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 .
Response to Amendment
The following is in response to the amendments filed on January 6, 2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-6, 8-13 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Azvine et al (US PG Pub 2020/0065692) in view of McFall et al (US PG Pub 2020/0327252) further in view of Sanchez de la Nava et al (“Artificial Intelligence for a personalized diagnosis and treatment of atrial fibrillation) further in view of Forlenza (“Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy”).
With respect to claim 1:
Azvine teaches:
A system comprising:
Data processing hardware in communication with the data processing hardware, the memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations, the operations comprising:
Receiving a plurality of input data from one or more data collection devices; (Paragraph [034], discloses receiving input data from data structures, tables or databases)
Converting the sub-data into a fuzzy domain by using fuzzy membership functions to create fuzzy data; (Paragraph [105], discloses using a membership value to produce fuzzy data)
Performing fuzzy inference operations on the fuzzy data using a fuzzy rule base; performing deffuzification operations to convert the fuzzy data into crisp data; (Paragraph [016], discloses using rules and defuzzification method to convert the fuzzy data into crisp data)
Azvine does not explicitly disclose:
Data associated with a patient;
Converting each input data of the plurality of input data to sub-data wherein converting each input data comprises:
Identifying, based on a type of the input data, a plurality of pre-defined sub-variables; and
Identifying, based on a value of the input data, one sub-variable of the plurality of pre-defined sub-variables as the sub-data;
Determining a heart disease diagnosis for the patient based on the crisp data;
Selecting, based on the heart disease diagnosis, a medication and a dosage amount of the medication to be administered to the patient to treat the patient’s symptoms associated with the heart disease diagnosis; and
Transmitting data indicating the medication and the dosage amount of the medication to an administration device associated with the patient, wherein the data causes the administration device to administer the dosage amount of the medication to the patient to treat the patient’s symptoms associated with the heart disease diagnosis.
McFall teaches:
Converting each input data of the plurality of input data to sub-data wherein converting each input data comprises: Identifying, based on a type of the input data, a plurality of pre-defined sub-variables; and Identifying, based on a value of the input data, one sub-variable of the plurality of pre-defined sub-variables as the sub-data; (Paragraph [034], discloses converting data to a resulting data set by identifying columns as having different categories (variables), choosing one of those categories and generating an anonymized dataset (sub-data))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teaching of Azvine and McFall, both in the same field of invention, because this would create a more efficient and privacy enhanced dataset to then be subject to fuzzy inference operations (See McFall Paragraphs [004]-[006])
The combination of Azvine and McFall does not appear to explicitly disclose:
Data associated with a patient;
Determining a heart disease diagnosis for the patient based on the crisp data;
Selecting, based on the heart disease diagnosis, a medication and a dosage amount of the medication to be administered to the patient to treat the patient’s symptoms associated with the heart disease diagnosis; and
Transmitting data indicating the medication and the dosage amount of the medication to an administration device associated with the patient, wherein the data causes the administration device to administer the dosage amount of the medication to the patient to treat the patient’s symptoms associated with the heart disease diagnosis.
Sanchez de la Nava teaches:
Data associated with a patient; Determining a heart disease diagnosis for the patient based on the data; (Page 5, Section AI Application in AF, discloses using an ML and patient data to diagnose heart disease)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teaching of Azvine, McFall and Sanchez de la Nava, all in the same field of invention, because this would allow AI techniques to improve early noninvasive diagnosis, developing more efficient therapies, and predicting long term clinical outcomes of patients with AF. (See Sanchez de la Nava, abstract)
The combination of Azvine, McFall and Sanchez de la Nava does not appear to explicitly disclose:
Selecting, based on the heart disease diagnosis, a medication and a dosage amount of the medication to be administered to the patient to treat the patient’s symptoms associated with the heart disease diagnosis; and
Transmitting data indicating the medication and the dosage amount of the medication to an administration device associated with the patient, wherein the data causes the administration device to administer the dosage amount of the medication to the patient to treat the patient’s symptoms associated with the heart disease diagnosis.
Forlenza teaches:
Selecting, based on the heart disease diagnosis, a medication and a dosage amount of the medication to be administered to the patient to treat the patient’s symptoms associated with the heart disease diagnosis; and Transmitting data indicating the medication and the dosage amount of the medication to an administration device associated with the patient, wherein the data causes the administration device to administer the dosage amount of the medication to the patient to treat the patient’s symptoms associated with the heart disease diagnosis. (Page 2, discloses an automated decision support system connected to an insulin pump, the ADS will use data to determine changes to insulin dosage and will communicate the changes to the insulin pump as to administer the correct dose)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teaching of Azvine, McFall, Sanchez de la Nava and Forlenza, all in the same field of invention, because this would allow for optimization of insulin dosing in a patient with a specific diagnosis, in this case diabetes. (see Forlenza, Page 2).
With respect to claim 2:
The combination of Azvine, McFall, Sanchez de la Nava and Forlenza teaches:
The operations further comprise displaying the heart disease diagnosis on a display of a healthcare provider device associated with a healthcare provider supervising the patient. (Sanchez de la Nava, Page 6, discloses the practitioner using the ML as a diagnostic tool, it follows that the diagnosis will be displayed for the healthcare provider)
With respect to claim 3:
The combination of Azvine, McFall, Sanchez de la Nava and Forlenza teaches:
The healthcare provider device is configured to be operated by the healthcare provider to modify the plurality of input data. (Forlenza, Page 2, discloses that the ADS would be able to take input from a healthcare provider)
With respect to claim 4:
The combination of Azvine, McFall, Sanchez de la Nava and Forlenza teaches:
The one or more data collection devices includes one or more of a Holter monitor, a blood pressure monitor, a cholesterol meter or monitor, a blood glucose meter or monitor, (Forlenza, Page 2) an electrocardiograph (ECG or EKG) monitor or machine, a heart rate monitor, an exercise or activity monitor, a gamma camera, a fluoroscope, a scale, an X-ray machine, a spirometer, a pulse oximeter, a capnography monitor, or a blood test machine.
With respect to claim 5:
The combination of Azvine, McFall, Sanchez de la Nava and Forlenza teaches:
The crisp data includes a numerical value indicating on a scale the likelihood that the patient has heart disease. (Sanchez de la Nava, Page 6, discloses determining a dice score)
With respect to claim 6:
The combination of Azvine, McFall, Sanchez de la Nava and Forlenza teaches:
The input data includes one or more of chest pain, blood pressure (systolic, diastolic), cholesterol, blood sugar (Forlenza, Page 2), resting electrocardiography, maximum heart rate, exercise, old peak, sex, thalium scan, age, slope (slope of peak exercise ST segment), color vessels (number of major vessels colored by fluoroscopy), body mass index (BMI), narrow vessels, FEVI/FVC ratio (Tiffeneau-Pinelli index), blood oxygen level, respiratory status, or c-reactive protein (CRP).
Claims 8-13 and 15-19 are rejected according claims 1-6.
Claim 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Azvine et al (US PG Pub 2020/0065692) in view of McFall et al (US PG Pub 2020/0327252) further in view of Sanchez de la Nava et al (“Artificial Intelligence for a personalized diagnosis and treatment of atrial fibrillation) further in view of Forlenza (“Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy”) and further in view of Basir et al (US PG Pub 20010047344).
With respect to claim 7:
The combination of Azvine, McFall, Sanchez de la Nava and Forlenza does not appear to explicitly disclose:
The deffuzification operations include one or more of a Weighted Average Formulate (WAF) Method, a Quality Method (QM), a Maximum-Weighted Average Formula (MAX-WAF), or a Center of Sums (COS) Method.
Basir teaches:
The deffuzification operations include one or more of a Weighted Average Formulate (WAF) Method, a Quality Method (QM), a Maximum-Weighted Average Formula (MAX-WAF), or a Center of Sums (COS) Method. (Paragraph [026], discloses using a center of sums deffuzification method)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teaching of Azvine, McFall, Sanchez de la Nava, Forlenza and Basir, all in the same field of invention, because this is a popular deffuzification method that leads to improvement in speed and accuracy. (see Basir, Paragraph [026])
Claims 14 and 20 are rejected according claim 7.
Response to Arguments
Claim Rejections - 35 USC § 101
The instant amendments overcome the previous 35 USC 101 rejection therefore it has been withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIELA D REYES whose telephone number is (571)270-1006. The examiner can normally be reached Monday-Friday, 7:30 am -5:00 pm.
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/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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