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 the Application
Claims 1-21 are currently pending in this case and have been examined and addressed below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/31/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-13 and 15 are drawn to a device (i.e., an apparatus). Claim 14 is drawn to a method (i.e., a process). As such, claims 1-15 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Independent Claim 1: An emergency medical treatment system programmed for use in connection with providing medical treatment to a patient in a prehospital environment, the system comprising:
a patient data display device programmed to receive and display data associated with the patient;
an environmental assessment device configured to capture audio, video, and/or acoustical signals associated with an emergency treatment site associated with the patient;
a patient monitoring device configured to be positioned on the patient and having multiple sensors programmed to collect physiological data or vitals data associated with the patient;
a patient data processing computer system configured for:
receiving one or more of:
sensor data from the patient monitoring device, emergency medical treatment protocol data, and/or historical health condition data associated with the patient;
and communicating at least a portion of the received data to the patient data display device;
an artificial intelligence (AI) system, operatively associated with the patient data processing computer system, comprising a generative AI module and a foundational/large language model (LLM) model, the AI system programmed for one or more of:
analyzing at least a portion of the data received by the patient data processing computer system, generating at least one output related to the patient in response to analyzing the data received by the patient data processing computer system, and/or generating a summary analysis of healthcare information related to treatment of the patient.
(Examiner notes: The above claim terms underlined are additional elements that fall under Step 2A - Prong Two analysis section detailed below)
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people). Therefore, providing medical treatment to a patient in a prehospital environment, receive and display data, receiving sensor data, emergency medical treatment protocol data, or historical health condition data, analyze a portion of the data received, generating an output related to the patient response to the data analysis, or generating a summary analysis of healthcare information related to the treatment of the patient are directed to managing personal interactions or personal behavior.
The dependent claim 2 is directed to recommending a healthcare treatment decision.
The dependent claim 3 is directed to generating at least one patient care summary customized for a specific audience.
The dependent claim 4 is directed to analyzing a portion of the sensor data and analyzing a portion of the received sensor data in comparison to a portion of historical health condition data associated with the patient.
The dependent claim 5 is directed to receiving a query by an emergency medical service provider.
The dependent claim 6 is directed to processing language based queries.
The dependent claim 7 is directed to processing image data associated with at least a portion of an emergency treatment scene associated with the prehospital environment.
The dependent claim 8 is directed to processing audio or acoustical data associated with the prehospital environment.
The dependent claim 9 is directed to documents regarding optimal patient care, state EMS protocols, historical examples of patient care summaries, pharmaceutical information, healthcare acronyms, and/or healthcare terms.
The dependent claim 10 is directed to a document tagged in regards to a priority treatment.
The dependent claim 11 is directed to imposing at least one constraint on at least a portion of output.
The dependent claim 12 is directed to restrict inclusion of personal health information (PHI) in output.
The dependent claim 13 is directed to routing an input in response to contents of a query.
The dependent claim 14 is directed to customize a patient encounter in connection with generating an output.
The dependent claim 15 is directed to tailor language communicated to the patient in response to an audience or population associated with the patient.
The dependent claim 16 is directed to generating a recommended treatment decision in connection with at least one local, state, and/or national guideline regarding a prehospital or emergency medical service health care protocol.
The dependent claim 17 is directed to generating at least one contraindication alert associated with a course of treatment for the patient in response to the patient data.
The dependent claim 18 is directed to parsing text from a text file derived from the audio file.
The dependent claim 19 is directed to the parsed text to generate a synthesized patient summary for at least a portion of the patient data.
The dependent claim 20 is directed to customize language of the synthesized patient summary in response to an audience or population associated with the synthesized patient summary.
Each of these steps of the preceding dependent claims 2-21 only serve to further limit or specify the features of independent claim 1 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
As such, the Examiner concludes that the preceding claims recite an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Claim 1 recites the use of a patient data display device, in this case to receive and display data. The claim also recites the use of an environmental assessment device configured to capture audio, video, and/or acoustical signals associated with an emergency treatment site associated with the patient and the use of a patient monitoring device configured to be positioned on the patient and having multiple sensors programmed to collect physiological data or vitals data associated with the patient. Additionally, claim 1 recites the use of a patient data processing computer system, in this case to receive sensor data, emergency medical treatment protocol data, or historical health condition data. The claim further recites the use of communicating at least a portion of the received data to the patient data display device. The use of the patient data display device, environmental assessment device configured to capture audio, video, and/or acoustical signals, patient data processing computer system, and communicating at least a portion of the received data to the patient data display device are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)). Furthermore, the use of the patient monitoring device configured to be positioned on the patient and having multiple sensors programmed to collect physiological data or vitals data associated with the patient, is only recited as being used in its ordinary capacity and is merely a tool to execute the abstract idea (MPEP § 2106.05(f)(2)).
Claims 1-8, 10, 16-17, and 19 recite the use of an artificial intelligence (AI) system, operatively associated with the patient data processing computer system, comprising a generative AI module and a foundational/large language model (LLM) model, in this case to analyzing a portion of the data received, generating an output related to the patient in response to analyzing the data, or generating a summary analysis of healthcare information related to treatment of the patient, recommending a healthcare treatment decision, generating a patient care summary customized for a specific audience, analyzing the at least a portion of the received sensor data in comparison to at least a portion of historical health condition data associated with the patient, receiving a query by an emergency medical service provider, processing language based queries, processing image data associated with at least a portion of an emergency treatment scene associated with the prehospital environment, processing audio or acoustical data associated with the prehospital environment, generating a recommended treatment decision in connection with at least one local, state, and/or national guideline regarding a prehospital or emergency medical service health care protocol, generating at least one contraindication alert associated with a course of treatment for the patient in response to the patient data, parsed text to generate a synthesized patient summary for at least a portion of the patient data, only recites the artificial intelligence (AI) system, operatively associated with the patient data processing computer system, comprising a generative AI module and a foundational/large language model (LLM) model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 5 recites the use of a chatbot interfacing with the foundational/LLM model, only as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 9 recites the use of a Al system programmed to access at least one database, in this case to documents regarding optimal patient care, state EMS protocols, historical examples of patient care summaries, pharmaceutical information, healthcare acronyms, and/or healthcare terms, only recites the Al system programmed to access at least one database as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 11 recites the use of the Al system programmed for enabling at least one guardrail programmed, in this case to imposing a constraint on a portion of output, only recites the Al system programmed for enabling at least one guardrail programmed as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 12 recites the use of the Al system including an output validator programmed, in this case to restrict inclusion of personal health information (PHI) in output, only recites the Al system including an output validator programmed as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 13 recites the use of the Al system including an agent framework, in this case to routing at least one input in response to contents of a query. The claim also recites the use of a type of foundational/LLM model. The use of the agent framework and a type of foundational/LLM model are only recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claims 14-15 recite the use of the Al system including response personalization functionality programmed, in this case to customize a patient encounter in connection with generating output and tailor language communicated to the patient in response to an audience or population associated with the patient, only recites the Al system including response personalization functionality programmed as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 18 recites the use of the patient data processing computer system including a speech-to-text module programmed, in this case to parsing text from a text file derived from the audio file. The claim also recites the use of converting audible speech into an audio file. The use of the patient data processing computer system including a speech-to-text module and converting audible speech into an audio file are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claim 20 recites the use of a the Al system including response personalization functionality programmed, in this case to customize language of the synthesized patient summary in response to an audience or population associated with the synthesized patient summary, only recites the Al system including response personalization functionality programmed as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 21 recites the use of the patient monitoring device comprises a device wearable, only as being used in its ordinary capacity and is merely a tool to execute the abstract idea (MPEP § 2106.05(f)(2)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception.
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as the patient data display device, environmental assessment device configured to capture audio, video, and/or acoustical signals associated with an emergency treatment site associated with the patient, patient monitoring device configured to be positioned on the patient and having multiple sensors programmed to collect physiological data or vitals data associated with the patient, patient data processing computer system, communicating at least a portion of the received data to the patient data display device, artificial intelligence (AI) system, operatively associated with the patient data processing computer system, comprising a generative AI module and a foundational/large language model (LLM) model, chatbot interfacing with the foundational/LLM model, Al system programmed to access at least one database, Al system programmed for enabling at least one guardrail programmed, Al system including an output validator programmed, agent framework and a type of foundational/LLM model, Al system including response personalization functionality programmed, patient data processing computer system including a speech-to-text module and converting audible speech into an audio file, Al system including response personalization functionality programmed, and patient monitoring device comprises a device wearable in independent claim 1 and dependent claims 2-21 are equivalent to adding the words “apply it” on a generic computer. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”). Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements are directed to generic computer component and functions being used to perform the abstract idea.
This conclusion is based on a factual determination. Applicant’s own disclosure in paragraphs [0031-0032] acknowledges that the “environmental assessment devices 208 such as a body camera device…a patient monitoring device 212 (e.g., a "Vital Vest" device) may be positioned on the patient 206 which is programmed to collect physiological or vital signs from the patient 206… the patient monitoring device 212 may be embodied as a watch, a forehead mounted sensor band, a ring, a belt, a harness, or a variety of other devices which can be configured to include sensors 212 for detecting and collecting signals derived from patient physiological data”. Paragraph [0037] discloses “the patient data processing device 402 may include a processor or controller (e.g., a small board computer (SBC) device, such as a "Raspberry Pi" device) for processing data communicated to or form the device 402. It can be seen that the device 402 can act as a central communication processor for the sensors 212A, for collecting cloud-based data, for accessing FHIR tools, for reporting results of execution of rules-based and machine learning algorithms, and for completing forms, among other tasks… In various embodiments, the patient data display device 210 and the device 402 may be combined into a single component or provided as separate components. In the separate component embodiment, the device 210 may be provided as an electronic tablet (e.g., an "iPad" device), for example, equipped with software components programmed for enabling data communication with the device 402”. Also, paragraphs [0051-0052] disclose “A retrieval-augmented generation (RAG) database 1606 can be provided as a knowledge base outside of the training data sources of the LLM 1504 for optimizing the output responses of the LLM 1604…The RAG database 1606 may be programmed to receive documents regarding optimal patient care, state EMS protocols, and how to document when a doctor provides online direction overriding EMS protocols, for example”. Paragraph [0053] acknowledges that the “chatbot can be provided as a computer program that simulates and processes human conversation (either written or spoken)”. Additionally, the specification discloses in paragraph [0058] "physiological foundational models which may include LLMs or large language and visual assistant (LLaVAs - trained large multimodal models designed to understand and generate content based on both visual inputs (images) and textual instructions), and/or other models trained on physiological sounds such as coughs, breathing, wheezing (e.g., Google's HeAR)". Furthermore, paragraphs [0061-0062] disclose "the foundational/LLM model 1710 may comprise a generic model such as GPT or Llama, for example, that are able to provide robust answers grounded in source documents fed to the system, as well as specific healthcare or physiological related models. The architectures and processes described herein may be model-agnostic and able to implement various LLMs, such as swAn open-source LLM, which can be used to configure the system locally rather than via a cloud computing architecture (assuming sufficient computational capability on the local network)…The use of agents, which can be code additions to an LLM framework, for example, that help to route a complex prompt or question to the correct pathway within the framework 1804".
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-21 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-7, 9, 11-12, 14-16 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (US-20210151145-A1)[hereinafter Dunn], in view of ELY (US-20250132038-A1)[hereinafter Ely].
As per Claim 1, Dunn discloses an emergency medical treatment system programmed for use in connection with providing medical treatment to a patient in a prehospital environment in paragraphs [0019] and [0027] and [0037] (patient monitoring system for providing medical treatment to a patient during an emergency medical event (synonymous to a patient in a prehospital environment)), the system comprising: a patient data display device programmed to receive and display data associated with the patient in paragraphs [0023-0024] and [0031] and [0038-0039] (a display to receive and display patient data); an environmental assessment device configured to capture audio, video, and/or acoustical signals associated with an emergency treatment site associated with the patient in paragraphs [0032] and [0092] (an environmental transducer device to capture audio signals associated with the triage location (synonymous to an emergency treatment site) associated with the patient); a patient monitoring device having multiple sensors programmed to collect physiological data or vitals data associated with the patient in paragraphs [0022] and [0038] (a remote device (synonymous to a patient monitoring device) to have multiple sensors that collects vitals associated with the patient); a patient data processing computer system in paragraphs [0083-0086] (a computer system) configured for: receiving one or more of: sensor data from the patient monitoring device, emergency medical treatment protocol data, and/or historical health condition data associated with the patient; and communicating at least a portion of the received data to the patient data display device in paragraphs [0023-0024] and [0031] and [0038-0039] and [0048] (receiving vitals from the remote device and health history (synonymous to historical health condition data) associated with the patient (Examiner notes that receiving vitals from the remote device and historical health condition meets the "one or more of" limitation") and communicating some or all of the patient data to the display).
Dunn discloses a patient monitoring device that has multiple sensors used to collect vitals associated with the patient, but does not disclose the patient monitoring device positioned on the patient. Additionally, Dunn does not disclose an artificial intelligence system. However, Ely discloses a patient monitoring device configured to be positioned on the patient and having multiple sensors programmed to collect physiological data or vitals data associated with the patient in paragraphs [0048-0069] (wearable device (synonymous to a patient monitoring device positioned on the patient) to collect vitals data associated with the patient); an artificial intelligence (AI) system, operatively associated with the patient data processing computer system, comprising a generative AI module and a foundational/large language model (LLM) model in paragraphs [0138-0139] and [0143] and [0147] and Figure 1 (content generation system (synonymous to an AI system), operatively associated with the service provider (synonymous to the patient data processing computer system), comprising generative AI models and a LLM model), the AI system programmed for one or more of: analyzing at least a portion of the data received by the patient data processing computer system, generating at least one output related to the patient in response to analyzing the data received by the patient data processing computer system, and/or generating a summary analysis of healthcare information related to treatment of the patient in paragraphs [0143] and [0148] (analyze a content item (synonymous to at least a portion of the data) provided by the service provider, generate content related to the patient in response to analyzing the content item received by the service provider (Examiner notes that analyzing a content item and generating content related to the patient in response to analyzing the received content item meets the "one or more of" limitation)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with a patient monitoring device to be positioned on the patient and an artificial intelligence system including a generative AI module and a foundational/large language model programmed to analyze a portion of the data, generating an output related to the patient in response to analyzing the data received, or generating a summary analysis of healthcare information related to treatment of the patient, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 2, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system programmed for recommending a healthcare treatment decision in paragraphs [0131-0138] and [0158] (generating a recommended treatment plan).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with recommending a healthcare treatment decision, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 3, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system programmed for generating at least one patient care summary customized for a specific audience in paragraphs [0102] and [0131-0138] and [0140] (generating content customized for a specific entity, wherein generated content includes a patient care summary).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with generating a patient care summary customized for a specific audience, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 5, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system programmed for receiving at least one query by an emergency medical service provider via a chatbot interfacing with the foundational/LLM model in paragraphs [0157-0158] (receiving a query by a user (synonymous to an emergency medical service provider) via a chatbot interfacing with the LLM model).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with receiving a query by an emergency medical service provider via a chatbot interfacing with the foundational/LLM model, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 6, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses wherein the foundational/LLM model comprises a large language model programmed for processing language based queries in paragraphs [0157-0158] (the LLM model processes language based queries).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with the LLM programmed for processing language based queries, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 7, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses wherein the foundational/LLM model comprises an image data model programmed for processing image data associated with at least a portion of an emergency treatment scene associated with the prehospital environment in paragraph [0141] (the LLM model includes multi-modal LLMs (synonymous to an image data model) for processing visual inputs (synonymous to image data associated with least a portion of an emergency treatment scene associated with the prehospital environment)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with an image data model programmed for processing image data associated with a portion of an emergency treatment scene associated with the prehospital environment, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 9, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system programmed to access at least one database comprising documents regarding optimal patient care, state EMS protocols, historical examples of patient care summaries, pharmaceutical information, healthcare acronyms, and/or healthcare terms in paragraphs [0071-0102] and [0140] and [0152] and Figure 2 (data storage includes medical encyclopedias and dictionaries, drug monographs and pharmacological references (Examiner notes that medical encyclopedias and dictionaries includes healthcare acronyms and healthcare terms and drug monographs and/or pharmacological references indicates pharmaceutical information) (Examiner notes that healthcare acronyms, healthcare terms, and pharmaceutical information meets the "or" limitation)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with accessing a database comprising documents, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 11, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system programmed for enabling at least one guardrail programmed for imposing at least one constraint on at least a portion of output of the foundational/LLM model in paragraphs [0012] and [0104-0126] (enabling one or more data generation constraints (synonymous to enabling at least one guardrail programmed for imposing at least one constraint) on the generated content (synonymous to at least a portion of output) of the LLM model).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with enabling a guardrail programmed for imposing a constraint on a portion of output of the LLM model, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 12, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system including an output validator programmed to restrict inclusion of personal health information (PHI) in output generated by the Al system in paragraphs [0012] and [0016-0027] (the anonymization component (synonymous to an output validator) to remove user identification information in machine-generated content (synonymous to output generated by the AI system)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with an output validator programmed to restrict inclusion of personal health information in output generated by the AI system, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 14, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system including response personalization functionality programmed to customize a patient encounter in connection with generating output of the Al system in paragraphs [0104-0126] and [0132-0137] and [0148] (the data generation request (synonymous to a response personalization functionality) to personalize patient encounters in connection with generating content of the content generation system).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with response personalization functionality programmed to customize a patient encounter in connection with generating output of the AI system, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 15, Dunn and Ely disclose the system of Claim 14.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the personalization functionality programmed to tailor language communicated to the patient in response to an audience or population associated with the patient in paragraphs [0104-0126] and [0131-0132] (tailor language communicated to the patient in response to specific audience groups associated with the patient).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with response personalization functionality programmed to tailor language communicated to the patient in response to an audience or population associated with the patient, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 16, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system programmed for generating a recommended treatment decision in connection with at least one local, state, and/or national guideline regarding a prehospital or emergency medical service health care protocol in paragraphs [0071-0102] and [0131-0138] and [0221] (generating a recommended treatment plan in connection with a geographical region guideline regarding an emergency medical service healthcare protocol).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with generating a recommended treatment decision in connection with a local, state, or national guideline regarding a prehospital or emergency medical service healthcare protocol, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
As per Claim 21, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses wherein the patient monitoring device comprises a device wearable by a patient in paragraphs [0048-0069] (a wearable device by the patient).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with the patient monitoring device including a device wearable by a patient, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (US-20210151145-A1)[hereinafter Dunn], in view of ELY (US-20250132038-A1)[hereinafter Ely], in view of FAN (US-20210174920-A1)[hereinafter Fan].
As per Claim 4, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system programmed for: analyzing at least a portion of the sensor data received by the patient data processing device in paragraphs [0143] and [0148] (analyze a content item (synonymous to at least a portion of the data)provided by the service provider, wherein the content item provided by the service provider includes sensor data).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with analyzing a portion of sensor data received by the patient data processing device, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
Dunn and Ely do not disclose the following limitations. However, Fan discloses analyzing the at least a portion of the received sensor data in comparison to at least a portion of historical health condition data associated with the patient in paragraphs [0041-0042] (analyzing physiological data (synonymous to the at least a portion of the received sensor data) in comparison to medical history (synonymous to at least a portion of historical health condition data) associated with the patient).
It would have been obvious to one of ordinary still in the art to include in the emergency medical treatment system of Dunn and Ely with analyzing a portion of sensor data in comparison to a portion of historical health condition data associated with the patient as taught by Fan since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably an emergency medical treatment system that analyzes a portion of sensor data in comparison to a portion of historical health condition data associated with the patient.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (US-20210151145-A1)[hereinafter Dunn], in view of ELY (US-20250132038-A1)[hereinafter Ely], in view of Ajay ("GLM-4-Voice: An end-to-end speech based Large Language Model (LLM)")[hereinafter Ajay].
As per Claim 8, Dunn and Ely disclose the system of Claim 1.
Dunn and Ely do not disclose the following limitations. However, Ajay discloses wherein the foundational/LLM model comprises an audio or sound model programmed for processing audio or acoustical data associated with the prehospital environment in the Introduction and GLM-4-Voice model architecture Figure (GPT-40 (synonymous to a LLM model) includes a voice model (synonymous to an audio or sound model) for processing audio (synonymous to audio or acoustical data associated with prehospital environment)).
It would have been obvious to one of ordinary still in the art to include in the emergency medical treatment system of Dunn and Ely with an audio or sound model programmed for processing audio or acoustical data associated with the prehospital environment as taught by Ajay since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably an emergency medical treatment system that includes an audio or sound model programmed for processing audio or acoustical data associated with the prehospital environment.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (US-20210151145-A1)[hereinafter Dunn], in view of ELY (US-20250132038-A1)[hereinafter Ely], in view of Madisetti et al. (US-20250190461-A1)[hereinafter Madisetti].
As per Claim 10, Dunn and Ely disclose the system of Claim 9.
Dunn and Ely do not disclose the following limitations. However, Madisetti discloses wherein at least one of documents is tagged to inform the foundational/LLM model regarding a priority treatment of the tagged document in paragraphs [0034] and [0185] and [0187] and [0196] (the document is tagged to inform the LLM model regarding the ranking and scoring (synonymous to a priority treatment) of the tagged document).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn and Ely, to be combined with one of the documents being tagged to inform the foundational/LLM model regarding a priority treatment of the tagged document, as disclosed by Madisetti, for the purpose of enhancing the attention span of LLMs and enabling more accurate and context-specific responses [0015-0016].
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (US-20210151145-A1)[hereinafter Dunn], in view of ELY (US-20250132038-A1)[hereinafter Ely], in view of Hettige et al. (US-20250094390-A1)[hereinafter Hettige].
As per Claim 13, Dunn and Ely disclose the system of Claim 1.
Dunn and Ely do not disclose the following limitations. However, Hettige discloses further comprising the Al system including an agent framework configured for routing at least one input to a type of foundational/LLM model in response to contents of a query in paragraphs [0031] and [0033-0034] and [0050] and [0088] (an agent framework to route an input to a type of LLM model in response to the user questions (synonymous to contents of a query)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn and Ely, to be combined with an agent framework for routing an input to a type of foundational/LLM model in response to contents of a query, as disclosed by Hettige, for the purpose of enhancing the attention span of LLMs and enabling more accurate and context-specific responses [0015-0016].
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (US-20210151145-A1)[hereinafter Dunn], in view of ELY (US-20250132038-A1)[hereinafter Ely], in view of Putter ("Drug-Diagnosis Contraindication Alert")[hereinafter Putter].
As per Claim 17, Dunn and Ely disclose the system of Claim 1.
Dunn and Ely do not disclose the following limitations. However, Putter discloses further comprising the Al system programmed for generating at least one contraindication alert associated with a course of treatment for the patient in response to the patient data in the 1st - 3rd paragraphs and Drug-Diagnosis Contraindication Figure (generating a contraindication alert associated with treating a patient in response to the patient data).
It would have been obvious to one of ordinary still in the art to include in the emergency medical treatment system of Dunn and Ely with generating a contraindication alert associated with a course of treatment for the patient as taught by Putter since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably an emergency medical treatment system that generates a contraindication alert associated with a course of treatment for the patient.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (US-20210151145-A1)[hereinafter Dunn], in view of ELY (US-20250132038-A1)[hereinafter Ely], in view of Sorkey et al. (US-10658074-B1)[hereinafter Sorkey].
As per Claim 18, Dunn and Ely disclose the system of Claim 1.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the patient data processing computer system including a speech-to-text module in paragraph [0157] (voice-enabled chat box using speech recognition (synonymous to speech-to-text module)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with a speech-to-text module, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
The combination of Dunn and Ely discloses the speech-to-text module, but does not explicitly disclose converting the audible speech into an audio file and parsing text from a text file from the audio file. However, Sorkey discloses further comprising the patient data processing computer system including a speech-to-text module in col 4 ln 21-50 (an automated medical transcription system) programmed for: converting audible speech into an audio file in the col 4 ln 21-45 (converting speech into one or more speech files), and parsing text from a text file derived from the audio file in col 5 ln 18-50, col 8 ln 61-64 (parsing text from a transcription (synonymous to a text file derived from the audio file)).
It would have been obvious to one of ordinary still in the art to include in the emergency medical treatment system of Dunn and Ely with a speech-to-text module programmed for converting audible speech into an audio file and parsing a text file derived from the audio file as taught by Sorkey since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably an emergency medical treatment system that includes a speech-to-text module programmed for converting audible speech into an audio file and parsing a text file derived from the audio file.
Claims 19-20 is rejected under 35 U.S.C. 103 as being unpatentable over Dunn et al. (US-20210151145-A1)[hereinafter Dunn], in view of ELY (US-20250132038-A1)[hereinafter Ely], in view of Sorkey et al. (US-10658074-B1)[hereinafter Sorkey], in view of Co et al. (US-20210090724-A1)[hereinafter Co].
As per Claim 19, Dunn, Ely, and Sorkey disclose the system of Claim 18.
Dunn, Ely, and Sorkey do not disclose the following limitations. However, Co discloses further comprising the Al system programmed for processing the parsed text to generate a synthesized patient summary for at least a portion of the patient data in paragraphs [0022] and [0036] and [0049-0052] (extracting text from the transcription to generate physician notes (synonymous to a synthesized patient summary) for a portion of patient data).
It would have been obvious to one of ordinary still in the art to include in the emergency medical treatment system of Dunn, Ely, and Sorkey with processing the parsed text to generate a synthesized patient summary for a portion of patient data as taught by Co since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably an emergency medical treatment system that processes the parsed text to generate a synthesized patient summary for a portion of patient data.
As per Claim 20, Dunn, Ely, Sorkey, and Co disclose the system of Claim 19.
Dunn does not disclose the following limitations. However, Ely discloses further comprising the Al system including response personalization functionality programmed to customize language of the patient summary in response to an audience or population associated with the patient summary in paragraphs [0104-0126] and [0131-0132] and [0140-0142] (customize language of generated patient summaries in response to specific audience groups associated with the patient summaries).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of an emergency medical treatment system, as disclosed by Dunn, to be combined with a response personalization functionality programmed to customize language of a patient summary in response to an audience or population associated with the patient summary, as disclosed by Ely, for the purpose of reducing the inaccuracies that may lead to improper training, improper diagnosis, inappropriate treatment recommendations, or suboptimal patient care protocols [0002].
The combination of Dunn, Ely, Sorkey discloses the response personalization functionality programmed to customize language of a patient summary in response to an audience or population associated with the patient summary but does not disclose customizing the language of the synthesized patient summary from the parsed text. However, Co discloses the synthesized patient summary in response to an audience or population associated with the synthesized patient summary in paragraphs [0022] and [0036] and [0049-0052] (the physician notes in response to the conversation between the patient and medical professional (synonymous to the audience) associated with the physician notes).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the synthesized patient summary of Co for the patient summary of the combination of Dunn, Ely, and Sorkey. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Radhakrishnan et al. (US-20190214129-A1) teaches on a triage classification system in a pre-hospital setting.
Yinghao Zhu et al. (“EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation”) teaches on a Retrieval-Augmented Generation driven framework to enhance multimodal HER predictive modeling to generate summaries of patients’ health statuses.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRYSTEN N WRIGHT whose telephone number is (571)272-5116. The examiner can normally be reached Monday thru Friday 8 - 5 pm, ET.
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/K.N.W./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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