DETAILED ACTION
Notice to Applicant
This communication is in response to the application submitted August 29, 2026, and amended September 3, 2025. Claims 21 – 99 are cancelled. Claims 1 – 20 are presented for examination.
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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 600 (Figures 6A and 6B). Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 2 is objected to because of the following informalities: The Applicant used the abbreviation “HL7” and “FHIR” without defining the abbreviation. Appropriate correction is required.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One
Claims 1 – 20 are drawn to a method and system, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One
Independent claims 1 and 12 recite enhancing clinical decision-making and workflow optimization comprising: collecting data from multiple healthcare sources, including health records, medical imaging, and patient-reported outcomes; analyzing the collected data, the analyzing including at least normalizing the multimodal data, to produce predictive outputs that indicate risk levels, anomaly detection alerts, or treatment prioritization flags; and presenting the predictive outputs in real time together with context-sensitive fields of the health record, thereby reducing redundant user interactions and streamlining clinical workflow, and delivering the generated insights to healthcare providers.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that presenting generated patient insights to healthcare providers to enhance patient care (paragraph 7 of the published specification). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a need to enhance clinical decision making and provide actionable insights (paragraph 7 of the published specification). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including:
Claim 1: “one or more processors executing instructions stored in memory”, “electronic”, “receiving structured data, unstructured text, and imaging pixel arrays via network communication interfaces”, “one or more processors”, “artificial intelligence (AI) or a machine learning (ML) model executed in hardware”, “multimodal data, generating embeddings of unstructured clinical notes, and combining the embeddings with imaging-derived feature vectors”, “clinician-facing display device”, “unified graphical user interface”, “unified interface”
Claim 2: “automating”, “one or more processors”, “executing standardized data exchange protocols, including HL7 and FHIR, to ensure interoperability with existing healthcare systems and standards”, “continuously updating, by the one or more processors, the Al or ML models”
Claim 3: “automating”, “autoscribing”, “natural language processing algorithms executed by the processors”
Claim 5: “one or more processors”, “wearable devices and remote monitoring systems into a unified platform stored in memory”
Claim 6: “display device to render a dashboard interface”
Claim 7: “encoded as structured attributes in memory”
Claim 8: “one or more processors”, “a trained machine learning model executed in hardware”
Claim 9: “synchronizing shared access”, “networked devices”, “secure communication tools through the graphical user interface”
Claim 10: “the one or more processors, encryption protocols, authentication routines, and access control”
Claim 11: “telehealth consultations and remote patient management by executing integrated audio/video communication protocols and secure data exchange between provider and patient devices”
Claim 12: “system”, “one or more processors”, “one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors”, “network communication interfaces”,” electronic”, “receiving structured data, unstructured text, and imaging pixel arrays via network communication interfaces”, “one or more processors”, “artificial intelligence (AI) or a machine learning (ML) model executed in hardware”, “multimodal data, generating embeddings of unstructured clinical notes, and combining the embeddings with imaging-derived feature vectors”, “clinician-facing display device”, “unified graphical user interface”, “unified interface”
Claim 13: “system”, “one or more processors”, “automate”, “continuously updating the Al or ML models”
Claim 14: “system”, “automated”, “autoscribing”
Claims 15, 18 – 19: “system”,
Claim 16: “system”, “one or more processors”, “wearable devices and remote monitoring systems into a unified platform stored in memory”
Claim 17: “system”, “dashboard interface”
Claim 20: “system”, “one or more processors”, “shared access”, “communication tools”
These features are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The published specification supports this conclusion as follows:
[0339] The platform may include one or more processors configured to execute instructions for data collection, analysis, visualization, and communication across multiple functional modules. These processors may include general purpose central processing units (CPUs) such as multi-core x86 or ARM processors to operate server operating systems, database management systems, and general application logic. In certain implementations, the processors may further include graphics processing units (GPUs) or tensor processing units (TPUs) to accelerate artificial intelligence (AI) and machine learning (ML) computations, including deep learning model training and inference for image analysis, natural language processing, and predictive analytics. Field-programmable gate arrays (FPGAs) may be incorporated to enable custom acceleration of specialized algorithms, such as signal processing for wearable device data or real-time encryption and decryption, while digital signal processors (DSPs) may be used for efficient handling of high-throughput data streams such as medical imaging files or continuous ECG signals. The processors may be housed in server-grade enclosures, blade server chassis, or compact embedded computing modules depending on deployment needs.
[0342] The platform may include one or more interface devices for interacting with healthcare providers and patients. Examples may include desktop workstations equipped with high-resolution displays for detailed medical imaging review, portable laptop computers, tablet devices for point-of-care decision-making, and wall-mounted touchscreen kiosks for patient check-in. Telehealth stations may be equipped with integrated cameras, microphones, biometric input devices, and dedicated lighting to facilitate remote consultations. Voice-enabled smart terminals may be deployed in sterile or high-activity environments to allow hands-free operation, while secure patient portals and mobile applications may allow patients to review health information, medication adherence history, and educational resources. Input devices may include keyboards, mice, medical-grade touchscreens, or voice recognition systems, while output devices may include visual displays, auditory alert systems, and haptic feedback mechanisms.
[0343] In some examples, the platform may integrate data from external sensors and wearable devices. Wearable devices may include smartwatches, fitness trackers, adhesive biosensors, or medical-grade continuous monitoring patches capable of measuring parameters such as heart rate, oxygen saturation, blood pressure, temperature, or glucose levels. Remote monitoring equipment may include homebased devices such as wireless weight scales, spirometers, or blood pressure cuffs. These devices may communicate with the platform using wireless protocols such as BLE, Zigbee, Wi-Fi, or cellular data connections. In certain cases, remote monitoring devices may transmit patient measurements at predefined intervals or in response to threshold events. Data may be buffered locally and batch-transmitted when network connectivity becomes available. The incoming data may be received by a secure gateway device that validates, encrypts, and forwards the data to analysis modules. The secure gateway may be implemented as a standalone appliance, a software client on a general-purpose computer, or an embedded module in a network router.
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2 – 11 and 13 – 20 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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.
Claim(s) 1 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Santiago (WO 2025/029357 A1) in view of Bennett et al., herein after Bennett (U.S. Publication Number 2015/0019241 A1).
Claim 1 (Original). Santiago teaches a method for enhancing clinical decision-making and workflow optimization (paragraph 17 discloses Clinical Decision Support (CDS) Hooks that allow healthcare software to offer decision support within workflows at the point of care), the method comprising:
collecting, by one or more processors executing instructions stored in memory (paragraph 162 discloses the memory includes a main memory, a static memory, and a storage unit, both accessible to the processors via the bus), data from multiple healthcare sources (Figure 8; paragraph 19 discloses the disease specific treatment algorithm is received from one or more different databases, such as a collection of ERDs (Electronic Reference Databases)), including electronic health records (EHRs), medical imaging (paragraph 19 discloses EMRs, EHRs, and Electronic Reference Databases (ERDs), where EHRs discloses information from all providers involved in a patient’s care; paragraph 76 discloses the AI (Artificial Intelligence) system can configure data from multiple different databases that are in their own non-standardized format into a single standardized format, the messages, queries, assessments, and alerts can be generated to communicate with physicians because the CQL (Clinical Quality Language) models are using standardized format), wherein the collecting includes receiving structured data (paragraph 182 discloses receiving structured or labeled data), unstructured text (paragraph 182 discloses receiving unstructured or unlabeled data), and imaging pixel arrays (paragraph 184 discloses pixels) via network communication interfaces (paragraph 102 discloses communication channels; paragraph 160 discloses the machine may operate as a standalone or networked to other machines);
presenting, by causing a clinician-facing display device to render a unified graphical user interface (paragraph 163 discloses the user output components may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), the predictive outputs in real time together with context-sensitive fields of the electronic health record (paragraph 105 discloses real-time, secure transmission from electronic medical records of a patient; paragraph 182 discloses using a trained model (e.g., trained machine-learning program) to generate predictions on new, unseen data; paragraph 194 discloses in prediction phase, the trained machine-learning program uses the features for analyzing query data to generate inferences, outcomes, or predictions, as examples of a prediction inference data; paragraph 199 discloses the system leverages contextual data from medical documents, clinical guidelines, and Clinical Quality Language (CQL) code, and serves as training material for AI algorithms, enabling the development of advanced decision support tools that assist clinicians in adhering to guidelines and making informed decisions), thereby reducing redundant user interactions and streamlining clinical workflow (This limitation is being interpreted by the Examiner as a statement of intended use. As such the limitation is not given any patentable weight in keeping with the guidelines of MPEP 7.37.09: a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim);
delivering the generated insights to healthcare providers through a unified interface (paragraph 222 discloses the user interface employs intuitive design principles, clear navigation structures, and interactive elements to enhance usability and facilitate efficient interaction with the system; paragraph 224 discloses the user interface facilitates interactive communication and feedback loops between users and the system. The user interface allows users to interact with generated recommendations, modify parameters, explore alternative options, and provide feedback on the system's performance. This interactivity promotes user engagement, collaboration, and continuous improvement of decision support functionalities).
Santiago fails to explicitly teach the following limitations met by Bennett as cited:
patient-reported outcomes (paragraph 25 discloses patient reported outcomes),
analyzing, by the one or more processors, the collected data using artificial intelligence (AI) or a machine learning (ML) model executed in hardware (paragraph 113 discloses embodiments of agents having personalized transition models-the integration of machine learning algorithms for optimal treatment selection for each patient; paragraph 114 discloses using machine learning methods to determine optimal treatments at a single timepoint for individual patients, which may be combined into the sequential decision AI framework by incorporating the output probabilities of those single-decision point treatment models into the transition models used by the sequential decision-making approaches), the analyzing including at least normalizing the multimodal data, generating embeddings of unstructured clinical notes, and combining the embeddings with imaging-derived feature vectors to produce predictive outputs that indicate risk levels, anomaly detection alerts, or treatment prioritization flags (Figure 8; paragraph 64 discloses normalized samples and/or samples of patients with a particular disease, which are mapped into a network structure and compared to other network structures; paragraph 79 discloses the transition model is to use the history of the health status (outcome data) to predict the probability of future health states; paragraph 85 discloses an Outcome Rating Scale (ORS) may be used as the basis for machine learning algorithms to predict individualized treatment response).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Santiago to further include clinical decision-making artificial intelligence object orients systems and methods of providing decision support for assisting medical treatment decision making using artificial intelligence as disclosed by Bennett.
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to expand the method of Santiago in this way to by providing the basis for clinical artificial intelligence that can deliberate in advance, form contingency plans to cope with uncertainty, and adjust to changing information in real-time (Bennett: paragraph 7).
Claim 2 (Original). Santiago and Bennett teach the method of claim 1. Santiago teaches a method further comprising:
automating, by the one or more processors, routine administrative tasks including appointment scheduling, billing, and documentation (paragraph 35 discloses the AI system can request an order set production for a patient including an expert opinion for a particular disease, prescriptions and corresponding dosages and schedules, diagnostic tests and procedures, care guidelines such as diet orders or activity orders, consultation orders such as for a specialist, clinical practice guidelines, patient specific information, discharge planning, precautions and contraindications related to treatment plans, outcome measures to track treatment and symptom scores, and/or the like);
providing, by the one or more processors, decision support alerts for potential issues including drug interactions or contraindications (paragraph 21 discloses a clinical decision support system to analyze patient data and provide relevant insights, alerts, and suggestions; paragraph 35 discloses precautions and contraindications related to treatment plans);
executing standardized data exchange protocols, including HL7 and FHIR, to ensure interoperability with existing healthcare systems and standards (paragraph 3 discloses Fast Healthcare Interoperability Resources (FHIR) standards framework developed by Health Level Seven International (HL 7), a not-for-profit standards developing organization that's dedicated to providing a comprehensive framework and related standards for the exchange, integration, sharing, and retrieval of electronic health information; paragraph 16 discloses FHIR is a standards framework for the exchange, integration, sharing, and retrieval of electronic health information and uses web-based technologies such as HTTP, HTML, CSS, JSON, XML, and RDF for data representation, which simplifies implementation and ensures greater interoperability between disparate health IT systems; paragraph 34 discloses the CDS hooks generate reporting that meet requirements for EMR systems that are FHIR compatible), and
continuously updating, by the one or more processors, the Al or ML models with newly received data to improve accuracy and relevance (paragraph 200 discloses validation and performance evaluation mechanisms ensure the accuracy and effectiveness of decision support artifacts, while real-time version control maintains up-to-date medical knowledge guidelines).
System claim 13 repeats the subject matter of claim 2. As the underlying processes of claim 13 have been shown to be fully disclosed by the teachings of Santiago and Bennett in the above rejections of claim 2; as such, these limitations (claim 13) are rejected for the same reasons given above for claim 2 and incorporated herein.
Claim 3 (Original). Santiago and Bennett teach the method of claim 2. Santiago teaches a method wherein the automated routine tasks include autoscribing of clinical notes using natural language processing algorithms executed by the processors (paragraph 43 discloses training the AI to handle variations in medical terminology by utilizing several strategies, including natural language processing (NLP), medical language processing, machine learning, and semantic understanding; paragraph 104 discloses the AI system provides a comprehensive healthcare framework that harnesses machine learning and natural language processing (NLP) to integrate seamlessly with Fast Healthcare Interoperability Resources (FHIR) and blockchain technology, real-time updates, enhancing interoperability and data security).
System claim 14 repeats the subject matter of claim 3. As the underlying processes of claim 14 have been shown to be fully disclosed by the teachings of Santiago and Bennett in the above rejections of claim 3; as such, these limitations (claim 14) are rejected for the same reasons given above for claim 3 and incorporated herein.
Claim 4 (Original). Santiago and Bennett teach the method of claim 1. Santiago teaches a method wherein the decision support alerts include predictive analytics generated by executing time-series analysis and risk stratification algorithms to identify high-risk patients (paragraph 21 discloses a clinical decision support system to analyze patient data and provide relevant insights, alerts, and suggestions; paragraph 24 discloses the set of text descriptions includes text describing a trend over the length of time, the trend represented by the plurality of patient parameters spanning a length of time; paragraph 182 discloses using a trained model (e.g., trained machine-learning program) to generate predictions on new, unseen data; paragraph 194 discloses in prediction phase, the trained machine-learning program uses the features for analyzing query data to generate inferences, outcomes, or predictions, as examples of a prediction inference data).
System claim 15 repeats the subject matter of claim 4. As the underlying processes of claim 15 have been shown to be fully disclosed by the teachings of Santiago and Bennett in the above rejections of claim 4; as such, these limitations (claim 15) are rejected for the same reasons given above for claim 4 and incorporated herein.
Claim 5 (Original). Santiago and Bennett teach the method of claim 1. Santiago teaches a method further comprising integrating, by the one or more processors, patient data from wearable devices and remote monitoring systems into a unified platform stored in memory (paragraph 160 discloses a wearable device).
System claim 16 repeats the subject matter of claim 5. As the underlying processes of claim 16 have been shown to be fully disclosed by the teachings of Santiago and Bennett in the above rejections of claim 5; as such, these limitations (claim 16) are rejected for the same reasons given above for claim 5 and incorporated herein.
Claim 6 (Original). Santiago and Bennett teach the method of claim 1. Santiago teaches a method further comprising providing, by causing a display device to render a dashboard interface, real-time data and visualizations of patient and population health metrics (paragraph 222 discloses the user interface employs intuitive design principles, clear navigation structures, and interactive elements to enhance usability and facilitate efficient interaction with the system; paragraph 224 discloses the user interface facilitates interactive communication and feedback loops between users and the system. The user interface allows users to interact with generated recommendations, modify parameters, explore alternative options, and provide feedback on the system's performance. This interactivity promotes user engagement, collaboration, and continuous improvement of decision support functionalities).
System claim 17 repeats the subject matter of claim 6. As the underlying processes of claim 17 have been shown to be fully disclosed by the teachings of Santiago and Bennett in the above rejections of claim 6; as such, these limitations (claim 17) are rejected for the same reasons given above for claim 6 and incorporated herein.
Claim 7 (Original). Santiago and Bennett teach the method of claim 1.
Santiago fails to explicitly teach the following limitations met by Bennett as cited:
wherein the collected data further includes genetic sequence information and lifestyle factors encoded as structured attributes in memory (paragraph 37 discloses genetic profiling; paragraph 41 discloses a computer memory representing genetic profiling information derived from patient sample data and populated into network models).
The motivation to combine the teachings of Santiago and Bennett is discussed in the rejection of claim 1 and incorporated herein.
System claim 18 repeats the subject matter of claim 7. As the underlying processes of claim 18 have been shown to be fully disclosed by the teachings of Santiago and Bennett in the above rejections of claim 7; as such, these limitations (claim 18) are rejected for the same reasons given above for claim 7 and incorporated herein.
Claim 8 (Original). Santiago and Bennett teach the method of claim 1.
Santiago fails to explicitly teach the following limitations met by Bennett as cited:
further comprising generating, by the one or more processors, personalized treatment recommendations using a trained machine learning model executed in hardware (paragraph 34 discloses systems may model personalized treatment simply by having each patient agent maintain their own individualized transition model; paragraph 113 discloses agents having Personalized transition models-the integration of machine learning algorithms for optimal treatment selection for each patient).
The motivation to combine the teachings of Santiago and Bennett is discussed in the rejection of claim 1 and incorporated herein.
System claim 19 repeats the subject matter of claim 8. As the underlying processes of claim 19 have been shown to be fully disclosed by the teachings of Santiago and Bennett in the above rejections of claim 8; as such, these limitations (claim 19) are rejected for the same reasons given above for claim 8 and incorporated herein.
Claim 9 (Original). Santiago and Bennett teach the method of claim 1. Santiago teaches a method further comprising facilitating collaboration among healthcare providers (paragraph 224 discloses the user interface allows users to interact with generated recommendations, modify parameters, explore alternative options, and provide feedback on the system's performance. This interactivity promotes user engagement, collaboration, and continuous improvement of decision support functionalities) by synchronizing shared access to patient data across networked devices (paragraph 108 discloses the AI system implements global FHIR CQL repository by establishing a global repository that accepts, validates, and compensates personalized Clinical Quality Language (CQL) submissions. This repository would use this content to deliver intelligently generated smart applications on demand, facilitating the sharing of validated artifacts with clinicians in or outside formal networks) and providing secure communication tools through the graphical user interface (paragraph 105 discloses the AI system implements electronic personalized profiles through the creation of electronic personalized profiles accessible across all patient devices, enabling real-time, secure transmission of patient data from electronic medical records (EMRs) to mobile devices in FHIR compatible formats).
System claim 20 repeats the subject matter of claim 9. As the underlying processes of claim 20 have been shown to be fully disclosed by the teachings of Santiago and Bennett in the above rejections of claim 9; as such, these limitations (claim 20) are rejected for the same reasons given above for claim 9 and incorporated herein.
Claim 10 (Original). Santiago and Bennett teach the method of claim 1. Santiago teaches a method further comprising implementing, by the one or more processors, encryption protocols, authentication routines, and access controls to ensure patient data privacy and compliance with regulatory standards (paragraph 230 discloses the validation module ensures that decision support artifacts comply with industry standards, guidelines, and regulations governing healthcare practices, data privacy, and ethical considerations. The validation module checks for adherence to standards such as Fast Healthcare Interoperability Resources (Fl-HR), Clinical Quality Language (CQL), and other relevant frameworks to maintain interoperability and data integrity).
Claim 11 (Original). Santiago and Bennett teach the method of claim 1. Santiago teaches a method further comprising enabling telehealth consultations and remote patient management by executing integrated audio/video communication protocols and secure data exchange between provider and patient devices (paragraph 107 discloses real-time, remote monitoring of patient status and early detection of adverse events as well as remote monitoring for home bound individuals with subacute or chronic diseases).
Claim 12 (Original). Santiago teaches a system for enhancing clinical decision-making and workflow optimization (paragraph 17 discloses Clinical Decision Support (CDS) Hooks that allow healthcare software to offer decision support within workflows at the point of care), the system comprising:
one or more processors (paragraph 162 discloses the memory includes a main memory, a static memory, and a storage unit, both accessible to the processors via the bus); and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors (paragraph 269 discloses a non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations), cause the one or more processors to:
collect, via network communication interfaces, data from multiple healthcare sources including electronic health records (EHRs), medical imaging systems (Figure 8; paragraph 19 discloses the disease specific treatment algorithm is received from one or more different databases, such as a collection of ERDs (Electronic Reference Databases); paragraph 76 discloses the AI (Artificial Intelligence) system can configure data from multiple different databases that are in their own non-standardized format into a single standardized format, the messages, queries, assessments, and alerts can be generated to communicate with physicians because the CQL (Clinical Quality Language) models are using standardized format), wherein the collecting includes receiving structured data (paragraph 182 discloses receiving structured or labeled data), unstructured text (paragraph 182 discloses receiving unstructured or unlabeled data), and imaging pixel arrays (paragraph 184 discloses pixels);
present the predictive outputs by causing a clinician-facing display device to render a unified graphical user interface (paragraph 163 discloses the user output components may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)) in real time together with context-sensitive fields of the electronic health record (paragraph 105 discloses real-time, secure transmission from electronic medical records of a patient; paragraph 182 discloses using a trained model (e.g., trained machine-learning program) to generate predictions on new, unseen data; paragraph 194 discloses in prediction phase, the trained machine-learning program uses the features for analyzing query data to generate inferences, outcomes, or predictions, as examples of a prediction inference data; paragraph 199 discloses the system leverages contextual data from medical documents, clinical guidelines, and Clinical Quality Language (CQL) code, and serves as training material for AI algorithms, enabling the development of advanced decision support tools that assist clinicians in adhering to guidelines and making informed decisions), thereby reducing redundant user interactions, streamlining clinical workflow (This limitation is being interpreted by the Examiner as a statement of intended use. As such the limitation is not given any patentable weight in keeping with the guidelines of MPEP 7.37.09: a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim), and
providing the generated insights to healthcare providers through the unified interface (paragraph 222 discloses the user interface employs intuitive design principles, clear navigation structures, and interactive elements to enhance usability and facilitate efficient interaction with the system; paragraph 224 discloses the user interface facilitates interactive communication and feedback loops between users and the system. The user interface allows users to interact with generated recommendations, modify parameters, explore alternative options, and provide feedback on the system's performance. This interactivity promotes user engagement, collaboration, and continuous improvement of decision support functionalities).
Santiago fails to explicitly teach the following limitations met by Bennett as cited:
patient-reported outcomes (paragraph 25 discloses patient reported outcomes),
analyze the collected data using an artificial intelligence (AI) or machine learning (ML) model executed in hardware (paragraph 113 discloses embodiments of agents having personalized transition models-the integration of machine learning algorithms for optimal treatment selection for each patient; paragraph 114 discloses using machine learning methods to determine optimal treatments at a single timepoint for individual patients, which may be combined into the sequential decision AI framework by incorporating the output probabilities of those single-decision point treatment models into the transition models used by the sequential decision-making approaches), the analyzing including at least normalizing the multimodal data, generating embeddings of unstructured clinical notes, and combining the embeddings with imaging-derived feature vectors to produce predictive outputs that indicate risk levels, anomaly detection alerts, or treatment prioritization flags (Figure 8; paragraph 64 discloses normalized samples and/or samples of patients with a particular disease, which are mapped into a network structure and compared to other network structures; paragraph 79 discloses the transition model is to use the history of the health status (outcome data) to predict the probability of future health states; paragraph 85 discloses an Outcome Rating Scale (ORS) may be used as the basis for machine learning algorithms to predict individualized treatment response).
The motivation to combine the teachings of Santiago and Bennett is discussed in the rejection of claim 1 and incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Baronov (WO 2025/226733 A1) discloses a clinical education method and system to assist in clinical diagnosis and treatment of patients.
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KRISTINE K. RAPILLO
Examiner
Art Unit 3626
/KRISTINE K RAPILLO/
Examiner, Art Unit 3682
Prosecution Insights