DETAILED ACTION
This action is in response to the initial filing on September 24, 2024. Claims 1-15 were filed. A preliminary amendment was filed on November 8, 2024. Claims 1-14 were amended. Claims 15 was cancelled. Claims 16-17 have been added. Claims 1-14 and 16-17 have been examined and are currently pending.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Information Disclosure Statement
The Information Disclosure Statement filed on January 7, 2025 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Objections
Claim 1 is objected to because of the following informalities: Independent claim 1 recites “the at least one read-in” in line 14 lacks antecedent basis. Appropriate correction is required.
Claim 1 is objected to because of the following informalities: Independent claim 1 recites “the at least one physiological parameter” in line 17 lacks antecedent basis. Appropriate correction is required.
Claim 1 is objected to because of the following informalities: Independent claim 1 recites “the result” in line 23 lacks antecedent basis. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: Dependent claim 6 recites “the accuracy” in line 2 lacks antecedent basis. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: Dependent claim 6 recites “the retrieved” in line 2 lacks antecedent basis. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: Dependent claim 8 recites “the measurements” in line 2 lacks antecedent basis. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: Dependent claim 9 recites “the predicted parameter” in line 4 lacks antecedent basis. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: Independent claim 12 recites “the patient” in line 4 lacks antecedent basis. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: Independent claim 12 recites “the at least one read-in” in line 14 lacks antecedent basis. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: Independent claim 12 recites “the at least one physiological parameter” in lines 16-17 lacks antecedent basis. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: Independent claim 12 recites “the result” in line 22 lacks antecedent basis. Appropriate correction is required.
Claim 14 is objected to because of the following informalities: Independent claim 14 recites “the execution” in line 2 lacks antecedent basis. Appropriate correction is required.
Claim 14 is objected to because of the following informalities: Independent claim 14 recites “the at least one read-in” in line 13 lacks antecedent basis. Appropriate correction is required.
Claim 14 is objected to because of the following informalities: Independent claim 14 recites “the at least one physiological parameter” in line 16 lacks antecedent basis. Appropriate correction is required.
Claim 14 is objected to because of the following informalities: Independent claim 14 recites “the result” in line 21 lacks antecedent basis. Appropriate correction is required.
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 1-14 and 16-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
ALICE/ MAYO: TWO-PART ANALYSIS
2A. First, a determination whether the claim is directed to a judicial exception (i.e., abstract idea).
Prong 1: A determination whether the claim recites a judicial exception (i.e., abstract idea).
Groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Mathematical concepts- mathematical relationships, mathematical formulas or equations, mathematical calculations.
Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Mental processes- concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
Prong 2: A determination whether the judicial exception (i.e., abstract idea) is integrated into a practical application.
Considerations indicative of integration into a practical application enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Improvement to the functioning of a computer, or an improvement to any other technology or technical field
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of a particular machine.
Effecting a transformation or reduction of a particular article to a different state or thing
Applying or using 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
Considerations that are not indicative of integration into a practical application enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea.
Adding insignificant extra-solution activity to the judicial exception.
Generally linking the use of the judicial exception to a particular technological environment or field of use.
2B. Second, a determination whether the claim provides an inventive concept (i.e., Whether the claim(s) include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
Considerations indicative of an inventive concept (aka “significantly more”) enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Improvement to the functioning of a computer, or an improvement to any other technology or technical field
Applying the judicial exception with, or by use of a particular machine.
Effecting a transformation or reduction of a particular article to a different state or thing
Applying or using 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 NOTE: The only consideration that does not overlap with the considerations indicative of integration into a practical application associated with step 2A: Prong 2.
Considerations that are not indicative of an inventive concept (aka “significantly more”) enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea.
Adding insignificant extra-solution activity to the judicial exception.
Generally linking the use of the judicial exception to a particular technological environment or field of use.
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. NOTE: The only consideration that does not overlap with the considerations that are not indicative of integration into a practical application associated with step 2A: Prong 2.
See also, 2019 Revised Patent Subject Matter Eligibility Guidance; Federal Register; Vol. 84, No. 4; Monday, January 7, 2019
Claims 1-14 and 16-17 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.
1: Statutory Category
Applicant’s claimed invention, as described in independent claim 1 is directed to a method, independent claim 12 is directed to a system, and independent claim 14 is directed to a hand-held device.
2(A): The claim(s) are directed to a judicial exception (i.e., an abstract idea).
PRONG 1: The claim(s) recite a judicial exception (i.e., an abstract idea).
Mental Processes
Independent claims 1 and 14 recite the limitations, “evaluating the at least one read-in value of the at least one first physiological parameter using the mathematical model, the evaluation being based on at least one of said combinations of parameter values while retrieving at least one of said multitude of combinations of parameter values, wherein the combination or combinations retrieved from the clinical database include(s) the value of the received at least one second parameter; and based on the result of said evaluating step outputting a prediction of a future development of at least one physiological state of the patient or at least one of the parameters of the patient”. Independent claim 12 recites the limitations, “an evaluating device for evaluating the at least one read-in value of the at least one first physiological parameter using the mathematical model based on at least one of said combinations of parameter values while retrieving at least one of said multitude of combinations of parameter values, wherein the at least one combination retrieved from the clinical database comprises the value of the received at least second parameter; and an output device for, based on the result of said evaluating step, outputting a prediction of a future development of at least one physiological state of the patient or at least one of the parameters of the patient, or suggesting future behavior or treatment for or of the patient.” The limitations described above in claims 1, 12 and 14 are directed to the abstract idea of mental processes. The limitations are directed to evaluating a value based on a mathematical model and a combination of values and predicting the state or parameters associated with a patient. These limitations can be performed in the human mind through observation, evaluation, and judgment of a value using a mathematical model and a combination of parameters.
Mathematical Concepts
Independent claims 1 and 14 recite the limitations, “providing a mathematical model for evaluating the at least one read-in value based on at least one of said multitude of combinations of parameter values comprised by the clinical database; evaluating the at least one read-in value of the at least one first physiological parameter using the mathematical model, the evaluation being based on at least one of said combinations of parameter values while retrieving at least one of said multitude of combinations of parameter values, wherein the combination or combinations retrieved from the clinical database include(s) the value of the received at least one second parameter; and based on the result of said evaluating step outputting a prediction of a future development of at least one physiological state of the patient or at least one of the parameters of the patient”. Independent claim 12 recites the limitations, “a mathematical model for evaluating the at least one read-in value based on at least one of said multitude of combinations of parameter values comprised by the clinical database; an evaluating device for evaluating the at least one read-in value of the at least one first physiological parameter using the mathematical model based on at least one of said combinations of parameter values while retrieving at least one of said multitude of combinations of parameter values, wherein the at least one combination retrieved from the clinical database comprises the value of the received at least second parameter; and an output device for, based on the result of said evaluating step, outputting a prediction of a future development of at least one physiological state of the patient or at least one of the parameters of the patient, or suggesting future behavior or treatment for or of the patient.” The limitations described above in claims 1, 12 and 14 are directed to the abstract idea of mathematical concepts. The limitations are applying mathematical concepts (e.g., mathematical relationships, mathematical formulas, or mathematical calculations) to determine and predict a future state or parameters associated with a patient.
PRONG 2: The judicial exception (i.e., an abstract idea) is not integrated into a practical application.
The applicant has not shown or demonstrated any of the requirements described above under "integration into a practical application" under step 2A. Specifically, the applicant's limitations are not "integrated into a practical application" because they are adding words "apply it" with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea (see MPEP 2106.05(f)). Additionally, improvements to the functioning of a computer or any other technology or technical field has not been shown or disclosed (see MPEP 2106.05(a)). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the applicant’s limitations are not “significantly more” because they are adding words “apply it” with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea (see MPEP 2106.05(f)). The applicant’s claimed limitations do not demonstrate an improvement to another technology or technical field, an improvement to the functioning of the computer itself, effecting a transformation or reduction of particular article to a different state or thing. The current application does not amount to 'significantly more' than the abstract idea as described above. The claim does not include additional elements or limitations individually or in combination that are sufficient to amount to significantly more than the judicial exception. Specifically, the individual elements of patient monitor, interdialytic measuring device, blood treatment apparatus, reporting tool, reading device, receiving device, evaluating device, output device, hand-held device, and control device amount to no more than implementing an idea with a computerized system and they are adding words “apply it” with the judicial exception, or mere instructions to implement an abstract idea merely as a tool to perform an abstract idea. The additional elements taken in combination add nothing more than what is present when the elements are considered individually. Therefore, based on the two-part Alice Corp. analysis, there are no meaningful limitations in the claims that transform the exception (i.e., abstract idea) into a patent eligible application.
Since the claim(s) recite a judicial exception and fails to integrate the judicial exception into a practical application, the claim(s) is/are “directed to” the judicial exception. Thus, the claim(s) must be reviewed under the second step of the Alice/ Mayo analysis to determine whether the abstract idea has been applied in an eligible manner.
2(B): The claims do not provide an inventive concept (i.e., The claim(s) do not include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
As discussed with respect to Step 2A Prong Two, the additional element(s) in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
For these reasons, there is no invention concept in the claim, and thus the claim is ineligible.
Dependent claims 2-11, 13, and 16-17 are rejected as ineligible subject matter under 35 U.S.C. 101 based on a rationale similar to the claims from which they depend. Dependent claim 2 recites “wearable”, “bioimpedance sensor”, “photoplethysmograph”, “accelerometer”, “blood glucose sensor”, “hemoglobin sensor”, “potassium sensor”, “calcium sensor”, “pulsometer”, “skin conductance sensor”, and “actigraph”. Dependent claim 3 recites “blood treatment apparatus” and “patient monitor”. Dependent claim 4 recites “smartphones” and “tablets”. Dependent claim 13 recites “smartphone”, “smart watch”, and “wearable”. Dependent claims 2-4 and 13 do not recite additional elements that amount to significantly more than the judicial exception.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-14 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bhagat et al. US Publication 20220361760 A1 in view of Yeh et al. US Publication 20230134865 A1.
Claims 1 and 14:
As per claims 1 and 14, Bhagat teaches a method and hand-held device comprising:
reading in, from a first data source, at least one value of at least one first parameter of a patient, the first parameter being a physiological parameter (paragraphs 0002, 0041, and 0050 “Biomarker tracking devices are capable of measuring multiple physiologic parameters of a patient. These physiologic parameters may include heart rate, electrocardiogram signals, blood volume changes, oxygen saturation, and other like signals and information. The biomarker tracking devices come in a variety of forms including smart watches, mobile phones, wearable devices, and the like.”);
receiving, from a second data source, at least one value of at least one second parameter, the second parameter being related to the patient or to a medical treatment of the patient, the first data source and the second data source being at least partly different from each other (paragraphs 0002, 0041, and 0050 “The biomarker tracking wearable band 1110 includes a plurality of sensors for measuring physiological, biological, and like measurements of a user. The sensors include, but is not limited to, ECG sensors for measuring heart rate and heart rate variability, PPG sensors for measuring heart rate, heart rate variability, blood pressure and blood oxygen saturation (SpO.sub.2), accelerometers for measuring step count and fall detection, and other like sensors.”);
wherein the first data source comprises at least one of a patient monitor or an interdialytic measuring device (paragraphs 0002 and 0040 “Biomarker tracking devices are capable of measuring multiple physiologic parameters of a patient. These physiologic parameters may include heart rate, electrocardiogram signals, blood volume changes, oxygen saturation, and other like signals and information. The biomarker tracking devices come in a variety of forms including smart watches, mobile phones, wearable devices, and the like.”);
and wherein the second data source comprises at least one of a patient monitor, an intradialytic measuring device, a blood treatment apparatus or a reporting tool for receiving input by the patient or medical staff (paragraphs 0002 and 0040 “Disclosed herein are implementations of biomarker tracking wearable bands and methods for making the wearable bands. The biomarker tracking wearable band provides multiple sensing modalities including activity monitoring, optical photoplethysmography (PPG) utilizing more than two light emitting diodes (LEDs) and photodiodes or photodetectors, and electrocardiography (ECG) utilizing printed Silver-Silver Chloride (Ag-AgCl) electrodes in a small form factor wearable band. In an implementation, the wearable band can be a wristband, ankle band, and the like.”).
Bhagat does not teach providing a clinical database comprising a multitude of combinations of values of a multitude of parameters, the values of the first parameter and the second parameter, wherein the multitude of values were gained from said patient or from a multitude of patients of a reference patient group. However, Yeh teaches an Intradialytic Analysis Method and Analysis Apparatus for Dialysis and further teaches, “The storage 110 may be any type of fixed or removable random access memory (RAM), read only memory (ROM), flash memory, hard disk drive (HDD), solid-state drive (SSD), or similar elements. In an embodiment, the storage 110 is configured to record codes, software modules, configuration layouts, data (for example, physiological parameters, biochemical test parameters, basic data, operating parameters, features, data collection at each time point, predicted results, etc.), or files, and the embodiment thereof will be detailed later.” (paragraph 0016) and “FIG. 2 is a flowchart of an intradialytic analysis method according to an embodiment of the disclosure. Please refer to FIG. 2. The processor 130 obtains one or more input features (Step S210). Specifically, the input features are input data for subsequent evaluation of blood pressure information and/or intradialytic hypotension. The processor 130 may obtain the input features or corresponding raw data via an input/output apparatus, the storage 110, an external storage apparatus, or a network” (paragraph 0020). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include providing a clinical database comprising a multitude of combinations of values of a multitude of parameters, the values of the first parameter and the second parameter, wherein the multitude of values were gained from said patient or from a multitude of patients of a reference patient group as taught by Yeh in order to store or maintain a set of values associated with one or more patients.
Bhagat does not teach providing a mathematical model for evaluating the at least one read-in value based on at least one of said multitude of combinations of parameter values comprised by the clinical database. However, Yeh teaches an Intradialytic Analysis Method and Analysis Apparatus for Dialysis and further teaches, “In an embodiment, the prediction model is established through one or more machine learning algorithms. The machine learning algorithm may be regression analysis algorithm, eXtreme gradient boosting (XGboost) algorithm, light gradient boosting machine (LightGBM), bootstrap aggregating (Bagged) algorithm, neural network algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, random forest algorithm, support vector regression algorithm, or other algorithms. The machine learning algorithm may analyze training data/samples to obtain rules therefrom, so as to predict unknown data through the rules. The prediction model is a machine learning model constructed after learning and inferences data to be evaluated accordingly.” (paragraph 0039) and “It should be noted that the training data of the prediction model is the same as or related to the parameters or data types corresponding to the input features. For example, the operating parameter of the dialysis machine, a physiological state of the tester, the basic data, and/or the external data. In some embodiments, the training data further includes actual data (that is, future blood pressure information and/or whether intradialytic hypotension actually occurs). Document 3 “Standard operation procedures (SOPs) for the management of a patient’s haemodialysis care” provided by the University Hospitals Birmingham in 2017 illustrates the correlation between the input features of the embodiment of the disclosure and the predicted future data.” (paragraph 0040). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include providing a mathematical model for evaluating the at least one read-in value based on at least one of said multitude of combinations of parameter values comprised by the clinical database as taught by Yeh in order to establish a method to analyze the set of values associated with one or more patients.
Bhagat does not teach evaluating the at least one read-in value of the at least one first physiological parameter using the mathematical model, the evaluation being based on at least one of said combinations of parameter values while retrieving at least one of said multitude of combinations of parameter values, wherein the combination or combinations retrieved from the clinical database include(s) the value of the received at least one second parameter. However, Yeh teaches an Intradialytic Analysis Method and Analysis Apparatus for Dialysis and further teaches, “In an embodiment, the prediction model is established through one or more machine learning algorithms. The machine learning algorithm may be regression analysis algorithm, eXtreme gradient boosting (XGboost) algorithm, light gradient boosting machine (LightGBM), bootstrap aggregating (Bagged) algorithm, neural network algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, random forest algorithm, support vector regression algorithm, or other algorithms. The machine learning algorithm may analyze training data/samples to obtain rules therefrom, so as to predict unknown data through the rules. The prediction model is a machine learning model constructed after learning and inferences data to be evaluated accordingly.” (paragraph 0039) and “It should be noted that the training data of the prediction model is the same as or related to the parameters or data types corresponding to the input features. For example, the operating parameter of the dialysis machine, a physiological state of the tester, the basic data, and/or the external data. In some embodiments, the training data further includes actual data (that is, future blood pressure information and/or whether intradialytic hypotension actually occurs). Document 3 “Standard operation procedures (SOPs) for the management of a patient’s haemodialysis care” provided by the University Hospitals Birmingham in 2017 illustrates the correlation between the input features of the embodiment of the disclosure and the predicted future data.” (paragraph 0040), and “In an embodiment, the processor 130 determines final future data according to the future data predicted by multiple prediction models. The machine learning algorithms used by the prediction models may be the same or different, and the final future data also includes the blood pressure information and the predicted result of intradialytic hypotension at the future time point. For example, FIG. 4 is a schematic diagram of determining final future data according to an embodiment of the disclosure. Please refer to FIG. 4. It is assumed that the prediction model includes a first classification model and there are i first classification models ML.sub.11 to ML.sub.1i (where i is a positive integer greater than one). The first classification models ML.sub.11 to ML.sub.1i sample the same, related, or similar training data TD but after training based on different machine learning algorithms (for example, regression analysis, XGBoost, neural network system, random forest, LASSO, support vector regression, neural network, etc.), the processor 130 uses the first classification models ML.sub.11 to ML.sub.1i to respectively predict future data P.sub.11 to P.sub.1i, and determines the blood pressure information in final future data P.sub.f based on the future data P.sub.11 to P.sub.1i. For example, a statistical result (for example, arithmetic mean, weighted mean, or median) of a predicted blood pressure PBP (that is, the blood pressure information predicted by the first classification models ML.sub.11 to ML.sub.1i) of the future data P.sub.11 to P.sub.1i is determined. The predicted blood pressure PBP may be used as one of the final future data.” (paragraph 0041). Therefore, it would have been obvious to one ordinary skilled in the art at the time of filing to modify Bhagat to include evaluating the at least one read-in value of the at least one first physiological parameter using the mathematical model, the evaluation being based on at least one of said combinations of parameter values while retrieving at least one of said multitude of combinations of parameter values, wherein the combination or combinations retrieved from the clinical database include(s) the value of the received at least one second parameter as taught by Yeh in order to generate an output or result based on evaluating the set of data associated with one or more patients.
Bhagat does not teach and based on the result of said evaluating step outputting a prediction of a future development of at least one physiological state of the patient or at least one of the parameters of the patient. However, Yeh teaches an Intradialytic Analysis Method and Analysis Apparatus for Dialysis and further teaches, “The intradialytic analysis method of an embodiment of the disclosure includes (but is not limited to) the following steps. One or more input features are obtained. The input features include variance relation between current data and previous data of an operating parameter related to a dialysis machine and data related to a tester. Future data is predicted according to the input features through one or more prediction models. The future data includes blood pressure information and a predicted result of intradialytic hypotension at a future time point.” (paragraph 0006), “Based on the above, the intradialytic analysis method and the analysis apparatus for dialysis of the embodiments of the disclosure further consider a new variable (for example, the variance relation between the current data and the previous data) that affects intradialytic hypotension to improve the accuracy of prediction. In this way, intradialytic hypotension that is about to occur for a patient can be predicted in advance, and nursing staff can be further notified to make the appropriate treatment to reduce the occurrence of interruption of dialysis, thereby reducing the mortality rate of patients and improving the quality of medical care.” (paragraph 0008) and “Please refer to FIG. 2. The processor 130 predicts future data according to the input features through one or more prediction models (Step S230). Specifically, the future data includes blood pressure information and a predicted result of intradialytic hypotension at a future time point. Taking FIG. 3 as an example, the future time point t+1 is 12:30, and the current time point t is 12 o’clock. In an embodiment, the blood pressure information at the future time point is a future systolic blood pressure (a future systolic blood pressure SBP.sub.t+1 as shown in FIG. 3). In some embodiments, the processor 13 may determine and/or compare a hypotension threshold value based on the blood pressure information for the evaluation of intradialytic hypotension. In an embodiment, the predicted result of intradialytic hypotension is an occurrence probability of intradialytic hypotension.” (paragraph 0036). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include based on the result of said evaluating step outputting a prediction of a future development of at least one physiological state of the patient or at least one of the parameters of the patient as taught by Yeh in order to anticipate or forecast a patient’s health condition or state.
Claim 12:
As per claim 12, Bhagat teaches a system comprising:
a first data source (paragraphs 0002, 0041, and 0050 “Biomarker tracking devices are capable of measuring multiple physiologic parameters of a patient. These physiologic parameters may include heart rate, electrocardiogram signals, blood volume changes, oxygen saturation, and other like signals and information. The biomarker tracking devices come in a variety of forms including smart watches, mobile phones, wearable devices, and the like.”);
a reading device for reading in, from the first data source, at least one value of at least one first parameter of the patient, the first parameter being a physiological parameter (paragraphs 0002, 0041, and 0050 “Biomarker tracking devices are capable of measuring multiple physiologic parameters of a patient. These physiologic parameters may include heart rate, electrocardiogram signals, blood volume changes, oxygen saturation, and other like signals and information. The biomarker tracking devices come in a variety of forms including smart watches, mobile phones, wearable devices, and the like.”);
a second data source, the first data source and the second data source being at least partly different from each other (paragraphs 0002, 0041, and 0050 “The biomarker tracking wearable band 1110 includes a plurality of sensors for measuring physiological, biological, and like measurements of a user. The sensors include, but is not limited to, ECG sensors for measuring heart rate and heart rate variability, PPG sensors for measuring heart rate, heart rate variability, blood pressure and blood oxygen saturation (SpO.sub.2), accelerometers for measuring step count and fall detection, and other like sensors.”);
a receiving device for receiving, from the second data source, at least one value of at least one second parameter, the second parameter being related to the patient or to a medical treatment of the patient (paragraphs 0002, 0041, and 0050 “The biomarker tracking wearable band 1110 includes a plurality of sensors for measuring physiological, biological, and like measurements of a user. The sensors include, but is not limited to, ECG sensors for measuring heart rate and heart rate variability, PPG sensors for measuring heart rate, heart rate variability, blood pressure and blood oxygen saturation (SpO.sub.2), accelerometers for measuring step count and fall detection, and other like sensors.”);
Bhagat does not teach a clinical database comprising a multitude of combinations of values of a multitude of parameters, those parameters including the first parameter and the second parameter, wherein the multitude of values were gained from said patient or from a multitude of patients of a reference patient group. However, Yeh teaches an Intradialytic Analysis Method and Analysis Apparatus for Dialysis and further teaches, “The storage 110 may be any type of fixed or removable random access memory (RAM), read only memory (ROM), flash memory, hard disk drive (HDD), solid-state drive (SSD), or similar elements. In an embodiment, the storage 110 is configured to record codes, software modules, configuration layouts, data (for example, physiological parameters, biochemical test parameters, basic data, operating parameters, features, data collection at each time point, predicted results, etc.), or files, and the embodiment thereof will be detailed later.” (paragraph 0016) and “FIG. 2 is a flowchart of an intradialytic analysis method according to an embodiment of the disclosure. Please refer to FIG. 2. The processor 130 obtains one or more input features (Step S210). Specifically, the input features are input data for subsequent evaluation of blood pressure information and/or intradialytic hypotension. The processor 130 may obtain the input features or corresponding raw data via an input/output apparatus, the storage 110, an external storage apparatus, or a network” (paragraph 0020). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include a clinical database comprising a multitude of combinations of values of a multitude of parameters, those parameters including the first parameter and the second parameter, wherein the multitude of values were gained from said patient or from a multitude of patients of a reference patient group as taught by Yeh in order to store or maintain a set of values associated with one or more patients.
Bhagat does not teach a mathematical model for evaluating the at least one read-in value based on at least one of said multitude of combinations of parameter values comprised by the clinical database. However, Yeh teaches an Intradialytic Analysis Method and Analysis Apparatus for Dialysis and further teaches, “In an embodiment, the prediction model is established through one or more machine learning algorithms. The machine learning algorithm may be regression analysis algorithm, eXtreme gradient boosting (XGboost) algorithm, light gradient boosting machine (LightGBM), bootstrap aggregating (Bagged) algorithm, neural network algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, random forest algorithm, support vector regression algorithm, or other algorithms. The machine learning algorithm may analyze training data/samples to obtain rules therefrom, so as to predict unknown data through the rules. The prediction model is a machine learning model constructed after learning and inferences data to be evaluated accordingly.” (paragraph 0039) and “It should be noted that the training data of the prediction model is the same as or related to the parameters or data types corresponding to the input features. For example, the operating parameter of the dialysis machine, a physiological state of the tester, the basic data, and/or the external data. In some embodiments, the training data further includes actual data (that is, future blood pressure information and/or whether intradialytic hypotension actually occurs). Document 3 “Standard operation procedures (SOPs) for the management of a patient’s haemodialysis care” provided by the University Hospitals Birmingham in 2017 illustrates the correlation between the input features of the embodiment of the disclosure and the predicted future data.” (paragraph 0040). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include a mathematical model for evaluating the at least one read-in value based on at least one of said multitude of combinations of parameter values comprised by the clinical database as taught by Yeh in order to establish a method to analyze the set of values associated with one or more patients.
Bhagat does not teach an evaluating device for evaluating the at least one read-in value of the at least one first physiological parameter using the mathematical model based on at least one of said combinations of parameter values while retrieving at least one of said multitude of combinations of parameter values, wherein the at least one combination retrieved from the clinical database comprises the value of the received at least second parameter. However, Yeh teaches an Intradialytic Analysis Method and Analysis Apparatus for Dialysis and further teaches, “In an embodiment, the prediction model is established through one or more machine learning algorithms. The machine learning algorithm may be regression analysis algorithm, eXtreme gradient boosting (XGboost) algorithm, light gradient boosting machine (LightGBM), bootstrap aggregating (Bagged) algorithm, neural network algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, random forest algorithm, support vector regression algorithm, or other algorithms. The machine learning algorithm may analyze training data/samples to obtain rules therefrom, so as to predict unknown data through the rules. The prediction model is a machine learning model constructed after learning and inferences data to be evaluated accordingly.” (paragraph 0039) and “It should be noted that the training data of the prediction model is the same as or related to the parameters or data types corresponding to the input features. For example, the operating parameter of the dialysis machine, a physiological state of the tester, the basic data, and/or the external data. In some embodiments, the training data further includes actual data (that is, future blood pressure information and/or whether intradialytic hypotension actually occurs). Document 3 “Standard operation procedures (SOPs) for the management of a patient’s haemodialysis care” provided by the University Hospitals Birmingham in 2017 illustrates the correlation between the input features of the embodiment of the disclosure and the predicted future data.” (paragraph 0040), and “In an embodiment, the processor 130 determines final future data according to the future data predicted by multiple prediction models. The machine learning algorithms used by the prediction models may be the same or different, and the final future data also includes the blood pressure information and the predicted result of intradialytic hypotension at the future time point. For example, FIG. 4 is a schematic diagram of determining final future data according to an embodiment of the disclosure. Please refer to FIG. 4. It is assumed that the prediction model includes a first classification model and there are i first classification models ML.sub.11 to ML.sub.1i (where i is a positive integer greater than one). The first classification models ML.sub.11 to ML.sub.1i sample the same, related, or similar training data TD but after training based on different machine learning algorithms (for example, regression analysis, XGBoost, neural network system, random forest, LASSO, support vector regression, neural network, etc.), the processor 130 uses the first classification models ML.sub.11 to ML.sub.1i to respectively predict future data P.sub.11 to P.sub.1i, and determines the blood pressure information in final future data P.sub.f based on the future data P.sub.11 to P.sub.1i. For example, a statistical result (for example, arithmetic mean, weighted mean, or median) of a predicted blood pressure PBP (that is, the blood pressure information predicted by the first classification models ML.sub.11 to ML.sub.1i) of the future data P.sub.11 to P.sub.1i is determined. The predicted blood pressure PBP may be used as one of the final future data.” (paragraph 0041). Therefore, it would have been obvious to one ordinary skilled in the art at the time of filing to modify Bhagat to include an evaluating device for evaluating the at least one read-in value of the at least one first physiological parameter using the mathematical model based on at least one of said combinations of parameter values while retrieving at least one of said multitude of combinations of parameter values, wherein the at least one combination retrieved from the clinical database comprises the value of the received at least second parameter as taught by Yeh in order to generate an output or result based on evaluating the set of data associated with one or more patients.
Bhagat does not teach and an output device for, based on the result of said evaluating step, outputting a prediction of a future development of at least one physiological state of the patient or at least one of the parameters of the patient, or suggesting future behavior or treatment for or of the patient. However, Yeh teaches an Intradialytic Analysis Method and Analysis Apparatus for Dialysis and further teaches, “The intradialytic analysis method of an embodiment of the disclosure includes (but is not limited to) the following steps. One or more input features are obtained. The input features include variance relation between current data and previous data of an operating parameter related to a dialysis machine and data related to a tester. Future data is predicted according to the input features through one or more prediction models. The future data includes blood pressure information and a predicted result of intradialytic hypotension at a future time point.” (paragraph 0006), “Based on the above, the intradialytic analysis method and the analysis apparatus for dialysis of the embodiments of the disclosure further consider a new variable (for example, the variance relation between the current data and the previous data) that affects intradialytic hypotension to improve the accuracy of prediction. In this way, intradialytic hypotension that is about to occur for a patient can be predicted in advance, and nursing staff can be further notified to make the appropriate treatment to reduce the occurrence of interruption of dialysis, thereby reducing the mortality rate of patients and improving the quality of medical care.” (paragraph 0008) and “Please refer to FIG. 2. The processor 130 predicts future data according to the input features through one or more prediction models (Step S230). Specifically, the future data includes blood pressure information and a predicted result of intradialytic hypotension at a future time point. Taking FIG. 3 as an example, the future time point t+1 is 12:30, and the current time point t is 12 o’clock. In an embodiment, the blood pressure information at the future time point is a future systolic blood pressure (a future systolic blood pressure SBP.sub.t+1 as shown in FIG. 3). In some embodiments, the processor 13 may determine and/or compare a hypotension threshold value based on the blood pressure information for the evaluation of intradialytic hypotension. In an embodiment, the predicted result of intradialytic hypotension is an occurrence probability of intradialytic hypotension.” (paragraph 0036). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include and an output device for, based on the result of said evaluating step, outputting a prediction of a future development of at least one physiological state of the patient or at least one of the parameters of the patient, or suggesting future behavior or treatment for or of the patient as taught by Yeh in order to anticipate or forecast a patient’s health condition or state
Claim 2:
As per claim 2, Bhagat and Yeh teach the method of claim 1 as described above and Bhagat further teaches wherein the at least one value of the first parameter or the at least one value of the second parameters parameter is measured by a wearable comprising at least one of a bioimpedance sensor, a photoplethysmograph, an accelerometer, a blood glucose sensor, a hemoglobin sensor, a sweat sodium (Na) and/or potassium (K) sensor, a calcium (Ca) sensor, a pulsometer, a skin conductance sensor, or an actigraph (paragraphs 0005 and 0041).
Claim 3:
As per claim 3, Bhagat and Yeh teach the method of claim 1 as described above and Bhagat further teaches wherein the first parameter and/or the second parameter comprises one or more parameters from the group consisting of:
sodium and potassium intake;
sodium muscle content;
drinking behavior pattern;
water intake;
water loss through perspiration and sweat;
water balance;
lean tissue index (LTI);
fat tissue index (FAT);
intracellular water (ICW);
extracellular water (ECW);
hydration state;
heart rate (paragraphs 0002 and 0005);
energy intake;
energy consumption;
sleep quality and duration;
physical activity;
and wherein the at least one second parameter comprises one or more parameters from a group consisting of blood pressure dynamics, heart rate dynamics, blood electrolyte profiles (Na, K, Ca), dialysate electrolyte composition (Na, K, Ca), fluid state, or administered medication, and wherein the at least one second parameter is retrieved from a blood treatment apparatus, a patient monitor or a clinical database as second data source during dialytic sessions (paragraphs 0002, 0005, 0040-0041, and 0044).
Claim 4:
As per claim 4, Bhagat and Yeh teach the method of claim 1 as described above and Bhagat further teaches wherein the at least one second parameter or the parameters retrieved from the clinical database comprise one or more parameters from a group consisting of clinical parameters, in particular age, sex, comorbidities, biochemical parameters, drugs, intradialytic events history, data gathered from electronic chart records, patients' symptoms, behavioral attitudes, beliefs and intradialytic or interdialytic events assessed with computer adaptive testing applications for self-reported outcomes on patients' smartphones or tablets (paragraph 0055);
Yeh further teaches and wherein the output is a prediction of future behavior or treatment for the patient related to at least one of:
intradialytic hypotension (IDH) (paragraphs 0008 and 0036). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include intradialytic hypotension as taught by Yeh in order to assist the clinician or physician to predict the patient’s current condition.
maximal tolerable ultrafiltration (UF) rate;
interdialytic weight gain;
or pre-dialysis systolic and diastolic blood pressure.
Claim 5:
As per claim 5, Bhagat and Yeh teach the method of claim 1 a described above and Yeh further teaches further comprising suggesting future behavior of or treatment for the patient that comprises suggesting a particular water and salt intake in an interdialytic period (paragraphs 0006 and 0008). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include further comprising suggesting future behavior of or treatment for the patient that comprises suggesting a particular water and salt intake in an interdialytic period as taught by Yeh in order to anticipate or forecast a patient’s health condition.
Claim 6:
As per claim 6, Bhagat and Yeh teach the method of claim 1 as described above and Yeh further teaches wherein the accuracy of the retrieved combination of the multitude of parameter values related to the patient is being assessed with regard to the predicted value, optimized and stored, in particular in the clinical database, as an optimized version (paragraphs 0041-0043). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include wherein the accuracy of the retrieved combination of the multitude of parameter values related to the patient is being assessed with regard to the predicted value, optimized and stored, in particular in the clinical database, as an optimized version as taught by Yeh in order to assist in improving the patient’s health condition.
Claim 7:
As per claim 7, Bhagat and Yeh teach the method of claim 1 as described above and Yeh further teaches comprising calibrating or optimizing the mathematical model, wherein the mathematical model is calibrated or optimized using the output prediction or suggestion (paragraph 0039). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include comprising calibrating or optimizing the mathematical model, wherein the mathematical model is calibrated or optimized using the output prediction or suggestion as taught by Yeh in order to improve the model’s prediction of a patient’s health condition.
Claim 8:
As per claim 8, Bhagat and Yeh teach the method of claim 1 as described above and Bhagat further teaches wherein the measurements from the first data source, comprise at least one of fluid state, blood pressure and heart rate (paragraph 0005).
Claim 9:
As per claim 9, Bhagat and Yeh teach the method of claim 1 as described above and Yeh further teaches wherein the output prediction is evaluated in an additional evaluating step using a measurement of the predicted parameter or state (paragraphs 0006 and 0008). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include wherein the output prediction is evaluated in an additional evaluating step using a measurement of the predicted parameter or state as taught by Yeh in order to validate the predicted or forecasted patient’s health condition.
Claim 10:
As per claim 10, Bhagat and Yeh teach the method of claim 1 as described above and Yeh further teaches wherein the output is a suggested future treatment of the patient suggesting or treatment parameters to be accomplished (paragraphs 0006 and 0008). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include wherein the output is a suggested future treatment of the patient suggesting or treatment parameters to be accomplished as taught by Yeh in order to develop goals to improve the outcome of a patient’s health condition.
Claim 11:
As per claim 11, Bhagat and Yeh teach the method of claim 1 as described above and Yeh further teaches wherein the output is a suggested future behavior or treatment for the patient ordering patient a next treatment session for the patient to take place (paragraphs 0006 and 0008). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include wherein the output is a suggested future behavior or treatment for the patient ordering patient a next treatment session for the patient to take place as taught by Yeh in order to schedule a treatment in advance for the patient to improve health outcomes.
Claim 13:
As per claim 13, Bhagat and Yeh teach system of claim 12 as described above and Bhagat further teaches wherein the second data source is or comprises a patient monitor, wherein the patient monitor is at least one of in particular a smartphone, a smart watch, or a wearable (paragraphs 0002 and 0040).
Claim 16:
As per claim 16, Bhagat and Yeh teach the hand-held device of claim 14 as described above and Bhagat further teaches comprising two interfaces programmed or configured to read in, from the first data source, the at least one value of the at least one first parameter of the patient, and to receive, from the second data source, the at least one value of the at least one second parameter (paragraphs 0002, 0041, and 0050).
Claim 17:
As per claim 17, Bhagat and Yeh teach the method of claim 9 as described above and Yeh further teaches wherein the mathematical model is calibrated or optimized using the result of the additional evaluating step (paragraphs 0039-0043). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Bhagat to include wherein the mathematical model is calibrated or optimized using the result of the additional evaluating step as taught by Yeh in order to validate the predicted or forecasted patient’s health condition.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Moissl et al. Method, Apparatus, and Device for Calculating or Approximating One or More Values Representing Parameters of a Patient
Moissl discloses a method for calculating or estimating or approximating one or more values representing parameters of a patient includes the step of interpolating or extrapolating of at least one later value of a first parameter taking into account at least one earlier value of the first parameter, at least one earlier and at least one later value of a second parameter, and a mathematical relation between the first and the second parameter. An apparatus, a blood treatment device, a digital storage device, a computer program product, and a computer program are also described.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW L HAMILTON whose telephone number is (571)270-1837. The examiner can normally be reached Monday-Thursday 9:30-5:30 pm EST.
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/MATTHEW L HAMILTON/Primary Examiner, Art Unit 3681