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 .
DETAILED ACTION
In the amendment filed 27 March 2026:
Claims 11-20 are new
Claims 1-10 are amended
Claims 1-20 are pending
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 29 April 2026 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 14 recites “[…] … wherein the plurality of categories comprise: … [ …]”. Since the classification cannot be all of the categories at once, Claims should instead recite ““[…] … wherein the plurality of categories comprise one of more of: … [ …]”. For the purposes of the prior art rejection, Examiner interprets Claim 14 to be the proposed version for purpose of clarity.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 9 and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a method, system and non-transitory, computer-readable media, which are within a statutory category.
Step 2A1
The limitations of:
Claim 1, 9-10 (Claim 1 being representative)
obtaining electrocardiogram data of the emergency patient at preset time intervals;
calculating a plurality of prediction values over a period of time, wherein each prediction value corresponds to a likelihood of occurrence of each of a plurality of diseases based on the electrocardiogram data;
computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time;
responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values;
and selecting a target institution or facility suitable for treatment of the emergency patient based on the level of severity,
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. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to determine where to transport emergency patients in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “obtaining, calulcating, computing, and selecting” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. 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 claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a computing device including at least one processor and one of more non-transitory, computer-readable media that implements the identified abstract idea. The computing device including at least one processor and one of more non-transitory, computer-readable media are not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of using a pre-trained neural network model to determine where to send emergency patients. 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 determine where to send emergency patients merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computing device including at least one processor and one of more non-transitory, computer-readable media 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 determine where to send emergency patients 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. 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).
Claims 2-8,11-20 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) 2 merely describe(s) determining one or more gradients, which further defines the abstract idea.
Claim(s) 3 merely describe(s) selecting determination methods, which further defines the abstract idea.
Claim(s) 4 merely describe(s) determining one or more gradients, which further defines the abstract idea.
Claim(s) 5 merely describe(s) selecting the target institution, which further defines the abstract idea.
Claim(s) 6 merely describe(s) the type of patient, which further defines the abstract idea.
Claim(s) 7 merely describe(s) extracting candidate hospitals, which further defines the abstract idea.
Claim(s) 8 merely describe(s) determining the types of patients, which further defines the abstract idea.
Claim(s) 11 merely describe(s) obtaining and extracting electrocardiogram data, which further defines the abstract idea.
Claim(s) 12 merely describe(s) selecting the target institution, which further defines the abstract idea.
Claim(s) 13 merely describe(s) computing the level of severity, which further defines the abstract idea.
Claim(s) 14 merely describe(s) classifying level of severity, which further defines the abstract idea.
Claim(s) 15 merely describe(s) selecting the target institution, which further defines the abstract idea.
Claim(s) 16 merely describe(s) obtaining electrocardiogram data, which further defines the abstract idea.
Claim(s) 17 merely describe(s) the digital pointer, which further defines the abstract idea.
Claim(s) 18 merely describe(s) transmitting electrocardiogram data, which further defines the abstract idea.
Claim(s) 19 merely describe(s) generating commands, which further defines the abstract idea.
Claim(s) 20 merely describe(s) reading electrocardiogram data and managing the emergency patient, which further defines the abstract idea.
Claim(s) 20 also includes the additional element of “a first and second server” which is analyzed the same as the “a computing device” and does not provide a practical application or significantly more for the same reasons.
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 1-3, 9-12, 15, 19-20 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over CHANG et al (Foreign Publication EP-4027352-A1) in view of Cao et al (US Publication No. 20210369131) in view of SPAGNOLO et al (US Publication No. 20200176119) in view of Schmid et al (US Publication No. 20220084680).
Regarding Claim 1
CHANG teaches a method for providing a transport service for an emergency patient and monitoring of a hospitalized patient based on electrocardiograms, the method being performed by a computing device including at least one processor, the method comprising:
calculating a plurality of prediction values over a period of time, wherein each prediction value corresponds to a likelihood of occurrence of each of a plurality of diseases based on the electrocardiogram data by using a pre-trained neural network model [CHANG at Para. 0010 teaches determining a severity of the patient on the basis of the acquired status information; CHANG at Para. 0016 teaches Further, the status information of the emergency patient includes at least one of biosignal information, age information, complained symptom information, existing medical history information, consciousness information, and electrocardiogram information, the emergency event possibility information includes at least one of intensive care unit hospitalization possibility information, STEMI possibility information, UA + NSTEM possibility information, LVO possibility information, cerebral infarction and cerebral hemorrhage possibility information, return of spontaneous circulation possibility information, and cardiac arrest recurrence possibility information, and the transport resource availability information may include at least one of real-time traffic information, location information of each candidate hospital, current position information, available sickbed information of each candidate hospital, duty doctor information of each candidate hospital, facility information of each candidate hospital, air ambulance location information, and air ambulance operation information; CHANG at Para. 0055 teaches in this case, the algorithm utilized to create the severity determination model may include at least one of supervised learning, support vector machines (SVM), random forest (RF), naive bayes (NB), artificial neural networks (ANN), decision tree, and Bayesian, but is not limited thereto];
and selecting a target institution or facility suitable for treatment of the emergency patient based on the level of severity [CHANG at Para. 0066 teaches for example, the emergency AI server 3000 may generate and include an optimal hospital selection model in advance and input the determined severity of the patient, the emergency event possibility information, and the transport resource availability information to the optimal hospital selection model to calculate the suitability for each candidate hospital; CHANG at Para. 0068 teaches in step S270, the emergency AI server 3000 may select the optimal transfer hospital.].
CHANG does not teach obtaining electrocardiogram data of the emergency patient at preset time intervals;
computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time;
responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values;
Cao teaches obtaining electrocardiogram data of the emergency patient at preset time intervals [Cao at Para. 0165 teaches the electrocardiogram basic rule reference data, such as a minimum time interval between two heartbeats, and a minimum interval between the P wave and R wave, is generated according to the description of basic rules of cardiomyocytes electrophysiological activities and electrocardiogram clinical diagnosis in authoritative electrocardiogram textbooks, and which is used for subdividing the primary classification information after classification of the heartbeat mainly based on the RR interval between the heartbeats and a medical significance of different heartbeat signals on each lead];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine neural network model of CHANG with the electrocardiogram data of Cao with the motivation to improve the level for analyzing the electrocardiogram automatically [Cao at Para. 0007].
CHANG/Cao do not teach computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time;
responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values;
SPAGNOLO teaches computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time [SPAGNOLO at Para. 0036 teaches using patient specific models of underlying physiological functions, i.e., the patient avatar, as well as the patient's current and past states, the patient trajectory calculator assesses the patient's severity and predicts the natural course of the condition and how various interventions will affect one or more patient physiological functions. The interventions may be interventions that are specific to a particular condition or set of conditions. Alternately, the interventions may be common to most conditions, such as patient supportive care; SPAGNOLO at Para. 0048 teaches referring now to FIG. 4, the patient trajectory predictor 60 allows a clinician to understand the current trends that are associated with a patient. The trends include towards a healthy state, towards organ failure, towards a particular disease state, and combinations of the above. The patient trajectory predictor offers a holistic analysis of patient outcomes related to multiple interventions, organizes important considerations toward care, and considers how the patient has been trending from symptom presentation to hospital admission to current patient status];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao with the trend of SPAGNOLO with the motivation to improve patient outcomes.
CHANG/Cao/SPAGNOLO do not teach responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values;
Schmid teaches responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values [Schmid at Para. 0160 teaches the stroke specialist is thus enabled to prepare a provisional diagnosis as to whether the potential stroke patient has a stroke and a corresponding degree of severity based on the transmitted (and displayed) medical information (interpret to combine with prediction values of CHANG)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO with the pattern matching of Schmid with the motivation to improve the stroke classification.
Regarding Claim 2
CHANG/Cao/SPAGNOLO/Schmid teach the method of claim 1,
CHANG/Cao/SPAGNOLO/Schmid further teach wherein computing the level of severity is further based on determining one or more gradients of the plurality of prediction values over the period of time and-one or more characteristics of the plurality of diseases [SPAGNOLO at Para. 0036 teaches using patient specific models of underlying physiological functions, i.e., the patient avatar, as well as the patient's current and past states, the patient trajectory calculator assesses the patient's severity and predicts the natural course of the condition and how various interventions will affect one or more patient physiological functions. The interventions may be interventions that are specific to a particular condition or set of conditions. Alternately, the interventions may be common to most conditions, such as patient supportive care; SPAGNOLO at Para. 0048 teaches referring now to FIG. 4, the patient trajectory predictor 60 allows a clinician to understand the current trends that are associated with a patient. The trends include towards a healthy state, towards organ failure, towards a particular disease state, and combinations of the above. The patient trajectory predictor offers a holistic analysis of patient outcomes related to multiple interventions, organizes important considerations toward care, and considers how the patient has been trending from symptom presentation to hospital admission to current patient status (trends interpreted as gradients)].
Regarding Claim 3
CHANG/Cao/SPAGNOLO/Schmid teach the method of claim 2,
CHANG/Cao/SPAGNOLO/Schmid further teach wherein in determining the one or more gradients of the plurality of prediction values over the period of time comprises selecting one of a plurality of determination methods according to the one or more characteristics of the plurality of diseases [SPAGNOLO at Para. 0036, 0048 (see Claim 2 for explanation)].
Regarding Claim 9
CHANG teaches one or more non-transitory, computer-readable media storing instructions for providing a transport service for an emergency patient and monitoring of a hospitalized patient based on electrocardiograms thereon that cause one or more processors to perform operations comprising:
calculating a plurality of prediction values over a period of time, wherein each prediction value corresponds to a likelihood of occurrence of each of a plurality of diseases based on the electrocardiogram data by using a pre-trained neural network model [CHANG at Para. 0010, 0016, 0055 (see Claim 1 for explanation)];
and selecting a target institution or facility suitable for treatment of the emergency patient based on level of severity [CHANG at Para. 0066, 0068 (see Claim 1 for explanation)].
CHANG does not teach obtaining electrocardiogram data of the emergency patient at preset time intervals;
computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time;
responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values;
Cao teaches obtaining electrocardiogram data of the emergency patient at preset time intervals [Cao at Para. 0165 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine neural network model of CHANG with the electrocardiogram data of Cao with the motivation to improve the level for analyzing the electrocardiogram automatically [Cao at Para. 0007].
CHANG/Cao do not teach computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time;
responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values;
SPAGNOLO teaches computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time [SPAGNOLO at Para. 0036, 0048 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao with the trend of SPAGNOLO with the motivation to improve patient outcomes.
Schmid teaches responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values [Schmid at Para. 0160 (see Claim 1 for explanation)};
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO with the pattern matching of Schmid with the motivation to improve the stroke classification.
Regarding Claim 10
CHANG teaches a system for providing a transport service for an emergency patient and monitoring of a hospitalized patient based on electrocardiograms, the system comprising:
one or more processors [CHANG at Para. 0124 teaches the control unit 3300 may be implemented by at least one processor. For example, the control unit 3300 may be implemented by one processor or a plurality of processors];
at least one memory operably coupled to the one or more processors [CHANG at Para. 0121 teaches the storage unit 3200 may be implemented by a non-volatile storage medium which consistently stores arbitrary data. For example, the storage unit 3200 may include not only a disk, an optical disk, and a magneto-optical storage device, but also a flash memory and/or a battery back-up memory based storage device, but is not limited thereto];
and computer-executable instructions stored in the at least one memory, wherein the computer- executable instructions, when executed by the one or more processors, cause the system to perform operations comprising:
calculating a plurality of prediction values over a period of time, wherein each prediction value corresponds to a likelihood of occurrence of each of a plurality of diseases based on the electrocardiogram data by using a pre-trained neural network model [CHANG at Para. 0010, 0016, 0055 (see Claim 1 for explanation)];
and selecting a target institution or facility suitable for treatment of the emergency patient based on level of severity [CHANG at Para. 0066, 0068 (see Claim 1 for explanation)].
CHANG does not teach obtaining electrocardiogram data of the emergency patient at preset time intervals;
computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time;
responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values;
Cao teaches obtaining electrocardiogram data of the emergency patient at preset time intervals [Cao at Para. 0165 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine neural network model of CHANG with the electrocardiogram data of Cao with the motivation to improve the level for analyzing the electrocardiogram automatically [Cao at Para. 0007].
CHANG/Cao do not teach computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time;
responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values;
SPAGNOLO teaches computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time [SPAGNOLO at Para. 0036, 0048 (see Claim 1 for explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao with the trend of SPAGNOLO with the motivation to improve patient outcomes.
Schmid teaches responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values [Schmid at Para. 0160 (see Claim 1 for explanation)};
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO with the pattern matching of Schmid with the motivation to improve the stroke classification.
Regarding Claim 11
CHANG/Cao/SPAGNOLO/Schmid teach the one or more non-transitory, computer-readable media of claim 9,
CHANG/Cao/SPAGNOLO/Schmid further teach wherein the instructions for obtaining electrocardiogram data further cause the one or more processors to perform operations comprising:
extracting electrocardiogram features from the electrocardiogram data, wherein the electrocardiogram features include at least one of P waves, QRS complexes, and T waves [Cao at Para. 0160 teaches in the P wave and T wave feature detection module, the features of the P wave, T wave, and QRS complex are extracted by calculating a position of a segmentation point of the QRS complex and a position of a segmentation point of the P wave and the T wave, which may be realized by QRS complex segmentation point detection, single-lead PT detection algorithms and multi-lead PT detection algorithms respectively].
Regarding Claim 12
CHANG/Cao/SPAGNOLO/Schmid teach the one or more non-transitory, computer-readable media of claim 9,
CHANG/Cao/SPAGNOLO/Schmid further teach wherein the instructions for selecting the target institution or facility further cause the one or more processors to perform operations comprising:
computing a candidate hospital suitability based on at least one of emergency room congestion, emergency facility information, and medical staff information [CHANG at Para. 0063 teaches the transport resource availability information is information about a resource consumed to transport the emergency patient and may include at least one of real-time traffic information, location information of each candidate hospital, current position information, available sickbed information of each candidate hospital, duty doctor information of each candidate hospital, facility information of each candidate hospital, air ambulance location information, and air ambulance operation information; CHANG at Para. 0010 (see Claim 1 for explanation)].
Regarding Claim 19
CHANG/Cao/SPAGNOLO/Schmid teach the system of claim 10,
CHANG/Cao/SPAGNOLO/Schmid further teach wherein the computer-executable instructions further cause the one or more processors to perform operations comprising:
generating one or more commands for providing route guidance to a selected hospital for the transport service;
and transmitting the one or more commands [CHANG at Para. 0113 teaches when the emergency AI server 3000 determines an optimal transport route, the emergency AI server may provide the information to the ambulance device 1000].
Regarding Claim 20
CHANG/Cao/SPAGNOLO/Schmid teach the system of claim 10,
CHANG/Cao/SPAGNOLO/Schmid further teach wherein the system further comprises a first server and a second server, wherein the first server is configured to read the electrocardiogram data and is communicably connected to a local device and the second server [CHANG at Para. 0046 teaches the emergency medical server 2000 may include various servers which provide information related to the emergency patient. For example, the emergency medical server 2000 may include National Emergency Department Information Service (NEDIS) server. The emergency medical server 2000 may acquire and include various information related to the emergency medical service and provide the information to the ambulance device 1000 and/or the emergency AI server 3000; CHANG at Para. 0056 teaches in step S240, the emergency medical server 2000 may calculate emergency event possibility information using the status information of the emergency patient; CHANG at Para. 0051 teaches The status information of the emergency patient may include at least one of biosignal information, age information, complained symptom information, existing medical history information, consciousness information, and electrocardiogram information, but is not limited thereto],
and wherein the second server is configured to manage the emergency patient and is communicably connected to multiple emergency facilities [CHANG at Para. 0046; CHANG at Para. 0047 teaches for example, the emergency medical server 2000 may acquire and contain at least one of real-time traffic information, location information of each hospital, current position information, available sickbed information of each hospital, duty doctor information of each hospital, facility information of each candidate hospital, air ambulance location information, and air ambulance operation information, and may also acquire and include various information without being limited thereto. Further, the emergency medical server may provide the acquired information to the ambulance device 1000 and/or the emergency AI server 3000 (interpreted as connected to multiple emergency facilities)].
Claim 4, 13 rejected under 35 U.S.C. 103(a) as being unpatentable over CHANG, Cao, SPAGNOLO, Schmid as applied to claim 1, 9, 10 above, and further in view of MA et al (Foreign Publication CN-114098638-A).
Regarding Claim 4
CHANG/Cao/SPAGNOLO/Schmid teach the method of claim 3,
CHANG/Cao/SPAGNOLO/Schmid do not teach wherein the plurality of determination methods for determining the one or more gradients comprises one or more of:
a first method comprising determining whether the plurality of prediction values computed by the pre-trained neural network model are in an upward trend; or a second method comprising determining whether the plurality of prediction values is in a downward trend or repeatedly falls and rises.
Ma teaches wherein the plurality of determination methods for determining the one or more gradients comprises one or more of:
a first method comprising determining whether the plurality of prediction values computed by the pre-trained neural network model are in an upward trend; or a second method comprising determining whether the plurality of prediction values is in a downward trend or repeatedly falls and rises [MA at Page 11 Para 2 teaches these important features may suggest to the physician which patient states are of greater concern in real-time care, such as in the case of fig. 4 when hemoglobin decreases, oxygen saturation decreases, and urinary nitrogen increases occur simultaneously, indicating that the patient is about to have a trend toward an increase in the SOFA score, i.e., an increase in disease severity (interpret to combine with neural network of CHANG); MA at Page 12 Para 8 teaches S6, explaining the predicted SOFA scoring trend: and taking variables corresponding to weight values higher than a weight threshold value under a certain class of the patient state weight matrix or the drug weight matrix as important patient states or important drugs respectively, prompting the change trend of the disease severity of the patient according to the important patient states, and obtaining a direct reason for the SOFA score increase according to the important drugs and the drug-related knowledge graph].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO, Schmid with the trend of MA with the motivation to improve disease prediction.
Regarding Claim 13
CHANG/Cao/SPAGNOLO/Schmid teach the one or more non-transitory, computer-readable media of claim 9,
CHANG/Cao/SPAGNOLO/Schmid do not teach wherein the level of severity is further computed based on a slope of a prediction value curve, a deviation of the plurality of prediction values past a cutoff, or an area of a prediction value curve.
MA teaches wherein the level of severity is further computed based on a slope of a prediction value curve, a deviation of the plurality of prediction values past a cutoff, or an area of a prediction value curve [MA at Page 11 Para 2 teaches these important features may suggest to the physician which patient states are of greater concern in real-time care, such as in the case of fig. 4 when hemoglobin decreases, oxygen saturation decreases, and urinary nitrogen increases occur simultaneously, indicating that the patient is about to have a trend toward an increase in the SOFA score, i.e., an increase in disease severity (interpret to combine with neural network of CHANG); MA at Page 12 Para 8 teaches S6, explaining the predicted SOFA scoring trend: and taking variables corresponding to weight values higher than a weight threshold value under a certain class of the patient state weight matrix or the drug weight matrix as important patient states or important drugs respectively, prompting the change trend of the disease severity of the patient according to the important patient states, and obtaining a direct reason for the SOFA score increase according to the important drugs and the drug-related knowledge graph].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO, Schmid with the severity of MA with the motivation to improve disease prediction.
Claims 5, 8 rejected under 35 U.S.C. 103(a) as being unpatentable over CHANG, Cao, SPAGNOLO, Schmid as applied to claim 1, 9, 10 above, and further in view of Op Den Buijs et al (US Publication No. 10593000).
Regarding Claim 5
CHANG/Cao/SPAGNOLO/Schmid teach the method of claim 1,
CHANG/Cao/SPAGNOLO/Schmid further teach wherein selecting the target institution or facility suitable for treatment of the emergency patient based on level of severity comprises:
[ … ] … (ii) location and traffic information including at least one of a location of the emergency patient, real-time traffic conditions, or a number of ambulances heading to each hospital [CHANG at Para. 0047 teaches for example, the emergency medical server 2000 may acquire and contain at least one of real-time traffic information, location information of each hospital, current position information, available sickbed information of each hospital, duty doctor information of each hospital, facility information of each candidate hospital, air ambulance location information, and air ambulance operation information, and may also acquire and include various information without being limited thereto];
computing, for each of the candidate hospitals, a corresponding degree of suitability based on capacity information including at least one of emergency room congestion, emergency facility information, or medical staff information of each of the candidate hospitals [CHANG at Para. 0047];
and determining a transport plan according to an order of priority of the candidate hospitals based on corresponding degrees of suitability [CHANG at Para. 0047].
CHANG/Cao/SPAGNOLO/Schmid do not teach classifying the emergency patient as either (i) a first type of patient who requires a specific procedure or (ii) a second type of patient who does not require a specific procedure based on (a) a severity classification used by emergency personnel and medical staff and (b) the level of severity computed based on the plurality of prediction values;
extracting candidate hospitals for treatment of the emergency patient from a group of hospitals based on
(i) classification of the emergency patient as a first type of patient or a second type of patient and … [ … ]
Op Den Buijs teaches classifying the emergency patient as either (i) a first type of patient who requires a specific procedure or (ii) a second type of patient who does not require a specific procedure based on (a) a severity classification used by emergency personnel and medical staff and (b) the level of severity computed based on the plurality of prediction values [Op Den Buijs at Para. 0019 teaches the information can indicate the different levels of intensity of the treatment program that are available, the medications (including the amounts and frequency) to be administered at each level, the test(s) required to be undertaken on or by the patient, whether hospital admission is required and/or whether surgical intervention is required (interpreted as specific procedure)];
extracting candidate hospitals for treatment of the emergency patient from a group of hospitals based on
(i) classification of the emergency patient as a first type of patient or a second type of patient and [Op Den Buijs at Para. 0019 teaches the information can indicate the different levels of intensity of the treatment program that are available, the medications (including the amounts and frequency) to be administered at each level, the test(s) required to be undertaken on or by the patient, whether hospital admission is required and/or whether surgical intervention is required (interpreted as specific procedure)] … [ … ]
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO, Schmid with the classification of Op Den Buijs with the motivation to reduce computation time of disease detection.
Regarding Claim 8
CHANG/Cao/SPAGNOLO/Schmid/Op Den Buijs teach the method of claim 5,
CHANG/Cao/SPAGNOLO/Schmid/Op Den Buijs further teach wherein computing a degree of suitability of a candidate hospital comprises providing a communication service configured to inquire the candidate hospital about whether to accept the emergency patient, wherein the candidate hospitals are inquired according to the order of priority [CHANG at Para. 0098 teaches in step S430, the emergency AI server 3000 may inquire the determined optimal transfer hospital about whether to accept the patient together with the weight information of the emergency patient. Further, the emergency AI server may receive a response to the inquiry from the hospital (interpreted to be based on order on priority)].
Claim 6 rejected under 35 U.S.C. 103(a) as being unpatentable over CHANG, Cao, SPAGNOLO, Schmid, Op Den Buijs as applied to claim 5 above, and further in view of of Aoki et al (Foreign Publication JP-6052875-B2).
Regarding Claim 6
CHANG/Cao/SPAGNOLO/Schmid/Op Den Buijs teach the method of claim 5,
CHANG/Cao/SPAGNOLO/Schmid/Op Den Buijs do not teach wherein the first type of patient includes at least one of (i) a patient who requires a procedure that can only be performed at a specific hospital or by specific medical staff or (ii) a patient whose level of severity is determined to be higher than a preset reference for each of the plurality of diseases.
Aoki teaches wherein the first type of patient includes at least one of (i) a patient who requires a procedure that can only be performed at a specific hospital or by specific medical staff or (ii) a patient whose level of severity is determined to be higher than a preset reference for each of the plurality of diseases [Aoki at Page 11 Para 7 teaches furthermore, when the transport destination for a specific disease or condition is determined in consideration of regional characteristics, the corresponding hospital is selected. In this example, when the child is a pediatric disease (S53A), the child corresponding hospital is determined as the transport destination (S54A), and when the patient is a special disease (S53B), the special disease corresponding hospital is determined as the transport destination ( S54B) In the case of mother transport (S53C), a mother transport compatible hospital is determined as the transport destination (S54C), and in the case of trauma (S55), other hospitals are selected as transport destinations.].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO, Schmid, Op Den Buijs with the procedure of Aoki with the motivation to improve patient prognosis.
Claim 7 rejected under 35 U.S.C. 103(a) as being unpatentable over CHANG, Cao, SPAGNOLO, Schmid, Op Den Buijs as applied to claim 5 above, and further in view of TAKIGAHIRA et al (Foreign Publication JP-2012208667-A).
Regarding Claim 7
CHANG/Cao/SPAGNOLO/Schmid/Op Den Buijs teach the method of claim 5,
CHANG/Cao/SPAGNOLO/Schmid/Op Den Buijs do not teach wherein extracting the candidate hospitals for the treatment of the emergency patient from the group of hospitals comprises:
computing an expected transport time information for the candidate hospitals;
and extracting the candidate hospitals based on the expected transport time information.
TAKIGAHIRA teaches wherein extracting the candidate hospitals for the treatment of the emergency patient from the group of hospitals comprises:
computing an expected transport time information for the candidate hospitals [TAKIGAHIRA at Page 3 Para 1 teaches the estimated time required for each transport destination candidate is calculated for each carrier candidate, and (7) the predicted time required for each route to each transport destination candidate by each transport candidate is compared with the doctor];
and extracting the candidate hospitals based on the expected transport time information [TAKIGAHIRA at Page 3 Para 1 teaches candidate selection means for preferentially selecting those with a short contact time with a patient, and (8) a communication means for giving a requester the estimated required time of each route to each transport destination candidate by each transport candidate].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO, Schmid, Op Den Buijs with the transport time of TAKIGAHIRA with the motivation to improve the survival rate and improve the prognosis.
Claim 14 rejected under 35 U.S.C. 103(a) as being unpatentable over CHANG, Cao, SPAGNOLO, Schmid as applied to claim 1, 9-10 above, and further in view of KIDO et al (Foreign Publication WO-2011152012-A1).
Regarding Claim 14
CHANG/Cao/SPAGNOLO/Schmid teach the one or more non-transitory, computer-readable media of claim 9,
CHANG/Cao/SPAGNOLO/Schmid do not teach wherein the instructions further cause the one or more processors to perform operations comprising classifying the level of severity into a plurality of categories, wherein the plurality of categories comprise:
a first category indicative of an upward trend of the plurality of prediction values;
a second category indicative of variation of the plurality of prediction values without the upward trend and below upper limit;
a third category indicative of variation of the plurality of prediction values between the upper limit and a lower limit;
and a fourth category indicative of variation of the plurality of prediction values below the lower limit.
KIDO teaches wherein the instructions further cause the one or more processors to perform operations comprising classifying the level of severity into a plurality of categories, wherein the plurality of categories comprise:
a first category indicative of an upward trend of the plurality of prediction values;
a second category indicative of variation of the plurality of prediction values without the upward trend and below upper limit;
a third category indicative of variation of the plurality of prediction values between the upper limit and a lower limit;
and a fourth category indicative of variation of the plurality of prediction values below the lower limit [KIDO at Page 9 Para 2 teaches in the disease severity test method of the present invention, the severity can be evaluated by the following method, specifically from the amount of ATP in the measured sample. In humans, it can be determined that the amount of ATP in a sample is abnormal when the lower limit of the normal value is less than 0.52 mM. Furthermore, the severity of illness in humans can be determined as (1) mild abnormality and (2) severe abnormality according to the value of ATP amount when the ATP amount is less than the lower limit of normal value 0.52 mM (interpreted as fourth category)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO, Schmid with the categories of KIDO with the motivation to better calculate severity of illness.
Claim 15-17 rejected under 35 U.S.C. 103(a) as being unpatentable over CHANG, Cao, SPAGNOLO, Schmid as applied to claim 1, 9-10 above, and further in view of Hathorn et al (US Publication No. 20140316812).
Regarding Claim 15
CHANG/Cao/SPAGNOLO/Schmid teach the one or more non-transitory, computer-readable media of claim 9,
CHANG/Cao/SPAGNOLO/Schmid further teach wherein selecting the target institution or facility suitable for treatment of the emergency patient is based on traffic information and location of the emergency patient in addition to the level of severity [CHANG at Para. 0010 (see Claim 1 for explanation)].
Regarding Claim 16
CHANG/Cao/SPAGNOLO/Schmid teach the system of claim 10,
CHANG/Cao/SPAGNOLO/Schmid do not teach wherein the computer-executable instructions for obtaining the electrocardiogram data further cause the one or more processors to perform operations comprising transmitting a digital pointer to at least one of the emergency patient, a guardian, or an emergency medical technician configured to establish connection to a local device.
Hathorn teaches wherein the computer-executable instructions for obtaining the electrocardiogram data further cause the one or more processors to perform operations comprising transmitting a digital pointer to at least one of the emergency patient, a guardian, or an emergency medical technician configured to establish connection to a local device [Hathorn at Para. 0055 teaches also, Lifesquare, Inc. (Menlo Park, Calif.) (lifesquare.com) also provides a QR code-based, webhosted, emergency first responder medical information system that securely relays essential health information to paramedics in an emergency (interpret to combine with electrocardiogram data of Cao, paramedic interpreted as emergency medical technician)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO, Schmid with the qr code of Hathorn with the motivation to improve patient intake efficiency.
Regarding Claim 17
CHANG/Cao/SPAGNOLO/Schmid/Hathorn teach the system of claim 16,
CHANG/Cao/SPAGNOLO/Schmid/Hathorn further teach wherein the digital pointer is a link or a QR code [Hathorn at Para. 0055 (see Claim 16 for explanation)].
Claim 18 rejected under 35 U.S.C. 103(a) as being unpatentable over CHANG, Cao, SPAGNOLO, Schmid as applied to claim 1,9-10 above, and further in view of Brown et al (US Publication No. 20100017471).
Regarding Claim 18
CHANG/Cao/SPAGNOLO/Schmid teach the system of claim 10,
CHANG/Cao/SPAGNOLO/Schmid do not teach wherein the computer-executable instructions further cause the one or more processors to perform operations comprising:
transmitting the electrocardiogram data, prediction values, and the level of severity to a selected hospital upon arrival by the emergency patient.
Brown teaches wherein the computer-executable instructions further cause the one or more processors to perform operations comprising:
transmitting the electrocardiogram data, prediction values, and the level of severity to a selected hospital upon arrival by the emergency patient [Brown at Para. 0015 teaches more particularly, the present disclosure relates to novel and advantageous systems and methods for transmitting EKG and defibrillator data from an EMS vehicle over a network to a hospital or the like that result in quicker and higher quality transmissions of EKG or defibrillator data from the EMS vehicle, and in most cases prior to the patient's arrival at a hospital].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of CHANG, Cao, SPAGNOLO, Schmid with the data transmission of Brown with the motivation to improve emergency care [Brown at Para. 0007].
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
That is, MPEP § 2106.05(a) has now been updated to require that an examiner (a) first identify if the specification and disclosed invention improves (i) technology or (ii) a technical field and (b) secondly, determine if the claim reflects the improvement. Applicant submits that the claims as amended are directed to a technical improvement at least by conserving computational resources and improving latency.
Regarding (a), the Examiner respectfully disagrees. It is unclear to one skilled in the art how the claims conserve computational resources or reduces latency. The computers are not performing any actions outside of their normal functions. Furthermore, while the Specification recites conserving resources, it is unclear to one skilled in the art how the claims lead to the statements made; there is nothing in the claim that a person having skill in the art would understand to result in conservation of computational reduced latency.
Accuracy of the plurality of prediction values is crucial. In furtherance of accuracy, one solution may be to continuously update calculation of the plurality prediction values. However, consistently and continuously processing such ECG data can be computationally burdensome due to the vast amount of data itself that needs to be transmitted and processed.
Instead, the claims, as amended, address such technical shortcomings by: "responsive to determining that the plurality of prediction values matches a pattern, causing finalization of calculation of the plurality of prediction values." As a non-limiting example, when the prediction values fall below a lower limit for a given time, the system may finalize computation of prediction values (see, e.g., paragraph [0063]). This may be because the patient is determined to have consistently low likelihood of disease. Similarly, as another non-limiting example, if the plurality of prediction values matches a particular pattern known to be associated with a certain level of severity, the system may finalize computation of prediction values. By discerning when to finalize calculation of prediction values, computational resource can be conserved while maintaining necessary accuracy of prediction values.
Regarding (b), the Examiner respectfully disagrees. While the Specification mentions accuracy, it is unclear to one skilled in the art how the claims improve the accuracy. If no technical improvement can be found, no practical application can be found. Further, improved accuracy appears to be an improvement to the abstract idea; which is not an “improvement” within the meaning of the word.
Further, the claims as amended recite "computing a level of severity for classification of the emergency patient based at least in part on the plurality of prediction values over the period of time," and "selecting a target ... facility suitable for treatment of the emergency patient based on the level of severity." By taking a plurality of prediction values over a period of time and distilling such vast data by computing a level of severity, a target facility can be selected faster, thereby reducing latency.
Regarding (c), the Examiner respectfully disagrees. There is no support in the Specification that supports the statement made that the claims improve latency. Further, (a) there is no “vast amount of data” claimed and (b) selecting a target facility faster is an improvement to the abstraction, which cannot provide a practical application or significantly more.
Rejection under 35 U.S.C. 103
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
SPURLOCK et al (Foreign Publication WO-2019071098-A2) discloses a method of predicting health status
NAIR et al (Foreign Publication WO-2011053333-A1) discloses a system and method for real time capture and communication of in-transit patient medical information.
HART et al (Foreign Publication WO-2021202467-A1) discloses a system for integrating medical device case filed with corresponding electronic patient care records.
THIS ACTION IS MADE FINAL, necessitated by amendment. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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