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 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 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1
Issue: The claim recite “receive and process computer executable data … wherein the computer executable data includes one or more of electronic health records (EHRs), real-time health monitoring , laboratory results, imaging data, patient preferences, or socio-economic parameters.” The limitation “computer executable” typically refers to code or script, but “laboratory results” are not considered code or script.
Suggested remedy: Provide clarification on whether the limitation refers to “code+data” or it encompasses only “data”.
The term “hyper-personalized” in claims 1, 3-7, 11, 13, and 15-16 is a relative term which renders the claim indefinite. The term “hyper-personalized” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. There is no indication in the specification that determines when the computer executable profile changes from personalized to hyper-personalized.
Claims 1, 3-7, 11, 13, and 15-16
Issue: The claims recite “hyper-personalized” without support in the specification that determines when a profile changes from personalizes to hyper personalized, hence would be considered a relative term.
Suggested remedy: Remove “hyper-personalized” from claims.
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: one or more processors(Para 0163, Arachi discloses processors) and a non-transitory memory storing computer executable instructions that(Para 0166, Arachi discloses memory), when executed by the one or more processors, implement: 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 data ingestion module) through encrypted communication channels(Para 0045, Arachi discloses end to end encryption of sensitive medical data), wherein the computer executable data includes one or more of electronic health records (EHRs)(Para 0248, Arachi discloses EHR), 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), or 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 comprises a multi-layered neural network(Para 0234, Arachi discloses deep learning neural network), a feature extraction algorithm(Para 0399, Arachi discloses a feature extraction AI model), a predictive analytics and modeling engine(Para 0204, Arachi discloses a computing platform for predictive analytics), and a temporal sequence analytics engine(Para 0426, Arachi discloses deep learning networks for time series data) executed by the one or more processors, and 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 the multi-layered neural network(Para 0234, Arachi discloses deep learning neural network) to analyze the computer executable data and the feature extraction algorithm(Para 0399, Arachi discloses a feature extraction AI model) to identify one or more latent variables indicative of clinical and non-clinical parameters of the subject(Para 0616, Arachi discloses various feature extraction techniques that can identify latent variables) while maintaining HIPAA-compliant access controls(Para 0540, Arachi discloses secure transmission necessary for healthcare regulation compliance); and generate, by the temporal sequence analytics engine and the predictive analytics and modelling engine, 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 executed by the one or more processors and 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(Para 0250, Arachi discloses social determinants of health) 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), wherein execution of a next best action of the set of the next best actions(Para 0252, Arachi discloses ranked recommendations) triggers an automated update(Para 0372, Arachi discloses input is sent to an electronic health record system) to the hyper-personalized computer executable profile(Para 0666, Arachi discloses the continuous updating of user profiles) and a subsequent recalibration(Para 0622, Arachi discloses calibration noise to guarantee privacy) of the pathway generator module in a closed loop.
Claim 2
Arachi discloses:
The system of claim 1, wherein the profile generation module further comprises: 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 executed by the one or more processors and 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).
Response to Arguments
35 U.S.C. 112(a), (b), and (f)
Applicant’s amendments have been fully considered and are persuasive. The 112(a), (b), and (f) has been withdrawn.
35 U.S.C. 101
101 rejection has been withdrawn due to the absence of abstract limitations.
35 U.S.C. 102
(Page 20) Regarding the assertion that Arachi discloses multiple distinct, unrelated embodiments.
Applicant's arguments filed have been fully considered but they are not persuasive. The 102 art is applied according to the claim interpretation under BRI. The claims do not differentiate or specifically outline the functions of the engines or algorithms and aim to generally apply computing functions. Application of generic computers/algorithms for specific applications allows the application of prior art in a more generalized fashion when there is a lack clearly defined structures of the computers and algorithms and the functions.
(Page 21-22) Regarding the assertion that Arachi does not disclose the amended claim architecture.
Applicant's arguments filed have been fully considered but they are not persuasive. Please refer to the revised 102 analysis above.
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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/ Supervisory Patent Examiner, Art Unit 3685
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