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 .
Status of Claims
Claim 1, as filed on September 2, 2025, was previously pending. On February 6, 2026, Applicant filed preliminary amendments to the claims, where Applicant: (1) canceled claim 1; and (2) added new claims 2-21 (the “February 6, 2026 Preliminary Amendment”). Therefore, claims 2-21, as recited in the February 6, 2026 Preliminary Amendment, are currently pending and subject to the non-final office action below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 16, 2026 is in compliance with the provisions of 37 CFR 1.97, and has been considered by the examiner.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a non-statutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-17, 19, and 20 of U.S. Patent No. 12,431,226. Although the claims at issue are not identical, they are not patentably distinct from each other as shown below.
Claim 2 in the Present Application (Application Serial No. 19/316,784)
Claim 1 of Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
2. A system comprising: at least one processor; and at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
1. A system comprising: at least one processor; and at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
accessing a treatment process corresponding to a disease of a patient from a first Electronic Health Record (EHR) system;
receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, the disease specific treatment algorithm including a decision tree that includes guidelines for treating a specific disease;
processing data corresponding to the treatment process by inputting the data into a machine learning model, the machine learning model configured to process treatment processes to generate Clinical Quality Language (CQL) models that trigger Clinical Decision Support (CDS) hooks;
processing data corresponding to the disease specific treatment algorithm by inputting the data into a Large Language Model (LLM), the LLM being trained to process disease specific treatment algorithms to generate Clinical Quality Language (CQL) models compatible for Fast Healthcare Interoperability Resources (FHIR) and configured to trigger Clinical Decision Support (CDS) hooks;
receiving one or more first CQL models from the machine learning model based on the processing of the data corresponding to the treatment process, the one or more first CQL models include at least a first CDS hook;
receiving one or more first COL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, the one or more first COL models include at least a first CDS hook;
receiving an update to a patient record of the patient from the first EHR system;
receiving an update to a patient record of the patient from the first EHR system;
generating an updated treatment process based on the update;
generating an updated disease specific treatment algorithm based on the update;
inputting the updated treatment processes to the machine learning model to receive one or more second CQL models from the machine learning model;
inputting the updated disease specific treatment algorithms to the LLM to receive one or more second CQL models from the LLM;
executing the one or more second CQL models triggering at least a second CDS
hook to generate an alert; and
executing the one or more second CQL models triggering at least a second CDS hook to generate an alert; and
causing transmission of the alert to the patient associated with the patient record.
causing transmission of the alert for a medical practitioner associated with the first EHR system.
Claim 3 in the Present Application (Application Serial No. 19/316,784)
Claim 1 (initial “receiving” step) Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
3. The system of claim 2, wherein the treatment process includes a disease specific treatment decision tree that includes guidelines for treating a specific disease.
1. A system comprising […] receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, the disease specific treatment algorithm including a decision tree that includes guidelines for treating a specific disease; […]
Claim 4 in the Present Application (Application Serial No. 19/316,784)
Claim 1 (“processing data” step) Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
4. The system of claim 2, wherein the CQL) models are compatible for Fast Healthcare Interoperability Resources (FHIR).
1. A system comprising […] processing data corresponding to the disease specific treatment algorithm by inputting the data into a Large Language Model (LLM), the LLM being trained to process disease specific treatment algorithms to generate Clinical Quality Language (CQL) models compatible for Fast Healthcare Interoperability Resources (FHIR) and configured to trigger Clinical Decision Support (CDS) hooks; […]
Claim 5 in the Present Application (Application Serial No. 19/316,784)
Claim 2 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
5. The system of claim 2, wherein the treatment process comprises a decision tree that applies information of a patient's EHR and outputs one or more treatment procedures for the patient.
2. The system of claim 1, wherein the disease specific treatment algorithm comprises a decision tree that applies information of a patient's EHR and outputs one or more treatment procedures for the patient.
Claim 6 in the Present Application (Application Serial No. 19/316,784)
Claim 3 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
6. The system of claim 2, wherein the one or more second CQL models include a decision tree that applies information of a patient's electronic health record and outputs one or more treatment procedures for the patient in a standardized form compatible with FHIR systems.
3. The system of claim 1, wherein the one or more second CQL models include a decision tree that applies information of a patient's electronic health record and outputs one or more treatment procedures for the patient in a standardized form compatible with FHIR systems.
Claim 7 in the Present Application (Application Serial No. 19/316,784)
Claim 4 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
7. The system of claim 2, wherein the machine learning model is trained to retrieve a patient’s EHR and apply the patient’s EHR to the one or more second CQL models to generate one or more treatment procedures for the patient.
4. The system of claim 1, wherein the LLM is trained to retrieve a patient’s EHR and apply the patient’s EHR to the one or more second COL models to generate one or more treatment procedures for the patient.
Claim 8 in the Present Application (Application Serial No. 19/316,784)
Claim 5 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
8. The system of claim 7, wherein the update of the patient records is applied to the one or more treatment procedures, wherein triggering at least the second CDS hook comprises an indication of a failure for the one or more treatment procedures.
5. The system of claim 4, wherein the update of the patient records is applied to the one or more treatment procedures, wherein triggering at least the second CDS hook comprises an indication of a failure for the one or more treatment procedures.
Claim 9 in the Present Application (Application Serial No. 19/316,784)
Claim 6 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
9. The system of claim 7, wherein retrieving the patient’s EHR comprises retrieving the patient’s EHR from the first EHR system.
6. The system of claim 4, wherein retrieving the patient’s EHR comprises retrieving the patient’s EHR from the first EHR system.
Claim 10 in the Present Application (Application Serial No. 19/316,784)
Claim 7 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
10. The system of claim 7, wherein the first EHR system selects the treatment process for the patient, ,wherein retrieving the patient’s EHR comprises retrieving the patient's EHR from a second EHR system different than the first EHR system.
7. The system of claim 4, wherein the first EHR system selects the disease specific treatment algorithm for the patient, wherein retrieving the patient’s EHR comprises retrieving the patient’s EHR from a second EHR system different than the first EHR system.
Claim 11 in the Present Application (Application Serial No. 19/316,784)
Claim 8 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
11. The system of claim 7, wherein the patient’s EHR comprises a digital version of a paper chart of an assessment by a medical practitioner of the patient.
8. The system of claim 4, wherein the patient’s EHR comprises a digital version of a paper chart of an assessment by a medical practitioner of the patient.
Claim 12 in the Present Application (Application Serial No. 19/316,784)
Claim 9 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
12. The system of claim 7, wherein the patient’s EHR indicates a plurality of diseases, wherein the operations farther comprise processing data associated with a plurality of treatment processes for individual diseases of the plurality of diseases using the machine learning model to generate the one or more treatments.
9. The system of claim 4, wherein the patient’s EHR indicates a plurality of diseases, wherein the operations further comprise processing data associated with a plurality of disease specific treatment algorithms for individual diseases of the plurality of diseases using the LLM to generate the one or more treatments.
Claim 13 in the Present Application (Application Serial No. 19/316,784)
Claims 10 and 11 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
13. The system of claim 2, wherein the operations farther comprise:
10. The system of claim 1, wherein the operations further comprise;
retrieving a patient’s EHR; and
retrieving a patient’s EHR; and
applying the patient’s EHR to the one or more second CQL models to generate one or more treatment procedures for the patient, wherein the update of the patient records is applied to the one or more treatment procedures,
applying the patient’s EHR to the one or more second CQL models to generate one or more treatment procedures for the patient, wherein the update of the patient records is applied to the one or more treatment procedures.
wherein triggering at least the second CDS hook comprises an indication of a failure for the one or more treatment procedures.
11. The system of claim 10, wherein triggering at least the second CDS hook comprises an indication of a failure for the one or more treatment procedures.
Claim 14 in the Present Application (Application Serial No. 19/316,784)
Claim 12 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
14. The system of claim 2, wherein triggering at least the second CDS hook is further based on an indication of a failure in the update to the patient record to prescribe a correct dosage of a medication.
12. The system of claim 1, wherein triggering at least the second CDS hook is further based on an indication of a failure in the update to the patient record to prescribe a correct dosage of a medication.
Claim 15 in the Present Application (Application Serial No. 19/316,784)
Claim 13 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
15. The system of claim 2, wherein triggering at least the second CDS hook is further
based on an indication of a failure in the update to the patient record to prescribe a medication at
a particular time.
13. The system of claim 1, wherein triggering at least the second CDS hook is further based on an indication of a failure in the update to the patient record to prescribe a medication at a particular time.
Claim 16 in the Present Application (Application Serial No. 19/316,784)
Claim 14 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
16. The system of claim 2, wherein triggering at least the second CDS hook is further based on an indication of a newly prescribed medication in the update to the patient record.
14. The system of claim 1, wherein triggering at least the second CDS hook is further based on an indication of a newly prescribed medication in the update to the patient record.
Claim 17 in the Present Application (Application Serial No. 19/316,784)
Claim 15 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
17. The system of claim 2, wherein triggering at least the second CDS hook is further based on an indication of a new patient record opened in the update to the patient record.
15. The system of claim 1, wherein triggering at least the second CDS hook is further based on an indication of a new patient record opened in the update to the patient record.
Claim 18 in the Present Application (Application Serial No. 19/316,784)
Claim 16 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
18. The system of claim 2, wherein the alert comprises a user selectable interface element, wherein in response to a user selection of the user selectable interface element, the system initiates communication with a third party server associated with a pharmacy to create, update, modify, or cancel a medication request.
16. The system of claim 1, wherein the alert comprises a user selectable interface element, wherein in response to a user selection of the user selectable interface element, the system initiates communication with a third party server associated with a pharmacy to create, update, modify, or cancel a medication request.
Claim 19 in the Present Application (Application Serial No. 19/316,784)
Claim 17 Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
19. The system of claim 2, wherein the operations further comprise:
17. The system of claim 1, wherein the operations further comprise:
training the machine learning model by:
training the LLM by:
identifying training treatment processes and corresponding expected training CQL models for the training treatment processes;
identifying training disease specific treatment algorithms and corresponding expected training CQL models for the training disease specific treatment algorithms;
applying the training treatment processes to the machine learning model to receive
output CQL models;
applying the training disease specific treatment algorithms to the LLM to receive output COL models;
compare the output CQL models with the expected training CQL models to determine a loss parameter for the machine learning model; and
compare the output CQL models with the expected training CQL models to determine a loss parameter for the LLM; and
update a characteristic of the machine learning model based on the loss parameter.
update a characteristic of the LLM based on the loss parameter.
Claim 20 in the Present Application (Application Serial No. 19/316,784)
Claim 19 of Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
20. A method comprising:
19. A method comprising:
accessing a treatment process corresponding to a disease of a patient from a first Electronic Health Record (EHR) system;
receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, the disease specific treatment algorithm including a decision tree that includes guidelines for treating a specific disease;
processing data corresponding to the treatment process by inputting the data into a machine learning model, the machine learning model configured to process treatment processes to generate Clinical Quality Language (CQL) models that trigger Clinical Decision Support (CDS) hooks;
processing data corresponding to the disease specific treatment algorithm by inputting the data into a Large Language Model (LLM), the LLM being trained to process disease specific treatment algorithms to generate Clinical Quality Language (CQL) models compatible for Fast Healthcare Interoperability Resources (FHIR) and configured to trigger Clinical Decision Support (CDS) hooks;
receiving one or more first CQL models from the machine learning model based on the processing of the data corresponding to the treatment process, the one or more first CQL models include at least a first CDS hook;
receiving one or more first COL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, the one or more first COL models include at least a first CDS hook;
receiving an update to a patient record of the patient from the first EHR system;
receiving an update to a patient record of the patient from the first EHR system;
generating an updated treatment process based on the update;
generating an updated disease specific treatment algorithm based on the update;
inputting the updated treatment processes to the machine learning model to receive one or more second CQL models from the machine learning model;
inputting the updated disease specific treatment algorithms to the LLM to receive one or more second CQL models from the LLM;
executing the one or more second CQL models triggering at least a second CDS
hook to generate an alert; and
executing the one or more second CQL models triggering at least a second CDS hook to generate an alert; and
causing transmission of the alert to the patient associated with the patient record.
causing transmission of the alert for a medical practitioner associated with the first EHR system.
Claim 21 in the Present Application (Application Serial No. 19/316,784)
Claim 20 of Patent No. US 12,431,226, issued on September 30, 2025 (with the patently indistinct limitations identified in bold and underlined font)
21. 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 comprising:
20. 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 comprising:
accessing a treatment process corresponding to a disease of a patient from a first Electronic Health Record (EHR) system;
receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, the disease specific treatment algorithm including a decision tree that includes guidelines for treating a specific disease;
processing data corresponding to the treatment process by inputting the data into a machine learning model, the machine learning model configured to process treatment processes to generate Clinical Quality Language (CQL) models that trigger Clinical Decision Support (CDS) hooks;
processing data corresponding to the disease specific treatment algorithm by inputting the data into a Large Language Model (LLM), the LLM being trained to process disease specific treatment algorithms to generate Clinical Quality Language (CQL) models compatible for Fast Healthcare Interoperability Resources (FHIR) and configured to trigger Clinical Decision Support (CDS) hooks;
receiving one or more first CQL models from the machine learning model based on the processing of the data corresponding to the treatment process, the one or more first CQL models include at least a first CDS hook;
receiving one or more first COL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, the one or more first COL models include at least a first CDS hook;
receiving an update to a patient record of the patient from the first EHR system;
receiving an update to a patient record of the patient from the first EHR system;
generating an updated treatment process based on the update;
generating an updated disease specific treatment algorithm based on the update;
inputting the updated treatment processes to the machine learning model to receive one or more second CQL models from the machine learning model;
inputting the updated disease specific treatment algorithms to the LLM to receive one or more second CQL models from the LLM;
executing the one or more second CQL models triggering at least a second CDS
hook to generate an alert; and
executing the one or more second CQL models triggering at least a second CDS hook to generate an alert; and
causing transmission of the alert to the patient associated with the patient record.
causing transmission of the alert for a medical practitioner associated with the first EHR system.
Notice to Applicant Regarding Patent Eligibility under 35 U.S.C. § 101
The inventive concept in the claimed invention is similar to the claims in the related U.S. Patent Number 12,431,226, issued on September 30, 2025. The claimed invention was analyzed under § 101 and is deemed to be eligible, because the claims do not appear to recite an abstract idea within the enumerated groupings of abstract ideas (Mathematical Concepts; Certain Methods of Organizing Human Activity; and Mental Processes). See MPEP § 2106.04. For example, the steps recited in independent claims 2, 20, and 21 directed to: (1) “accessing a treatment process corresponding to a disease of a patient from a first Electronic Health Record (EHR) system”; (2) “processing data corresponding to the treatment process by inputting the data into a machine learning model, the machine learning model configured to process treatment processes to generate Clinical Quality Language (CQL) models that trigger Clinical Decision Support (CDS) hooks”; (3) “receiving one or more first CQL models from the machine learning model based on the processing of the data corresponding to the treatment process, the one or more first CQL models include at least a first CDS hook”; (4) “receiving an update to a patient record of the patient from the first EHR system”; (5) “generating an updated treatment process based on the update”; (6) “inputting the updated treatment processes to the machine learning model to receive one or more second CQL models from the machine learning model”; (7) “executing the one or more second CQL models triggering at least a second CDS hook to generate an alert”; and (8) “causing transmission of the alert to the patient associated with the patient record”, do not appear to fall within Mathematical Concepts; Certain Methods of Organizing Human Activity; and Mental Processes enumerated groupings of abstract ideas, because a majority of the steps comprise accessing a treatment process from an EHR system, inputting data into machine learning models, and generating an updated treatment process based on updates to a patient record. Therefore, under Step 2A, Prong Two of the 2019 Revised Patent Subject Matter Eligibility Guidance (collectively includes the guidance in the January 7, 2019 Federal Register Notice and the October 2019 update issued by the USPTO as incorporated into the MPEP), the claims as a whole are not directed to a judicial exception, and thus are eligible under § 101. This concludes the § 101 analysis. See MPEP § 2106.04(II).
Claims 1-20 are deemed to be allowable over the prior art for the following reasons.
McNair et al. (Pat. No. US 12,020,814) teaches a system for providing clinical decision support. McNair, Col. 16, line 13. Column 32, lines 25-30 teach that the monitoring system receives an update when the criteria are met for the patient being at risk for a particular disease or condition, which may be determined upon obtaining additional information about the patient, or when an update to a condition program results in an updated condition risk score (i.e., receiving an update to a patient record of the patient from the first EHR system). McNair, Col. 32, lines 25-30. Column 32, lines 42-43 teach that the alerting service 312 facilitates displaying alerts or notifications on a graphical user interface (i.e., triggering the CDS to generate an alert). McNair, Col. 32, lines 42-43. For example, column 32, lines 61-63 teach that the first medical organization may receive an alert, notification, or update (such as a risk score update) (i.e., generating an alerting based on an update to the patient record). McNair, Col. 32, lines 61-63. Column 33, lines 26-27 teach that alert or notification may appear on the patient’s EMR (i.e., causing transmission of the alert to the patient associated with the first EHR system), or may be sent directly to the clinician responsible for treating the patient. McNair, Col. 33, lines 26-27.
Sinha et al. (Pub. No. US 2023/0335258) teaches a platform providing methods and systems for prevention and/or treatment of a health condition. Sinha, Abstract. Paragraph [0128] teaches that the system includes functions pathways as identified by machine learning algorithms, associated with health and disease conditions (e.g. gut health, mental health, metabolic disorders etc.) (i.e., receiving a disease specific treatment algorithm corresponding to a disease of a patient). Sinha, paragraph [0128]. Paragraph [0151] teaches that generating the personalized intervention plan for a subject can implement large language models (LLMs) based upon personalized traits of each subject (i.e., processing the data by inputting the data into a machine learning model). Sinha, paragraph [0151]. Paragraph [0261] teaches that any of the algorithms can implement any one or more of a decision tree. Sinha, paragraph [0261].
Paisley (Pub. No. AU 2021/200650) teaches a system and method for enabling users, such as healthcare providers, to create, customize, and manage one or more clinical decision support systems. Paisley, paragraph [0001]. Paragraph [0093] teaches that the system 202 (or 110) may use natural language processing method (or program) for the analysis of presenting/current systems. Paisley, paragraph [0093]. The Natural Language Processing Program is trained on medical terminology and can be applied to an unstructured data file such as Doctor and nursing notes, e.g., History notes. Id. Paragraph [0048] teaches that CDSS [clinical decision support system] enhances decision-making in the clinical workflow, and includes a variety of tools, such as, computerized alerts and reminders to care providers, medical practitioners, and patients. Paisley, paragraph [0048].
Paragraph [0076] teaches that to make the system 110 compatible with any EHR, two types of API's (application program interfaces) developed by HL7 (Health Level 7 standards) are used FHIR (Fast Healthcare Interoperability Resources) and CDS (Clinical Decision Support) Hooks. Paisley, paragraph [0076]. Further, paragraph [0076] teaches that CDS Hooks API describes a hook-based pattern for invoking clinical decision support from within a clinician's workflow in the EHR system 104A. Id. The CDS Hooks API supports synchronous, workflow triggered CDSS calls returning information and suggestions and can launch a user-facing smart application when the CDSS (e.g., CDSS 106A) requires additional interaction. Id. Paragraph [0206] teaches that the CDSS may read data from the EHR system via the FHIR or CDS connect interface. Paisley, paragraph [0206]. The data from the EHR system may be copied into a temporary file and a logfile in the CDSS as it is generated in the EHR system and read. Id. When specific triggers are read in the EHR system, such as Diagnosis, Investigations, Treatment the words may trigger the CDSS via the CDS hooks interface for the appropriate response. Id. The CDSS is activated to commence the appropriate algorithm or method (i.e., diagnosis, investigation, treatment method) stored in the storage module and display the response in its own interface. Id.
Xi Yang et al., A large language model for electronic heath records, NPJ Digital Medicine 5:194 (2022) (hereinafter referred to as Yang) teaches that there is an increasing interest in developing AI systems to improve healthcare delivery and health outcomes using electronic health records (EHRs). Yang, Introduction Section, Column 1 on p.1, First Paragraph. Yang further teaches that a critical step is to extract and capture patient characteristics form longitudinal EHRs. Id. In this study, Yang developed a large clinical language model, using less than ninety billion words of text from the de-identified clinical notes of University of Florida (UF) Health, PubMed articles, and Wikipedia. Yang trained the large language models for five different clinical NLP tasks using experts’ annotations form six public benchmark datasets. Yang, Fine-tune GatorTron for Five Clinical NLP tasks, Evaluation Matrices, and Benchmark Datasets Section, Column 1 on p.6, Last Paragraph.
However, McNair; Sinha; Paisley; and Yang, do not teach a system and method, comprising: (1) “receiving one or more first CQL models from the machine learning model based on the processing of the data corresponding to the treatment process, the one or more first CQL models include at least a first CDS hook”; (2) “receiving an update to a patient record of the patient from the first EHR system”; (3) “generating an updated treatment process based on the update”; (4) “inputting the updated treatment processes to the machine learning model to receive one or more second CQL models from the machine learning model”; and (5) “executing the one or more second CQL models triggering at least a second CDS hook to generate an alert”, as described in 2, 20, and 21, in combination with the other limitations described in independent claims 2, 20, and 21.
Conclusion
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Faxed replies should be directed to the central fax at (571) 273-8300.
Mailed replies should be addressed to:
United States Patent and Trademark Office:
Commissioner of Patents and Trademarks
P.O. Box 1450
Alexandria, VA 22313-1450
Hand delivered responses should be brought to the United States Patent and Trademark Office Customer Service Window:
Randolph Building
401 Dulany Street
Alexandria, VA 22314-1450
/N.A.A./Examiner, Art Unit 3686
/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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