DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “module configured to”(claims 1 and 7), “engine configured to”(claims 8 and 12), “pathway refinement system configured to”(claim 9), “generator configured to”(claim 11).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation “module”, “generator”, and “engine” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The written description fails to provide the justified links for the module, generator, and engine structure other than broadly stating the functions. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Provided specification does not have any written description that demonstrates possession of the claimed invention. Please refer to 35 112(b) justification above. The structure of the recited “module”, “engine”, and “generator” is not described in the specification.
In part, claim 13 recites the following:
“… receive and process computer executable data from one or more data sources through encrypted communication channels, wherein the computer executable data includes at least one of electronic health records (EHRs), real-time health monitoring data, laboratory results, imaging data, patient preferences, and socio-economic parameters…”
The underlined claim language requires only one of the listed elements, which conflicts with the
language that follows requiring all of the elements (specifically dependent claim 15). The aforementioned features of claim 13 renders it indefinite due to the optionality of the limitations found in the beginning of the claim and those same limitations losing optionality later on. Dependent claims 14-20 are rejected through dependency.
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 abstract
idea without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. However, as will be shown below, Claims 1-20 are nonetheless unpatentable under 35 U.S.C. 101.
Claim 1 is representative of the inventive concept and recites:
A system for generating hyper-personalized care pathways for a subject, the system comprising: a data ingestion module configured to:
receive and process computer executable data from one or more data sources through encrypted communication channels, wherein the computer executable data includes at least one of electronic health records (EHRs), real-time health monitoring data, laboratory results, imaging data, patient preferences, and socio-economic parameters;
implement blockchain-based verification protocols to ensure data integrity during transmission;
a profile generation module operatively coupled to the data ingestion module, wherein the profile generation module is configured to:
generate encrypted data containers for storing the computer executable data;
synthesize the computer executable data into a hyper-personalized computer executable profile by applying a machine learning algorithm and predictive analytics while maintaining HIPAA-compliant access controls;
generate a multi-dimensional computer executable representation of current health status and predicted future healthcare needs of the subject within the encrypted data containers;
and a pathway generator module operatively coupled to the profile generation module, wherein the pathway generator module is configured to:
determine a set of next best actions for the subject based on the hyper- personalized computer executable profile while maintaining data privacy through role-based access controls;
transmit the next best actions through secure communication protocols, the next best actions comprising dynamically generated computer traceable interventions tailored to one or more clinical and non-clinical parameters associated with the subject;
and update the set of next best actions in real-time as new data becomes available with the data ingestion module while maintaining an encrypted audit trail of all updates.
*Claim 13 recites similar limitations as claim 1, but for a method
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
determining, updating, and maintaining) or using pen and paper. Other than reciting generic computer components/functions such as “system”, “module”, and “computer“, nothing in the claims precludes the highlighted portions from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions of maintaining and implementing also covers behavioral or interactions between people (i.e. a computer and user interface), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case a person is able to physically follow the steps to collect and analyze data), hence the claim falls under “Certain Methods of Organizing Human Activity”.
Dependent claims 2-12 and 14-20 recite additional subject matter which further narrows or
defines the abstract idea embodied in the claims.
Step 2A Prong Two:
This judicial exception is no integrated into a practical application. In particular, the claims recite the
following additional limitations:
Claim 1 recites: “system”, “module”, “computer“, “receive and process computer executable data from one or more data sources through encrypted communication channels, wherein the computer executable data includes at least one of electronic health records (EHRs), real-time health monitoring data, laboratory results, imaging data, patient preferences, and socio-economic parameters”, “generate encrypted data containers for storing the computer executable data”, and “transmit the next best actions through secure communication protocols, the next best actions comprising dynamically generated computer traceable interventions tailored to one or more clinical and non-clinical parameters associated with the subject”, “blockchain”.
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of
are recited as being performed by a : “system”, “module”, “computer“ , and “machine learning algorithm”. A computer is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The algorithm is used to generally apply the abstract idea without limiting how it functions. The algorithm is described at a high level such that it amounts to using a computer with generics models to apply the abstract idea.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “receive and process computer executable data from one or more data sources through encrypted communication channels, wherein the computer executable data includes at least one of electronic health records (EHRs), real-time health monitoring data, laboratory results, imaging data, patient preferences, and socio-economic parameters”, “generate encrypted data containers for storing the computer executable data”, “transmit the next best actions through secure communication protocols, the next best actions comprising dynamically generated computer traceable interventions tailored to one or more clinical and non-clinical parameters associated with the subject”, and “blockchain”.
Dependent claims 2, 8-9, 11-12, and 15 recite “engine”
Dependent claims 3, 7, 10, and 16 recite “storing”
Dependent claims 3, 9-13, and 16 recite “algorithm”
Dependent claims 8-9 and 18 recite “AI model”
Dependent claims 2 and 14 recite “neural network”
Dependent claims 2-3, 8-9, 11, 16, 18, and 20 recite “model”
Dependent claim 6 recites “API”
Dependent claims 9 and 17 recite “input”
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of
are recited as being performed by an “engine”, “algorithm”, “AI model”, “neural network”, “API”, and “model”. The models, networks, and algorithms are used to generally apply the abstract idea without limiting how it functions. The “engine” and “API” are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The models, networks, and algorithms are described at a high level such that it amounts to using a computer with generics models/algorithms/networks to apply the abstract idea.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the
recitation of “storing” and “input”.
Dependent claims 4-5 do not include any additional elements beyond those
already recited in independent claims 1 and 13 and dependent claims 2-3, 6-12, and 14-20, and hence do not integrate the aforementioned abstract idea into a practical application. 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 any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1 and 13 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: A system in claim 1; amount to no
more than mere instructions to apply an exception to the abstract idea. Additionally, the additional
limitations, other than the abstract idea per se, amount to no more than limitations which amount to
elements that have been recognized as well-understood, routine, and conventional activity in particular
fields as demonstrated by the recitation of:
Receiving which refers to the process where a computer or device acquires information transmitted from another source (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional.
Generating (Encryption) refers to the process of creating cryptographic keys user for encryption and decryption of data (Para 0029, Boyle(US 20230142618 A1) discloses: “ In addition, all or some of links can be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), virtual private networks (VPNs), Internet Protocol security (IPsec), etc.”) in a manner that would be well-understood, routine, and conventional.
Transmit, which refers to sending/receiving digital information between devices using various communication channels (OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)) in a manner that would be well-understood, routine, and conventional.
Blockchain refers to the creation of a decentralized ledger that records and stores data in a secure manner (Para 0175, Sillifant(US 20230185477 A1) discloses: While conventional blockchains store every transaction to achieve validation, a blockweave permits secure decentralization without the usage of the entire chain, thereby enabling low cost on-chain storage of data.”) in a manner that would be well-understood, routine, and conventional.
Storing, which refers to the process of saving digital information in a medium or system (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) in a manner that would be well-understood, routine, and conventional.
Input, which refers to the information or instructions that are fed into a computer or system to
initiate a process or generate an output (Para 67, Nielen(US10666702B1) discloses: “This conventional input can include, for example, a push button, touch pad, touch screen, wheel, joystick, keyboard, mouse, trackball, keypad or any other such device or element whereby a user can input a command to the device.”) in a manner that would be well-understood, routine, and conventional.
Dependent claims 4-5 do not include any additional elements beyond those already recited in independent claims 1 and 13 and dependent claims 2-3, 6-12, and 14-20. Therefore, they are not deemed to be significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite and additional elements not already recited in independent claims 1 and 13, hence do not amount to “significantly more” than the abstract idea.
Claim Rejections – 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Arachi(US20250014731A1).
Claim 1
Arachi discloses:
A system for generating hyper-personalized care pathways for a subject, the system comprising: a data ingestion module configured to: receive and process computer executable data from one or more data sources(Figure 1, #120, Arachi discloses analysis system which can be considered a fata ingestion module) through encrypted communication channels(Para 0045, Arachi discloses end to end encryption of sensitive medical data), wherein the computer executable data includes at least one of electronic health records (EHRs)(Para 0248, Arachi discloses HER), real-time health monitoring data(Para 0455, Arachi discloses sensor data from a medical device), laboratory results(Para 0248, Arachi discloses laboratory tests), imaging data(Para 0422, Arachi discloses image data), patient preferences(Para 0250, Arachi discloses patient preferences), and socio-economic parameters(Para 0250, Arachi discloses social determinants of health); implement blockchain-based verification protocols to ensure data integrity during transmission(Para 0065, Arachi discloses blockchain for verification); a profile generation module operatively coupled to the data ingestion module(Para 0650, Arachi discloses the building of user profiles using data integration techniques), wherein the profile generation module is configured to: generate encrypted data containers for storing the computer executable data(Para 0596, Arachi discloses encryption and storage of health related data); synthesize the computer executable data into a hyper-personalized computer executable profile by applying a machine learning algorithm and predictive analytics(Para 0650, Arachi discloses machine learning algorithms and predictions used to create a profile) while maintaining HIPAA-compliant access controls(Para 0540, Arachi discloses secure transmission necessary for healthcare regulation compliance); generate a multi-dimensional computer executable representation of current health status(Para 0513, Arachi discloses outputting of a health status) and predicted future healthcare needs(Para 0656, Arachi discloses algorithms generating personalized recommendations) of the subject within the encrypted data containers; and a pathway generator module operatively coupled to the profile generation module, wherein the pathway generator module is configured to: determine a set of next best actions for the subject based on the hyper- personalized computer executable profile(Para 0608, Arachi discloses treatment recommendations based on information gathered for profile) while maintaining data privacy through role-based access controls(Para 0652, Arachi discloses role-based access controls); transmit the next best actions through secure communication protocols(Para 0595, Arachi discloses securely transmitting health related information), the next best actions comprising dynamically generated computer traceable interventions tailored to one or more clinical and non-clinical parameters associated with the subject(Para 0131 Arachi discloses a recommendation based on information received from a medical device); and update(Para 0433, Arachi discloses adjusting recommendations based on updated knowledge base) the set of next best actions in real-time as new data becomes available(Para 0148, Arachi discloses a computing system performing operations and transmitting data in real time) with the data ingestion module while maintaining an encrypted audit trail of all updates(Para 0267, Arachi discloses updates stored on a secure and auditable record).
Claim 2
Arachi discloses:
The system of claim 1, wherein the profile generation module further comprises: a multi-layered neural network configured to analyze synthesized computer executable data(Para 0234, Arachi discloses deep learning neural network), wherein the neural network employs a feature extraction technique(Para 0237, Arachi discloses feature extraction) to identify one or more latent variables indicative of the clinical and non-clinical parameters of the subject(Para 0616, Arachi discloses various feature extraction techniques that can identify latent variables); and a predictive modeling engine incorporating temporal sequence analytics to anticipate a change in the current health status of the subject(Para 0248, Arachi discloses multi-layered neural network used to model temporal dependencies to determine health status), the predictive modeling engine being trained on a plurality of historical datasets associated with the subject to refine the hyper- personalized computer executable profile iteratively(Para 0366, Arachi discloses algorithms trained on historical medical data).
Claim 3
Arachi discloses:
The system of claim 1, wherein the hyper-personalized computer executable profile comprises: a multidimensional data structure stored in a non-transitory computer-readable medium, wherein the data structure integrates one or more of: demographic parameters mapped to healthcare utilization models(Para 0396, Arachi discloses patient demographics); clinical data encoded in accordance with HL7 FHIR schema for standardization and interoperability; and genetic and biomolecular markers processed through feature extraction algorithms(Para 0426, Arachi discloses genetic algorithms).
Claim 4
Arachi discloses:
The system of claim 3, wherein the hyper-personalized computer executable profile further comprises a set of predictive analytics data, wherein the set of predictive analytics data includes a probabilistic risk assessment(Para 0287, Arachi discloses determining probability of state or condition), quantified using gradient-boosted decision trees trained on a multi-modal dataset(Para 0328, Arachi discloses decision tree training) to assess probabilities of one or more future medical events.
Claim 5
Arachi discloses:
The system of claim 3, wherein the hyper-personalized computer executable profile further comprises a dynamic feedback data set, wherein the data set reflects adjustments to care pathways(Para 0444, Arachi discloses continuous fine-tuning the output of algorithms based on changing data) based on real-time subject monitoring data(Para 0211, Arachi discloses continuous monitoring of incoming data streams) and previous intervention outcome(Para 0362, Arachi discloses AI support system learning and adapting based on patient outcome).
Claim 6
Arachi discloses:
The system of claim 3, wherein the hyper-personalized computer executable profile further comprises a structured data interface, wherein the structured data interface is configured to facilitate extraction of actionable healthcare insights from the data structure(Para 0050, Arachi discloses a GUI for obtaining AI analysis output and action steps) and allows integration with an external healthcare delivery system using machine- interpretable APIs(Para 0393, Arachi discloses integration of third party APIs such as telemedicine systems).
Claim 7
Arachi discloses:
The system of claim 1, further comprising: a security module configured to: implement HIPAA-compliant encryption protocols(Para 0579, Arachi discloses encryption protocols) for all stored and transmitted data(Para 0540, Arachi discloses secure transmission of health information to maintain trust and compliance with healthcare regulations); maintain blockchain-based verification of data integrity(Para 0242, Arachi discloses blockchain providing a record of data ensuring integrity and auditability of data); generate secure audit logs of all data access and modifications(Para 0242, Arachi discloses blockchain providing a record of data ensuring integrity and auditability of data); and enforce role-based access controls for all system interactions(Para 0652, Arachi discloses role-based access controls).
Claim 8
Arachi discloses:
The system of claim 1, further comprising a feedback component that is configured to incorporate outcomes of the next best actions into the hyper-personalized computer executable profile to refine subsequent recommendations by the pathway generator module(Para 0254, Arachi discloses: “At a high level, the reinforcement learning module 2212[CAN BE A PATHWAY GENERATOR MODULE] is responsible for adapting the personalized diagnoses and treatment recommendations based on the feedback and outcomes of the patients and the providers.”), wherein the feedback component includes a generative AI feedback engine(Para 0228, Arachi discloses AI feedback engine), the generative AI feedback engine comprises a scenario simulation engine configured to construct multiple potential next best actions(Para 0433, Arachi discloses a list of prioritized recommended next steps) by leveraging a generative AI model trained on domain-specific computer executable datasets including subject data, wherein the model generates probabilistic outcomes(Para 0287, Arachi discloses determining probability of state or condition) and evaluates effectiveness of the potential next best actions based on subject-specific data(Para 0386, Arachi discloses patient outcomes as training data for the models).
Claim 9
Arachi discloses:
The system of claim 8, wherein the AI feedback component is coupled to a real-time feedback acquisition interface, wherein the feedback acquisition interface is configured to collect the subject data from one or more of a patient monitoring device(Para 0455, Arachi discloses sensor data from a medical device), healthcare provider input(Para 0255, Arachi discloses provider input), and next best action response metrics(Para 0255, Arachi discloses clinical outcomes) to refine simulated next best actions, and wherein the AI feedback component further comprising: an iterative optimization module operatively coupled to the scenario simulation engine, wherein the scenario simulation engine employs a reinforcement learning algorithm to prioritize the potential next best actions based on predefined metrics(Para 0433, Arachi discloses a list of prioritized recommended next steps), including clinical efficacy(Para 0254, Arachi discloses patient outcomes), patient satisfaction(Para 0210, Arachi discloses user satisfaction), and resource utilization(Para 0210, Arachi discloses resource utilization); and a pathway refinement system configured to dynamically update the potential next best actions(Para 0433, Arachi discloses adjusting recommendations based on updated knowledge base) by incorporating real-time changes in the subject data and contextual feedback into the generative AI model.
Claim 10
Arachi discloses:
The system of claim 1, wherein the profile generation module implements: encrypted data containers for storing patient profiles(Para 0596, Arachi discloses encryption and storage of health related data); secure multi-party computation protocols(Para 0242, Arachi discloses multi-party computation protocol) for distributed data processing(Para 0050, Arachi discloses advanced data handling protocols); privacy-preserving machine learning algorithms that maintain data confidentiality during analysis(Para 0246, Arachi discloses differential privacy mechanisms for models); and secure key management protocols for controlling access to encrypted data(Para 0585 Arachi discloses secure key management).
Claim 11
Arachi discloses:
The system of claim 1, wherein the pathway generator module further comprises a next-best-actions generator configured to dynamically analyze the hyper-personalized profile of the subject synthesized by the profile generation module in conjunction with a plurality of computer executable clinical guidelines(Para 0609, Arachi discloses AI system processing clinical guidelines), historical treatment efficacy datasets(Para 0366, Arachi discloses algorithms trained on historical medical data), and real-world evidence databases(Para 0636, Arachi discloses OU-ISIR Gait Database, which is a real-world evidence database), wherein the next-best-actions generator utilizes a machine learning algorithm, including supervised learning models and reinforcement learning frameworks, to generate the set of next best actions, and wherein the next-best-actions generator is operatively coupled to a predictive modeling engine that applies predictive modeling techniques to account for temporal factors(Para 0248, Arachi discloses multi-layered neural network used to model temporal dependencies to determine health status) and treatment timelines(Para 0596, Arachi discloses a treatment plan) by generating intervention schedules(Para 0572, Arachi discloses scheduling a consult) optimized based on specified parameters.
Claim 12
Arachi discloses:
The system of claim 1, wherein the pathway generator module further comprises a multi-tiered prioritization engine configured to stratify the set of next-best-actions into categories comprising immediate, short-term, and long-term interventions(Para 0120, Arachi discloses AI that determines urgency level of a recommendation), wherein the multi- tiered prioritization engine incorporates a prioritization algorithm to evaluate one or more specified computer executable parameters, and wherein a prioritization outcome is communicated to provider system or a subject system through a user interface block in real time(Para 0433, Arachi discloses a list of prioritized recommended next steps on a user interface).
Claim 13
Claim 13 recites similar limitation as claim 1. See claim 1 analysis.
Claim 14
Claim 14 recites similar limitation as claim 2. See claim 2 analysis.
Claim 15
Arachi discloses:
The method of claim 13, wherein determining the set of next best actions further comprises: analyzing the hyper-personalized computer executable profile using a predictive analytics engine to quantify the probabilities of one or more future medical events(Para 0287, Arachi discloses determining probability of state or condition); and generating a prioritized list of interventions(Para 0433, Arachi discloses a list of prioritized recommended next steps) based on predefined metrics, including clinical efficacy(Para 0368, Arachi discloses prioritization based on likelihood of correct diagnosis), subject preferences(Para 0250, Arachi discloses patient preferences), and socio-economic feasibility(Para 0250, Arachi discloses social determinants of health).
Claim 16
Claim 16 recites similar limitation as claim 3. See claim 3 analysis.
Claim 17
Claim 17 recites similar limitation as claim 9. See claim 9 analysis.
Claim 18
Arachi discloses:
The method of claim 17, further comprising: utilizing a generative AI feedback mechanism to simulate one or more potential next best actions by constructing the potential next best actions using a generative AI model trained on domain-specific datasets; and evaluating effectiveness of the one or more potential next best actions based on subject-specific data and predefined success metrics(Para 0460, Arachi discloses the AI system continuously learns and adapts based various datasets and outcomes).
Claim 19
Arachi discloses:
The method of claim 13, further comprising monitoring subject adherence to the next best actions through an integration of wearable device telemetry(Para 0262, Arachi discloses wearables), wherein adherence metrics are dynamically fed back into the pathway generator module to recalibrate the next best actions(Para 0388, Arachi discloses monitoring patient for adherence and updating the recommendations/diagnosis accordingly) .
Claim 20
Arachi discloses:
The method of claim 13, wherein synthesizing the computer executable data further comprises: preprocessing heterogeneous data formats using a data harmonization pipeline that includes one or more of natural language processing for unstructured text(Para 0360, Arachi discloses using NLP for unstructured text), Fourier transformations for signal data(Para 0616, Arachi discloses Fourier transform), and ontology-based mapping for categorical data; and implementing a multi-task deep learning model to concurrently perform of or more of identifying risk factors, predicting disease progression, and generating health insights(Para 0042, Arachi discloses personalized insights).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Miller(US20200302296A1) discloses a method for optimizing educational outcomes using artificial intelligence.
Callcut(US12001965B2) discloses a system for distributed privacy-preserving computing on protected data
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685