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 .
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 7-14, and Claims 3-6 and 15-16 by dependency, 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, Claim 2 recites “during a one hour period before the diagnosis time, at least one of fainting or all of….” The use of the term “or” (i.e., “or all of”) in combination with the term “at least one of” (i.e., “at least one of fainting or all of”) renders the scope of the claim indefinite, as it is unclear from the use of the word “or” what particularly is contemplated to be a member of the group of elements included by the term “at least one of.” See MPEP 2111.03; MPEP 2117.
The issue could be resolved by amending Claim 2 to recite “during a one hour period before the diagnosis time, at least one of fainting and all of….”
Regarding Claim 7, Claim 7 recites “at least one of the suspect of MI fuzzy set … or the high-risk for MI fuzzy set.” The use of the term “or” in conjunction with the term “at least one of” renders Claim 7 indefinite for the same reasons as explained above with respect to Claim 2.
Regarding Claim 8, Claim 8 recites “at least one of: the in favor of MI fuzzy set… or at least one of the typical MI fuzzy set or the high-risk for MI fuzzy set.” The use of the term “or” in conjunction with the term “at least one of” renders Claim 8 indefinite for the same reasons as explained above with respect to Claim 2.
Regarding Claim 9, Claim 9 recites “at least one of the typical MI fuzzy set or the high-risk for MI fuzzy set.” The use of the term “or” in conjunction with the term “at least one of” renders Claim 9 indefinite for the same reasons as explained above with respect to Claim 2.
Regarding Claim 10, Claim 10 recites “at least one of the typical MI fuzzy set or the high-risk for MI fuzzy set” and “at least one of: the in favor of MI fuzzy set and the no MI symptom fuzzy set; or at least one of the typical MI fuzzy…” The use of the term “or” in conjunction with the term “at least one of” renders Claim 10 indefinite for the same reasons as explained above with respect to Claim 2.
Regarding Claim 11, Claim 11 recites “and at least one of: the in favor of MI fuzzy set and at least one of the no MI symptom fuzzy set or the atypical MI fuzzy set; or at least one of the typical MI fuzzy set or the high-risk for MI fuzzy set.” The use of the term “or” in conjunction with the term “at least one of” renders Claim 11 indefinite for the same reasons as explained above with respect to Claim 2.
Regarding Claim 12, Claim 12 recites “and at least one of: the in favor of MI fuzzy set; the typical MI fuzzy set; the high-risk for MI fuzzy set; or the suspect of MI fuzzy set and the no MI symptom fuzzy set.” The use of the term “or” in conjunction with the term “at least one of” renders Claim 12 indefinite for the same reasons as explained above with respect to Claim 2.
Regarding Claim 13, Claim 13 recites “at least one of the high risk age for female fuzzy set, the medium risk age for female fuzzy set, or the low risk age for female fuzzy set; and at least one of the in favor of MI fuzzy set, the typical MI fuzzy set, or the high-risk for MI fuzzy set” The use of the term “or” in conjunction with the term “at least one of” renders Claim 13indefinite for the same reasons as explained above with respect to Claim 2.
Regarding Claim 14, Claim 14 recites “and at least one of: the in favor of MI fuzzy set and the atypical MI fuzzy set; or at least one of the typical MI fuzzy set or the high-risk for MI fuzzy set.” The use of the term “or” in conjunction with the term “at least one of” renders Claim 13indefinite for the same reasons as explained above with respect to Claim 2.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Eligibility Step 1 – The Four Categories of Statutory Subject Matter
Claims 1-16 each fall within one of the four categories of statutory subject matter.
Eligibility Step 2A, Prong One
Claims 1-16 recite abstract ideas:
Regarding Independent Claim 1:
“generating, utilizing one or more processors, a denoised ECG signal by applying a first wavelet transform on the raw ECG signal” recites a mathematical concept, and more particularly a mathematical calculation when afforded its broadest reasonable interpretation. See MPEP 2106.04(a)(2)(I). “A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation.” MPEP 2106.04(a)(2)(I)(C). A wavelet transform is such a mathematical operation.
“generating, utilizing the one or more processors, an artifact-free ECG signal by applying a second wavelet transform on the denoised ECG signal” recites a mathematical concept, and more particularly a mathematical calculation when afforded its broadest reasonable interpretation. See MPEP 2106.04(a)(2)(I). “A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation.” MPEP 2106.04(a)(2)(I)(C). A wavelet transform is such a mathematical operation.
“generating, utilizing the one or more processors, a filtered ECG signal by applying a finite impulse response (FIR) filter on the artifact-free ECG signal” recites a mathematical concept, and more particularly a mathematical calculation when afforded its broadest reasonable interpretation. See MPEP 2106.04(a)(2)(I). “A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation.” MPEP 2106.04(a)(2)(I)(C). A finite impulse response filter is such a mathematical operation.
“extracting, utilizing the one or more processors, an averaged ECG signal from the filtered ECG signal, the averaged ECG signal comprising a QRS complex, an ST segment, and a T wave” recites a mathematical concept, and more particularly a mathematical calculation when afforded its broadest reasonable interpretation. See MPEP 2106.04(a)(2)(I). “A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation.” MPEP 2106.04(a)(2)(I)(C). The claimed “extracting” is being interpreted as a feature extraction step, with the extracted features subsequently being acquired. Such feature extraction is a mathematical operation.
“acquiring a plurality of ECG features from the averaged ECG signal and the filtered ECG signal” recites a mental process when afforded its broadest reasonable interpretation. The claimed acquiring could practically be performed in the human mind. See MPEP 2106.04(a)(2)(III). For example, a human could observe data reflective of the averaged ECG signal (e.g., a graph or spreadsheet) and exercise judgment to acquire ECG features therefrom.
It is noted that the limitation “extracting, utilizing the one or more processors, an averaged ECG signal…” is being interpreted as a feature extraction step, and the limitation “acquiring a plurality of ECG features from the averaged ECG signal…” is being interpreted as manipulation of the extracted features.
“generating, utilizing the one or more processors, a plurality of clinical symptoms fuzzy sets associated with the plurality of clinical symptoms” recites a mental process when afforded its broadest reasonable interpretation. The claimed generating could practically be performed in the human mind. See MPEP 2106.04(a)(2)(III). For example, a human could observe medical literature describing pertinent clinical symptoms and exercise judgment to design a plurality of fuzzy sets associated therewith.
“generating, utilizing the one or more processors, a plurality of gender-age fuzzy sets associated with the gender and the age” recites a mental process when afforded its broadest reasonable interpretation. The claimed generating could practically be performed in the human mind. See MPEP 2106.04(a)(2)(III). For example, a human could observe medical literature describing pertinent age-gender considerations relative to heart attack risk and exercise judgment to design a plurality of fuzzy sets associated therewith.
“generating, utilizing the one or more processors, a plurality of ECG fuzzy sets associated with the plurality of ECG features” recites a mental process when afforded its broadest reasonable interpretation. The claimed generating could practically be performed in the human mind. See MPEP 2106.04(a)(2)(III). For example, a human could observe medical literature describing pertinent ECG features relative to heart attack risk and exercise judgment to design a plurality of fuzzy sets associated therewith.
“generating, utilizing the one or more processors, a myocardial infarction (MI) class corresponding to occurrence of an MI in the subject and a non-MI class corresponding to an absence of MI in the subject” recites a mental process when afforded its broadest reasonable interpretation. The claimed generating could practically be performed in the human mind. See MPEP 2106.04(a)(2)(III). For example, a human could exercise judgment to create such a myocardial infarction (MI) class and a non-MI class.
“designing, utilizing the one or more processors, a fuzzy inference system based on a set of rules, each rule of the set of rules comprising mapping a respective combination of a respective clinical symptoms fuzzy set of the plurality of clinical symptoms fuzzy sets, a respective gender-age fuzzy set of the plurality of gender-age fuzzy sets, and a respective ECG fuzzy set of the plurality of ECG fuzzy sets to one of the MI class or the non-MI class” recites a mental process when afforded its broadest reasonable interpretation. The claimed designing could practically be performed in the human mind. See MPEP 2106.04(a)(2)(III). For example, a human could observe data and literature descriptive of typical associations characterized by the fuzzy sets, exercise judgment to create a set of rules based thereupon, and again exercise judgment to design a fuzzy inference system based on those rules.
“mapping each of the plurality of clinical symptoms, the gender, the age, and the plurality of ECG features to a respective fuzzy input of a plurality of fuzzy inputs” recites a mental process when afforded its broadest reasonable interpretation. The claimed mapping could practically be performed in the human mind. See MPEP 2106.04(a)(2)(III). For example, a human could exercise judgment to assign respective clinical symptoms, gender, age, and ECG features to a respective fuzzy inputs, thereby “mapping” such symptoms, gender, age, and ECG features as claimed.
“determining, utilizing the fuzzy inference system, an occurrence of the heart attack in the subject by applying the plurality of fuzzy inputs to the fuzzy inference system” recites a mathematical concept, and more particularly a mathematical calculation when afforded its broadest reasonable interpretation. See MPEP 2106.04(a)(2)(I). “A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation.” MPEP 2106.04(a)(2)(I)(C). Such determining using a fuzzy inference system as claimed is a mathematical operation.
Regarding Claims 2-16, Claims 2-16 depend from and further limit Claim 1, and recite abstract ideas for the same reasons as does Claim 1.
Eligibility Step 2A, Prong Two
Claims 1-16 do not recite additional elements that integrate the judicial exception into a practical application:
Regarding Independent Claim 1:
“utilizing the one or more processors” (i.e., that the above-indicated abstract ideas are done “utilizing the one or more processors”) is a generic computer structure for performing a generic computer function, and thus simply amounts to using a computer as a tool to implement the abstract idea. See MPEP 2106.05(f).
“acquiring a plurality of clinical symptoms from the subject” is insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application because it amounts to mere data gathering.
“acquiring a gender of the subject, the gender comprising one of a male or a female” is insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application because it amounts to mere data gathering.
“acquiring an age of the subject” is insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application because it amounts to mere data gathering.
“acquiring a raw electrocardiography (ECG) signal from the subject at a diagnosis time period” is insignificant extra-solution activity insufficient to integrate the judicial exception into a practical application because it amounts to mere data gathering.
Regarding Claims 2-16, Claims 2-16 do not recite any additional elements.
Eligibility Step 2B
Claims 1-16 do not amount to significantly more than the abstract ideas recited therein:
Regarding Independent Claim 1
“utilizing the one or more processors” does not contribute an inventive concept. The claimed “one or more processors” are generic computer structures for performing generic computer functions, and are simply appended to the claimed abstract ideas. See MPEP 2106.05(I)(A).
“acquiring a plurality of clinical symptoms from the subject” does not contribute an inventive concept. Such acquiring clinical symptoms is well-understood, routine and conventional in the art. See, e.g., Goff DC, Sellers DE, McGovern PG, et al. Knowledge of Heart Attack Symptoms in a Population Survey in the United States: The REACT Trial. Arch Intern Med. 1998;158(21):2329–2338 at Pg. 2330, Left Column, Second Paragraph (“Our purpose herein is to describe knowledge regarding heart attack symptoms reported by participants…”); Pg. 2335, Table 3 (Table 3 depicts clinical symptoms reported by patients and characterized as existing “knowledge” at Pg. 2330, Left Column, Second Paragraph); S. ARSLANKAYA et al., "Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks," International Journal of Computational and Experimental Science and ENgineering (IJCESEN), Vol. 7-No.1 (2021)pp. 1-8, March 2021 at Pg. 2, Left Column, Second Paragraph.
“acquiring a gender of the subject, the gender comprising one of a male or a female” does not contribute an inventive concept. Such acquiring gender is well-understood, routine and conventional in the art. See, e.g., Goff DC, Sellers DE, McGovern PG, et al. Knowledge of Heart Attack Symptoms in a Population Survey in the United States: The REACT Trial. Arch Intern Med. 1998;158(21):2329–2338 at Pg. 2330, Left Column, Second Paragraph (“Our purpose herein is to describe knowledge regarding heart attack symptoms reported by participants…”); Pg. 2335, Table 3 (Table 3 depicts clinical symptoms reported by patients and characterized as existing “knowledge” at Pg. 2330, Left Column, Second Paragraph, and includes breakdown by gender); S. ARSLANKAYA et al., "Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks," International Journal of Computational and Experimental Science and ENgineering (IJCESEN), Vol. 7-No.1 (2021)pp. 1-8, March 2021 at Pg. 2, Left Column, Second Paragraph.
“acquiring an age of the subject” does not contribute an inventive concept. Such acquiring subject age is well-understood, routine and conventional in the art. See, e.g., Goff DC, Sellers DE, McGovern PG, et al. Knowledge of Heart Attack Symptoms in a Population Survey in the United States: The REACT Trial. Arch Intern Med. 1998;158(21):2329–2338 at Pg. 2330, Left Column, Second Paragraph (“Our purpose herein is to describe knowledge regarding heart attack symptoms reported by participants…”); Pg. 2335, Table 3 (Table 3 depicts clinical symptoms reported by patients and characterized as existing “knowledge” at Pg. 2330, Left Column, Second Paragraph, and includes breakdown by age); S. ARSLANKAYA et al., "Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks," International Journal of Computational and Experimental Science and ENgineering (IJCESEN), Vol. 7-No.1 (2021)pp. 1-8, March 2021 at Pg. 2, Left Column, Second Paragraph.
“acquiring a raw electrocardiography (ECG) signal from the subject at a diagnosis time period” does not contribute an inventive concept. Such acquiring a raw ECG signal is well-understood, routine and conventional in the art. See, e.g., S. Ansari et al., "A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records," in IEEE Reviews in Biomedical Engineering, vol. 10, pp. 264-298, 2017 at Pg. 264, Right Column, Second Paragraph (“The 12-lead electrocardiogram (ECG) is the primary screening tool for … MI.”).
Regarding Claims 2-16, Claims 2-16 do not recite any additional elements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over US 6507753 B1 to Xue et al. (“Xue”) in view of S. Ansari et al., "A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records," in IEEE Reviews in Biomedical Engineering, vol. 10, pp. 264-298, 20171 (“Ansari”) and A. Adeli et al., "A Fuzzy Expert System for Heart Disease Diagnosis," Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, Vol I, IMECS 2010, March 17-19, 2010, Hong Kong (“Adeli”) as evidenced by J. Presedo et al., "Fuzzy modelling of the expert's knowledge in ECG-based ischaemia detection," Fuzzy Sets and Systems Volume 77, Issue 1, 15 January 1996, Pages 63-75 (“Presedo”).
Regarding Independent Claim 1, Xue teaches:
A method for early detection of a heart attack in a subject, the method comprising: (Title, “Method and apparatus to detect acute cardiac syndromes in specified groups of patients using ECG”);
acquiring a plurality of clinical symptoms from the subject; (Abstract, “…ECG data are acquired from a patient experiencing ACS symptoms.”);
acquiring a gender of the subject, the gender comprising one of a male or a female; (Col. 5, Ln. 50-52, “After the standard ECG analysis is implemented 100, the patient's gender and age are checked at 102.”);
acquiring an age of the subject; (Col. 5, Ln. 50-52, “After the standard ECG analysis is implemented 100, the patient's gender and age are checked at 102.”);
acquiring a raw electrocardiography (ECG) signal from the subject at a diagnosis time period; (Col. 5, Ln. 50-52, “After the standard ECG analysis is implemented 100, the patient's gender and age are checked at 102;” see Col. 1, Ln. 52 through Col. 2, Ln. 26 elaborating on Xue’s “standard ECG analysis”);
generating … a denoised ECG signal… (Col. 4, Ln. 12-18, “The ECG device 10 has a signal conditioner 34 that receives the ECG signals and filters noise, sets thresholds, segregating signals, and provides the appropriate number of ECG signals for the number of leads 28 to an A/D converter 36 which converts the analog signals to digital signals for processing by a microcontroller 38, or any other type of processing unit.”);
generating, utilizing the one or more processors, a myocardial infarction (MI) class corresponding to occurrence of an MI in the subject and a non-MI class corresponding to an absence of MI in the subject; (Fig. 4, “Acute Inferior MI 118” and “No Acutre Inferior MI 142;” Col. 5, Ln. 49 through Col. 6, Ln 17).
determining, utilizing the fuzzy inference system, an occurrence of the heart attack in the subject by applying the plurality of fuzzy inputs to the fuzzy inference system (Abstract, “…the present invention provides a method and apparatus to improve diagnosis of acute cardiac syndromes (ACS), such as acute myocardial infarction…;” Claim 21, “The method of claim 17 further comprising identifying whether the patient has an ACS and whether the ACS is acute anterior myocardial infarction or acute inferior myocardial infarction;” Col. 2, Ln. 38-42, “Either a fuzzy logic or a neural network can be used for pattern recognition in addition to classical pattern recognition methods such as linear discriminant function analysis and thresholding.”)
Xue differs from the invention of Claim 1 in two main ways. First, Xue uses a simpler method of denoising, filtering and feature extraction than claimed. Second, Xue uses clinical symptoms, gender and age to reduce its threshold for determining myocardial infarction via fuzzy logic rather than as a fuzzy set used in such determination, with Xue’s fuzzy logic being described in less detail.
Accordingly, Xue does not disclose:
generating, utilizing one or more processors, a denoised ECG signal by applying a first wavelet transform on the raw ECG signal;
That is, Xue teaches generating a denoised ECG signal, but does not teach doing so by “utilizing one or more processors” or “by applying a first wavelet transform on the raw ECG signal,” opting instead to denoise via a signal conditioner.
generating, utilizing the one or more processors, an artifact-free ECG signal by applying a second wavelet transform on the denoised ECG signal;
generating, utilizing the one or more processors, a filtered ECG signal by applying a finite impulse response (FIR) filter on the artifact-free ECG signal;
extracting, utilizing the one or more processors, an averaged ECG signal from the filtered ECG signal, the averaged ECG signal comprising a QRS complex, an ST segment, and a T wave;
acquiring a plurality of ECG features from the averaged ECG signal and the filtered ECG signal;
generating, utilizing the one or more processors, a plurality of clinical symptoms fuzzy sets associated with the plurality of clinical symptoms;
generating, utilizing the one or more processors, a plurality of gender-age fuzzy sets associated with the gender and the age;
generating, utilizing the one or more processors, a plurality of ECG fuzzy sets associated with the plurality of ECG features;
designing, utilizing the one or more processors, a fuzzy inference system based on a set of rules, each rule of the set of rules comprising mapping a respective combination of a respective clinical symptoms fuzzy set of the plurality of clinical symptoms fuzzy sets, a respective gender-age fuzzy set of the plurality of gender-age fuzzy sets, and a respective ECG fuzzy set of the plurality of ECG fuzzy sets to one of the MI class or the non-MI class;
mapping each of the plurality of clinical symptoms, the gender, the age, and the plurality of ECG features to a respective fuzzy input of a plurality of fuzzy inputs;
Ansari describes “…Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records” (Title). Ansari is analogous art.
Ansari teaches:
generating, utilizing one or more processors, a denoised ECG signal by applying a first wavelet transform on the raw ECG signal; (Pg. 270, Right Column, Second Paragraph, “Another popular method for denoising the ECG signal uses wavelet transform.”);
generating, utilizing the one or more processors, an artifact-free ECG signal by applying a second wavelet transform on the denoised ECG signal; (Pg. 269, Left Column, Fourth Paragraph, “Discrete wavelet transform (DWT) [81] has also been used for BW removal.”);
Ansari teaches both baseline wander (“BW”) removal and noise removal via wavelet transform as part of such “pre-processing” as described at Ansari’s Pg. 268, Right Column, First Paragraph (“There has been a growing number of studies that use the ECG waveform to automatically detect these conditions. These methods are generally composed of four steps, preprocessing, ECG wave quantification, feature extraction, and classification.”).
generating, utilizing the one or more processors, a filtered ECG signal by applying a finite impulse response (FIR) filter on the artifact-free ECG signal; (Pg. 269, Right Col., Third Paragraph, “A variety of methods have been used in the literature to reduce the effect of noise in the ECG signal. The simplest and most common approach is low-pass filtering. For example, Minchole et al. [71] and Garcıa et al. [36] used a linear-phase finite impulse response (FIR) filter with a cutoff frequency of 25 Hz.”);
extracting, utilizing the one or more processors, an averaged ECG signal from the filtered ECG signal, the averaged ECG signal comprising a QRS complex, an ST segment, and a T wave; (Pg. 270, Left Column, Second Paragraph, “Beat averaging is another approach for reducing the effect of noise and interference in the ECG signal, i.e., a sequence of consecutive beats are averaged together to create an aggregate beat that is a better representative of heart’s electrical activity;” Pg. 273, Fig. 3, “Depiction of different morphological features that different studies have extracted from the ECG waveform,” shows QRS complex, an ST segment, and a T wave as commonly extracted features);
acquiring a plurality of ECG features from the averaged ECG signal and the filtered ECG signal; (Pg. 272, Right Column, Third Paragraph, “Once the ECG signal has been preprocessed and segmented, various methodologies are used to extract informative features that will allow for downstream detection of MI or ischemia;” Pg. 273, Fig. 3);
designing, utilizing the one or more processors, a fuzzy inference system based on a set of rules, (Pg. 279, Right Column, Last Paragraph through Pg. 280, Right Column First Paragraph, “Classification algorithms can formalize clinical guidelines for MI and ischemia diagnosis using ECG signal. … Expert knowledge has also been modeled with fuzzy logic in [34], where the authors constructed a linguistic filter with persistence and separation criterions to detect ST and T episodes.”);
each rule of the set of rules comprising mapping a respective combination of … a respective ECG fuzzy set of the plurality of ECG fuzzy sets to one of the MI class or the non-MI class (Pg. 279, Right Column, Last Paragraph through Pg. 280, Right Column First Paragraph, “Classification algorithms can formalize clinical guidelines for MI and ischemia diagnosis using ECG signal. … Expert knowledge has also been modeled with fuzzy logic in [34], where the authors constructed a linguistic filter with persistence and separation criterions to detect ST and T episodes.”);
Ansari’s reference “[34]” is Presedo. Presedo elaborates beyond Ansari’s summary to describe such a set of rules as claimed at Pg. 68, Right Column, Third Paragraph through Pg. 70, Left Column, Second Paragraph under the heading “4. Fuzzy detection of ischaemie episodes”).
Ansari’s summary of Presedo and Presedo itself both allude to use of such a “clinical symptoms fuzzy sets” and “gender-age fuzzy sets” as claimed, but do not provide sufficient detail to on their own fairly disclose either. This deficiency is addressed below.
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Xue with the teachings of Ansari (i.e., to denoise Xue’s raw ECG signal by applying a first wavelet transform on the raw ECG signal using a processor in the manner of Ansari) in order to balance smoothness and efficiency in the denoising (Ansari at Pg. 270, Right Column, Second Paragraph).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Xue with the teachings of Ansari (i.e., to modify Xue so as to additionally generate an artifact-free ECG signal by applying a second wavelet transform on the denoised ECG signal in the manner of Ansari) in order to mitigate artifact from baseline wander (Ansari at Pg. 269, Left Column, Fourth Paragraph).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Xue with the teachings of Ansari (i.e., to modify Xue so as to additionally generate a filtered ECG signal by applying a finite impulse response (FIR) filter on the artifact-free ECG signal in the manner of Ansari) in order to further reduce the impact of noise in the signal (Ansari at Pg. 269, Right Col., Third Paragraph).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Xue with the teachings of Ansari (i.e., to use beat averaging prior to extraction in the manner of Ansari) in order to reduce the influence of noise (Ansari at Pg. 270, Left Column, Second Paragraph).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Xue with the teachings of Ansari (i.e., to extract an averaged ECG signal from the filtered ECG signal comprising a QRS complex, an ST segment, and a T wave in the manner of Ansari and to subsequently acquire such features as taught by Ansari) in order to extract informative features that will allow for downstream detection of MI or ischemia (Ansari at Pg. 272, Right Column, Third Paragraph).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of claimed invention to modify the method of Xue with the collective teachings of Ansari (i.e., to modify Xue such that its simple denoising and filtering process is replaced with a more comprehensive process such as those of Ansari) in order to facilitate automatic initial diagnosis of patients with symptoms associated with ischemia or MI in accordance with modern techniques (Ansari at Pg. 268, Right Column, First Paragraph).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of claimed invention to modify the method of Xue with the teachings of Ansari (i.e., to configure Xue’s fuzzy logic in the manner of Ansari) in order to facilitate MI and ischemia diagnosis using ECG signal (Ansari at Pg. 279, Right Column, Last Paragraph through Pg. 280, Right Column First Paragraph).
Adeli describes “A Fuzzy Expert System for Heart Disease Diagnosis” (Title). Adeli is analogous art.
Adeli teaches:
generating, utilizing the one or more processors, a plurality of clinical symptoms fuzzy sets associated with the plurality of clinical symptoms; (Pg. 2 of 6, Left Column, Second Paragraph, “Input fields (attributes) are chest pain type, blood pressure, cholesterol, resting blood sugar, resting maximum heart rate, sex, electrocardiography (ECG), exercise, old peak (ST depression induced by exercise relative to rest), thallium scan and age. The output field refers to the presence of heart disease in the patient. It is integer value from 0 (no presence) to 4 (distinguish presence (values 1, 2, 3 and 4)); increasing value shows increasing heart disease risk.”);
Adeli’s chest pain type, blood pressure, cholesterol, resting blood sugar are such “clinical symptoms” as claimed per Para. [0114] of the Present Specification (Present Specification at Para. [0114], “For each individual, a vector containing only zero or one value was generated describing the feeling or not feeling of the clinical sign and symptoms of upper chest pain, middle chest pain, upper abdomen pain, pain in the neck, pain in the jaw, pain in the right shoulder, pain in the left shoulder, pain inside the right arm, pain inside the left arm, pain between shoulders in back, fainting, sweating, shortness of breath, light headedness, vomiting, nausea, history of diabetes, history of hypertension, and history of hyperlipidemia.” (emphasis added)).
generating, utilizing the one or more processors, a plurality of gender-age fuzzy sets associated with the gender and the age; (Pg. 2 of 6, Left Column, Second Paragraph, “Input fields (attributes) are chest pain type, blood pressure, cholesterol, resting blood sugar, resting maximum heart rate, sex, electrocardiography (ECG), exercise, old peak (ST depression induced by exercise relative to rest), thallium scan and age. The output field refers to the presence of heart disease in the patient. It is integer value from 0 (no presence) to 4 (distinguish presence (values 1, 2, 3 and 4)); increasing value shows increasing heart disease risk.”);
generating, utilizing the one or more processors, a plurality of ECG fuzzy sets associated with the plurality of ECG features; (Pg. 2 of 6, Left Column, Second Paragraph, “Input fields (attributes) are chest pain type, blood pressure, cholesterol, resting blood sugar, resting maximum heart rate, sex, electrocardiography (ECG), exercise, old peak (ST depression induced by exercise relative to rest), thallium scan and age. The output field refers to the presence of heart disease in the patient. It is integer value from 0 (no presence) to 4 (distinguish presence (values 1, 2, 3 and 4)); increasing value shows increasing heart disease risk.”);
each rule of the set of rules comprising mapping a respective combination of a respective clinical symptoms fuzzy set of the plurality of clinical symptoms fuzzy sets, a respective gender-age fuzzy set of the plurality of gender-age fuzzy sets, (Pg. 5 of 6, Left Column, First Paragraph, “Rule base is the main part in fuzzy inference system and quality of results in a fuzzy system depends on the fuzzy rules. This system includes 44 rules. Antecedent part of all rules has one section. This system designed with another rule bases (64 rules, 15 rules, 10 rules and 5 rules) and results showed in 44 rules system are best in comparison with results of the other rule bases. In the other hand, results with 44 rules tend to the expert’s idea and laboratory results. The rules have been shown in FIG.9;” Pg. 5 of 6, Fig. 9 shows Adeli’s rules);
mapping each of the plurality of clinical symptoms, the gender, the age, and the plurality of ECG features to a respective fuzzy input of a plurality of fuzzy inputs; (Pg. 2 of 6, Left Column, Second Paragraph).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of combined Xue and Ansari with the teachings of Adeli (i.e., to configure the fuzzy logic system of combined Xue and Ansari in the manner of Adeli wherein clinical symptoms, gender and age are used in the manner of Adeli) in order to increase classification accuracy (Adeli at Pg. 1 of 6, Right Column, Third Paragraph).
Allowable Subject Matter
Claims 2-16 are rejected under 35 USC 112(b) and 35 USC 101, and are dependent upon a base claim rejected under both 35 USC 101 and 35 USC 103, but are otherwise potentially allowable over prior art.
The following is a statement of reasons for the indication of potential allowability over prior art:
Claim 2 recites “wherein acquiring the plurality of clinical symptoms comprises assessment of: a first clinical symptom comprising: on/off pain with a continuous duration of at least five minutes during a one hour period before the diagnosis time in at least one of a first plurality of regions having a total size larger than three times of a size of a fingertip of the subject, the first plurality of regions comprising upper chest, middle chest (sternum), upper abdomen, neck, jaw, right shoulder, left shoulder, inside right arm, inside left arm, and between shoulders in back.” The prior art does not fairly teach, suggest or disclose acquiring as a first symptom such “on/off pain” having the duration claimed and in the regions claimed. Further, the prior art does not fairly teach, suggest or disclose acquiring as a first symptom such “on/off pain” having the duration claimed and in the regions claimed for use as part of a fuzzy set in a fuzzy logic system.
The closest prior art is Goff DC, Sellers DE, McGovern PG, et al. Knowledge of Heart Attack Symptoms in a Population Survey in the United States: The REACT Trial. Arch Intern Med. 1998;158(21):2329–2338. At Pg. 2333, Table 3, Goff lists “Chest pain or discomfort,” “Arm pain or numbness,” and “Jaw or neck pain” as “Reported Signs or Symptoms of Heart Attack,” with the intent that they be assessed as symptoms. However, Goff does not reasonably teach, disclose or suggest “on/off pain” having the duration claimed and in the regions claimed, and makes no mention of fuzzy logic.
A. Adeli et al., "A Fuzzy Expert System for Heart Disease Diagnosis," Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, Vol I, IMECS 2010, March 17-19, 2010, Hong Kong (“Adeli”) and S. ARSLANKAYA et al., "Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks," International Journal of Computational and Experimental Science and ENgineering (IJCESEN), Vol. 7-No.1 (2021)pp. 1-8, March 2021 at Pg. 2, Left Column, Second Paragraph teach fuzzy logic systems which assess various symptoms in making their respective determinations, but neither remedies the deficiencies of Goff. It would not have been obvious to modify the prior art to acquire as a first symptom such “on/off pain” having the duration claimed and in the regions claimed for use as part of a fuzzy set in a fuzzy logic system, because the prior art simply does not contain these details. Given the nature of fuzzy logic, including such details would require substantial rework of prior art systems.
Claims 3-16 depend from and further limit Claim 2, and are potentially allowable over prior art for the same reasons as explained above with respect to Claim 2.
Conclusion
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/C.J.M./Examiner, Art Unit 3796
/Jennifer Pitrak McDonald/Supervisory Patent Examiner, Art Unit 3796
1 The Examiner notes that S. Ansari et al., “A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records,” in IEEE Reviews in Biomedical Engineering, vol. 10, pp. 264-298, 2017 was cited by the International Searching Authority in the Written Opinion dated January 6, 2023.