Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The office action is being examined in response to the application filed by the applicant on October 11th, 2025.
Claims 1 – 5, 7 – 8, 14, and 19 – 20 have been amended and are hereby entered
Claims 1 - 20 are pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant’s arguments filed on October 11th, 2025 have been fully considered but they are not persuasive.
Regarding applicant’s arguments against the 101 rejection of claims 1- 20 on pages 7 – 9: Applicant argues that the amended claims are directed to an improvement in computer functionality under Enfish, a solution rooted in computer technology under DDR Holdings, and contain an inventive concept under BASCOM. These arguments are not persuasive for the reasons below.
Step 2A Prong 1: Applicant asserts the claims are not directed to an abstract idea because they “improve efficiency and risk determination” and “dynamically adjust patient communities using ML/AI.” However, the claims, even as amended, remain directed to (1) collecting user health and behavioral data, (2) organizing users into communities, (3) de-identifying and authorizing data sharing, (4) analyzing data using AI/ML, and (5) predicting outcomes and generating risk determinations. These activities fall squarely within the abstract idea groupings of: mental processes (evaluation, categorization, prediction, risk assessment) as well as methods of organizing human activity (managing interactions between patients and providers; forming communities based on conditions; decision support systems). The mere use of “machine learning” or “AI” does not remove the claims from the abstract idea category.
The claims do not recite a specific improvement to ML architecture, training methodology, model structure, data transformation technique, or database structure. Instead, they use ML as a tool to analyze data and generate predictions—which is a classic data analysis abstract idea. Thus, under Step 2A, Prong One, the claims remain directed to abstract ideas.
Step 2A Prong 2: Applicant argues that the invention “operates in a novel and non-conventional manner.” However, the claims recite generic processors and servers, recite ML/AI at a functional level, do not recite a particular algorithm, do not recite a specific improvement to data security, nor do they recite a technical improvement in database performance. The claims do not improve computer speed/improve memory usage/improve network throughput/ML architecture/nor improve system resource management—instead they apply generic computing to perform data collection and predictive analysis. Under MPEP 2106.05(a)-(c), the claims do not integrate the exception into a practical technological application.
Step 2B: Applicant relies on BASCOM to argue that the ordered combination provides an inventive concept. Here, the ordered combination is: (a) collect data, (b) de-identify data, (c) store data, (d) analyze data with ML, (e) provide predictions, and (f) organize communities. This is a common data-processing pipeline, as there is no generic placement of components, no distributed architecture innovation, no novel network configuration, nor any structural rearrangement of computing components. The combination is merely the automation of risk assessment and decision support.
Accordingly, the rejection under 35 U.S.C. § 101 is maintained.
Regarding the Applicant’s arguments of rejection under 35 U.S.C. § 103 on pages 8 – 9: Applicant argues the amended independent claims 1, 8 and 14 are patentable because neither the Homchowdhury nor Gandi references teach:
An AI model trained to output indications of medical efficacy and risk assessment at the user interface and suggest engagement with online patient communities
A simulated training model comprising a risk assessment tool graphically depicting probabilities, consequences, and cost-based analysis
Selecting search terms to generate an AI-driven recommendation based on health status and user inputs
These arguments are not persuasive.
Gandi explicitly teaches (1) applying data-driven analytics as well as predictive modeling, (2) generating outcome predictions and risk determinations, as well as (3) presenting predictive results to users via an interface. Predicting outcomes inherently includes assessing the likelihood of treatment success, risk of adverse events as well as comparative efficacy. Even if Gandi does not use the exact phrase “medical efficacy,” the predictive modeling of health outcomes necessarily teaches outputting treatment success probabilities and risk indications. Under In re Peterson, a reference need not use identical terminology; teaching the same concept satisfies the limitation.
Homchowdury teaches (1) connecting users with others based on health conditions, (2) online health interaction environments, and (3) patient engagement systems. It would have been obvious to one of ordinary skill to combine Gandhi’s predictive analytics with Homchowdhury’s community engagement system so that predictive outputs inform or suggest community participation.
Accordingly, the rejection of claims 1 – 20 under 35 U.S.C. § 103 as being unpatentable over Homchowdhury in view of Gandhi is maintained.
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 an abstract idea without significantly more, and therefore does not recite patent-eligible subject matter.
Step 1: The claims are directed to a system (claim 1 - 13) and a method (claim 14 - 20) for a digital platform for health users, which fall within the statutory categories of invention (process, machine, and manufacture) under 35 U.S.C. § 101.
Step 2A Prong 1: The claims are directed to (1) collecting, categorizing, and analyzing health-related and behavioral user data, (2) enabling users to join health-related communities, (3) de-identifying and authorizing data release, and (4) generating predictive insights or synthetic personas using machine learning and artificial intelligence (AI), which falls into the abstract category of mental processes and certain methods of organizing human activity, specifically managing personal behavior or relationships or interactions between people. For instance, claim 1 recites:
one or more servers including a plurality of health data, wherein the plurality of health data comprises learned user data inputs, shared content, and information from one or more application program interfaces (APIs);
one or more processors programmed to receive the plurality of health data tailored to a designated person to create a user profile, including, user demographics and health status; and
a shared database directed by the server to store the data for import and export to the one or more APIs;
wherein the server is programmed to: (a) receive a request from the user at a user interface to create or join at least one online patient community categorized by one or more health conditions, (b) receive a first request from the user to delineate personal identifiable information (PII) and de-identify the PII as de-identified data, (c) receive a second request from the user to select and authorize release of the identified or de- identified data to one or more shared databases, and (d) access the data by way of the processor, wherein the user queries data to request data analytics and the processor sorts relevant data and trends using machine learnings and artificial intelligence (AI) models to predict outcomes, treatments, and disease progression using user-derived data, wherein the AI model is trained to output indications of medical efficacy and risk assessment to the user at the user interface, and further suggest engagement with one or more of the online patient communities.
Such activities are fundamentally conceptual and can be performed mentally or with pen and paper, such as by a clinician manually reviewing patient data, de-identifying it, and sharing insights with colleagues or support groups. The claims recite an abstract idea consistent with “mental processes” and “certain methods of organizing human activity, specifically managing personal behavior or relationships or interactions between people” groupings set forth in MPEP 2106.04(a)(2)(III).
Step 2A Prong 2: For independent claim 1, The claims do not integrate the abstract idea into a practical application. While the claims are nominally tied to a “digital platform,” “processors,” and “user interface.” The specification and claim language fail to recite any specific improvement to the functioning of a computer, software, or network technology.
The claimed invention merely uses generic computing components to automate a fundamental mental task—organizing, analyzing, sharing health data, as well as forming communities based on health conditions. The additional elements, such as the “server” or “processor”, as stated above, are both recited at a high level of generality and merely apply the abstract idea using a generic computing environment, which is not sufficient to integrate the idea into a practical application (see MPEP 2106.05(f)). These elements do not themselves amount to an improvement to the interface or computer, to a technology, or to another technical field.
Step 2B: For independent claim 1, as indicated in the Step 2A Prong 2 analysis, the additional element(s) in the claims are merely, using a generic computer device or computing technologies and/or other machinery merely as a tool to a mere instruction to practice the invention. This is because the claimed invention must improve upon conventional functioning of a computer, or upon conventional technology or technological processes a technical explanation as to how to implement the invention should be present in the specification. The rationale set forth for the 2nd prong of the eligibility test is also applicable and re-evaluated in the Step 2B analysis. Therefore, the rationale is sufficient for its rejection basis as it is not patent eligible and no comments are necessary as it is also consistent with MPEP 2106.
For dependent claims 2 – 20, these claims cover or fall under the same abstract idea of a method of organizing human activity and mental processes. They describe additional limitations steps of:
Claims 2 – 20: further describes the abstract idea of the digital platform for social interaction between users, creating an artificial intelligence profile comprising data generated from real patient data, de-identifying data from the user profiles in order to synthesize health conditions, as well as creating hypothetical clinical experiences for training. Thus, being directed to the abstract idea group of organizing interpersonal interactions and mental processes as these functions encompass observation, evaluation, judgment, and opinion and can be performed mentally or in pen and paper.
Step 2A Prong 2 and Step 2: For dependent claims, these claims do not include additional elements, but further instruct one to practice the abstract idea by using general computer components that merely are used as a tool. Thus, it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Therefore, these claim limitations amount to no more than mere instructions to apply the exceptions using generic computer components and or computing technologies (e.g., that are merely deployed to be used as a tool; see MPEP 2106.05(f)).
Additionally, these elements and their limitations are “merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Therefore, claims 1 – 20 are rejected under 35 U.S.C. § 101 for being directed to an abstract idea without sufficient integration into a practical application, and the additional elements do not add significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 20 are rejected under 35 U.S.C. 102 as being unpatentable over Homchowdhury (US20120278095 A1) in view of Gandhi (US 20160270659 A1).
Regarding claim 1:
Homchowdhury discloses:
one or more servers including a plurality of health data, wherein the plurality of health data comprises learned user data inputs, shared content, and information from one or more application program interfaces (APIs); (In ¶0021: teaches “In one embodiment, the present system comprises a versatile cloud computing software platform where doctors and researchers may use contemporary social networking tools to communicate with patients and the extended care team online, on tablets and via mobile phones, sharing personal health information and medical records within a HIPAA privacy and security compliant environment. “ (¶0004]: teaches a plurality of modules that may input data and metadata.
one or more processors programmed to receive the plurality of health data tailored to a designated person to create a user profile, including, user demographics and health status; and (In ¶0099; Figs. 2 - 3: teaches “The various embodiments of “Pods” (e.g. “CarePods”, “SocialPods” and the like—the terms may be used interchangeably) described herein (and as further depicted in FIGS. 1-10) create a unique place in the cloud that unifies communication and tools needed to coordinate, manage and provide care to a patient. The CarePod has the ability to provide controlled access to various people involved in the care of a patient within and between Doctor's offices to the various parts of a CarePod, such as the communication tools, the charts and records etc.” Additionally, ¶0110; Figs. 2 – 3: teaches “(v) Data that are gathered with the patient's CarePod about the patient's genomic, proteinomic, and metabonomic profiles from various sources.”)
a shared database directed by the server to store the data for import and export to the one or more APIs; (In ¶0072: teaches “Any metadata associated with the transcoding, if any, may be updated in a database or storage.”)
a) receive a request from the user at a user interface to create or join at least one online patient community categorized by one or more health conditions, (In ¶0058; Figs. 3 – 4: teaches “Social pod 308 may be created by a provider, a physician or researcher 304 via the present system. Provider 304 alerts the system that a new “Care” pod is to be created and provider 304 may populate the pod by listing individuals (e.g. patient 306) and have the system invite patient 306 via some identified means of communication (e.g. by providing the patient's email address to the system) at 310.”)
(b) receive a first request from the user to delineate personal identifiable information (PII) and de-identify the PII as de-identified data, (In ¶0089; Figs. 8A and 8B: teaches “ De-identification module 806 may be implemented within the system on top of, or in communication with, query module or communication module or the like. In response, de-identification module 806 may strip out information or data which may be linked to, and help identify, any given patient.”)
(c) receive a second request from the user to select and authorize release of the identified or de- identified data to one or more shared databases, and (In ¶0090; Figs. 8A and 8B: teaches “Such a message, as noted above, may be posted in various forms (e.g. text, voice or video), and it may be desirable that de-identifier module 806 strip out any such identifying data.”)
Although Homchowdhury discloses predicting measures for success of a treatment plan for a user’s diagnosis, Homchowdhury does not disclose a processor using machine learning and artificial intelligence (AI) driven analytics to predict outcomes, treatments, and disease progression using user-derived data, Gandhi teaches:
(d) access the data by way of the processor, wherein the user queries data to request data analytics and the processor sorts relevant data and trends using machine learnings and artificial intelligence (AI) models to predict outcomes, treatments, and disease progression using user-derived data, wherein the AI model is trained to output indications of medical efficacy and risk assessment to the user at the user interface, and further suggest engagement with one or more of the online patient communities. (In ¶0047; Fig. 1: teaches “The system 100, for example, may be able to process historical patient health data, medical records, and notes previously recorded by clinical investigators 119 (e.g., using machine learning techniques) to develop a personalized model for a particular patient, which may predict how the patient may respond if given a particular drug treatment.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Homchowdhury’s disclosed system and method for creating and managing treatment protocols within trusted health-user communities with using machine learning and/or artificial intelligence driven analytics to predict outcomes, treatments, and disease progression using user derived data, as taught by Gandhi, as there is “a need exists for a remote monitoring tool that provides for early and targeted stratification of patients, which may result in improved drug success rates, increased drug response predictability, and improved identification of causal links between drug treatments and adverse events.”
Regarding claim 2:
Homchowdhury discloses:
wherein the health status comprises data from digital and health systems, including health data, bloodwork, testing data, wearable data, imaging data, implant data, medical device data, or data which describes symptoms, complaint, injury, health condition, condition status, disease-state, treatment, surgery, therapy, or medication of the user, or a combination 2 thereof, and medical efficacy is determined by the user, one or more health providers, or clinical outcome studies. (In ¶0095; Fig. 9: teaches “Possible interfaces might include therapeutic item box 906, where text may be entered by users regarding aspects of the treatment and a set of reminders for treatment may be set in accordance with the treatment plan (e.g. take medications every day, or describe symptoms once a week, take and record vital signs once a month or the like).”)
Regarding claim 3:
Homchowdhury discloses:
wherein the extracted data comprise social interactions, digital engagement across cloud-based platforms, metadata, cookies, reactions to digital media content, time spent with online communities, data entered by the user from electronic medical record (EMR), and data from medical devices, wearable devices or implanted devices, the processor further programmed to update the data. (In ¶0026: teaches “the present embodiment may require that the physician communicate with a patient who is authenticated at the time of communication to the patient. In addition, the system stores and/or otherwise archives the interaction between the physician and the patient to form a part of the latter's EHR.” Additionally, ¶0045: teaches “One or more moderators may oversee some types of programs, being able to remove objectionable content and update content files. Other types of programs may be completely unsupervised and self-directed.”)
Regarding claim 4:
Homchowdhury discloses:
wherein communication between users occurs at the user interface external to a health provider portal. (In ¶0077; Fig. 6: teaches “Users of the present system may connect by a myriad of communication pathways. For example, users may connect via phone (602), mobile or otherwise, and by a browser 604 through standard interfaces 606.”)
Regarding claim 5:
Homchowdhury discloses:
wherein the user select and deselect the extracted data configured to be integrated in the analytics of the processor to create a user controlled social health network. (In ¶0069: teaches “a patient may not feel like talking directly to a doctor, or writing a lengthy email about conditions and results; but the patient might be amenable to uploading an audio or video file describing such. So, users and applications can use a multimedia content server/network—such as “anicaport” to affect solutions.”)
Regarding claim 6:
Homchowdhury discloses:
wherein the plurality of health data comprises data input by a plurality of users and data aggregated from external resources, clinical studies, and clinical practice. (In ¶0066; Fig. 4: teaches “External ecosystem integration engine 406 may present a set of RESTful API, that allows it to exchange its data with third party systems and using (when applicable) industry standards such as HL7 etc. These API's will allow external systems to send information to the present system, e.g. a medical device or EHR system.”)
Regarding claim 9:
Homchowdhury discloses:
wherein the parameters input by the user comprise an election of one or more licensed health providers associated with the user. (In ¶0028; Fig. 1: teaches “a set of entities that might comprise a prototypical environment of trusted users. Users (collectively labeled 102) are shown interconnectedly with the present system 100 and, possibly, connected amongst themselves apart from system 100. A set of users might comprise the following types of individuals: physicians 102 a, practice staff and nurses 102 b, researchers 102 c, consulting physicians 102 d, payor and donors 102 e, patient's friends and family members 102 f, patients 102 g and students 102 h.”)
Regarding claim 10:
Homchowdhury discloses:
wherein the licensed health provider comprises a care provider including an individual or a system entity comprising: medical doctors, doctors of osteopathic medicine, dentists, specialists, surgeons, nurses, physician assistants, dental assistants, alternative care providers, aestheticians, hospital systems, health-affiliated educational institutes and organizations, managed care living facilities, mental health organizations, and other care provider groups. (In ¶0030: teaches “To appreciate the flexibility of communities that the present system could enable, trusted communities might comprise one, two, or any number of members depending on their specific purpose. For mere exemplary purposes, communities may consist of: a single member using a self-directed therapeutic intervention, doctor+doctor, doctor to pharmacy, doctor to health insurance agent, doctor+patient, doctor+patient+family, doctor+multiple patients.”) Alternatively, ¶0058: teaches “Social pod 308 may be created by a provider, a physician or researcher 304 via the present system.”)
Regarding claim 11:
Homchowdhury discloses:
wherein the user digitally submits a request for an electronic medical record (EMR) from the licensed health provider, the user authenticates the request, and the licensed health provider directs an uploaded version of the EMR to the user interface of the digital platform. (In ¶0080; Fig. 6: teaches “the present system may also support parallel and separate communication threads among various subsets of a community, ensuring selective and appropriate access to communications, personal health information, and medical reports. The present system may automatically deposit every communication and medical record into a EHR and EMR repository. “)
Regarding claim 12:
Homchowdhury discloses:
wherein the licensed health provider is provided a digital request to engage with the digital platform at a secondary user interface, and a secondary processor accesses the shared database to drive predictive analytics at a secondary server exclusive to a plurality of licensed health providers. (In ¶0157 – 0159; Fig. 14: teaches “CarePod Treatment Plan module may send a monitor request to such Instrument module. First, it is possible to tell how well an individual patient is responding to his/her treatment plan.. caregivers may be able to alter the Treatment Plan during its course. Secondly, this data may be used to discern the overall efficacy of the Treatment Plan—and possibly use it for predictive measures for success of a treatment plan for a given patient.”)
Regarding claim 13:
Homchowdhury discloses:
The digital platform of claim 12, wherein the licensed health provider inputs at the secondary user interface one or more of provider demographics, education, residency, or clinical experience, and authorizes release of de-identified medical data from persons or machines to the shared database. (In ¶0090; Fig. 8: teaches “At 810, physician may post a message or a response to the social pod. Such a message, as noted above, may be posted in various forms (e.g. text, voice or video), and it may be desirable that de-identifier module 806 strip out any such identifying data. “)
Regarding claim 14:
Homchowdhury discloses:
receiving user data to initiate at least one user profile; (In ¶0008; Fig. 3: teaches “a flow chart of one embodiment of creating and authenticating a social pod within the context of a medical application.”)
implementing a machine-learned model defining a relationship between a plurality of users and the health status of at least one user; (In ¶0147; Fig. 12: teaches “Treatment plans tend to address a particular condition of a patient that may respond to therapy. It will be appreciated that the notion of treatment plan and condition for treatment may be considered broadly. The condition may be a disease condition, a congenital condition, a mental condition or any other possible condition of a patient for which treatment plans may have efficacy.”)
creating one or more communities by applying demographics of the users, user inputs, the health status, and associated one or more health providers; and (In ¶0021: teaches “The present system should also be flexible to allow users (i.e. HCPs, issue groups or the like) to create specific on-line communities to address particular conditions, diseases or other health-related conditions or issues.” Alternatively, ¶0049; Fig. 1: teaches “For example, system 100 may provide the following: (1) establish networked infrastructure for programs for health, education, prevention, wellness, treatment and/or research (104); (2) enable automated and/or distributed funding of programs from donors, granting organizations, payors and private payors (106); (3) establish micro social networks of trusted relationships around the program;“)
causing a representation of the recommendation to be presented at a user interface;
Homchowdhury does not disclose selecting search terms to generate artificial intelligence (AI) driven response to guide decision-making. However, Gandhi teaches:
selecting search terms to generate artificial intelligence (Al) driven recommendation based on the health status and the user inputs to guide decision-making. (In ¶0025; Fig. 2: teaches “The data analytics engine 230 may generate different analytical models that look at the frequency, correlation and deviation between different parameters to identify meaningful patterns within the data, and may provide a score and confidence value for the pattern that is identified.” Furthermore, ¶0025: also teaches “ The data analytics engine 230, for instance, may implement clustering analytics using k-means or latent Dirichlet allocation (LDA) techniques to generate models that group different sets of information having certain common parameters.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to combine Homchowdhury’s disclosed system and method for creating and managing treatment protocols within trusted health-user communities with selecting search terms to generate artificial intelligence (Al) driven response to guide decision-making, as taught by Gandhi, as there is “a need exists for a remote monitoring tool that provides for early and targeted stratification of patients, which may result in improved drug success rates, increased drug response predictability, and improved identification of causal links between drug treatments and adverse events.”
Regarding claim 15:
Homchowdhury discloses:
wherein the data from the user profiles is de-identified to synthesize simulated health conditions. (In ¶0088; Figs. 8A – 8B: teaches “One possible useful feature of a system made in accordance with the principles of the present invention might be the unlinking of patient data from the positive identification of the patient herself.”)
Regarding claim 16:
Homchowdhury does not disclose generating a plurality of synthetic data from profiles. However, Gandhi teaches:
wherein the processor generates a plurality of synthetic data from profiles. (In ¶0025; Figs. 1 – 2: teaches “The data analytics engine 230, for instance, may implement clustering analytics using k-means or latent Dirichlet allocation (LDA) techniques to generate models that group different sets of information having certain common parameters. These models, and other analytics models, may provide a score and confidence value for the different patterns or groupings that are observed. “[Examiner’s Note: In reference to the applicant’s specification, “one profile includes synthetic data artificially generated from real patient data to create an AI profile”. The Gandhi reference utilizes a data analytics engine to process information within the patient profile database in order to provide the analytical functionality above.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to combine Homchowdhury’s disclosed system and method for creating and managing treatment protocols within trusted health-user communities with wherein the processor generates a plurality of synthetic data from profiles, as taught by Gandhi, as there is “a need exists for a remote monitoring tool that provides for early and targeted stratification of patients, which may result in improved drug success rates, increased drug response predictability, and improved identification of causal links between drug treatments and adverse events.”
Regarding claim 17:
Homchowdhury discloses:
wherein the synthetic data creates at least one Al profile that represents an artificially represented person (ARP).
Regarding claim 18:
Homchowdhury discloses:
wherein the simulated health conditions create hypothetical clinical experiences to engage with a trainee. (In ¶0028; Fig. 1: teaches “A set of users might comprise the following types of individuals: physicians 102 a, practice staff and nurses 102 b, and students 102 h.” Additionally, ¶0067; Fig. 4: teaches “Therapeutics and Research Management Engine 408 is that part of the system 400 that comprises sufficient hardware and logic to create, store, disseminate, and dynamically manage treatment plans and pathways for trusted users on the system.”)
Regarding claim 19:
Homchowdhury discloses:
wherein a feedback loop uses outcomes to predict care pathways, such that a user or trainee can elects one or more treatments to structure a care plan for the user with a specified health condition and create a simulated learning model to determine efficacy of treatment. (In ¶0099; Figs. 1 – 10: teaches “The CarePod has the ability to provide controlled access to various people involved in the care of a patient within and between Doctor's offices to the various parts of a CarePod, such as the communication tools, the charts and records etc. This capability may allow the parties involved to have a single and common place to go to find the information they need about a patient, even if they are physically distant as well organizationally separated—i.e they could be part of two different Healthcare providers in two different parts of the world.”)
Regarding claim 20:
Although Homchowdhury discloses providing treatment plans as well as predictive measures for success of a treatment plan, Homchowdhury does not explicitly disclose the artificial intelligence aspect of the claim limitation. However, Gandhi teaches:
wherein the simulated learning model uses a structured artificial intelligence (Al) profile, and the Al profile responds to one or more Al generated treatment plans, and wherein the one or more processors generates, from the Al profile, graphical depictions representing risk assessment and success rates corresponding to the Al generated treatment plans. (In ¶0047; Fig. 1: teaches “The system 100, for example, may be able to process historical patient health data, medical records, and notes previously recorded by clinical investigators 119 (e.g., using machine learning techniques) to develop a personalized model for a particular patient, which may predict how the patient may respond if given a particular drug treatment.” Additionally, ¶0047 also teaches “The tradeoff analytics, may suggest an optimal drug treatment (e.g., a particular type and dosage quantity) or different drug treatment options having different predicted outcomes (e.g., an aggressive treatment option or a conservative treatment option that may have different risks and likelihood of success) based on the model that is generated.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Homchowdhury’s disclosed system and method for creating and managing treatment protocols within trusted health-user communities with simulated health conditions structured in an artificial intelligence (Al) profile, and the Al profile responds to one or more Al generated treatment plans, and wherein the one or more processors generates, from the Al profile, graphical depictions representing risk and success rates corresponding to the Al generated treatment plans, as taught by Gandhi, as there is “a need exists for a remote monitoring tool that provides for early and targeted stratification of patients, which may result in improved drug success rates, increased drug response predictability, and improved identification of causal links between drug treatments and adverse events.”
Claims 7 and 8 are rejected under 35 U.S.C. 102 as being unpatentable over Homchowdhury (US20120278095 A1) in view of Gandhi (US 20160270659 A1) in further view of Bahrami (US20170308671A1).
Regarding claim 7:
Homchowdhury discloses creating a profile within a patient’s CarePod comprising of data generated from the patient’s genomic, proteinomic, and metabonomic profiles and metadata from various sources. Gandhi teaches utilizing a data analytics engine paired with an NLP system and ML to process patient information and medical records to provide analytical functionality [¶0025; Figs. 1 – 2]. However, neither reference teach creating an artificially represented person (ARP). Thus, Bahrami teaches:
wherein the one or more processors is further programmed to create an artificial intelligence (Al) profile, the Al profile comprising synthetic data artificially generated by the processor from real patient data to represent an artificially represented person (ARP). (In ¶091): teaches “the techniques described herein may enable users to define the model and the data structures in the multidimensional space by a semantic graph and probability density functions for generating synthetic patient data.”
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to combine Homchowdhury’s disclosed system and method for creating and managing treatment protocols within trusted health-user communities and creating an Al profile, the Al profile comprising synthetic data artificially generated from real patient data to create the Al profile, as taught by Gandhi, with Bahrami’s multidimensional model of synthetic patient data, in order to effectively create a visual representation of patient charts to provide reliable and consistent resolutions to health conflicts.
Regarding claim 8:
The combination of Homchowdhury, Gandhi and Bahrami, as shown in the rejection above, discloses the limitations of claim 7, respectively.
Homchowdhury does not disclose the limitations below. Thus, Bahrami teaches:
an artificially represented person (ARP) created by user selection and deselection of digital data, including personal data, health data, test results and imaging of one or more persons, such that artificial intelligence (AI) modeling generates an AI profile at the user interface, wherein the ARP provides a simulated training model to diagnose, treat, and create medical scenarios that predict success rates, failure, efficacy of treatments, therapies, surgery, medical procedures, medical devices, and wearables; [¶0053 - 055]: Using the synthetic data generator, selected groups of biomarkers are used in order to assist in generating synthetic data that may be able to capture “a variety of types of data present in health, healthcare, and medical domains. Some synthetically-generated datasets for use in data driven algorithms, examples of which are described herein, have a large number of dimensions.”
wherein the simulated training model further comprises a risk assessment tool that graphically depicts probabilities, consequences, and cost-based analysis of the user selections. [¶0060]: teaches “Such aggregation can then reveals further opportunities, help to mitigate risk and provide early warnings.” Additionally, [¶0070]: teaches the disclosure being paired with Machine learning systems in order to effectively generate personalized health risk assessments.
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to combine Homchowdhury’s disclosed system and method for creating and managing treatment protocols within trusted health-user communities and wherein the Al profile represents an artificially represented person (ARP), as taught by Gandhi, with that of a synthetic data generator, as taught by Bahrami, in order to effectively create a visual representation of patient charts to provide reliable and consistent resolutions to health conflicts.
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Yang (US20100076913 A1) is pertinent because it is directly related to finding “dynamic social networks by applying a dynamic stochastic block model to generate one or more dynamic social networks, wherein the model simultaneously captures communities and their evolutions, and inferring best-fit parameters for the dynamic stochastic model with online learning and offline learning.”
Dyment (US20150081465 A1) is pertinent because it is directed to “a fitness health and wellness social e-commerce platform that provides an e-commerce platform that operates as a social network in which the interaction between fitness enthusiasts is what drives revenue into the e-commerce components.”
Green (US8606593 B1) is pertinent because it is related to “systems and methods of utilizing the data captured by an integrated medical software system. More particularly, the present invention relates to systems and methods of utilizing the data captured in an integrated medical software system to conduct medical research, to maintain disease registries, to analyze the quality and safety of healthcare providers, and to conduct composite clinical and financial analytics.”
Maguire (US9807183 B2) is pertinent because it is directly related to “crowd-sourced computer-implemented methods and systems of collecting and transforming portable device data.”)
Bureau (US20180115543 A1) is pertinent because it is directed to “methods and systems for providing an online communication platform for a targeted community of people.”
Abousy (US20090177495 A1) is pertinent because it is related to “an intelligent health care management system, and more particularly to a system, method and device for maintaining, updating, and intelligently analyzing patient information to provide diagnostic and therapeutic information.”
Manning (US7979286 B2) is pertinent because it is directly related to “health care, and more specifically relates to healthcare management, healthcare cost analysis, financial services, and healthcare service analysis.”
Lynch (US20150149208 A1) is pertinent because it is directed to “aggregating patient medical records, and in particular, to aggregating and organizing medical records in a manner that protects the identity of the patient.”
Heywood (US20170206327 A1) is pertinent because it is related to “a method of using self-reported health data in online communities to predict significant health events in life-changing illnesses to improve the lives of individuals and to improve”
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bill Chen whose telephone number is (571)270-0660. The examiner can normally be reached Monday - Friday 8:30am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached on (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BILL CHEN/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626