DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The amendments and remarks filed on 03JUN2025 have been entered and considered.
Claims 1-10, 21, & 23-31 are currently pending.
Claims 1-3, 5, 9-10, 21, 23-24, 26-27, & 31 have been amended.
Claims 7 & 28 have been canceled by applicant.
No claims have been added or withdrawn.
New matter has been added.
Claims 1-6, 8-10, 21, 23-27, & 29-31 are under examination.
Response to Arguments
Applicant's amendments filed 03JUN2025 regarding the claim objections have been fully considered and have been found to be persuasive. Therefore, the objection has been withdrawn.
Applicant's arguments filed 03JUN2025 regarding the rejections under 35 USC 101 have been fully considered and have been found to be not persuasive. Parts deemed not persuasive discussed below:
Applicant states (see Pages 12-14 of the Remarks):
“Obtaining the ECG data from a terminal, and using the first and second classification models to identify the abnormal heartbeat data in the ECG data and heartbeat data of each heartbeat cycle are not mental processes, as they are technology-specific elements where a human mind or mental process cannot be substituted.”
The examiner is not persuaded. Data acquisition through an electrode or sensor is a generic task which doesn’t make claims patent eligible as all data for any application/device will be collected from the electrode/sensor. Artificial intelligence models and machine learning are a mimic of the human mind and requires training by people to be developed. It is merely an automation of tasks, steps, calculations, etc., previously performed by a human technician. The inclusion of models in the claims does not make claims non-abstract. Therefore, the 101 rejection is being maintained.
“Furthermore, “generating an abnormality comparison image by combining the normal heartbeat waveform and the abnormal heartbeat waveform into a single image, wherein the abnormality comparison image displays a difference between the abnormal heartbeat waveform and the normal heartbeat waveform” and “displaying the abnormality comparison image to a user.” This is because one cannot generate an image that is displayed to a user using a mental process.”
The examiner is not persuaded. The image comparison can be represented as a human overlaying 2 plotted sets of data to make comparisons. The displaying to a user can be represented as this person then showing another technician the comparison. The task of displaying the results to the user provides no structure for displaying or task which doesn’t make claims patent eligible as all most data collection units will have a displaying of data processing results, whether this is calculations, transformations, or just filtering. This limitation does not provide patentable subject matter for the claim.
Applicant's arguments filed 03JUN2025 regarding the rejections under 35 USC 103 have been fully considered and have been found to be not persuasive. Parts deemed not persuasive discussed below:
Applicant states (see Pages 14-17 of the Remarks):
“However, Applicant respectfully asserts that neither Bae nor Brian teach, or even suggests “determining, by the terminal, whether the ECG data comprises abnormal heartbeat data by inputting the ECG data of the complete cardiac monitoring cycle into a first classification model” as claimed. Instead, Bae only generically describes determining whether an ECG has abnormal heartbeat data: “Arrhythmia may be diagnosed based on the shape of a single extracted waveform of the signal.” Notably, Bae fails to teach, or even suggest, “inputting the ECG data of the complete cardiac monitoring cycle into a first classification model.” This is because Bae fails to make any teaching related to any “classification model having one or more neural network layers.” However, Brian fails to teach any analysis of the ECG data or identification of the abnormal heartbeat data using the model. Indeed, none of the references teach nay specific method or step for identifying the whether the ECG data comprises abnormal heartbeat data, as claimed. Yu merely teaches using preset feature points for comparison when determining whether a heartbeat cycle is abnormal. Yu, p. 2, Il. 26-28. Nothing in Yu teaches or suggest any conditional use of a preset electrocardiogram waveform, of that the preset electrocardiogram waveform is used to “generate the normal heartbeat waveform for each heartbeat cycle of the plurality of heartbeat cycles” that is then used to generate “an abnormality comparison image” displayed that is to a user.”
The examiner is not persuaded. Bae is shown to utilize artificial intelligence for processing data in ¶0062 “In this example, a secondary determination, performed by a secondary determining unit of a server, determines whether an abnormal signal indicates arrhythmia using an artificial neural network. Also, a type of arrhythmia of the abnormal signal may be determined.”. This citation shows there is multiple determination units which may utilize AI. Brian is cited to teach the specified classification models. Since they share a similar field of endeavor, one would see both references and understand that the models of Brian can be substituted into the device of Bae as the AI model for data processing. Therefore, the examiner mainta9ins that the references teach the claim limitations.
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 1-6, 8-10, 21, 23-27, & 29-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite:
Claim 1 is to a method.
Claim 21 is to a terminal device (i.e., a System)
Therefore, claims 1-6, 8-10, 21, 23-27, & 29-31 are directed to a statutory category of invention.
Step 2A, Prong One
Regarding the independent claims 1 & 21, the limitations of, “determining, by the terminal, that the ECG data comprises abnormal heartbeat data by inputting the ECG data of the complete cardiac monitoring cycle into a first classification model having one or more neural network layers, wherein determining whether the ECG data comprise the abnormal heartbeat data indicates whether any heartbeat cycle in the complete cardiac monitoring cycle has abnormal heartbeat data”, “determining, by the terminal and in response to determining that the ECG data comprises the abnormal heartbeat data exists, whether the heartbeat data for each heartbeat cycle of the plurality of heartbeat cycles is abnormal by inputting the heartbeat data for one or more heartbeat cycles of the plurality of heartbeat cycles into a second classification model”, “generating a diagnosis report based on the abnormality comparison image”, “displaying the abnormality comparison image to a user”, “using, in response to at least one heartbeat cycle of the plurality of heartbeat cycles having normal heartbeat data, the normal heartbeat data to generate the normal heartbeat waveform for each heartbeat cycle of the plurality of heartbeat cycles, including any of the heartbeat cycles having abnormal heartbeat data”, “using, in response to each heartbeat cycle of the plurality of heartbeat cycles having abnormal heartbeat data, a preset electrocardiogram waveform to generate the normal heartbeat waveform for each heartbeat cycle of the plurality of heartbeat cycles, including any of the heartbeat cycles having abnormal heartbeat data “, “obtaining, by a terminal, from a measurement device, electrocardiography (ECG) data for a complete cardiac monitoring cycle, wherein the ECG data comprises heartbeat data for a plurality of heartbeat cycles of the complete cardiac monitoring cycle”, “generating, by the terminal and based on the ECG data, an abnormal heartbeat waveform corresponding to the abnormal heartbeat data”, “generating, by the terminal, a normal heartbeat waveform based on the ECG data, wherein the generation of the normal heartbeat waveform comprises”, and “generating an abnormality comparison image by combining the normal heartbeat waveform and the abnormal heartbeat waveform, wherein the abnormality comparison image displays a difference between the abnormal heartbeat waveform and the normal heartbeat waveform” are mental processes. The limitations as drafted, covers performance of the limitations that can be performed by a human using a pen and paper under the broadest reasonable interpretation standard. For example, combining two waveforms to create a comparison image, and generating a diagnosis report based on the comparison image encompasses nothing more than a user plotting a ECG data on a piece of paper or computer program and determining where the two waveforms do not match. If claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in human mind or by a human using a pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a)(2)(III).
Step 2A, Prong Two
Regarding the independent claims 1 & 21, this judicial exception is not integrated into a practical application. In particular, the claims recite additional elements of a “terminal”, which is a generic computer structure; a “first and second classification models”, which are merely generic computer implementation and automation of the abstract idea (i.e. a mental process); the steps for “Obtaining data”, which are insignificant extra-solution activity (i.e. data gathering); and “Display…”, which is insignificant post-solution activity (i.e. data reporting). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
Regarding the independent claim 1, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “terminal”, which is a generic computer structure; a “first and second classification models”, which are merely generic computer implementation and automation of the abstract idea (i.e. a mental process); the steps for “Obtaining data”, which are insignificant extra-solution activity (i.e. data gathering); and “Display…”, which is insignificant post-solution activity (i.e. data reporting); which are activities well-understood, routine, and conventional in the field of cardiology and all uses of the recited judicial exception require the pre-solution activity of data gathering. Therefore, the claim is not patent eligible.
Claims 2-3 & 23-24 further defines the abstract idea in the “determining” steps, additionally only introduces a data gathering step of “obtaining”, and a mathematical concept step “performing calculation based on the plurality of first prevalence probabilities, the plurality of second prevalence probabilities, and preset probability weights.”. These additional elements are not sufficient to amount to significantly more than the judicial exception. The additional elements amounts to no more than mere pre-solution activity of data gathering and processing, is well-understood, routine, and conventional in the field of cardiology. This does not amount of an inventive concept as all uses of the recited judicial exception require the pre-solution activity of data gathering. Therefore, the claim is not patent eligible.
Claims 4 & 25 further defines data gathering, such as obtaining probabilities and extracting features, and introduces the additional element “Classification model”. These additional elements are not sufficient to amount to significantly more than the judicial exception. The additional elements amounts to no more than mere pre-solution activity of data gathering and processing, is well-understood, routine, and conventional in the field of cardiology. This does not amount of an inventive concept as all uses of the recited judicial exception require the pre-solution activity of data gathering. Additionally, this judicial exception is not integrated into a practical application as the tasks are recited at high levels of generality (i.e. The commands themselves do not have any supporting structures to perform such commands). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Therefore, the claim is not patent eligible.
Claims 5 & 26 further defines the mathematical concept abstract idea of combining and generating waveforms. These additional elements are not sufficient to amount to significantly more than the judicial exception. The additional elements amounts to no more than mere pre-solution activity of data gathering and processing, is well-understood, routine, and conventional in the field of cardiology. This does not amount of an inventive concept as all uses of the recited judicial exception require the pre-solution activity of data gathering. Therefore, the claim is not patent eligible.
Claims 6, 8 , 27, & 29 further define the abstract idea of “determining” steps, additionally only introduces a data gathering step of “obtaining”, and introduces the additional element “waveform correction model” These additional elements are not sufficient to amount to significantly more than the judicial exception. The additional elements amounts to no more than mere pre-solution activity of data gathering and processing, is well-understood, routine, and conventional in the field of cardiology. This does not amount of an inventive concept as all uses of the recited judicial exception require the pre-solution activity of data gathering. Additionally, this judicial exception is not integrated into a practical application as the tasks are recited at high levels of generality (i.e. The commands themselves do not have any supporting structures to perform such commands). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Therefore, the claim is not patent eligible.
Claims 9 & 30 further defines the abstract ideas of “aligning the abnormal heartbeat waveform with the normal heartbeat waveform based on a time axis”, and “displaying the aligned abnormal heartbeat waveform and the aligned normal heartbeat waveform based on a same amplitude axis to obtain the abnormality comparison image.”. These additional elements are not sufficient to amount to significantly more than the judicial exception. The additional elements amounts to no more than mere pre-solution activity of data gathering and processing, is well-understood, routine, and conventional in the field of cardiology. This does not amount of an inventive concept as all uses of the recited judicial exception require the pre-solution activity of data gathering. Therefore, the claim is not patent eligible.
Claims 10 & 31 further defines the abstract ideas of displaying data. These additional elements are not sufficient to amount to significantly more than the judicial exception. The additional elements amounts to no more than mere pre-solution activity of data gathering and processing, is well-understood, routine, and conventional in the field of cardiology. This does not amount of an inventive concept as all uses of the recited judicial exception require the pre-solution activity of data gathering. Therefore, the claim is not patent eligible.
Therefore, claims 1-6, 8-10, 21, 23-27, & 29-31 are directed to an abstract idea without significantly more.
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.
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.
Claims 1-2, 5-6, 8-10, 21, 23, 26-27, & 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over Bae et al. (US Publication No. 20140142448; Previously Cited) in view of Chen et al. (US Patent No. 10,729,351), and Yu (CN Publication No. 108652635; Previously Cited).
Regarding claims 1 & 21, Bae discloses A terminal device, comprising: diagnosis report generation method (Bae ¶0060 “In 552, the medical team enters a feedback based on a diagnosis result and transmits the feedback to the server and the gateway, and in 533 a diagnosed analysis result is displayed to the user in the gateway so that the user performs an action based on the diagnosis result.”; Figure 5), comprising: obtaining by a terminal, from a measurement device, electrocardiography (ECG) data for a complete cardiac monitoring cycle, wherein the ECG data comprises heartbeat data for a plurality of heartbeat cycles of the complete cardiac monitoring cycle (Bae ¶0083 “To efficiently monitor the ECG signal over a 24 hour period, the sensor 710 may primarily analyze the ECG of the patient and determine whether the patient is in normal or abnormal condition. Based on this analysis, the sensor 710 may transmit data to the server 730 only in cases of necessity; for example, when an abnormal condition is detected.”); a memory (Bae ¶0088 “The software or instructions and any associated data, data files, and data structures may be embodied permanently or temporarily in any type of machine, component, physical or virtual equipment, computer storage medium or device, or a propagated signal wave capable of providing instructions or data to or being interpreted by the processing device.”); a processor (Bae ¶0087 “A processing device may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a field-programmable array, a to programmable logic unit, a microprocessor, or any other device capable of running software or executing instructions.”); and a non-transitory computer-readable storage medium storing a program to be executed by the processor (Bae ¶0089 “For example, the software or instructions and any associated data, data files, and data to structures may be recorded, stored, or fixed in one or more non-transitory computer-readable storage media.”), generating, by the terminal, a normal heartbeat waveform based on the ECG data , wherein the generation of the normal heartbeat waveform comprises: using, in response to at least one heartbeat cycle of the plurality of heartbeat cycles having normal heartbeat data, the normal heartbeat data to generate the normal heartbeat waveform for each heartbeat cycle of the plurality of heartbeat cycles, including any of the heartbeat cycles having abnormal heartbeat data (Bae ¶0043 “A type and frequency of a waveform 321 generated before the arrhythmia occurs and a waveform 322 generated after the arrhythmia occurs may be determined, and a condition of a patient may be diagnosed”); displaying the abnormality comparison image to a user. (Bae ¶0084 “Additionally, the abnormal signal to be analyzed by the medical team 740 through primary analysis may automatically be analyzed by determining the type of arrhythmia and displaying the results to the medical team 740”).
Bae does not disclose determining, by the terminal, whether the ECG data comprises abnormal heartbeat data by inputting the ECG data of the complete cardiac monitoring cycle into a first classification model having one or more neural network layers, wherein determining whether the ECG data comprise the abnormal heartbeat data indicates whether any heartbeat cycle in the complete cardiac monitoring cycle has abnormal heartbeat data; determining, by the terminal and in response to determining that the ECG data comprises the abnormal heartbeat data exists, whether the heartbeat data for each heartbeat cycle of the plurality of heartbeat cycles is abnormal by inputting the heartbeat data for one or more heartbeat cycles of the plurality of heartbeat cycles into a second classification model; combining a normal heartbeat waveform and the abnormal heartbeat waveform to obtain an abnormality comparison image, wherein the abnormality comparison image displays a difference between the abnormal heartbeat waveform and the normal heartbeat waveform; and using, in response to each heartbeat cycle of the plurality of heartbeat cycles having abnormal heartbeat data, a preset electrocardiogram waveform to generate the normal heartbeat waveform for each heartbeat cycle of the plurality of heartbeat cycles, including any of the heartbeat cycles having abnormal heartbeat data.
Chen in a similar field of endeavor of heartbeat classification teaches determining, by a terminal, whether the ECG data comprises abnormal heartbeat data by inputting the ECG data of the complete cardiac monitoring cycle into a first classification model having one or more neural network layers, wherein determining whether the ECG data comprise the abnormal heartbeat data indicates whether any heartbeat cycle in the complete cardiac monitoring cycle has abnormal heartbeat data; determining, by the terminal and in response to determining that the ECG data comprises the abnormal heartbeat data exists, whether the heartbeat data for each heartbeat cycle of the plurality of heartbeat cycles is abnormal by inputting the heartbeat data for one or more heartbeat cycles of the plurality of heartbeat cycles into a second classification model (Figure 18 showing multiple layered Machine Learning algorithms 1810-1837, and normal and abnormal data is displayed at 1806); combining a normal heartbeat waveform and the abnormal heartbeat waveform to obtain an abnormality comparison image, wherein the abnormality comparison image displays a difference between the abnormal heartbeat waveform and the normal heartbeat waveform (Chen Column 12 Lines 27-30 “Based on the comparison, the processor can determine differences from the normal data. All information calculated by the processor can also be displayed on display 730.”); and using, in response to each heartbeat cycle of the plurality of heartbeat cycles having abnormal heartbeat data, a preset electrocardiogram waveform to generate the normal heartbeat waveform for each heartbeat cycle of the plurality of heartbeat cycles, including any of the heartbeat cycles having abnormal heartbeat data (Chen Column 17 Lines 4-12 “Signal processor 830 can be software implemented on another processor of the ECG device, such as a processor of display device 840. Signal processor 830 can also be a remote server that receives the detected and amplified difference voltage signal from detector 820, detects or calculates one or more subwaveforms within and/or in the interval between the P, Q, R, S, T, U, and J waveforms, and sends the detected and amplified different voltage signal and the one or more subwaveforms to display device 840.”). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device of Bae with the methods for determining, by the terminal, whether the ECG data comprises abnormal heartbeat data by inputting the ECG data of the complete cardiac monitoring cycle into a first classification model having one or more neural network layers, wherein determining whether the ECG data comprise the abnormal heartbeat data indicates whether any heartbeat cycle in the complete cardiac monitoring cycle has abnormal heartbeat data; determining, by the terminal and in response to determining that the ECG data comprises the abnormal heartbeat data exists, whether the heartbeat data for each heartbeat cycle of the plurality of heartbeat cycles is abnormal by inputting the heartbeat data for one or more heartbeat cycles of the plurality of heartbeat cycles into a second classification model combining a normal heartbeat waveform and the abnormal heartbeat waveform to obtain an abnormality comparison image, wherein the abnormality comparison image displays a difference between the abnormal heartbeat waveform and the normal heartbeat waveform; and using, in response to each heartbeat cycle of the plurality of heartbeat cycles having abnormal heartbeat data, a preset electrocardiogram waveform to generate the normal heartbeat waveform for each heartbeat cycle of the plurality of heartbeat cycles, including any of the heartbeat cycles having abnormal heartbeat data, as taught by Chen, for the purposes of allowing greater efficiency and more accurate diagnosis (Bae ¶0053).
Bae in combination with Chen does not disclose generating an alert based on the abnormality comparison image. Yu in a similar field of endeavor of medical monitoring teaches generating an alert based on the abnormality comparison image (Yu Page 2 Lines 51-53 “The heart impact map signal stored in the monitoring database is analyzed to determine whether the heart impact map signal has a preset abnormal feature, and an early warning signal is generated when an abnormal feature occurs.”). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device of Bae in combination with Chen with the methods for generating an alert based on the abnormality comparison image, as taught by Yu, for the purposes of allowing greater efficiency and more accurate diagnosis (Bae ¶0053).
Regarding claims 2 & 23, Bae in combination with Chen and Yu teaches the limitations of claims 1 & 21. Bae further discloses wherein obtaining, based on the ECG data, an abnormal heartbeat waveform corresponding to the abnormal heartbeat data further comprises: obtaining, for each heartbeat cycle, an abnormal heartbeat waveform in the heartbeat cycle in response to the heartbeat data in the heartbeat cycle being abnormal (Bae ¶0049 “In this example, when the measured signal is determined to have an abnormal waveform by the primary determining unit 414, a predetermined interval of the signal may be transmitted to the gateway 420 through a transmitter 413. The signal of the predetermined interval ("the abnormal signal") may be the ECG signal determined to have the abnormal waveform by the primary determining unit 414, and may include data corresponding to two minutes before or after the abnormal waveform.”; ¶0059).
Regarding claims 5 & 26, Bae in combination with Chen and Yu teaches the limitations of claims 1-2; 21 & 23 respectively. Bae further discloses wherein using the normal heartbeat data to generate the normal heartbeat waveform for each heartbeat cycle of the plurality of heartbeat cycles comprises: adding, for each heartbeat cycle, a heartbeat waveform corresponding to the heartbeat cycle to a normal heartbeat waveform set if the heartbeat data in the heartbeat cycle is normal (Bae ¶0047 “In this example, the ECG sensor unit 410 measures a normal waveform or an arrhythmia waveform using an ECG measuring unit 411. A signal with the measured waveform is stored in an ECG storage unit 412. In an example, data ranging from about two minutes before or after a point in time at which arrhythmia occurs may be stored in the ECG storage unit 412 after being determined. Additionally, all measured data may be stored in the storage unit 412.”; ¶0071 “A scheme of extracting the R waveform may be used to determine whether a measured ECG signal is a normal signal or an abnormal signal by a primary determining unit.”); and generating the normal heartbeat waveform based on at least one heartbeat waveform in the normal heartbeat waveform set. (Bae ¶0043 “A type and frequency of a waveform 321 generated before the arrhythmia occurs and a waveform 322 generated after the arrhythmia occurs may be determined, and a condition of a patient may be diagnosed”).
Regarding claims 6 & 27, Bae in combination with Chen and Yu teaches the limitations of claims 1-2, 5; 21, 23, 26 respectively. Bae further discloses wherein generating the normal heartbeat waveform based on at least one heartbeat waveform in the normal heartbeat waveform set comprises: obtaining feature data of each band of each heartbeat waveform in the normal heartbeat waveform set (Bae ¶0018 “The determining of whether the measured biosignal has a normal waveform may comprise determining whether the ECG has a normal waveform based on a rhythm and a feature point of the ECG”; ¶0068 “Referring to FIG. 6, Feature points extracted from the ECG signal include the heart rate, the QRS duration 624, the PR interval 612, the QT interval 625, a type of a T waveform, and the like. The feature points of the signal may be periodically extracted, and the heart rate, the QRS duration 624, the PR interval 612, the QT interval 625, and the type of the T waveform may be morphometric characteristics of the ECG signal”); inputting the feature data of each band into a waveform correction model corresponding to each band to obtain a standard waveform of each band; and combining a plurality of standard waveforms to obtain the normal heartbeat waveform. (Bae ¶0070-¶0071).
Regarding claims 8 & 29, Bae in combination with Chen and Yu teaches the limitations of claims 1-2; 21 & 23 respectively. Bae further discloses wherein determining whether the heartbeat data in each heartbeat cycle in the ECG data is abnormal comprises determining whether the heartbeat data in each heartbeat cycle in the ECG data is abnormal based on whether the ECG data is arrhythmia data, and wherein the method further comprises, determining, before determining whether the heartbeat data in each heartbeat cycle in the ECG data is abnormal, whether the ECG data is arrhythmia data. (Bae ¶0015-¶0016 “The transmitter may be further configured to transmit only an interval of the ECG, and the interval may include only a portion of the ECG showing arrhythmia and two minutes before and after that portion. In another general aspect, there is provided a method of remotely managing a disease, the method including determining whether a measured biosignal has a normal waveform; transmitting the biosignal in response to the biosignal being determined to have an abnormal waveform; and receiving the transmitted biosignal”).
Regarding claims 9 & 30, Bae in combination with Chen and Yu teaches the information of claims 1 & 21. Neither Bae or Yu teach wherein generating the abnormality comparison image: aligning the abnormal heartbeat waveform with the normal heartbeat waveform based on a time axis; and displaying the aligned abnormal heartbeat waveform and the aligned normal heartbeat waveform based on a same amplitude axis to obtain the abnormality comparison image. Brian in a similar field of endeavor of ECG diagnostic tools teaches generating the abnormality comparison image: aligning the abnormal heartbeat waveform with the normal heartbeat waveform based on a time axis; and displaying the aligned abnormal heartbeat waveform and the aligned normal heartbeat waveform based on a same amplitude axis to obtain the abnormality comparison image. Chen further teaches wherein generating the abnormality comparison image: aligning the abnormal heartbeat waveform with the normal heartbeat waveform based on a time axis; and displaying the aligned abnormal heartbeat waveform and the aligned normal heartbeat waveform based on a same amplitude axis to obtain the abnormality comparison image. Brian in a similar field of endeavor of ECG diagnostic tools teaches generating the abnormality comparison image: aligning the abnormal heartbeat waveform with the normal heartbeat waveform based on a time axis; and displaying the aligned abnormal heartbeat waveform and the aligned normal heartbeat waveform based on a same amplitude axis to obtain the abnormality comparison image (Chen Abstract “The ECG waveform is converted to a frequency domain waveform, which, in turn, is separated into two or more different frequency domain waveforms, which, in turn, are converted into a plurality of time domain cardiac electrophysiological subwaveforms and discontinuity points between these subwaveforms. The plurality of subwaveforms and discontinuity points are compared to a database of subwaveforms and discontinuity points for normal and abnormal patients or to a set of rules developed from the database.”). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device of Bae in combination with Chen and Yu with the methods for generating the abnormality comparison image: aligning the abnormal heartbeat waveform with the normal heartbeat waveform based on a time axis; and displaying the aligned abnormal heartbeat waveform and the aligned normal heartbeat waveform based on a same amplitude axis to obtain the abnormality comparison image, as taught by Chen, for the purposes of allowing greater efficiency and more accurate diagnosis (Bae ¶0053).
Regarding claims 10 & 31, Bae in combination with Chen an Yu teaches the information of claims 1, 9; 21, 30. Neither Bae or Yu teaches displaying the diagnosis report, wherein the diagnosis report comprises a complete waveform diagram, the complete waveform diagram comprises a heartbeat waveform in each heartbeat cycle in the ECG data, at least one abnormal heartbeat waveform is marked in the complete waveform diagram, and each abnormal heartbeat waveform corresponds to one abnormality comparison image and displaying, based on whether an operation triggered for any abnormal heartbeat waveform in the complete waveform diagram is detected, an abnormality comparison image corresponding to the abnormal heartbeat waveform, an arrhythmia disease corresponding to the abnormal heartbeat waveform, and an overall prevalence probability of the arrhythmia disease. Chen teaches displaying the diagnosis report, wherein the diagnosis report comprises a complete waveform diagram, the complete waveform diagram comprises a heartbeat waveform in each heartbeat cycle in the ECG data, at least one abnormal heartbeat waveform is marked in the complete waveform diagram, and each abnormal heartbeat waveform corresponds to one abnormality comparison image (Chen Column 22 Lines 51-61 “Display device 1940 also displays one or more markers at the location of the at least one subwaveform or the one or more discontinuity points on the ECG waveform and identifies the one or more markers as a normal or abnormal cardiac electrophysiological signal. For example, FIGS. 35 and 36 show how one or more markers are displayed on ECG waveforms to indicate normal or abnormal cardiac electrophysiological signals. The one or more markers can be identified as a normal or abnormal cardiac electrophysiological signal using symbols, colors, or text, for example.”); and displaying, based on whether an operation triggered for any abnormal heartbeat waveform in the complete waveform diagram is detected, an abnormality comparison image corresponding to the abnormal heartbeat waveform, an arrhythmia disease corresponding to the abnormal heartbeat waveform, and an overall prevalence probability of the arrhythmia disease (Chen Figure 13 as described in Column 16 Lines 9-20 “As shown in block 1360, the signal processor further analyzes the saah ECG waveforms to produce saah ECG data. This saah ECG data is sent to display 1380. Additionally, as shown in block 1370, the signal processor further analyzes the saah to obtain endocardium and epicardium data. This data is compared to recorded normal and abnormal data. The signal processor then produces automatic pattern recognition diagnosis (APD) information, and this information is sent to display 1380. APD information is, for example, patterns and/or colors that allow a user to easily and quickly determine that normal or abnormal endocardium and/or epicardium data was found.”). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device of Bae in combination with Chen and Yu with the methods for displaying the diagnosis report, wherein the diagnosis report comprises a complete waveform diagram, the complete waveform diagram comprises a heartbeat waveform in each heartbeat cycle in the ECG data, at least one abnormal heartbeat waveform is marked in the complete waveform diagram, and each abnormal heartbeat waveform corresponds to one abnormality comparison image and displaying, based on whether an operation triggered for any abnormal heartbeat waveform in the complete waveform diagram is detected, an abnormality comparison image corresponding to the abnormal heartbeat waveform, an arrhythmia disease corresponding to the abnormal heartbeat waveform, and an overall prevalence probability of the arrhythmia disease, as taught by Chen, for the purposes of allowing a accurate diagnosis (Bae ¶0053).
Claims 3-4 & 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Bae et al. (US Publication No. 20140142448; Previously Cited) in view of Chen et al. (US Patent No. 10,729,351), Yu (CN Publication No. 108652635; Previously Cited), and Brian et al. (US Publication No. 20100217144; Previously Cited).
Regarding claims 3 & 24, Bae in combination with Chen and Yu teaches the limitations of claims 1-2; 21 & 23 respectively. The combination does not further teach obtaining a plurality of first prevalence probabilities based on a prevalence probability of each of a plurality of arrhythmia diseases based on the ECG data; obtaining a plurality of second prevalence probabilities based on a prevalence probability of each of the plurality of arrhythmia diseases based on the heartbeat data in each heartbeat cycle; performing one or more calculations based on the plurality of first prevalence probabilities, the plurality of second prevalence probabilities, and preset probability weights, to obtain a plurality of overall prevalence probabilities based on a prevalence probability of each arrhythmia disease of the plurality of arrhythmia diseases; and determining, based on the plurality of overall prevalence probabilities, whether the heartbeat data in each heartbeat cycle is abnormal.
Brian in a similar field of endeavor of cardiac predictive and diagnostic systems further teaches obtaining a plurality of first prevalence probabilities based on a prevalence probability of each of a plurality of arrhythmia diseases based on the ECG data; obtaining a plurality of second prevalence probabilities based on a prevalence probability of each of the plurality of arrhythmia diseases based on the heartbeat data in each heartbeat cycle; performing one or more calculations based on the plurality of first prevalence probabilities, the plurality of second prevalence probabilities, and preset probability weights, to obtain a plurality of overall prevalence probabilities based on a prevalence probability of each arrhythmia disease of the plurality of arrhythmia diseases; and determining, based on the plurality of overall prevalence probabilities, whether the heartbeat data in each heartbeat cycle is abnormal. (Brian ¶0034 “As an example, logistic regression analysis can be used to estimate the probability of a new patient being a member of a particular disease or event-risk group based strictly on his/her ECG variables. Classification of patients can be made on the basis of whether or not the predicted probability of being in a disease or event-risk group is greater than or less than, for example, 0.5.”; Abstract “A plurality of ECG Superscore formulae, created from multiple parameter ECG measurements including those from advanced ECG techniques, can be optimized using additive multivariate statistical models or pattern recognition procedures, with the results compared against a large database of ECG measurements from individuals with known cardiac conditions and/or previous cardiac events…. ECG Superscores have retrospectively optimized accuracy for identifying and screening individuals for underlying heart disease and/or for determining the risk of future cardiac event…”). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device of Bae in combination with Chen and Yu with the methods for obtaining a plurality of first prevalence probabilities based on a prevalence probability of each of a plurality of arrhythmia diseases based on the ECG data; obtaining a plurality of second prevalence probabilities based on a prevalence probability of each of the plurality of arrhythmia diseases based on the heartbeat data in each heartbeat cycle; performing one or more calculations based on the plurality of first prevalence probabilities, the plurality of second prevalence probabilities, and preset probability weights, to obtain a plurality of overall prevalence probabilities based on a prevalence probability of each arrhythmia disease of the plurality of arrhythmia diseases, as taught by Brian, for the purposes of allowing greater efficiency and more accurate diagnosis (Bae ¶0053).
Regarding claims 4 & 25, Bae in combination with Chen, Yu, and Brian teaches the limitations of claims 1-3; 21, 23, 24 respectively. Bae further discloses extracting a feature between heartbeat data in all heartbeat cycles in the ECG data, to obtain first feature data; and obtaining the plurality of second prevalence probabilities based on the prevalence probability of each of the plurality of arrhythmia diseases based on the heartbeat data in each heartbeat cycle comprises: extracting a feature of the heartbeat data in each heartbeat cycle, to obtain second feature data of the heartbeat data in each heartbeat cycle (Bae ¶0048 “Whether the measured ECG signal has a normal waveform or an abnormal waveform may be determined based on rhythms of an ECG using an R waveform and whether a feature point of the measured ECG signal exists.”; ¶0014 “The transmitter may be further configured to transmit only an interval of the ECG, and the second determining unit may be further configured to determine the type of arrhythmia based on a rhythm and a feature point of an R wave of the transmitted interval.).
Bae in combination with Chen and Yu does not disclose inputting the first feature data into a preset first classification model, to obtain the plurality of first prevalence probabilities; and inputting each piece of second feature data into a preset second classification model, to obtain the plurality of second prevalence probabilities in the heartbeat data in each heartbeat cycle. Brian teaches inputting the first feature data into a preset first classification model, to obtain the plurality of first prevalence probabilities; and inputting each piece of second feature data into a preset second classification model, to obtain the plurality of second prevalence probabilities in the heartbeat data in each heartbeat cycle (Brian ¶0039 “In the presently preferred embodiment of the invention, ECG Superscores are derived from one or more additive models, support vector machines, discriminant analyses, neural networks, recursive partitioning analyses, or classification and regression tree analyses, many of these techniques being referred to as pattern recognition techniques by those experienced in the art. The Superscores are then used to predict, offline or in real time if desired: … In their practical application in the presently preferred embodiment, the Superscores either: 1) combine the results derived strictly from three or more advanced ECG techniques; or 2) combine the results from one or more conventional ECG techniques with those from two or more advanced ECG techniques.”; Figure 8 Showing a plurality of probabilities produced from the analysis such as detailed in ¶0039). Before the effective filing date, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device of Bae in combination with Chen and Yu with the methods for inputting the first feature data into a preset first classification model, to obtain the plurality of first prevalence probabilities; and inputting each piece of second feature data into a preset second classification model, to obtain the plurality of second prevalence probabilities in the heartbeat data in each heartbeat cycle, as taught by Brian, for the purposes of allowing greater efficiency and more accurate diagnosis (Bae ¶0053).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu et al. (US Publication No. 20220233129)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/MEGAN T FEDORKY/
Examiner, Art Unit 3796
/ALLEN PORTER/Primary Examiner, Art Unit 3796
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