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 .
Notice to Applicant
Claims 1- 28 have been examined in this application. This communication is the first action on the merits. Information Disclosure Statement (IDS) filed 12/04/2023 is acknowledged.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “unit(s)” and “module” in claims 1 -10 and 19-21.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Examiner suggests amended claim language such as “A business operations associated device comprising a computer system, executing a program comprising:” to avoid 112f claim interpretation and 112b issues.
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.
Claims 1 -10 and 19 -28 are 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 pre-AIA the applicant regards as the invention.
Claim limitation “data collection unit”, “data processing unit”, “model generation unit”, “information collection unit”, “determination module” and “validation module” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2-10 depend from claim 1, and claims 20-28 depend from claim 19 and are rejected for same reasons as claim 1 and claim 19.
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- 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-28 are directed to hyperkalemia prediction.
Claim 1 and Claim 19 recite a system for hyperkalemia prediction and Claim 11 recites a method for hyperkalemia prediction, which include collecting electrocardiogram data of multiple hyperkalemia patients; generating a training dataset for machine learning based on the electrocardiogram data collected; and constructing a neural network model for predicting a hyperkalemia using an electrocardiogram based on the training dataset provided (Claim 1 and Claim 11). A smart band worn by a user, and measuring an electrocardiogram of the user; collecting information on the electrocardiogram of the user measured by the smart band; and determining whether the user has thee hyperkalemia by applying the electrocardiogram of the user collected to a neural network model pre-constructed to predict the hyperkalemia according to the electrocardiogram (Claim 19).
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing personal behavior. The recitation of “data collection unit”, “data processing unit”, “model generation unit”, “information collection unit”, “determination module” and “smart band”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing personal behavior. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “data collection unit”, “data processing unit”, “model generation unit”, “information collection unit”, “determination module” and “smart band “is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 11 and claim 19 recite using one or more machine learning analysis techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning processing is solely used a tool to perform the instructions of the abstract idea. Accordingly, the 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 also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in prediction analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “data collection unit”, “data processing unit”, “model generation unit”, “information collection unit”, and “determination module” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-10, 12-18 and 20-28 recite collects electrocardiogram data of patients who have developed symptoms of hyperkalemia; collects electrocardiogram data of the hyperkalemia patient data from a time before a first preset reference time from the time of the symptom onset of the hyperkalemia to a time after a second preset reference time from the time of the symptom onset of the hyperkalemia; classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into abnormal-state data, and classifies the ECG data from the time of the symptom onset of the hyperkalemia to a time before a first preset reference time into normal-state data, and generates the training dataset to include the normal- state data and the abnormal-state data which are classified; classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into abnormal-state data, and classifies the ECG data from the time of the symptom onset of the hyperkalemia to a time after a second preset reference time into normal-state data, and generates the training dataset to include the normal- state data and the abnormal-state data which are classified; classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into the abnormal-state data, and also classifies, into the normal-state data, ECG data other than the ECG data among the ECG data from the time before the first preset reference time from the time of the symptom onset of the hyperkalemia to the time after the second preset reference time from the time of the symptom onset of the hyperkalemia, and generates the training dataset to include the normal-state data and the abnormal-state data which are classified; classifies some randomly selected data of the data classified from the ECG data provided by the data collection unit into the training dataset, and classifies the remaining data into a sample set for testing or validating the neural network model; wherein the ECG data is ECG lead II signals data; wherein the symptom of the hyperkalemia is chronic renal failure (CRF); analyzing the performance of the neural network model by applying the sample set to the neural network model; wherein the smart band is worn on a wrist of the user; constructing the neural network mode using electrocardiogram data of the hyperkalemia patient, and providing the constructed neural network model to the determination module; collecting electrocardiogram data of multiple hyperkalemia patients; a data processing unit generating a training dataset for machine learning based on the electrocardiogram data collected by the data processing unit; and a model generation unit constructing a neural network model for predicting a hyperkalemia using an electrocardiogram based on the training dataset provided by the data processing unit; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 11 and 19. Regarding Claims, 2-8, and 21-27, and the additional elements of “data collection unit”, “data processing unit”, “model construction unit”; “determination module”; “ model generation unit “; it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claims 7, 10, 17-18, 21-22, and 27-28 and the additional element of neural network- the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-28 are rejected under 35 U.S.C. 103 as being unpatentable over Yoon Duk Yong, KR 20190066332A, [hereinafter Yoon], in view of Galloway, Conner D., et al. "Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram." JAMA cardiology 4.5 (2019): 428-436, [hereinafter Galloway].
Regarding Claim 1,
Yoon teaches
A system for constructing a hyperkalemia prediction algorithm through an electrocardiogram, the system comprising: a data collection unit collecting electrocardiogram data of multiple hyperkalemia patients (Yoon Par. 1-3-The present invention relates to an apparatus and method for predicting potassium concentration in a blood using electrocardiographic data, The present invention relates to an apparatus and method for predicting potassium concentration in a blood using electrocardiogram (ECG) data, and more particularly, to a method and apparatus for generating a predicted model of potassium concentration in a blood using ECG data, And a method for generating a blood potassium concentration prediction model using electrocardiogram data for generating a prediction model. Hyperkalemia is an electrolyte displacement that can lead to fatal heart arrhythmias. Proper management of hyperkalemia is becoming more important because of the increased incidence of hyperkalemia-related diseases such as diabetes, coronary artery disease, and chronic kidney disease. Hyperkalemia and changes in hypokalemia or potassium levels are all associated with increased risk of death and life-threatening arrhythmias. Mortality, hospitalization and mortality in patients with renal or cardiac disease can follow a gradual change in potassium levels.”) ;
a data processing unit generating a training dataset for machine learning based on the electrocardiogram data collected by the data collection unit (Yoon Par. 19-20- Preferably, the prediction model generation apparatus may further include a learning evaluator that evaluates the degree of learning of the potassium concentration prediction model using a loss function, and learns the potassium concentration prediction model by an error inversion method. According to another aspect of the present invention, there is provided a method of predicting a blood potassium concentration prediction model using electrocardiogram data, the preprocessor dividing each of a plurality of electrocardiogram data mapped with potassium concentration into predetermined lengths, Generating a training data set by minimizing-maximum normalizing each of the electrocardiogram data, generating a training data set, extracting feature points from each training data, and analyzing the extracted feature points in a time-series manner to generate a potassium concentration prediction model .”);
and a model generation unit constructing a neural network model for predicting…based on the training dataset provided by the data processing unit. (Yoon Par. 18-20- Preferably, the predictive model generator includes at least one CNN (Convolution Neural Network) layer for extracting feature points from each training data, an RNN (Recurrent Neural Network) for sequentially calculating the potassium concentration by sequentially receiving the feature points extracted from the CNN layer, Network layer.)
Yoon teaches prediction analysis and the feature is expounded upon by Galloway:
… predicting a hyperkalemia using an electrocardiogram ... (Galloway Pg. 429- Question Can a deep-learning model classify hyperkalemia from the electrocardiogram (ECG) in patients with chronic kidney disease? Findings In this validation study, a deep neural network was trained using more than 1.5 million ECGs recorded from 1994 to 2017 from approximately 450 000 patients seen at the Mayo Clinic in Minnesota and validated on nearly 62 000 ECGs from the Mayo Clinic in Minnesota, Florida, and Arizona. Using 2 or 4 ECG leads, a deep-learning model detected hyperkalemia with high sensitivity and negative predictive value, with an area under the curve between 0.853 and 0.901. Meaning Deep learning may enable noninvasive screening for hyperkalemia in at-risk patients with chronic kidney disease ;Pg 432-433.“ The number of false-positive, false-negative, true positive, and true-negative results for each model, as well as accuracy, is presented in eTable 3 in the Supplement. Between50% and70%of patients in the validation data sets did not have hyperkalemia predicted by the DLM, with less than 1% of all test results being false-negative; on the other hand, up to 42% of all test results were false-positive,
Yoon and Galloway are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Yoon, as taught by Galloway, by utilizing hyperkalemia analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Yoon with the motivation of improving the detection of hyperkalemia (Galloway Pg. 429).
Regarding Claim 2 and Claim 12, Yoon in view of Galloway teach The system of claim 1,… and The method of claim 11,…
wherein the data collection unit collects electrocardiogram data of patients who have developed symptoms of hyperkalemia. (Yoon Pg. 9- Doctors have failed to evaluate the potassium concentration using EKG information. This may be because it is somewhat difficult for humans to determine the cutoff because the change in ECG pattern is continuous and minimal compared to changes in serum potassium concentration. However, since the potassium concentration prediction model according to the present invention can accurately measure changes in the potassium concentration pattern using electrocardiographic data and consistently determines the cutoff, the potassium concentration and the normal range of hyperkalemia are determined, The concentration itself can be evaluated.)
Regarding Claim 3 and Claim 13, Yoon in view of Galloway teach The system of claim 2,… and The method of claim 12,…
wherein the data collection unit collects electrocardiogram data of the hyperkalemia patient data from a time before a first preset reference time from the time of the symptom onset of the hyperkalemia to a time after a second preset reference time from the time of the symptom onset of the hyperkalemia. (Yoon Pg. 4- The collecting unit 111 stores the electrocardiogram data at predetermined time intervals (for example, every 10 seconds), and the electrocardiogram data in which the potassium concentration values are matched can be used for the analysis.)
Regarding Claim 4 and Claim 14, Yoon in view of Galloway teach The system of claim 2,… and The method of claim 12,…
wherein the data processing unit classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into abnormal-state data, and classifies the ECG data from the time of the symptom onset of the hyperkalemia to a time before a first preset reference time into normal-state data, and generates the training dataset to include the normal- state data and the abnormal-state data which are classified. (Yoon Pg. 5- The classifying unit 116 classifies some of the electrocardiogram data normalized by the normalizing unit 115 into a training data set. In order to generate a prediction model for predicting the potassium concentration using the electrocardiogram data, a training data set is required, and overfitting of a prediction model for a training data set is detected and data for setting a hyperparameter And need data to assess the generalizability of the predictive model. The classification unit 116 may classify the normalized electrocardiogram data into a training data set, a test data set, and a validity verification data set at a predetermined ratio (e.g., 8: 1: 1). The training data set is used for prediction model generation and the test data set is used for overfitting detection and hyperparameter setting of the prediction model and the validation data set can be used to evaluate the generalization possibility of the prediction model have.; Pg. 8- The predictive model generation device classifies training data sets, test data sets, and validity data sets by removing no potassium concentration data, uninterpretable noise data, and outlier data from the electrocardiogram data collected from 531 patients, respectively do. As a result, 1,071,000 electrocardiogram data from 425 patients, 106,615 ECG data from 53 patients, and 137,165 ECG data from 53 patients, which are in turn sorted into training data sets, test data sets, and validation data sets Lt; I RTI > At this time, the predictive model generation device assumes that 1,065,000 electrocardiogram data are finally registered in 425 patients by adjusting the potassium concentration distribution through resampling among the training data sets.)
Regarding Claim 5 and Claim 15, Yoon in view of Galloway teach The system of claim 2,… and The method of claim 12,…
wherein the data processing unit classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into abnormal-state data, and classifies the ECG data from the time of the symptom onset of the hyperkalemia to a time after a second preset reference time into normal-state data, and generates the training dataset to include the normal- state data and the abnormal-state data which are classified. (Yoon Pg. 5- The classifying unit 116 classifies some of the electrocardiogram data normalized by the normalizing unit 115 into a training data set. In order to generate a prediction model for predicting the potassium concentration using the electrocardiogram data, a training data set is required, and overfitting of a prediction model for a training data set is detected and data for setting a hyperparameter And need data to assess the generalizability of the predictive model. The classification unit 116 may classify the normalized electrocardiogram data into a training data set, a test data set, and a validity verification data set at a predetermined ratio (e.g., 8: 1: 1). The training data set is used for prediction model generation and the test data set is used for overfitting detection and hyperparameter setting of the prediction model and the validation data set can be used to evaluate the generalization possibility of the prediction model have.; Pg. 8- The predictive model generation device classifies training data sets, test data sets, and validity data sets by removing no potassium concentration data, uninterpretable noise data, and outlier data from the electrocardiogram data collected from 531 patients, respectively do. As a result, 1,071,000 electrocardiogram data from 425 patients, 106,615 ECG data from 53 patients, and 137,165 ECG data from 53 patients, which are in turn sorted into training data sets, test data sets, and validation data sets Lt; I RTI > At this time, the predictive model generation device assumes that 1,065,000 electrocardiogram data are finally registered in 425 patients by adjusting the potassium concentration distribution through resampling among the training data sets.)
Regarding Claim 6 and Claim 16, Yoon in view of Galloway teach The system of claim 2,… and The method of claim 12,…
wherein the data processing unit classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into the abnormal-state data, and also classifies, into the normal-state data, ECG data other than the ECG data among the ECG data from the time before the first preset reference time from the time of the symptom onset of the hyperkalemia to the time after the second preset reference time from the time of the symptom onset of the hyperkalemia, and generates the training dataset to include the normal-state data and the abnormal-state data which are classified. (Yoon Pg. 5- The classifying unit 116 classifies some of the electrocardiogram data normalized by the normalizing unit 115 into a training data set. In order to generate a prediction model for predicting the potassium concentration using the electrocardiogram data, a training data set is required, and overfitting of a prediction model for a training data set is detected and data for setting a hyperparameter And need data to assess the generalizability of the predictive model. The classification unit 116 may classify the normalized electrocardiogram data into a training data set, a test data set, and a validity verification data set at a predetermined ratio (e.g., 8: 1: 1). The training data set is used for prediction model generation and the test data set is used for overfitting detection and hyperparameter setting of the prediction model and the validation data set can be used to evaluate the generalization possibility of the prediction model have.; Pg. 8- The predictive model generation device classifies training data sets, test data sets, and validity data sets by removing no potassium concentration data, uninterpretable noise data, and outlier data from the electrocardiogram data collected from 531 patients, respectively do. As a result, 1,071,000 electrocardiogram data from 425 patients, 106,615 ECG data from 53 patients, and 137,165 ECG data from 53 patients, which are in turn sorted into training data sets, test data sets, and validation data sets Lt; I RTI > At this time, the predictive model generation device assumes that 1,065,000 electrocardiogram data are finally registered in 425 patients by adjusting the potassium concentration distribution through resampling among the training data sets.)
Regarding Claim 7 and Claim 17, Yoon in view of Galloway teach The system of claim 4,… and The method of claim 14,…
wherein the data processing unit classifies some randomly selected data of the data classified from the ECG data provided by the data collection unit into the training dataset, and classifies the remaining data into a sample set for testing or validating the neural network model (Yoon Pg. 5- The balancing processor performs sampling so that the potassium concentration is uniformly distributed in the training data set. That is, the balancing processor classifies the category of the training data according to the potassium concentration mapped to each training data, based on the category in which the potassium concentration is divided into a certain number of categories, samples the electrocardiogram data for each of the classified categories Potassium concentration can be distributed in a balanced manner. At this time, the balancing processor may randomly extract the electrocardiogram data for each category, and the electrocardiogram data for each category may be increased from the number before the sampling. For example, if the collector samples electrocardiogram data at a 250 kHz rate, the balancing processor can sample training data for each category at a 125 Hz rate. Therefore, the balancing processor can increase the training data by the number of electrocardiogram data before re-sampling in units of categories..)
Regarding Claim 8 Yoon in view of Galloway teach The system of claim 1,… wherein the ECG data collected by the data collection unit is ECG lead II signals data. (Yoon Pg. 9- Further, the electrocardiographic data used in the present invention may have a more important clinical implication by using only the lead II ECG signal, which is a more practical ECG signal than the conventional V3, V4 or V5 in actual setting. That is, in the related art, one electrocardiogram signal including the T wave most conspicuous among V3, V4, or V5 was selected and used, that is, three lead signals were required. The present invention uses only the lead II signal, There are more important clinical implications because they are the most commonly used in the real world.)
Regarding Claim 9,
Yoon teaches prediction analysis and the feature is expounded upon by Galloway:
wherein the symptom of the hyperkalemia is chronic renal failure (CRF) (Galloway Pg.428- For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.
Yoon and Galloway are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Yoon, as taught by Galloway, by utilizing hyperkalemia analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Yoon with the motivation of improving the detection of hyperkalemia (Galloway Pg. 429).
Regarding Claim 10 and Claim 18, Yoon in view of Galloway teach The system of claim 7,… and The method of claim 17,…
further comprising: a validation module analyzing the performance of the neural network model by applying the sample set to the neural network model. (Yoon Pg. 5- The training data set is used for prediction model generation and the test data set is used for overfitting detection and hyperparameter setting of the prediction model and the validation data set can be used to evaluate the generalization possibility of the prediction model have.)
Regarding Claim 11,
Yoon teaches
A method for constructing a hyperkalemia prediction algorithm through an electrocardiogram, the method comprising: a data collection step of colleting, by a data collection unit, electrocardiogram data of multiple hyperkalemia patients; (Yoon Par. 1-3-The present invention relates to an apparatus and method for predicting potassium concentration in a blood using electrocardiographic data, The present invention relates to an apparatus and method for predicting potassium concentration in a blood using electrocardiogram (ECG) data, and more particularly, to a method and apparatus for generating a predicted model of potassium concentration in a blood using ECG data, And a method for generating a blood potassium concentration prediction model using electrocardiogram data for generating a prediction model. Hyperkalemia is an electrolyte displacement that can lead to fatal heart arrhythmias. Proper management of hyperkalemia is becoming more important because of the increased incidence of hyperkalemia-related diseases such as diabetes, coronary artery disease, and chronic kidney disease. Hyperkalemia and changes in hypokalemia or potassium levels are all associated with increased risk of death and life-threatening arrhythmias. Mortality, hospitalization and mortality in patients with renal or cardiac disease can follow a gradual change in potassium levels.”) ;
a data processing step of generating, by a data processing unit, a training dataset for machine learning based on the electrocardiogram data collected in the data collection step; (Yoon Par. 19-20- Preferably, the prediction model generation apparatus may further include a learning evaluator that evaluates the degree of learning of the potassium concentration prediction model using a loss function, and learns the potassium concentration prediction model by an error inversion method. According to another aspect of the present invention, there is provided a method of predicting a blood potassium concentration prediction model using electrocardiogram data, the preprocessor dividing each of a plurality of electrocardiogram data mapped with potassium concentration into predetermined lengths, Generating a training data set by minimizing-maximum normalizing each of the electrocardiogram data, generating a training data set, extracting feature points from each training data, and analyzing the extracted feature points in a time-series manner to generate a potassium concentration prediction model .”);
and a model generation step of constructing, by a model generation unit, a neural network model for predicting …based on the training dataset. (Yoon Par. 18-20- Preferably, the predictive model generator includes at least one CNN (Convolution Neural Network) layer for extracting feature points from each training data, an RNN (Recurrent Neural Network) for sequentially calculating the potassium concentration by sequentially receiving the feature points extracted from the CNN layer, Network layer.)
Yoon teaches prediction analysis and the feature is expounded upon by Galloway:
… predicting a hyperkalemia using an electrocardiogram ... (Galloway Pg. 429- Question Can a deep-learning model classify hyperkalemia from the electrocardiogram (ECG) in patients with chronic kidney disease? Findings In this validation study, a deep neural network was trained using more than 1.5 million ECGs recorded from 1994 to
2017 from approximately 450 000 patients seen at the Mayo Clinic in Minnesota and validated on nearly 62 000 ECGs from the Mayo Clinic in Minnesota, Florida, and Arizona. Using 2 or 4 ECG leads, a deep-learning model detected hyperkalemia with high sensitivity and negative predictive value, with an area under the curve between 0.853 and 0.901. Meaning Deep learning may enable noninvasive screening for
hyperkalemia in at-risk patients with chronic kidney disease ;Pg 432-433.“ The number of false-positive, false-negative, true positive, and true-negative results for each model, as well as accuracy, is presented in eTable 3 in the Supplement. Between50% and70%of patients in the validation data sets did not have hyperkalemia predicted by the DLM, with less than 1% of all test results being false-negative; on the other hand, up to 42% of all test results were false-positive,
Yoon and Galloway are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Yoon, as taught by Galloway, by utilizing hyperkalemia analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Yoon with the motivation of improving the detection of hyperkalemia (Galloway Pg. 429).
Regarding Claim 19,
Yoon teaches
A hyperkalemia prediction system using an electrocardiogram, comprising: a smart band worn by a user, and measuring an electrocardiogram of the user (Yoon- Pg. 9 Meanwhile, the potassium concentration monitoring apparatus 1000 may be implemented (or mounted) on a portable device. The portable electronic device may be a laptop computer, a mobile phone, a smart phone, a tablet PC, a mobile internet device (MID), a personal digital assistant (PDA), an enterprise digital assistant ), A portable game console (handheld console), an e-book, or a smart device. For example, a smart device can be implemented as a smart watch or a smart band.”) ;
an information collection unit collecting information on the electrocardiogram of the user measured by the smart band; (Yoon Par. 1-3-The present invention relates to an apparatus and method for predicting potassium concentration in a blood using electrocardiographic data, The present invention relates to an apparatus and method for predicting potassium concentration in a blood using electrocardiogram (ECG) data, and more particularly, to a method and apparatus for generating a predicted model of potassium concentration in a blood using ECG data, And a method for generating a blood potassium concentration prediction model using electrocardiogram data for generating a prediction model. Hyperkalemia is an electrolyte displacement that can lead to fatal heart arrhythmias. Proper management of hyperkalemia is becoming more important because of the increased incidence of hyperkalemia-related diseases such as diabetes, coronary artery disease, and chronic kidney disease. Hyperkalemia and changes in hypokalemia or potassium levels are all associated with increased risk of death and life-threatening arrhythmias. Mortality, hospitalization and mortality in patients with renal or cardiac disease can follow a gradual change in potassium levels.”; Pg. 9- portable device) ;
and a determination module determining whether the user has thee hyperkalemia by applying the electrocardiogram of the user collected by the information collection unit to a neural network model pre-constructed to predict …. (Yoon Par. 18-20- Preferably, the predictive model generator includes at least one CNN (Convolution Neural Network) layer for extracting feature points from each training data, an RNN (Recurrent Neural Network) for sequentially calculating the potassium concentration by sequentially receiving the feature points extracted from the CNN layer, Network layer.)
Yoon teaches prediction analysis and the feature is expounded upon by Galloway:
… the hyperkalemia according to the electrocardiogram (Galloway Pg. 429- Question Can a deep-learning model classify hyperkalemia from the electrocardiogram (ECG) in patients with chronic kidney disease? Findings In this validation study, a deep neural network was trained using more than 1.5 million ECGs recorded from 1994 to 2017 from approximately 450 000 patients seen at the Mayo Clinic in Minnesota and validated on nearly 62 000 ECGs from the Mayo Clinic in Minnesota, Florida, and Arizona. Using 2 or 4 ECG leads, a deep-learning model detected hyperkalemia with high sensitivity and negative predictive value, with an area under the curve between 0.853 and 0.901. Meaning Deep learning may enable noninvasive screening for hyperkalemia in at-risk patients with chronic kidney disease ;Pg 432-433.“ The number of false-positive, false-negative, true positive, and true-negative results for each model, as well as accuracy, is presented in eTable 3 in the Supplement. Between50% and70%of patients in the validation data sets did not have hyperkalemia predicted by the DLM, with less than 1% of all test results being false-negative; on the other hand, up to 42% of all test results were false-positive,
Yoon and Galloway are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Yoon, as taught by Galloway, by utilizing hyperkalemia analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Yoon with the motivation of improving the detection of hyperkalemia (Galloway Pg. 429).
Regarding Claim 20,
The hyperkalemia prediction system using an electrocardiogram of claim 19, wherein the smart band is worn on a wrist of the user (Yoon- Pg. 9 Meanwhile, the potassium concentration monitoring apparatus 1000 may be implemented (or mounted) on a portable device. The portable electronic device may be a laptop computer, a mobile phone, a smart phone, a tablet PC, a mobile internet device (MID), a personal digital assistant (PDA), an enterprise digital assistant ), A portable game console (handheld console), an e-book, or a smart device. For example, a smart device can be implemented as a smart watch or a smart band.”).
Regarding Claim 21, Yoon in view of Galloway teach The hyperkalemia prediction system using an electrocardiogram of claim 19, further comprising:…
a model construction unit constructing the neural network mode using electrocardiogram data of the hyperkalemia patient, and providing the constructed neural network model to the determination module. (Yoon Par. 18-20- Preferably, the predictive model generator includes at least one CNN (Convolution Neural Network) layer for extracting feature points from each training data, an RNN (Recurrent Neural Network) for sequentially calculating the potassium concentration by sequentially receiving the feature points extracted from the CNN layer, Network layer.)
Regarding Claim 22, Yoon in view of Galloway teach The hyperkalemia prediction system using an electrocardiogram of claim 21,…
wherein the model construction unit includes a data collection unit collecting electrocardiogram data of multiple hyperkalemia patients; (Yoon Par. 1-3-The present invention relates to an apparatus and method for predicting potassium concentration in a blood using electrocardiographic data, The present invention relates to an apparatus and method for predicting potassium concentration in a blood using electrocardiogram (ECG) data, and more particularly, to a method and apparatus for generating a predicted model of potassium concentration in a blood using ECG data, And a method for generating a blood potassium concentration prediction model using electrocardiogram data for generating a prediction model. Hyperkalemia is an electrolyte displacement that can lead to fatal heart arrhythmias. Proper management of hyperkalemia is becoming more important because of the increased incidence of hyperkalemia-related diseases such as diabetes, coronary artery disease, and chronic kidney disease. Hyperkalemia and changes in hypokalemia or potassium levels are all associated with increased risk of death and life-threatening arrhythmias. Mortality, hospitalization and mortality in patients with renal or cardiac disease can follow a gradual change in potassium levels.”) ;
a data processing unit generating a training dataset for machine learning based on the electrocardiogram data collected by the data processing unit; (Yoon Par. 19-20- Preferably, the prediction model generation apparatus may further include a learning evaluator that evaluates the degree of learning of the potassium concentration prediction model using a loss function, and learns the potassium concentration prediction model by an error inversion method. According to another aspect of the present invention, there is provided a method of predicting a blood potassium concentration prediction model using electrocardiogram data, the preprocessor dividing each of a plurality of electrocardiogram data mapped with potassium concentration into predetermined lengths, Generating a training data set by minimizing-maximum normalizing each of the electrocardiogram data, generating a training data set, extracting feature points from each training data, and analyzing the extracted feature points in a time-series manner to generate a potassium concentration prediction model .”);
and a model generation unit constructing a neural network model for predicting …based on the training dataset provided by the data processing unit. (Yoon Par. 18-20- Preferably, the predictive model generator includes at least one CNN (Convolution Neural Network) layer for extracting feature points from each training data, an RNN (Recurrent Neural Network) for sequentially calculating the potassium concentration by sequentially receiving the feature points extracted from the CNN layer, Network layer.)
Yoon teaches prediction analysis and the feature is expounded upon by Galloway:
… predicting a hyperkalemia using an electrocardiogram ... (Galloway Pg. 429- Question Can a deep-learning model classify hyperkalemia from the electrocardiogram (ECG) in patients with chronic kidney disease? Findings In this validation study, a deep neural network was trained using more than 1.5 million ECGs recorded from 1994 to
2017 from approximately 450 000 patients seen at the Mayo Clinic in Minnesota and validated on nearly 62 000 ECGs from the Mayo Clinic in Minnesota, Florida, and Arizona. Using 2 or 4 ECG leads, a deep-learning model detected hyperkalemia with high sensitivity and negative predictive value, with an area under the curve between 0.853 and 0.901. Meaning Deep learning may enable noninvasive screening for
hyperkalemia in at-risk patients with chronic kidney disease ;Pg 432-433.“ The number of false-positive, false-negative, true positive, and true-negative results for each model, as well as accuracy, is presented in eatable 3 in the Supplement. Between50% and70%of patients in the validation data sets did not have hyperkalemia predicted by the DLM, with less than 1% of all test results being false-negative; on the other hand, up to 42% of all test results were false-positive,
Yoon and Galloway are directed to predictive analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Yoon, as taught by Galloway, by utilizing hyperkalemia analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Yoon with the motivation of improving the detection of hyperkalemia (Galloway Pg. 429).
Regarding Claim 23, Yoon in view of Galloway teach The hyperkalemia prediction system using an electrocardiogram of claim 22,,…
wherein the data collection unit collects electrocardiogram data of the hyperkalemia patient data from a time before a first preset reference time from the time of the symptom onset of the hyperkalemia to a time after a second preset reference time from the time of the symptom onset of the hyperkalemia. (Yoon Pg. 4- The collecting unit 111 stores the electrocardiogram data at predetermined time intervals (for example, every 10 seconds), and the electrocardiogram data in which the potassium concentration values are matched can be used for the analysis.)
Regarding Claim 24, Yoon in view of Galloway teach The hyperkalemia prediction system using an electrocardiogram of claim 22,…
wherein the data processing unit classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into abnormal-state data, and classifies the ECG data from the time of the symptom onset of the hyperkalemia to the time before the first preset reference time into normal-state data, and generates the training dataset to include the normal-state data and the abnormal-state data which are classified. (Yoon Pg. 5- The classifying unit 116 classifies some of the electrocardiogram data normalized by the normalizing unit 115 into a training data set. In order to generate a prediction model for predicting the potassium concentration using the electrocardiogram data, a training data set is required, and overfitting of a prediction model for a training data set is detected and data for setting a hyperparameter And need data to assess the generalizability of the predictive model. The classification unit 116 may classify the normalized electrocardiogram data into a training data set, a test data set, and a validity verification data set at a predetermined ratio (e.g., 8: 1: 1). The training data set is used for prediction model generation and the test data set is used for overfitting detection and hyperparameter setting of the prediction model and the validation data set can be used to evaluate the generalization possibility of the prediction model have.; Pg. 8- The predictive model generation device classifies training data sets, test data sets, and validity data sets by removing no potassium concentration data, uninterpretable noise data, and outlier data from the electrocardiogram data collected from 531 patients, respectively do. As a result, 1,071,000 electrocardiogram data from 425 patients, 106,615 ECG data from 53 patients, and 137,165 ECG data from 53 patients, which are in turn sorted into training data sets, test data sets, and validation data sets Lt; I RTI > At this time, the predictive model generation device assumes that 1,065,000 electrocardiogram data are finally registered in 425 patients by adjusting the potassium concentration distribution through resampling among the training data sets.)
Regarding Claim 25, Yoon in view of Galloway teach The hyperkalemia prediction system using an electrocardiogram of claim 22,,…
wherein the data processing unit classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into abnormal-state data, and classifies the ECG data from the time of the symptom onset of the hyperkalemia to the time after the second preset reference time into normal-state data, and generates the training dataset to include the normal-state data and the abnormal-state data which are classified. (Yoon Pg. 5- The classifying unit 116 classifies some of the electrocardiogram data normalized by the normalizing unit 115 into a training data set. In order to generate a prediction model for predicting the potassium concentration using the electrocardiogram data, a training data set is required, and overfitting of a prediction model for a training data set is detected and data for setting a hyperparameter And need data to assess the generalizability of the predictive model. The classification unit 116 may classify the normalized electrocardiogram data into a training data set, a test data set, and a validity verification data set at a predetermined ratio (e.g., 8: 1: 1). The training data set is used for prediction model generation and the test data set is used for overfitting detection and hyperparameter setting of the prediction model and the validation data set can be used to evaluate the generalization possibility of the prediction model have.; Pg. 8- The predictive model generation device classifies training data sets, test data sets, and validity data sets by removing no potassium concentration data, uninterpretable noise data, and outlier data from the electrocardiogram data collected from 531 patients, respectively do. As a result, 1,071,000 electrocardiogram data from 425 patients, 106,615 ECG data from 53 patients, and 137,165 ECG data from 53 patients, which are in turn sorted into training data sets, test data sets, and validation data sets Lt; I RTI > At this time, the predictive model generation device assumes that 1,065,000 electrocardiogram data are finally registered in 425 patients by adjusting the potassium concentration distribution through resampling among the training data sets.)
Regarding Claim 26, Yoon in view of Galloway teach The hyperkalemia prediction system using an electrocardiogram of claim 22,…
wherein the data processing unit classifies the ECG data at the time of the symptom onset of the hyperkalemia among the ECG data provided by the data collection unit into the abnormal-state data, and classifies, into the normal-state data, ECG data other than the ECG data among the ECG data from the time before the first preset reference time from the time of the symptom onset of the hyperkalemia to the time after the second preset reference time from the time of the symptom onset of the hyperkalemia, and generates the training dataset to include the normal-state data and the abnormal-state data which are classified. (Yoon Pg. 5- The classifying unit 116 classifies some of the electrocardiogram data normalized by the normalizing unit 115 into a training data set. In order to generate a prediction model for predicting the potassium concentration using the electrocardiogram data, a training data set is required, and overfitting of a prediction model for a training data set is detected and data for setting a hyperparameter And need data to assess the generalizability of the predictive model. The classification unit 116 may classify the normalized electrocardiogram data into a training data set, a test data set, and a validity verification data set at a predetermined ratio (e.g., 8: 1: 1). The training data set is used for prediction model generation and the test data set is used for overfitting detection and hyperparameter setting of the prediction model and the validation data set can be used to evaluate the generalization possibility of the prediction model have.; Pg. 8- The predictive model generation device classifies training data sets, test data sets, and validity data sets by removing no potassium concentration data, uninterpretable noise data, and outlier data from the electrocardiogram data collected from 531 patients, respectively do. As a result, 1,071,000 electrocardiogram data from 425 patients, 106,615 ECG data from 53 patients, and 137,165 ECG data from 53 patients, which are in turn sorted into training data sets, test data sets, and validation data sets Lt; I RTI > At this time, the predictive model generation device assumes that 1,065,000 electrocardiogram data are finally registered in 425 patients by adjusting the potassium concentration distribution through resampling among the training data sets.)
Regarding Claim 27, Yoon in view of Galloway teach The hyperkalemia prediction system using an electrocardiogram of claim 24,,…
wherein the data processing unit classifies some randomly selected data of the data classified from the ECG data provided by the data collection unit into the training dataset, and classifies the remaining data into a sample set for testing or validating the neural network model (Yoon Pg. 5- The balancing processor performs sampling so that the potassium concentration is uniformly distributed in the training data set. That is, the balancing processor classifies the category of the training data according to the potassium concentration mapped to each training data, based on the category in which the potassium concentration is divided into a certain number of categories, samples the electrocardiogram data for each of the classified categories Potassium concentration can be distributed in a balanced manner. At this time, the balancing processor may randomly extract the electrocardiogram data for each category, and the electrocardiogram data for each category may be increased from the number before the sampling. For example, if the collector samples electrocardiogram data at a 250 kHz rate, the balancing processor can sample training data for each category at a 125 Hz rate. Therefore, the balancing processor can increase the training data by the number of electrocardiogram data before re-sampling in units of categories.)
Regarding Claim 28, Yoon in view of Galloway teach The hyperkalemia prediction system using an electrocardiogram of claim 27, further comprising:,…
a validation module analyzing the performance of the neural network model by applying the sample set to the neural network model. (Yoon Pg. 5- The training data set is used for prediction model generation and the test data set is used for overfitting detection and hyperparameter setting of the prediction model and the validation data set can be used to evaluate the generalization possibility of the prediction model have.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20200008696A1 to Sirendi et al.- Abstract-“ The present invention relates to a method of analysing cardiac data relating to a patient, comprising: providing cardiac data relating to the patient—optionally by using a means for providing physiological data (20); determining one or more properties of the data, wherein the or each property is determined over a particular context length, the context length being selected based on the or each property—optionally using an analysis module (24); comparing the or each property against a respective predetermined threshold value, thereby to indicate a probability of the patient experiencing a cardiac event—optionally using a means for providing an output (26); and providing an output based on the comparison. A system and apparatus corresponding to this method is also disclosed.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624