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 .
The present Office Action is in response to the Request for Continued Examination dated 02 September 2025.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02 September 2025has been entered.
DETAILED ACTION
In the amendment filed 02 September 2025:
Claims 39-41 are cancelled
Claims 21,23-24,26,28-30 and 36-37 are amended
Claims 21-30,32-33,35-38 are pending
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 21-30,32-33,35-38 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.
Claims 21, 26, 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a methods and a server, which are within a statutory category. The limitations of:
Claims 21, 26, 30 (21 is representative)
determining candidate hospitals according to a search radius;
transporting an emergency patient;
record the voice of the emergency patient and to photograph the emergency patient in order to generate status information about the emergency patient during transport, the status information generated;
automatically creating an emergency activity log based on the recorded emergency patient's condition during transport;
transmitting the status information;
determining a severity of the emergency patient based on the status information;
calculating emergency event possibility information based on the status information;
acquiring transport resource availability information about the candidate hospitals, the transport resource availability information acquired from the emergency medical server;
calculating a fitness of each candidate hospital based on the severity of the emergency patient, the emergency event possibility information, and the transport resource availability information;
and determining the optimal transfer hospital based on the fitness of each candidate hospital,
wherein the status information is acquired according to the emergency activity log by recognizing syllables of words spoken by a paramedic and by recognizing photographed images of the emergency patient,
wherein the transport resource availability information includes location information of each candidate hospital and further includes real-time traffic information and current position information with respect to the real-time traffic information and the location information of each candidate hospital,
stores the transport resource availability information and real-time resource information related to the candidate hospitals and the search radius, providing information related to an emergency medical service, the information being stored and provided
calculate the fitness of each candidate hospital during transport of the emergency patient, that are continuously updated, by continuously inputting data acquired throughout the transport of the emergency patient, the data being input and including
first data of the severity of the emergency patient acquired based on the status information, the first data received only during transport of the emergency patient,
second data of the emergency event possibility information acquired from the ambulance device based on the status information, the second data received only during the transport of the emergency patient,
and third data of the transport resource availability information acquired from the emergency medical server based on the location information of each candidate hospital, the real-time traffic information, and the current position information with respect to the real-time traffic information, the third data received and
configured to:
determine an order of suitability among the candidate hospitals based on the fitness of the optimal transfer hospital compared with a predetermined value;
select one hospital having a highest suitability among the candidate hospitals as the optimal transfer hospital;
transmit information on the order of suitability among the candidate hospitals and the selected one hospital;
transmit, to the selected one hospital, weight information of the emergency patient together with an inquiry as to whether the selected one hospital can accept the emergency patient;
receive, from the selected one hospital, a response to the inquiry, the selected one hospital determining whether to accept the emergency patient based on the weight information of the emergency patient and a number of emergency patients accepted during the transport of the emergency patient, the selected one hospital further transmitting the response;
and update the weight information of the emergency patient and information about the number of accepted emergency patients and the order of suitability among the candidate hospitals in real-time.
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting an emergency AI server, emergency medical server, cloud server, local server, nationally based server, control unit and storage unit, the claimed invention amounts to managing personal behavior or interaction between people. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The claim further recites the additional element of continuously updating a machine learning model to find the best candidate hospital to transport a patient to during an emergency. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to find the best candidate hospital to transport a patient to during an emergency merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (at least one of supervised learning, support vector machines (SVM), random forest (RF), naive bayes (NB), artificial neural networks (ANN), decision tree, and Bayesian) and thus fails to add an inventive concept to the claims.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of an emergency AI server, emergency medical server, cloud server, local server, control unit and storage unit, that implement the identified abstract idea. The emergency AI server, emergency medical server, cloud server, local server, nationally based server, control unit and storage unit are not described by the applicant and is recited at a high-level of generality (i.e., generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims further recite the additional element of an ambulance device capable of transporting an emergency patient, an ambulance device including a camera capable of audiovisual recording, machine learning models and storage device. The ambulance device capable of transporting an emergency patient, an ambulance device including a camera capable of audiovisual recording, machine learning models and storage device merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
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, the additional elements of using an emergency AI server, emergency medical server, cloud server, local server, nationally based server, control unit and storage unit to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to find the best candidate hospital to transport a patient to during an emergency was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (at least one of supervised learning, support vector machines (SVM), random forest (RF), naive bayes (NB), artificial neural networks (ANN), decision tree, and Bayesian). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an ambulance device capable of transporting an emergency patient, an ambulance device including a camera capable of audiovisual recording, machine learning models and storage device were determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. MPEP 2106.05(f) indicates that merely saying “apply it” cannot provide significantly more. Accordingly, even in combination, these additional elements do not provide significantly more. As such the claims are not patent eligible.
Claims 22-25,27-29,32-33,35-38 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 22, 27, 32 merely describe(s) what the status information contains, which further defines the abstract idea.
Claim(s) 23, 28, 36 merely describe(s) expanding search radius to find a candidate hospital, which further defines the abstract idea.
Claim(s) 24, 29, 37 merely describe(s) expanding search radius to find a candidate hospital, which further defines the abstract idea.
Claim(s) 25, 38 merely describe(s) utilization of an air ambulance, which further defines the abstract idea.
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.
The Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 21-22 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over AOKI et al (Foreign Publication WO-2012098613-A1) in view of BAE et al (Foreign Publication KR-20060037684-A) in view of Kim et al (US Publication No. 20180322961) in view of MARUYAMA et al (Foreign Publication JP-2011048775-A) in view of PEETERS et al (US Publication No. 20160157739) in view of Gounares et al (US Publication No. 20090198733) in view of Simoudis et al (US Publication No. 20200363220).
Regarding Claim 21
AOKI teaches a method for determining an optimal transfer hospital, the method comprising:
determining candidate hospitals according to a search radius [AOKI at Page 10 Para 8 teaches the server sorts the list of medical institutions that can be transported based on the judgment of severity and urgency and the characteristics and list of pre-registered medical institutions according to the distance from the site and the busyness of each medical institution];
automatically creating, by the ambulance device, an emergency activity log based on the recorded emergency patient's condition during transport [AOKI at Page 10 Para 7 teaches the ambulance team records data necessary for the determination of “severity / emergency” in the patient's past history, current medical history, and physical findings (S7). Based on this recorded data, the server uses a severity / emergency determination algorithm incorporated internally based on the “Employment Standards for Transporting and Accepting Victims” determined by each local government. A determination is made (S8)];
determining, in the emergency Al server, a severity of the emergency patient based on the status information [AOKI at Page 10 Para 7];
acquiring, in the emergency Al server, transport resource availability information about the candidate hospitals, the transport resource availability information acquired from the emergency medical server [AOKI at Page 10 Para 8];
first data of the severity of the emergency patient acquired from the ambulance device based on the status information, the first data received only during transport of the emergency patient [AOKI at Page 16 Para 3 teaches the severity determination unit 251c calculates the severity of the patient based on the registered information and various indexes. For example, when the consciousness state, blood pressure, pulse rate, respiration, body temperature, SpO2, etc. are input by the ambulance team on the screen 502 as shown in FIG. 25, the severity determination unit 201e, based on the input information, It is possible to determine the urgency level of the disease state and display it on the screen],
wherein the emergency Al server is further configured to:
determine an order of suitability among the candidate hospitals based on the fitness of the optimal transfer hospital compared with a predetermined value [AOKI at Page 10 Para 8];
select one hospital having a highest suitability among the candidate hospitals as the optimal transfer hospital [AOKI at Page 14 Para 7 teaches in addition to this, the transport destination candidate is selected in consideration of the suspected disease and its severity];
transmit, to the ambulance device, information on the order of suitability among the candidate hospitals and the selected one hospital [AOKI at Page 4 Para 5 teaches Hereinafter, an example of screen transition in the terminal will be described with reference to FIGS. Screen data on the terminal is generated under the control of the display control unit 301b of the server. Information input by the terminal is registered in the information registration unit 301d of the server. The list displayed on the terminal is created by the server list creation unit 301f];
transmit, to the selected one hospital, weight information of the emergency patient together with an inquiry as to whether the selected one hospital can accept the emergency patient [AOKI at Page 14 Para 4 teaches in the initial branch screen 502, when one of endogenous adult CPA, endogenous child CPA, extrinsic CPA, and DNR-compatible CPA is designated in the input designation area of a highly urgent disease displayed on the upper left, FIG. As shown in FIG. 32, the screen transits to an acceptability status screen 509 regarding CPA. On this screen 509, the occurrence status (map), the activity status of today's list hospital, and current patient information are displayed (patient information interpreted as weight information of the emergency patient); AOKI at Page 14 Para 5 teaches when the hospital is selected from the list of activity status of today's list hospital on the acceptability screen 509, the screen changes to an acceptability input screen 511 as shown in FIG. In this screen 511, the name of the medical institution to be inquired, the telephone number, and the name of the on-duty doctor are displayed on the upper side, and the telephone, background / transport source, acceptable, and difficult to accept buttons are displayed in the middle];
receive, from the selected one hospital, a response to the inquiry, the selected one hospital determining whether to accept the emergency patient based on the weight information of the emergency patient [AOKI at Page 14 Para 5 teaches when the terminal is operated at a medical institution or the like and the difficult to accept button is pressed, a difficult to accept reason input panel 513 as shown in FIG. 36 is displayed. On this panel 513, it is possible to select a reason for difficulty in acceptance.] and a number of emergency patients accepted during the transport of the emergency patient, the selected one hospital further transmitting the response to the ambulance device [AOKI at Page 6 Para 9 teaches by the way, the medical institution displays the availability of beds and resources for each department or specialty by “○”, “×”, etc., and the emergency team determines the transport destination based on the signature (emergency team transport destination interpreted to be determined on the ambulance device)];
and update the weight information of the emergency patient and information about the number of accepted emergency patients and the order of suitability among the candidate hospitals in real-time [AOKI at Page 10 Para 8 teaches the server updates the list based on this notification (S13). In this way, the medical institution and the emergency team make the best decision while sharing the same information (patient outbreak status, busyness of each medical institution, patient status, etc.). In addition, when the ambulance team makes an inquiry, if it is difficult to accept due to the situation of the medical institution, the information can be shared by touching the ambulance team].
AOKI does not teach transporting an emergency patient in an ambulance device including a camera capable of audiovisual recording;
automatically operating the camera to record the voice of the emergency patient and to photograph the emergency patient in order to generate status information about the emergency patient during transport, the status information generated by the camera being stored in a storage device provided in the ambulance device;
transmitting the status information from the ambulance device to an emergency artificial intelligence (AI) server that communicates with an emergency medical server provided separately from the emergency Al server;
calculating, in the emergency medical server, emergency event possibility information based on the status information;
calculating, in the emergency Al server, a fitness of each candidate hospital based on the severity of the emergency patient, the emergency event possibility information, and the transport resource availability information;
and determining, in the emergency Al server, the optimal transfer hospital based on the fitness of each candidate hospital,
wherein the status information is acquired according to the emergency activity log by recognizing syllables of words spoken by a paramedic of the ambulance device and by recognizing photographed images of the emergency patient,
wherein the transport resource availability information includes location information of each candidate hospital and further includes real-time traffic information and current position information of the ambulance device with respect to the real-time traffic information and the location information of each candidate hospital,
wherein the emergency medical server includes a local server and stores the transport resource availability information and real-time resource information related to the candidate hospitals and the search radius, the local server including a nationally based server providing information related to an emergency medical service to the ambulance device, the information stored in the emergency medical server being provided to the ambulance device and the emergency Al server,
wherein the emergency Al server is configured to perform machine learning to calculate the fitness of each candidate hospital during transport of the emergency patient, includes a cloud server, and stores a plurality of machine learning models that are continuously updated, the machine learning performed in the plurality of machine learning models by continuously inputting data acquired from the emergency medical server and the ambulance device throughout the transport of the emergency patient, the data being input to an optimal hospital selection model of the plurality of machine learning models and including
second data of the emergency event possibility information acquired from the ambulance device based on the status information, the second data received only during the transport of the emergency patient,
and third data of the transport resource availability information acquired from the emergency medical server based on the location information of each candidate hospital, the real-time traffic information, and the current position information of the ambulance device with respect to the real-time traffic information, the third data received only from the emergency medical server, and
BAE teaches transporting an emergency patient in an ambulance device including a camera capable of audiovisual recording [BAE at Page 2 Para 1 teaches inside the ambulance, at least a camera for photographing a patient's condition, a microphone for receiving an audio signal inside the vehicle and sending it to an emergency center, a speaker for outputting an audio signal transmitted from the emergency center, and an ambulance; BAE at Page 8 Para 8 teaches as described above, the present invention has an advantage in that an emergency center in a hospital or the like can identify a patient's condition in an ambulance vehicle through video and audio, thereby enabling a first-aid for a specialist];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine patient severity of AOKI with the camera of BAE with the motivation to improve monitoring of a patient in an ambulance car.
AOKI/BAE do not teach automatically operating the camera to record the voice of the emergency patient and to photograph the emergency patient in order to generate status information about the emergency patient during transport, the status information generated by the camera being stored in a storage device provided in the ambulance device;
transmitting the status information from the ambulance device to an emergency artificial intelligence (AI) server that communicates with an emergency medical server provided separately from the emergency Al server;
calculating, in the emergency medical server, emergency event possibility information based on the status information;
calculating, in the emergency Al server, a fitness of each candidate hospital based on the severity of the emergency patient, the emergency event possibility information, and the transport resource availability information;
and determining, in the emergency Al server, the optimal transfer hospital based on the fitness of each candidate hospital,
wherein the status information is acquired according to the emergency activity log by recognizing syllables of words spoken by a paramedic of the ambulance device and by recognizing photographed images of the emergency patient,
wherein the transport resource availability information includes location information of each candidate hospital and further includes real-time traffic information and current position information of the ambulance device with respect to the real-time traffic information and the location information of each candidate hospital,
wherein the emergency medical server includes a local server and stores the transport resource availability information and real-time resource information related to the candidate hospitals and the search radius, the local server including a nationally based server providing information related to an emergency medical service to the ambulance device, the information stored in the emergency medical server being provided to the ambulance device and the emergency Al server,
wherein the emergency Al server is configured to perform machine learning to calculate the fitness of each candidate hospital during transport of the emergency patient, includes a cloud server, and stores a plurality of machine learning models that are continuously updated, the machine learning performed in the plurality of machine learning models by continuously inputting data acquired from the emergency medical server and the ambulance device throughout the transport of the emergency patient, the data being input to an optimal hospital selection model of the plurality of machine learning models and including
second data of the emergency event possibility information acquired from the ambulance device based on the status information, the second data received only during the transport of the emergency patient,
and third data of the transport resource availability information acquired from the emergency medical server based on the location information of each candidate hospital, the real-time traffic information, and the current position information of the ambulance device with respect to the real-time traffic information, the third data received only from the emergency medical server, and
KIM teaches operating the camera to record the voice of the emergency patient and to photograph the emergency patient in order to generate status information about the emergency patient during transport, the status information generated by the camera being stored in a storage device provided in the ambulance device [KIM at Para 0042 teaches in general, a voice module 104, in various embodiments, is configured to receive and/or record voice audio data from a user (e.g., a patient, an athlete, another user, or the like) and/or to assess and/or diagnose the presence and/or severity of one or more medical conditions (e.g., injuries, illnesses, diseases, or the like) based on collected voice audio data (interpret to combine with ambulance device of AOKI)];
wherein the status information is acquired according to the emergency activity log by recognizing syllables of words spoken by a paramedic of the ambulance device and by recognizing photographed images of the emergency patient [KIM at Para. 0067 teaches the speech recognition results may be in any appropriate format and include any appropriate information. For example, the speech recognition results may include a word lattice that includes multiple possible sequences of words, information about pause fillers, and the timings of words, syllables, vowels, pause fillers, or any other unit of speech; KIM at Para. 0153 teaches in one embodiment, the detection module 1106 may extract one or more image features from image data (e.g., one or more images, video, or the like of the user, of the user's face, of another body part of the user associated with a medical condition, or the like) from an image sensor such as a camera of a computing device 102, and may input the one or more image features into a model (e.g., with extracted voice features or the like)],
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of AOKI, BAE with the speech recognition of KIM with the motivation to improve the performance of a medical condition diagnosis system.
AOKI/BAE/KIM do not teach transmitting the status information from the ambulance device to an emergency artificial intelligence (AI) server that communicates with an emergency medical server provided separately from the emergency Al server;
calculating, in the emergency medical server, emergency event possibility information based on the status information;
calculating, in the emergency Al server, a fitness of each candidate hospital based on the severity of the emergency patient, the emergency event possibility information, and the transport resource availability information;
and determining, in the emergency Al server, the optimal transfer hospital based on the fitness of each candidate hospital,
wherein the transport resource availability information includes location information of each candidate hospital and further includes real-time traffic information and current position information of the ambulance device with respect to the real-time traffic information and the location information of each candidate hospital,
wherein the emergency medical server includes a local server and stores the transport resource availability information and real-time resource information related to the candidate hospitals and the search radius, the local server including a nationally based server providing information related to an emergency medical service to the ambulance device, the information stored in the emergency medical server being provided to the ambulance device and the emergency Al server,
wherein the emergency Al server is configured to perform machine learning to calculate the fitness of each candidate hospital during transport of the emergency patient, includes a cloud server, and stores a plurality of machine learning models that are continuously updated, the machine learning performed in the plurality of machine learning models by continuously inputting data acquired from the emergency medical server and the ambulance device throughout the transport of the emergency patient, the data being input to an optimal hospital selection model of the plurality of machine learning models and including
second data of the emergency event possibility information acquired from the ambulance device based on the status information, the second data received only during the transport of the emergency patient,
and third data of the transport resource availability information acquired from the emergency medical server based on the location information of each candidate hospital, the real-time traffic information, and the current position information of the ambulance device with respect to the real-time traffic information, the third data received only from the emergency medical server, and
MARUYAMA teaches transmitting the status information from the ambulance device to an emergency artificial intelligence (AI) server that communicates with an emergency medical server provided separately from the emergency Al server [MARUYAMA at Page 3 Para 5 teaches the vehicle terminal 2 is mounted on an emergency vehicle such as an ambulance and wirelessly communicates with the management server 3 to acquire and display identification information of a medical institution such as a hospital that should go from the emergency site that is the current location. As a result, the ambulance crew can transport the emergency patient and the injured person to an appropriate medical institution without hesitation on site. The management server 3 is installed in a general center having jurisdiction over a predetermined area, and communicates with the vehicle terminal 2 and the hospital server 4 so that when there is an inquiry about an accepted hospital from the vehicle terminal 2, each hospital server 4 receives each hospital.];
wherein the emergency medical server includes a local server and stores the transport resource availability information and real-time resource information related to the candidate hospitals and the search radius, the local server including a nationally based server providing information related to an emergency medical service to the ambulance device, the information stored in the emergency medical server being provided to the ambulance device and the emergency Al server [MARUYAMA at Page 3 Para 1 teaches however, when receiving the inquiry of the receiving hospital from the terminal and means for storing the hospital location information in advance for each hospital, the inquiry of the corresponding time is transmitted to the hospital server for each hospital, As a response, the means for receiving and storing the corresponding time from the hospital server, the location information of the terminal included in the inquiry of the accepting hospital, and the prestored information of each hospital Based on the location information, for each hospital, a means for calculating and storing the travel time from the current position of the emergency vehicle to the hospital and the larger of the corresponding time and the travel time for each hospital Means for storing the time until the hospital can accept the patient and the minimum of the reception times of each hospital, and the hospital associated with the minimum value is selected as the receiving hospital. And means for transmitting information related to the hospital to the terminal],
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of AOKI, BAE, KIM with the servers of MARUYAMA with the motivation to pick the most qualified hospital for the patient.
AOKI/BAE/KIM/MARUYAMA do not teach calculating, in the emergency medical server, emergency event possibility information based on the status information;
calculating, in the emergency Al server, a fitness of each candidate hospital based on the severity of the emergency patient, the emergency event possibility information, and the transport resource availability information;
and determining, in the emergency Al server, the optimal transfer hospital based on the fitness of each candidate hospital,
wherein the transport resource availability information includes location information of each candidate hospital and further includes real-time traffic information and current position information of the ambulance device with respect to the real-time traffic information and the location information of each candidate hospital,
wherein the emergency Al server is configured to perform machine learning to calculate the fitness of each candidate hospital during transport of the emergency patient, includes a cloud server, and stores a plurality of machine learning models that are continuously updated, the machine learning performed in the plurality of machine learning models by continuously inputting data acquired from the emergency medical server and the ambulance device throughout the transport of the emergency patient, the data being input to an optimal hospital selection model of the plurality of machine learning models and including
second data of the emergency event possibility information acquired from the ambulance device based on the status information, the second data received only during the transport of the emergency patient,
and third data of the transport resource availability information acquired from the emergency medical server based on the location information of each candidate hospital, the real-time traffic information, and the current position information of the ambulance device with respect to the real-time traffic information, the third data received only from the emergency medical server, and
PEETERS teaches calculating, in the emergency medical server, emergency event possibility information based on the status information [PEETERS at Para. 0033 teaches by ‘one or more parameters indicative of a probability of Return of Spontaneous Circulation’ may be understood a number which is indicative of a probability of Return of Spontaneous Circulation];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of AOKI, BAE, KIM, MARUYAMA with the emergency event possibility of PEETERS with the motivation to better understand the patient’s condition to better match to a candidate hospital.
AOKI/BAE/KIM/MARUYAMA/PEETERS do not teach calculating, in the emergency Al server, a fitness of each candidate hospital based on the severity of the emergency patient, the emergency event possibility information, and the transport resource availability information;
and determining, in the emergency Al server, the optimal transfer hospital based on the fitness of each candidate hospital,
wherein the transport resource availability information includes location information of each candidate hospital and further includes real-time traffic information and current position information of the ambulance device with respect to the real-time traffic information and the location information of each candidate hospital,
wherein the emergency Al server is configured to perform machine learning to calculate the fitness of each candidate hospital during transport of the emergency patient, includes a cloud server, and stores a plurality of machine learning models that are continuously updated, the machine learning performed in the plurality of machine learning models by continuously inputting data acquired from the emergency medical server and the ambulance device throughout the transport of the emergency patient, the data being input to an optimal hospital selection model of the plurality of machine learning models and including
second data of the emergency event possibility information acquired from the ambulance device based on the status information, the second data received only during the transport of the emergency patient,
and third data of the transport resource availability information acquired from the emergency medical server based on the location information of each candidate hospital, the real-time traffic information, and the current position information of the ambulance device with respect to the real-time traffic information, the third data received only from the emergency medical server, and
PEETERS teaches calculating, in the emergency medical server, emergency event possibility information based on the status information [PEETERS at Para. 0033 teaches by ‘one or more parameters indicative of a probability of Return of Spontaneous Circulation’ may be understood a number which is indicative of a probability of Return of Spontaneous Circulation (interpreted as emergency event possibility information)];
Gounares teaches the emergency Al server, a fitness of each candidate hospital based on the severity of the emergency patient, the emergency event possibility information, and the transport resource availability information [Gounares at Para. 0004 teaches the subject innovation relates to systems and/or methods that facilitate identifying an optimal medical facility to transport a patient involved in an emergency medical incident. Moreover, the subject innovation relates to systems and/or methods that facilitate routing a patent to medical care and/or routing medical care to a patient. A match component can be utilized in order to identify an optimal selection of a medical facility to transport a patient based on evaluating a portion of data related to at least one of a patient in need of emergency medical attention, a medical facility, and/or a route and respective traffic between the medical facility and the patient. In general, the match component can examine the portion of data to select a medical facility, a route or set of directions, and/or a patient needs (e.g., medical assets, specialists, etc.) based upon a patient location, traffic evaluation, a patient health condition, available medical support (e.g., devices, medicine, personnel, etc.), predicted traffic, patient authorized medical data, and/or patient status (e.g., vital signs, injury, etc.) (interpret to combine with emergency event possibility information of PEETERS)];
and determining, in the emergency Al server, the optimal transfer hospital based on the fitness of each candidate hospital [Gounares at Para. 0004],
wherein the transport resource availability information includes location information of each candidate hospital and further includes real-time traffic information and current position information of the ambulance device with respect to the real-time traffic information and the location information of each candidate hospital [Gounares at Para. 0037 teaches in general, the contingency component 402 can provide online adaptive flows based on real time monitoring of at least one of a patient, traffic, prediction of traffic, etc. to make decisions and updates based on information (e.g., stuck in traffic, construction, detours, etc.); Gounares at Para. 0047 teaches moreover, travel time can be analyzed based on predicting traffic along the route from the patient's location to the medical facility's location. For instance, characteristics such as personnel, staffing, assets (e.g., devices in a vehicle/facility, drugs, equipment, needles, oxygen, scanning devices, etc.), resources, patient flow, modes of transportation, and the like can be considered in determining if a medical facility suites a particular patient],
[ … ] … calculate the fitness of each candidate hospital during transport of the emergency patient [Gounares at Para. 0035 teaches the system 300 can further include a data store 310 that can include any suitable data related to a patient, an incident that requires medical assistance, an accident, a medical facility, traffic on a route between an accident and a medical facility, etc. It is to be appreciated that such information or data can be at least one of dynamically gathered, collected and periodically updated, and/or any suitable combination thereof. For example, a periodic update can be requested or retrieved from medical facilities based on duration of time. In general, the data store 310 can include, but not limited to including, patient data, accident or incident data, transportation/traffic data, medical facility information, and/or any suitable combination thereof.], … [ … ] … the machine learning performed in the plurality of machine learning models by continuously inputting data acquired from the emergency medical server and the ambulance device throughout the transport of the emergency patient, the data being input to an optimal hospital selection model of the plurality of machine learning models and including [Gounares at Para. 0035]
second data of the emergency event possibility information acquired from the ambulance device based on the status information, the second data received only during the transport of the emergency patient [Gounares at Para. 0022 teaches the match component 102 can evaluate the portion of data (e.g., patient data, medical facility data, and/or traffic data) such as, but not limited to, traffic patterns, previous traffic flows, databases of ambulance flows, medical facility assets (e.g., devices in a vehicle/facility, drugs, equipment, needles, oxygen, scanning devices, etc.), medical professional staffing (e.g., employees on staff, specialists, experts, surgeons, credentials of employees, etc.), patient status during transport, severity of injuries, a location of patient, a location of the emergency situation, received medical data (e.g., EMT report, initial prognosis, blood pressure, vital stats, heart rate, historic medical data, etc.), history of traffic patterns, emergency vehicle routes, directions, travel distance, and/or any other suitable data associated with transporting a patient to a medical facility 104 in an emergency situation],
and third data of the transport resource availability information acquired from the emergency medical server based on the location information of each candidate hospital, the real-time traffic information, and the current position information of the ambulance device with respect to the real-time traffic information, the third data received only from the emergency medical server [Gounares at Para. 0037 teaches the system 400 can employ a contingency component 402 that enables a pre-calculated or dynamically generated contingency plan for a patient being transported in an emergency situation. In general, the contingency component 402 can provide online adaptive flows based on real time monitoring of at least one of a patient, traffic, prediction of traffic, etc. to make decisions and updates based on information (e.g., stuck in traffic, construction, detours, etc.). In other words, the contingency component 402 can re-assess a transport for a patient based on a circumstance or event that may have been unexpected in a match initiated by the match component 108], and
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of AOKI, BAE, KIM, MARUYAMA, PEETERS with the fitness calculation of Gounares with the motivation to provide increased efficiency, decrease errors and costs, and more importantly save lives.
AOKI/BAE/KIM/MARUYAMA/PEETERS/Gounares do not teach wherein the emergency Al server is configured to perform machine learning to … [ … ] … includes a cloud server, and stores a plurality of machine learning models that are continuously updated,
Simoudis teaches wherein the emergency Al server is configured to perform machine learning to [Simoudis at Para. 0056 teaches the platform may be capable of generating a personalized transportation plan for a user, to process, recommend, and/or present personalized mobility data, routing data, scheduling data, traffic data, and many other forms of data. In some instances, machine learning techniques can be utilized to create a personalized transportation plan that includes predicted destinations, travel schedules (e.g., begin time, end time), options for transaction-based purchase of goods, services, and content during transportation, types of vehicles (e.g., types of autonomous vehicles such as sedans or vans, brands), types of transportation modes (e.g., autonomous vehicle, public transportation (such as train, light rail, or city bus), shuttle, ride-sharing, ride-hailing, shared trip or private trip, walking, bicycle, e-scooter, taxi, etc.), and others.] … [ … ] … includes a cloud server, and stores a plurality of machine learning models that are continuously updated [Simoudis at Para. 0058 teaches the one or more databases 107, 109 may utilize any suitable database techniques. For instance, structured query language (SQL) or “NoSQL” database may be utilized for storing the user profile data, historical data, predictive model or algorithms used for generating a personalized transportation plan, map or other data; Simoudis at Para. 0070 teaches FIG. 13 shows examples of data processed or used by the transportation plan generator 1107. The transportation plan generator may be configured to analyze user data, vehicle data (e.g., data stored in the vehicle database or real-time vehicle data), and third-party data, and use machine learning, planning, and reasoning algorithms to create a transportation plan that is tailored to each user on a day-by-day basis, and updated as-necessary in near real-time during the day],
It would have been prima facie obvious skill in the art, at the tim