DETAILED ACTION
All references to the instant specification have been cited using PG Pub US20240366144A1.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 for application filed in KR on Sept. 25, 2021 (10-2021-0126785).
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference for application filed in KR 10-2022-0118440, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on Jun. 30, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on Feb. 3, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement filed Mar. 15, 2024 fails to comply with 37 CFR 1.98(a)(3)(i) because it does not include a concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information, of each reference listed that is not in the English language.
The attached citation of JP 2020-074949 does not include an explanation of relevancy and an English translation has not been made available. It has been placed in the application file, but the information referred to therein has not been considered.
Specification
The disclosure is objected to because of the following informalities: “However, FIG. 1 shows” in in ¶[0050] should be changed to “However, FIG. 2 shows”.
Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 11 is objected to because of the following informalities: “direction” should be added after the word “right” to add clarity. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 and 3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 2, the limitation of “measured with 12 multiple leads” is unclear. It seems unclear whether there are 12 sets of leads or 12 leads. For the purposes of examination, the limitation will be interpreted as “measured with 12 leads”.
Regarding claim 3, the limitation of “the neural network model includes a second sub-neural network model that has been trained based on at least six limb leads or six precordial leads” is unclear. The neural network model is trained based on electrocardiogram data so it seems unclear how the neural network model is being trained based on the leads. For the purposes of examination, the limitation will be interpreted as “the neural network model includes a second sub-neural network model that has been trained based on electrocardiogram data measured with at least six limb leads or six precordial leads”.
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.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it recites a computer program stored in a computer-readable storage medium. MPEP 2106.03(II) states “a claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter… a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.” To narrow the claim to those embodiments that fall within a statutory category, the claim can be amended to “non-transitory computer-readable storage medium”.
Claim 1- 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract ideas of “diagnosing thyroid dysfunction based on an electrocardiogram”, “acquiring electrocardiogram data”, “estimating a probability of occurrence of thyroid dysfunction for a subject of measurement of the electrocardiogram data based on the electrocardiogram data”, and “wherein the neural network model is trained based on correlations between thyroid function and changes in electrocardiogram characteristics” without significantly more.
Step 1:
Claims 1-13 recite a method and claim 15 recites a computing device, a machine. Therefore, the claims fall within the statutory categories. Claim 14 is directed to non-statutory subject matter as set forth above. However, claim 14, if amended to claim to those embodiments that fall within a statutory category (e.g., “non-transitory computer-readable storage medium”), the following rejection of claim 14 is ineligible for the reasons provided below.
Step 2A, Prong 1:
Claims 1, 14, and 15 recite limitations of diagnosing thyroid dysfunction based on an electrocardiogram, acquiring electrocardiogram data, estimating a probability of occurrence of thyroid dysfunction for a subject of measurement of the electrocardiogram data based on the electrocardiogram data, and wherein the neural network model is trained based on correlations between thyroid function and changes in electrocardiogram characteristics. The limitations, as drafted, describe a process that, under its broadest reasonable interpretation, includes performance of the limitation in the mind except for the claim 1 recitation of
“computing device including at least one processor” and “pre-trained neural network model”, the claim 14 recitation of “computer program stored in a computer-readable storage medium” and “pre-trained neural network model”, and the claim 15 recitation of “computing device”, “a processor including at least one core”, “a memory including program codes that are executable on the processor” and “pre-trained neural network model”. That is, other than reciting that a system is performing these tasks, nothing in the claims precludes the steps from practically being performed in the human mind. MPEP 2106.04(a)(2)(III) states that the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. For example, aside from the recitation of “computing device”, “processor”, “a processor including at least one core”, “a memory including program codes that are executable on the processor” and “pre-trained neural network model” language, the claim encompasses the user obtaining ECG waveform data, comparing it to patterns in thyroid functions against changes in ECG characteristics, and determining the likelihood of a thyroid dysfunction. These limitations are a mental process.
Step 2A, Prong 2:
The claims recite “computing device including at least one processor”, “computing device”, “a processor including at least one core”, “a memory including program codes that are executable on the processor” and “pre-trained neural network model” to perform the abstract steps. These components read on a computer implemented system and are recited at a high level of generality, i.e., as a generic processor, performing a generic computer function of processing data (see in ¶[0045], ¶[0049]-¶[0051], and ¶[0055] of the specification). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional limitation does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount 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 on a generic computer cannot integrate a judicial except into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if it is more than what is well- understood, routine, conventional activity in the field. The specification in ¶[0045], ¶[0049]-¶[0051], and ¶[0055] does not provide any indication that the computer and computer programming is anything other than a generic, off-the-shelf computer component. Court decisions cited in MPEP 2106.05(d)(II) indicate that computer‐ implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim, as a whole, amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). Accordingly, a conclusion that the generic computer functions merely being used to implement an abstract idea is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
Dependent claims 2-13 further limit the process of diagnosing thyroid dysfunction based on an electrocardiogram, acquiring electrocardiogram data, estimating a probability of occurrence of thyroid dysfunction for a subject of measurement of the electrocardiogram data based on the electrocardiogram data, and wherein the neural network model is trained based on correlations between thyroid function and changes in electrocardiogram characteristics. Therefore, these claims further limit the abstract idea already indicated in independent claim 1 and they are ineligible for the same reasons provided for claim 1 above.
For these reasons, there is no inventive concept in the claims and thus they are ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 8, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (KR1020060117546, published Nov. 17, 2006, hereinafter referred to as “Lee”) in view of Deepika et al. (EAI Endorsed Transactions on Energy Web, Aug. 26, 2020, Vol. 8, Issue 32, cited in IDS filed on Mar. 15, 2024 and hereinafter referred to as “Deepika”), and in further view of Yang et al. (US 20190090774 A1, published Mar. 28, 2019, hereinafter referred to as “Yang”).
Regarding claim 1, Lee teaches a method of diagnosing a disease based on an electrocardiogram (Fig. 2 “A control unit (500) that determines whether a user has a disease and the type of disease based on the user's electrocardiogram signal” in ¶[0021]), the method comprising: acquiring electrocardiogram data (Fig. 2, “and input unit (100) that receives the user’s electrocardiogram signal” in ¶[0021]); wherein the neural network model is trained based on the disease and electrocardiogram characteristics Fig. 2 “The feature values extracted from the above feature extraction unit (300) are trained through a neural network to determine a disease diagnosis for the input electrocardiogram signal” ¶[0021]).
Lee does not disclose the method of diagnosing thyroid dysfunction based on an electrocardiogram, the method being performed by a computing device including at least one processor, and estimating a probability of occurrence of thyroid dysfunction for a subject of measurement of the electrocardiogram data based on the electrocardiogram data by using a pre-trained neural network model; wherein the neural network model is trained based on correlations between thyroid function and changes in electrocardiogram characteristics.
Deepika teaches ECG parameters in relation to thyroid dysfunction and this analysis is used to assess the relationship among outright thyroxine (T4) and thyrotropin (TSH) levels with ECG parameters (beat, PR intervals, QRS term, QT between times, and JT between times) (pg. 2). The process involved training using TSH and FT4 levels as well as the parameters in Table 2 which correspond to electrocardiogram characteristics (pg. 4 section 3.3.3). The classification technique is used to categorize the hypothyroid and hyperthyroid illnesses. The efficiency of classifiers is evaluated using the confusion matrix on the subjects of accuracy, sensitivity, and specificity. The C4.5 algorithm gives 93.58% which is providing better accuracy than determination stump tree accuracy and also C4.5 Algorithm provides very minimum error rate than decision stump (pg. 9).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to diagnose thyroid dysfunction based on the electrocardiogram, estimate the probability of occurrence of the thyroid dysfunction using the pre-trained neural network, and train the neural network based on correlations between thyroid function and changes in ECG as taught by Deepika in the method of Lee. This is obvious because the device in Lee is capable of learning the material in Deepika and it is known that thyroid disorders affect ECGs. Further, the system is not 100% accurate so it would be obvious to provide a probability of occurrence since the medical diagnosis is uncertain and this allows for optimization of medical resources.
Lee and Deepika do not disclose the method being performed by a computing device including at least one processor.
Yang’s invention relates to systems and methods for localizing activity in cardiac tissue using data acquired non-invasively via electrocardiography (¶[0003]). The computing device 710 in Fig. 7 may include one or more processors and memory for storing instructions that are executed by the one or more processors to provide the functionality being discussed (¶[0062]). The device consists of a processor and memory having instructions that, when executed by the processor, are configured to: use the electrocardiography device to record electrical activity in the heart of a subject; feed the electrical data to one or more neural networks; and receive from the one or more neural networks an identification of a segment of the heart at which an arrhythmia originates (¶[0013]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to implement the method of Lee and Deepika onto a computing device including at least one processor as taught by Yang. This is obvious because it provides a medium for the neural network to work.
Regarding claim 2, Lee discloses wherein the neural network model includes a first sub-neural network model that has been trained based on electrocardiogram data measured with 12 leads (¶[0006-0008] and neural networks are composed of sub-neural networks).
Deepika further teaches the 12-lead electrocardiograph as being the standard measurement (pg. 2).
Regarding claim 8, Lee teaches wherein the correlations between the disease and changes in electrocardiographic characteristics are based on electrocardiographic characteristics, including at least one of a length of a QT interval, and QRS duration (“Five features, including the heart rate, QRS duration, PR interval, QT interval, and T wave type, are input into the neural network” in ¶[0056]).
Lee does not teach correlations between thyroid function and changes in electrocardiographic characteristics.
Deepika teaches ECG parameters in relation to thyroid dysfunction and this analysis is used to assess the relationship among outright thyroxine (T4) and thyrotropin (TSH) levels with ECG parameters (beat, PR intervals, QRS term, QT between times, and JT between times) (pg. 2). The process involved training using TSH and FT4 levels as well as the parameters in Table 2 which correspond to electrocardiogram characteristics (pg. 4 section 3.3.3). The classification technique is used to categorize the hypothyroid and hyperthyroid illnesses.
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to correlate between thyroid function and changes in electrocardiographic characteristics as taught by Deepika in the method of Lee in order to more accurately assess thyroid disorders.
Regarding claim 13, Lee does not teach wherein estimating the probability of occurrence of thyroid dysfunction for the subject of measurement of the electrocardiogram data based on the electrocardiogram data by using the pre-trained neural network model comprises: estimating the probability of occurrence of thyroid dysfunction for the subject of measurement of the electrocardiogram data by inputting biological data including at least one of age and gender, together with the electrocardiogram data, to the neural network model.
Deepika teaches that Subclinical thyroid dysfunction is defined as a typical condition, where serum thyrotrophic hormone (TSH) levels is found to be below (hyperthyroidism) or above (hypothyroidism) the reference interval with traditional free thyroxin (FT4) levels. It is commonly prevailing in older (age) girls (gender) (pg. 1).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to include age or gender as additional inputs in the neural network as taught by Deepika in the method of Lee since thyroid dysfunction is more common in older women and can then be used to use to strengthen the probability measurement.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Deepika, in further view of Yang (hereinafter “modified Lee”) as applied to claim 1 above, in further view of Wellens (The New England Journal of Medicine, Feb. 4, 1999, Vol. 340, No. 5., pg. 381, hereinafter referred to as “Wellens”).
Regarding claim 3, modified Lee teaches the method of claim 1.
Although Lee teaches six limb leads and six precordial leads, modified Lee does not disclose wherein the neural network model includes a second sub-neural network model that has been trained based on electrocardiogram data measured with at least six limb leads or six precordial leads.
Wellens’ article discusses the value of precordial leads in an electrocardiogram. The article teaches that traditionally, 12 leads are recorded, 6 leads on the extremities (limb leads) and 6 on the precordium. This has been the standard approach for almost half a century. The extremity leads give a more distant image of the electrical activity of the heart. For example, leads II and III record electrical activity from the inferior wall. The precordial leads, because they are unipolar and closer to the heart, primarily reflect the cardiac electrical activity directly beneath the electrode. Therefore, because of their position on the chest wall leads V2 to V6 primarily give information on the process of activation and repolarization of the anterior and lateral aspects of the left ventricle. Lead V1 provides information on the interventricular septum and the superior part of the right ventricle (pg. 381).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to train the neural network model with data measured with at least six limb leads or six precordial leads on as taught by Wellens in the method of modified Lee in order to obtain signals related to different positions of the heart.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over modified Lee as applied to claim 1 above, in further view of Witvliet et al. (Journal of Electrocardiology, 2021, Vol. 66, pg 33-37, available online on Mar. 4, 2021).
Regarding claim 4, modified Lee teaches the method of claim 1.
Modified Lee does not disclose wherein the neural network model includes a third sub-neural network model that has been trained based on electrocardiogram data measured with single leads.
Witvliet’s study relates to an overview of the usefulness and potential pitfalls when implementing 1 L-ECGs into everyday clinical practice. The study teaches that single-lead electrocardiograms are increasingly used in (pre)clinical settings for the detection and monitoring of a range of rhythm and conduction disorders (abstract). The intended use of 1 L-ECGs has been detecting AF and it has been validated for this purpose. 1 L-ECGs can be a less time-consuming alternative for 12 L-ECG (pg. 33-34).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to train the neural network based on ECG data measured with single leads as taught by Witvliet in the method of modified Lee because one lead ECG is capable of detecting Afib and is becoming a more common and less time consuming alternative for 12 lead ECG.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over modified Lee as applied to claim 1 above, in further view of Oommen (Towards Data Science, Nov. 28, 2020, hereinafter referred to as “Oommen”) and the Institute of Medicine (Medicare Coverage of Routine Screening for Thyroid Dysfunction, 2003, Washington, DC: The National Academies Press. Pg. 21, hereinafter “Institute of Medicine”).
Regarding claim 5, modified Lee discloses the method of claim 1.
Although Deepika’s study assesses the relationship among outright thyroxine (T4) and thyrotropin (TSH) levels with ECG parameters in order to classify subclinical thyroid dysfunction (pg.1 and 3), modified Lee does not disclose wherein: the neural network model includes a neural network including a plurality of residual blocks; and the neural network including the residual blocks receives the electrocardiogram data and outputs a probability of occurrence of overt hyperthyroidism.
Oommen teaches the benefits of residual blocks in deep neural networks. Residual blocks create an identity mapping to activations earlier in the network to thwart the performance degradation problem associated with deep neural architectures (Oommen).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to have a neural network comprising residual blocks as taught by Oommen in the method of modified Lee in order to combat performance degradation seen in deep neural networks.
Modified Lee and Oommen (hereinafter “modified Lee”) do not disclose the neural network including the residual blocks receives the electrocardiogram data and outputs a probability of occurrence of overt hyperthyroidism.
Institute of medicine teaches that overt hyperthyroidism is defined by a low serum TSH concentration and a high serum free T4 concentration (Institute of Medicine pg. 22).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to output the probability of occurrence of overt hyperthyroidism in the method of modified Lee because modified Lee’s device is capable of diagnosing using the TSH and T4 concentration conditions of subclinical hyperthyroidism and would then be able to apply the same concept to overt hyperthyroidism.
Regarding claim 6, modified Lee and Oommen inherently teach wherein the overt hyperthyroidism corresponds to a case where a free thyroxine level is higher than a predetermined reference range or a case where a thyroid-stimulating hormone level is lower than a reference range.
Overt hyperthyroidism is defined by a low serum TSH concentration (thyroid-stimulating hormone level is lower than a reference range) and a high serum free T4 concentration (free thyroxine level is higher than a predetermined reference range) (Institute of Medicine pg. 22).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over modified Lee as applied to claim 1 above, in further view of Zhang et al. (Zhang, J. et al. “MLBF-Net: A Multi-Lead-Branch Fusion Network for Muti-Class Arrythmia Classification Using 12-Lead ECG”. Cardiovascular Devices and Systems. IEEE Journal of Transitional Engineering in Health and Medicine. 15 Mar. 2021. Vol.9, 2021. Doi: 10.1109/JTEHM.2021.3064675, hereinafter referred to as “Zhang”).
Regarding claim 7, modified Lee teaches the method of claim 1 including the probability of occurrence of thyroid dysfunction as shown in claim 1.
Modified Lee does not disclose wherein: the neural network model includes neural networks corresponding to a plurality of respective leads of the electrocardiogram data; and outputs of the neural networks are concatenated into one to derive the probability of occurrence of thyroid dysfunction.
Zhang’s study relates to deep neural networks and automatic arrhythmia detection using 12-lead electrocardiograms. The study teaches that in the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix (neural networks corresponding to a plurality of respective leads of the electrocardiogram data; and outputs of the neural networks are concatenated), and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information (abstract).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to include neural networks corresponding to a plurality of respective leads as taught by Zhang in the method of modified Lee in order to extract useful information.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over modified Lee as applied to claim 8 above, in further view of Klein et al. (Klein, I. et al. “Thyroid Disease and the Heart”. Circulation. 09 Oct. 2007. Vol.116:15, p.1725-1734. https://doi.org/10.1161/CIRCULATIONAHA.106.678326 , hereinafter referred to as “Klein”).
Regarding claim 9, modified Lee teaches the method of claim 8.
Although Lee teaches sinus tachycardia may be caused by hyperthyroidism (¶[0023]), modified Lee does not disclose wherein the probability of occurrence of thyroid dysfunction increases as the frequency of tachycardia increases.
Klein’s study relates to cardiovascular signals in relation to thyroid disease. The study teaches sinus tachycardia is the most common rhythm disturbance and is recorded in almost all patients with hyperthyroidism. An increase in resting heart rate is characteristic of this disease (pg. 1730).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to increase the probability of occurrence of thyroid dysfunction as the frequency of tachycardia increases as taught by Klein in the method of modified Lee because tachycardia is recorded in almost all patients with hyperthyroidism so the increase in tachycardia frequency may signify a thyroid dysfunction.
Regarding claim 10, modified Lee teaches the method of claim 8.
Although Lee teaches the method including extracting information based on the QT interval and using it for disease classification (¶[0016] and claim 11), modified Lee does not teach wherein the probability of occurrence of thyroid dysfunction increases as the length of the QT interval increases.
Klein teaches a variety of case reports have demonstrated that hypothyroidism may cause a prolongation of the QT interval that predisposes the patient to ventricular irritability (pg. 1731).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to increase the probability of occurrence of thyroid dysfunction as the length of the QT interval increases as taught by Klein in the method of modified Lee because hypothyroidism may cause a prolongation of the QT interval so the presence of a longer QT interval may signify a thyroid dysfunction.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over modified Lee as applied to claim 8 above, in further view of Tayal et al. (Tayal, B. et al. “Thyroid dysfunction and electrocardiographic changes in subjects without arrhythmias: a cross-sectional study of primary healthcare subjects from Copenhagen”. BMJ Open. 2019;9:e023854. doi:10.1136/ bmjopen-2018-023854, hereinafter referred to as “Tayal”).
Regarding claim 12, modified Lee teaches the method of claim 8.
Modified Lee does not disclose wherein the probability of occurrence of thyroid dysfunction increases as the QRS duration becomes shorter.
Tayal’s study relates to investigating associations of both overt and subclinical thyroid dysfunction with common ECG parameters in a large primary healthcare population. In the study, overt hyperthyroid subjects had a shorter QRS duration (88.5±9.6) in comparison with the euthyroid (93.5±10.5 ms) subjects (pg. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to increase the probability of occurrence of thyroid dysfunction as the QRS duration becomes shorter as taught by Tayal in the method of modified Lee because overt hyperthyroid subjects had a shorter QRS duration so the shortening of the QRS duration may indicate a thyroid dysfunction.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Deepika and Yang.
Regarding claim 14, Lee teaches diagnosing a disease based on an electrocardiogram (Fig. 2 “A control unit (500) that determines whether a user has a disease and the type of disease based on the user's electrocardiogram signal” in ¶[0021]), the operations include operations of: acquiring electrocardiogram data (Fig. 2, “and input unit (100) that receives the user’s electrocardiogram signal” in ¶[0021]); wherein the neural network model is trained based on the disease and electrocardiogram characteristics Fig. 2 “The feature values extracted from the above feature extraction unit (300) are trained through a neural network to determine a disease diagnosis for the input electrocardiogram signal” ¶[0021]).
Lee does not disclose a computer program stored in a computer-readable storage medium, the computer program performing operations for diagnosing thyroid dysfunction based on an electrocardiogram when executed on one or more processors and estimating a probability of occurrence of thyroid dysfunction for a subject of measurement of the electrocardiogram data based on the electrocardiogram data by using a pre-trained neural network model; wherein the neural network model is trained based on correlations between thyroid function and changes in electrocardiogram characteristics.
Deepika teaches ECG parameters in relation to thyroid dysfunction and this analysis is used to assess the relationship among outright thyroxine (T4) and thyrotropin (TSH) levels with ECG parameters (beat, PR intervals, QRS term, QT between times, and JT between times) (pg. 2). The process involved training using TSH and FT4 levels as well as the parameters in Table 2 which correspond to electrocardiogram characteristics (pg. 4 section 3.3.3). The classification technique is used to categorize the hypothyroid and hyperthyroid illnesses. The efficiency of classifiers is evaluated using the confusion matrix on the subjects of accuracy, sensitivity, and specificity. The C4.5 algorithm gives 93.58% which is providing better accuracy than determination stump tree accuracy and also C4.5 Algorithm provides very minimum error rate than decision stump (pg. 9).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to diagnose thyroid dysfunction based on the electrocardiogram, estimate the probability of occurrence of the thyroid dysfunction using the pre-trained neural network, and train the neural network based on correlations between thyroid function and changes in ECG as taught by Deepika in the method of Lee. This is obvious because device in Lee is capable of learning the material in Deepika and it is known that thyroid disorders affect ECGs. Further, the system is not 100% accurate so it would be obvious to provide a probability of occurrence since the medical diagnosis is uncertain and this allows for optimization of medical resources.
Lee and Deepika do not teach a computer program stored in a computer-readable storage medium, the computer program performing operations based on an electrocardiogram when executed on one or more processors.
Yang teaches computerized functions may be implemented as instructions stored using one or more machine-readable media, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (¶[0067]). The device consists of a processor and memory having instructions that, when executed by the processor, are configured to: use the electrocardiography device to record electrical activity in the heart of a subject; feed the electrical data to one or more neural networks; and receive from the one or more neural networks an identification of a segment of the heart at which an arrhythmia originates (¶[0013]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to implement the device of Lee and Deepika with a computer program stored in a computer-readable storage medium as taught by Yang. This is obvious because it provides a medium for the neural network to work.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Deepika, in further view of Yang, as evidenced by Silberschatz et al. (Silberschatz, A. et al. “Operating System Concepts” 4 May 2018. ISBN 9781119124894, pg. 18).
Regarding claim 15, Lee teaches a computing device for diagnosing a disease based on an electrocardiogram (Fig. 2 “A control unit (500) that determines whether a user has a disease and the type of disease based on the user's electrocardiogram signal” in ¶[0021]), and acquiring electrocardiogram data (Fig. 2, “and input unit (100) that receives the user’s electrocardiogram signal” in ¶[0021]); wherein the neural network model is trained based on the disease and electrocardiogram characteristics Fig. 2 “The feature values extracted from the above feature extraction unit (300) are trained through a neural network to determine a disease diagnosis for the input electrocardiogram signal” ¶[0021]).
Lee does not disclose a computing device for diagnosing thyroid dysfunction based on an electrocardiogram, the computing device comprising: a processor including at least one core; and memory including program codes that are executable on the processor; and estimating a probability of occurrence of thyroid dysfunction for a subject of measurement of the electrocardiogram data based on the electrocardiogram data by using a pre-trained neural network model; wherein the neural network model is trained based on correlations between thyroid function and changes in electrocardiogram characteristics.
Deepika teaches ECG parameters in relation to thyroid dysfunction and this analysis is used to assess the relationship among outright thyroxine (T4) and thyrotropin (TSH) levels with ECG parameters (beat, PR intervals, QRS term, QT between times, and JT between times) (pg. 2). The process involved training using TSH and FT4 levels as well as the parameters in Table 2 which correspond to electrocardiogram characteristics (pg. 4 section 3.3.3). The classification technique is used to categorize the hypothyroid and hyperthyroid illnesses. The efficiency of classifiers is evaluated using the confusion matrix on the subjects of accuracy, sensitivity, and specificity. The C4.5 algorithm gives 93.58% which is providing better accuracy than determination stump tree accuracy and also C4.5 Algorithm provides very minimum error rate than decision stump (pg. 9).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to diagnose thyroid dysfunction based on the electrocardiogram, estimate the probability of occurrence of the thyroid dysfunction using the pre-trained neural network, and train the neural network based on correlations between thyroid function and changes in ECG as taught by Deepika in the method of Lee. This is obvious because device in Lee is capable of learning the material in Deepika and it is known that thyroid disorders affect ECGs. Further, the system is not 100% accurate so it would be obvious to provide a probability of occurrence since the medical diagnosis is uncertain and this allows for optimization of medical resources.
Lee and Deepika do not disclose the computing device comprising: a processor including at least one core; and memory including program codes that are executable on the processor.
Yang teaches the computing device 710 in Fig. 7 may include one or more processors and memory for storing instructions that are executed by the one or more processors to provide the functionality being discussed (¶[0062]). The device consists of a processor and memory having instructions that, when executed by the processor, are configured to: use the electrocardiography device to record electrical activity in the heart of a subject; feed the electrical data to one or more neural networks; and receive from the one or more neural networks an identification of a segment of the heart at which an arrhythmia originates (¶[0013]). Further, processors inherently contain at least one core as evidenced in Silberschatz “processor – a physical chip that contains one or more CPUs” and “core – the basic computation unit of the CPU” (Sillerschatz pg. 18).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of filing to implement the method of Lee and Deepika onto a computing device including at least one processor with at least one core and a memory with program codes as taught by Yang and evidenced by Sillerschatz. This is obvious because it provides a medium for the neural network to work.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hardesty (Hardesty, L. “Neural networks everywhere: New chip reduces neural networks’ power consumption by up to 95 percent, making them practical for battery-powered devices.” MIT News Office. 13 Feb 2018 < https://news.mit.edu/2018/chip-neural-networks-battery-powered-devices-0214>) - neural net is an abstraction: The “nodes” are just weights stored in a computer’s memory and implies that a neural network needs a memory to work
Abdulla et al. (Abdulla, R. et al. “Algorithmic Approach to Pediatric ECG Interpretation.” Pediatric Electrocardiography. Springer, (2016). Cham. https://doi.org/10.1007/978-3-319-26258-1_10) – discusses the importance of age and gender in reading an ECG as well as mentions what p-waves, t-waves, and QRS axis between 90-180 degrees signify generally, but not in respect to thyroids
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/E.N.C./Patent Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792