output of this stage (610) is a definition of the dynamic complexity base model of the subject environment or patient mode,” [0085]).

Regarding claim 6, Ata further discloses: wherein the digital human replica system is configured to: identify the participant users using customizable identity and biometric information, or receive feedback on physical human health or medical processes (“For example, one set of parameters may represent exposure of the patient to a disease, a change in the patient's health attributes based on known trends within the population of the patient's environment, an injury suffered by the patient, and other changes relating to the patient or the patient's environment that may affect the patient's health,” [0059]).

Regarding claim 7, Ata further discloses: wherein the non-participant users are identified by de-identified information created by: age groups, gender, or biometric group characteristics (“such an occurrence probability may be determined based on historical data about the patient model, epidemiological data (e.g., data derived from a given population that relates health attributes and incidences of various changes exhibited by the population), historical simulation data, data about comparable patient models, the patient's health attributes, and/or other sources,” [0067]).


Regarding claim 9, Ata further discloses: wherein the digital human simuli system comprises a simulation process dynamic visualization subsystem for dynamically visualizing changed data (“FIG. 28 shows a HDT graph created using the perturbation method, which visually illustrates how the dynamics of multimorbidity may negatively impact a patient's health in a particular scenario and provides an indication of how much time is available to find an effective treatment,” [0236]).

Regarding claim 10, Ata further discloses: wherein the digital human agent system, through one or more deep neural network models, performs training and learning based on input data comprising: real-world doctors' treatment and prescription strategies, nursing and service strategies for nurses, therapists, research, experimentation, or development strategies for researchers, regulations and services for government agencies, wherein the digital human agent system generates digital human simuli models based on the input data, and wherein the digital human simuli models interact with a baseline digital human replica model (“Using already available AI, deep learning and neural computation technologies, the robot should be able to provide the doctor and patient a robust diagnosis and recommend a treatment plan in 20 minutes or less using the proposed process,” [0247]).

Regarding claim 11, Ata further discloses: wherein the digital human agent system creates deep neural network models of:(a) doctors, nurses, or therapists to treat the digital human simuli and provide virtual instructions and services to physical persons on their healthcare and disease treatment process; (b) researchers and experimental groups to conduct virtual tests and surveys on the digital human replica and the digital human simuli to conduct research and development of new medical devices, drugs, treatment plans, or general health and behavioral studies; (c) health insurance providers that interact with the digital human replica about coverage and billing costs, insurance claims for ongoing treatments and services, and different treatment plans and insurance coverage and billing options; (d) governing bodies, regulators, and digital security agencies to oversee, monitor and enforce critical health, medical, privacy protection, data security policy and regulations, freedom of information and disinformation suppression in virtual worlds;(e) public health workers and platform users to provide related government services comprising medical material distribution, medical forms, and insurance claims; or (f) professionals who supervise scientific research and production services, and self- diagnosis services and volunteers who provide health diagnosis and treatment services to family members and family communities, wherein the digital human agent system undertakes medical treatment and care services, virtual simulation and modeling application (“Based on those findings, a report may be generated for the patient 105 (235). The report may indicate the identified risks, and may also provide a diagnosis of the patient 105 and one or more remedies/interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome,” [0060]).

Regarding claim 12, Ata further discloses: wherein the present and past medical and health data comprises: real time data, events data, and historical data and paper records (“The health attributes may include a range of data, test results, evaluation and analysis of the patient, including genetic traits, a blood lipid profile, medical imaging reports, medical records detailing the patient's medical history, and the patient's environment (e.g., climate, location, and related external factors that may affect the patient's health),” [0006]).

Regarding claim 13, Ata further discloses: wherein the data acquisition system comprises an interface interacting with users and contributors of the medical and healthcare service platform to facilitate biomedical, healthcare, data inputs and labeling, and wherein the interface comprises one or more of: an interface for regular individual participants, an interface for doctors and hospitals, an interface for companies and R&D groups, and an interface to interact with datasets and data feeds provided by public agencies (“Such selection of differing parameters is described herein, and in particular with reference to FIG. 7. When selecting input parameters to detect an adverse outcome resulting from a patient model's dynamic complexity, a number of variables can be selected for permutation. For example, input parameters can be permutated to simulate the patient catching a given disease, a change in one or more of the patient's health attributes (e.g., a change in the patient's blood lipid profile, a change in the patient's lifestyle or environment), an acute adverse outcome such as an injury, and/or one or more interventions, such as treatment of physical therapy, a medication, or a positive lifestyle change (e.g., quitting smoking, beginning an exercise routing or diet). Further, the length of time over which the model patient is emulated may be varied, and this variation may be managed by an AI process based on the outcomes to be investigated. Such variation in time may be employed, with or without other permutations, to determine whether the input parameters result in an adverse outcome over a different (e.g., longer) length of time,” [0061]).

Regarding claim 14, Ata does not disclose but Jarrell further teaches: wherein the interface comprises: a wearable body health sensor interface, a digital human body multimodal data interface, or a digital human body medical record conversion interface (receiving real-time data from sensors/wearables in [0221]-[0222]).
Each element is taught by either Ata, Jarrell, or Li. The medical data inputs of Ata being sensors as taught by Jarrell does not affect the normal functioning of the elements of the claim which are taught by Ata and Li. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Jarrell with the teachings of Ata and Li since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.


Regarding claim 14, Ata does not disclose but Jarrell further teaches: wherein the medical and health data are acquired from: wearable sensors, medical records, or lab tests (receiving real-time data from sensors/wearables in [0221]-[0222]).
The motivation to combine is the same as in claim 13.

Regarding claim 16, Ata further discloses: wherein the data acquisition system is configured to: clean collected data; perform desensitization processing on collected data; perform data privacy protection processing on the collected data; or visualize the collected data (“a representation of the health attributes into the health dependency graph.,” [0006]).

Regarding claim 17, Ata further discloses: wherein the medical and healthcare service platform constructs a spatial-temporal virtual world that simulates external factors related to human health, disease, conditions, and wherein the external factors comprise one or more of: social activity, travel behavior, family environment, hospital, occupation, workplace, transportation, community, and city (“Inputs of this stage (605) include information regarding the patient (e.g., health attributes) and construct of the environment and patient model that are its static definition, including functional definitions (how each component of the patient model operates and interacts with other components) and physical definitions (layered architecture). The output of this stage (605) is a definition of the static complexity base of the subject environment or patient model,” [0079]).

Regarding claim 18, Ata further discloses: comprising an auxiliary and interactive system interfacing between the virtual world of digital humans and real-world personal healthcare and government public health management to perform one or more of: auxiliary diagnosis and treatment, health care, nursing care, public health management, and smart device and robot interaction, wherein the auxiliary diagnosis and treatment, health care, and nursing care comprises: real- world medical diagnosis and treatment; emergency responses; diagnosis and treatment of rare diseases; offline personal health care; family daily care, emotional care, or psychological counseling; real-world offline community health management services; or medical resource retrieval, distribution and sharing, equalization and fairness services (“Using the situational data revealed from the iterative analysis methodology, the ultimate objective is to improve cancer detection and treatment outcomes for individual patients (FIG. 21),” [0204]), and 
--wherein the public health management: conducts real-world government regulation, control disease, or infectious disease control; conducts national health education; prevents quack doctors or false medical information; or prevents and reduces major injury events with high accident rate and low rescue success rate (“The active monitoring of IAM process outcomes would thereby provide a fully vetted platform to support individualized and proactive patient risk avoidance and suggest personalized treatment protocols when necessary,” [0204]; “an iterative process that creates a symbiotic relationship between physical and in silico research can deepen the understanding of cancer progression, identify promising new areas for scientific research and improve the certainty of predictive diagnosis and treatment outcomes for a specific patient,” [0153]).

Regarding claim 19, Ata does not disclose but Jarrell further teaches: comprising a hybrid data warehouse system for storing the medical and health data, wherein the hybrid data warehouse system comprises: a centralized data warehouse and a federated data warehouse, wherein the centralized data warehouse has an upload interface for users to upload their healthcare and medical data directly to the medical and healthcare service platform, and wherein the federated data warehouse has an application interface for the participating parties to share converted aggregate data and models from protected data sources within each party (“System 100 includes a data warehouse 120 which provides data storage and management for information used by the system 100 and includes an application 150 that utilizes the data in data warehouse 120 and input from an author and trainee to teach cognitive skills to the trainee,” [0041]; “In step 210 normal patient data is received, for example, from visible human data stored in normal human data 124 in data warehouse 120,” [0056]; “In some embodiments the shared data is stored in a common area of data warehouse 120. In some embodiments, some or all of the shared data is replicated in each instance of organism data, e.g., in the normal human data 124 and abnormalities data 126,” [0134]; “The ontological classes, polymesh data and image data used herein can serve as a defacto standard for the “patient sharable object” for the medical care industry,” [0206]).
Each element is taught by either Ata, Jarrell, or Li. The data warehouse techniques of Jarrell do not affect the normal functioning of the elements of the claim which are taught by Ata and Li. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Jarrell with the teachings of Ata and Li since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.

Regarding claim 20, Ata does not disclose but Li further teaches wherein the digital data currency system awards digital currency for one or more of: contributors based on entire data contribution and generation life cycles (paying per task, i.e., based on entire data contribution, on page 19), contributions to early detection, tracking, and prevention of infectious diseases, success rate (paying based on winning contributions on page 29), and positive user feedback for healthcare worker agents, and successful applications and adoptions for research and discovery results from research and discovery agents. 
The motivation to combine is the same as in claim 1.

Claims 2-3 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Ata (USP App. Pub. No. 2022/0076841) in view of Jarrell (USP App. Pub. No. 2008/0015418), Li (Li, W., Wu, Wj., Wang, Hm. et al. Crowd intelligence in AI 2.0 era. Frontiers Inf Technol Electronic Eng 18, 15–43 (2017). https://doi.org/10.1631/FITEE.1601859), and Barricelli (B. R. Barricelli, E. Casiraghi and D. Fogli, "A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications," in IEEE Access, vol. 7, pp. 167653-167671, 2019, doi: 10.1109/ACCESS.2019.2953499)
Regarding claim 2, Ata does not expressly disclose but Barricelli teaches: wherein the digital human replica system provides a representation of physical human bodies, and wherein the representation comprises one or more of: 3D contour body model, multi-dimensional anatomical model, multi- dimensional data feature tensor, and spatiotemporal transformation of health and disease state (“A physiological model virtualized by a DT would allow physicians to make in silico predictions of how the real organ might behave in any given situation. The automated analysis provided by CAD systems would allow evaluating the effectiveness of tailored treatments, paving the way to the expansion of precision medicine,” page 167663).
One of ordinary skill in the art would have been motivated to expand the health modeling platform of Ata, Jarrell, and Li to include the digital twin modeling techniques of Barricelli because this would allow “tailored treatments, paving the way to the expansion of precision medicine,” (page 167663).

Regarding claim 3, Ata does not expressly disclose but Barricelli teaches: wherein the representation further comprises one or more of medical diagnosis and treatment; pharmaceutical use state quantity; diet, living and healthcare habits state quantity; environmental impact state quantity; virtual detection monitoring and observation modeling; and a process of psychophysiological changes for a full life cycle (“AI-based applications and digital twins still require a lot of human intervention, particularly in scenarios where they are used to test new features and modifcations of physical assets, or when they are exploited to provide answers such as diagnosis and treatments.”).
The motivation to combine is the same as in claim 2.

Regarding claim 8, Ata does not expressly disclose but Barricelli teaches: wherein the digital human simuli system comprises an organ or part simulation subsystem that simulates a change process by using organs or parts of a constructed digital human replica (“A physiological model virtualized by a DT would allow physicians to make in silico predictions of how the real organ might behave in any given situation.”).
The motivation to combine is the same as in claim 2.

Conclusion
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/JOSHUA B BLANCHETTE/Primary Examiner, Art Unit 3624