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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 18/727,252, filed on 07/08/2024.
Claim Objections
Claims 1-15 are objected to because of the following informalities:
Regarding claims 1 and 12, the term “Computer-implemented method” in line 1 is missing a prerequisite “a” such that it reads “A computer-implemented method”.
Regarding claims 2-11, the term “Computer-implemented method” in line 1 is missing a prerequisite “the” such that it reads “The computer-implemented method”.
Regarding claim 13, the term “System for classification” in line 1 is missing a prerequisite “a” such that it reads “A system for classification”.
Regarding claim 14, the term “Computer program” in line 1 and “program code” in line 1 is missing a prerequisite “a” such that it reads “A computer program” and “a program code”.
Regarding claim 15, the term “Computer readable data carrier” in line 1 is missing a prerequisite “a” such that it reads “A computer readable data carrier”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Regarding claim 13, the claim recites “means for applying a machine learning algorithm” in line 8 and “means for outputting a second data set” in line 13. For the purposes of examination, Examiner interprets the “means for applying a machine learning algorithm” as a computer and the “means for outputting a second data set” as data processing to output relevant data.
Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Regarding claim 13, the limitations of a “means for applying a machine learning algorithm” in line 8 and a “means for outputting a second data set” in line 13 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Specifically, regarding claim 13, the limitations of a “means for applying a machine learning algorithm” and a “means for outputting a second data set” are not sufficiently disclosed in Applicant’s specification as there is not a corresponding structure to describe what structural elements are said means. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 1-15 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 claims 1 and 12-13, the term “medical relevance” renders the claim indefinite because it is unclear as to what the metes and bounds of this limitation is within the claim. The term “medical relevance” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purposes of examination, Examiner interprets “medical relevance” as any relevant metric pertaining to medical data. Claims 2-11 and 14-15, which are dependent from claim 1, are rejected for the same reason as set forth for claim 1 above.
Regarding claim 10, the term “the cardiac curve data” renders the claim indefinite because it is unclear as to whether “the cardiac curve data” includes both the “first cardiac curve data” and “second cardiac curve data” or another cardiac curve data set. For the purposes of examination, Examiner interprets “the cardiac curve data” as including the “first cardiac curve data” and “second cardiac curve data”.
Regarding claim 12, the term “the second class representing a medically not relevant deviation between the first cardiac current curve data and the second cardiac current curve data from the first cardiac current curve data and the second cardiac current curve data” renders the claim indefinite because it is unclear as to whether “from the first cardiac current curve data and the second cardiac current curve data” is redundant or another data input. For the purposes of examination, Examiner interprets the claim to read “the second class representing a medically not relevant deviation between the first cardiac current curve data and the second cardiac current curve data” as “from the first cardiac current curve data and the second cardiac current curve data” appears to be redundant.
Regarding claim 15, the claim recites the limitation "a computer program" in line 1. There is insufficient antecedent basis for this limitation in the claim. For the purposes of examination, Examiner interprets “a computer program” as “the computer program”.
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 14 and 15 are 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 per se. MPEP 2106.03(I) states “a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category.” Claims 14 and 15 are directed to “computer program with program code” and “computer readable data carrier storing a computer program”, respectively.
Claims 1-15 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 1-15 fall within one of the four categories of statutory subject matter. Claims 1-11 are drawn to “a method” (i.e., a process), claim 12 drawn to “a method” (i.e., a process), and claim 13 is drawn to “a system” (i.e., a machine), and thus fall within one of the four statutory categories. Claims 14 and 15 recite a program and therefore directed to a non-statutory subject matter as set forth above. However, claims 14 and 15, if amended to claim those embodiments that fall within a statutory category (e.g., “non-transitory computer-readable storage medium”), the claims 14 and 15 are further ineligible for the reasons provided below.
Eligibility Step 2A, Prong One
Claims 1-15 recite an abstract idea:
Regarding independent claims 1, 12, and 13, the limitations of “applying a machine learning algorithm to the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data for classification of the medical relevance of the deviation between the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data; and outputting a second data set comprising at least a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data” in independent claim 1, “training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification of the first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or the second class representing a medically not relevant deviation between the first cardiac current curve data and the second cardiac current curve data from the first cardiac current curve data and the second cardiac current curve data” in independent claim 12, and “means for applying a machine learning algorithm to the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data for classification of the medical relevance of the deviation between the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data; and means for outputting a second data set comprising at least a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data” in independent claim 13
are directed to an abstract idea.
The limitations, as drafted, describe a process that, under its broadest reasonable interpretation, includes performance of the limitation in the mind except for the recitations of “an implantable medical device” in independent claims 1, 12, and 13.
Additionally, other than reciting that “an implantable medical device” in independent claims 1, 12, and 13 is performing these tasks, nothing in the claim precludes the steps from practically being performed in the human mind or being considered as methods of organizing human activity since these steps are performed by computational models.
MPEP 2106.04(a)(2)(II) states that the sub-grouping "managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions and 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 “an implantable medical device”, the claim encompasses analysis of cardiac current curves to determine relevant deviations between said cardiac current curves. This determination does not require any structure to perform the step, where a person or medical professional may determine such deviations by analyzing relevant patient data, such that the claims are directed to organizing human activity. The claims do not require the use of a computer beyond the recitation of a general-purpose processor to gather information about a subject, therefore they are not self-evidently patent eligible.
Eligibility Step 2A, Prong Two
Claims 1-15 do not recite additional elements that integrate the judicial exception into a practical application. The claims recite “an implantable medical device” to perform the abstract steps. This component reads on a computer implemented system and method and is recited at a high level of generality, i.e., as a generic processor, performing a generic computer function of processing data. 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.
Eligibility Step 2B
Claims 1, 12, and 13 do not amount to significantly more than the abstract ideas recited therein. 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. Applicant’s specification on page 9, lines 15-20 does not provide any indication that the computer 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.
Regarding dependent claims 2-11 and 14-15, the limitations of these claims further define the limitations already indicated as being directed to the abstract idea as recited in independent claim 1.
Therefore, these additional elements do not amount to significantly more than the judicial exception and the claimed subject matter appears to be ineligible under §101.
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.
Claims 1-6, 8, 10-11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sipe et al. (hereinafter “Sipe”) (U.S. Pub. No. 2023/0395248 A1) in view of Sullivan et al. (hereinafter “Sullivan”) (U.S. Pub. No. 2019/0216350 A1, IDS Reference No. 1 from IDS dated 07/08/2024) and Seo et al. (hereinafter “Seo”) (U.S. Pub. No. 2022/0338743 A1).
Regarding claim 1, Sipe teaches a computer-implemented method for classification of a medical relevance of a deviation between cardiac current curves (Abstract, where “Data samples derived from raw physiological data captured by sensors of a wearable medical device monitoring a patient's heart, such as a first ECG channel data sample, a second ECG channel data sample, and/or a cardio-vibrational data sample, are applied to a multi-tier machine learning engine to determine a cardiac condition measure … A first tier of the multi-tier machine learning engine may analyze the physiological data sample(s) using first machine learning classifier(s) to obtain a first result. The first result may then be applied to second machine learning classifier(s) of a second tier of the machine learning engine, along with physiological metrics and/or patient clinical information, to determine the cardiac condition measure,” ¶[0003], where “the present disclosure relates to a method for determining a cardiac condition measure of a patient wearing a wearable medical device based on machine learning analysis”), comprising the steps of:
providing a first data set comprising first cardiac current curve data of a patient acquired during a first time interval (¶[0073], where “Turning to FIG. 2A, example training data 200 illustrates first ECG (e.g., 15-second data) samples 202a through 202j … Each data sample may represent a different discrete timeframe of capture”) and at least second cardiac current curve data of the patient acquired during a second time interval (¶[0073], where “Turning to FIG. 2A, example training data 200 illustrates … second ECG (e.g., 4-second data) samples 204a through 204j … Each data sample may represent a different discrete timeframe of capture”), said first time interval and said second time interval differing from each other (¶[0073], where “Each data sample may represent a different discrete timeframe of capture”);
applying a machine learning algorithm to the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data for classification of the medical relevance (¶[0073], where “A data set including the first and second ECG data samples 202, 204, for example, may be provided to train both a first (e.g., side-to-side channel) data classifier and a second (e.g., front-to-back channel) data classifier of a fully connected machine learning model,” ¶[0078], where “As illustrated in FIG. 1A, in some embodiments, the output of each of the machine learning classifiers 124a, 124b, and 124c is provided to a classification analysis engine 120 to produce a first result 110. The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure”); and
outputting a second data set comprising at least a first class between the first cardiac current curve data and the second cardiac current curve data and/or a second class between the first cardiac current curve data and the second cardiac current curve data (¶[0078], where “As illustrated in FIG. 1A, in some embodiments, the output of each of the machine learning classifiers 124a, 124b, and 124c is provided to a classification analysis engine 120 to produce a first result 110. The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure.” Examiner interprets the output representative of one or more features as a second data set, where the features are further classifications that are a first or second class.).
Although Sipe teaches that “The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure” (¶[0078]), where such classification output based on relevant cardiac features by the machine learning model is interpreted as a second data set, Sipe does not teach an implantable medical device, nor explicitly teach that the one or more relevant features includes deviation between the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data nor a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data.
Sullivan teaches a system and method for medical premonitory event estimation (Abstract), and further teaches an implantable medical device (¶[0234], where “Parameters or metrics of a subject that may be monitored include various parameters of the subject's ECG,” ¶[0254], where “Disclosed embodiments, however, are not limited to wearable medical devices, and a medical device for monitoring any one or more of the parameters of a subject listed herein may include an implantable medical device”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sullivan, which teaches an implantable medical device, with the invention of Sipe in order to monitor any one or more of the parameters of a subject (Sullivan ¶[0254]).
Although Sipe teaches that “The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure” (¶[0078]), where such classification output based on relevant cardiac features by the machine learning model is interpreted as a second data set, neither Sipe nor Sullivan explicitly teach that the one or more relevant features includes deviation between the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data nor a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data.
Seo teaches an electronic device capable of providing information about various suspected diseases other than arrhythmia using electrocardiogram and additional biometric sensing information and a method for providing health information based on electrocardiogram (¶[0008]), and further teaches that the one or more relevant features includes deviation between the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data (¶[0096], where “the processor 210 may compare the first ST interval and first QRS interval of the measured first ECG waveform and the second ST interval and second QRS interval of the previously obtained second ECG waveform (e.g., normal ECG waveform) and, if the difference between the first ST interval and the second ST interval and the difference between the first QRS interval and the second QRS interval are a designated threshold variation or more, identify that the suspected disease is hypoglycemia,” ¶[0098], where “the processor 210 according to an embodiment may obtain a plurality of ECG waveforms for the user periodically or with a designated time interval or a time difference and may identify changes between the plurality of electrocardiogram waveforms … The processor 210 according to an embodiment may provide information regarding the suspected disease.” Examiner interprets identification of changes or differences between electrocardiogram waveforms as deviation between said waveforms.); and
a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data (¶[0096], where “the processor 210 may compare the first ST interval and first QRS interval of the measured first ECG waveform and the second ST interval and second QRS interval of the previously obtained second ECG waveform (e.g., normal ECG waveform) and, if the difference between the first ST interval and the second ST interval and the difference between the first QRS interval and the second QRS interval are a designated threshold variation or more, identify that the suspected disease is hypoglycemia,” ¶[0098], where “the processor 210 according to an embodiment may obtain a plurality of ECG waveforms for the user periodically or with a designated time interval or a time difference and may identify changes between the plurality of electrocardiogram waveforms … The processor 210 according to an embodiment may provide information regarding the suspected disease.” Examiner interprets identification of changes or differences between electrocardiogram waveforms as deviation between said waveforms, where a deviation will either be present or not be present. Furthermore, such deviation will be medically relevant since it identifies a disease.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Seo, which teaches that the one or more relevant features includes deviation between the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data; and a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data, with the modified invention of Sipe in order to identify changes between the ECG waveforms and determine disease presence (Seo ¶[0098]).
Regarding claim 2, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Seo teaches that the first class representing the medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data comprises a plurality of subclasses each representing a medical indication, in particular a cardiac disorder (¶[0095], where “The processor 210 according to an embodiment may identify the presence and type of arrhythmia based on the first parameter set (e.g., the presence or absence of the P wave and/or regularity of the RR interval) among the electrocardiogram factors (e.g., parameters) of the electrocardiogram waveform 420. The processor 210 according to an embodiment may compare at least some (e.g., at least some parameter sets among the plurality of parameter sets) of the ECG factors of the measured first ECG waveform and at least one some (e.g., at least some parameter sets among the plurality of parameter sets) of the ECG factors of the previously obtained second ECG waveform (e.g., the normal ECG waveform) and identify the suspected disease based on at least one ECG factor for which a difference by a designated threshold or more is made therebetween. For example, the suspected disease may include hypercalcemia, hyperkalemia, dehydration, rehydration, hyperglycemia, and/or hypoglycemia.” Examiner interprets that the different suspected diseases are subclasses representing a medical indication.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Seo, which teaches that the first class representing the medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data comprises a plurality of subclasses each representing a medical indication, in particular a cardiac disorder, with the modified invention of Sipe in order to identify changes between the ECG waveforms and determine disease presence (Seo ¶[0098]).
Regarding claim 3, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Sullivan teaches that the second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data comprises changes in position, respiration (¶[0259], where “the wearable medical device 100 may include additional sensors, other than the ECG sensing electrodes 112, capable of monitoring the physiological condition or activity of the subject. For example, sensors capable of measuring … respiration rate … may also be provided”) and/or physical exertion.
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sullivan, which teaches that the second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data comprises changes in position, respiration and/or physical exertion, with the modified invention of Sipe in order to monitor the physiological condition or activity of the subject (Sullivan ¶[0259]).
Regarding claim 4, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Sipe teaches that the second data set further comprises a third class representing erroneous cardiac current curve data not suitable for application of the machine learning algorithm for classification of the medical relevance of the deviation between cardiac current curves (¶[0073], where “Other transformations may be applied to the ECG side-to-side sample data 108a and/or the ECG front-to-back sample data 108b of FIG. 1A and FIG. 1B, such as transformations to reduce noise/artifacts and/or to reduce size and complexity of data being transferred across a network for analysis.” Examiner interprets the noise/artifacts as a third classification that is removed and not inputted into the machine learning algorithm since the data is removed.).
Regarding claim 5, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Sullivan teaches that if the deviation between the pre-acquired first cardiac current curve data and the pre- acquired at least second cardiac current curve data is classified to be a medically relevant deviation according to the first class, a notification is sent to a communication device of a health care provider (¶[0375], where “a subject's event estimation of risk score being above the threshold can lead to a direct notification to … the subject's medical team … For example, a notification of the subject's elevated risk status can take the form of text message, email, or contact by telephone ... Notification of elevated risk can also be sent to the subject's medical team, including a primary care physician and a cardiology specialist with recommendations that they contact their patient to evaluate the subject's adherence to optimized medical therapy”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sullivan, which teaches that if the deviation between the pre-acquired first cardiac current curve data and the pre- acquired at least second cardiac current curve data is classified to be a medically relevant deviation according to the first class, a notification is sent to a communication device of a health care provider, with the modified invention of Sipe in order to notify a physician of a subject’s elevated risk (Sullivan ¶[0375]).
Regarding claim 6, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Seo teaches that the medically relevant deviation between the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data comprises changes in P waves, PQ segment, QRS complex, J point, ST segment, T waves (¶[0062], where “the processor 210 according to an embodiment may identify the suspected disease by analyzing ECG factors (e.g., parameters) of ECG waveform based on the measured first electrocardiogram waveform and the previously obtained second electrocardiogram waveform (normal electrocardiogram waveform or representative electrocardiogram waveform). For example, the parameters may include a plurality of feature points (e.g., P-wave, QRS complex, T-wave) or sections associated with a plurality of feature points (e.g., segments or duration associated with P-wave, QRS complex, and/or T-wave)”), U waves, TP respectively UP segment and/or a QRS morphology for ischemia, infarction and/or conduction disorders.
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Seo, which teaches that the medically relevant deviation between the pre-acquired first cardiac current curve data and the pre-acquired at least second cardiac current curve data comprises changes in P waves, QRS complex, and/or T waves, with the modified invention of Sipe in order to identify the suspected disease by analyzing ECG factors (Seo ¶[0062]).
Regarding claim 8, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Sullivan teaches that the first data set further comprises additional medical parameters comprising a patient activity, a thoracic impedance (¶[0259], where “the wearable medical device 100 may include additional sensors, other than the ECG sensing electrodes 112, capable of monitoring the physiological condition or activity of the subject. For example, sensors capable of measuring … thoracic impedance … the activity level of the subject may also be provided”) and/or electrode readings of the implantable medical device.
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sullivan, which teaches that the first data set further comprises additional medical parameters comprising a patient activity or a thoracic impedance, with the modified invention of Sipe in order to monitor the physiological condition or activity of the subject (Sullivan ¶[0259]).
Regarding claim 10, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Seo teaches that the cardiac current curve data is acquired by the implantable medical device at predetermined intervals (¶[0098], where “Referring to FIG. 5, the processor 210 according to an embodiment may obtain a plurality of ECG waveforms for the user periodically or with a designated time interval or a time difference and may identify changes between the plurality of electrocardiogram waveforms”) and/or on request (¶[0051], where “if the electronic device 101 should perform a function or a service automatically, or in response to a request from a user or another device, the electronic device 101, instead of, or in addition to, executing the function or the service, may request the one or more external electronic devices to perform at least part of the function or the service”), and wherein the cardiac current curve data is transmitted to a central server via a patient communication device or smartphone (¶[0051], where “commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 via the server 108 … The external electronic devices 102 or 104 each may be a device of the same or a different type from the electronic device 101,” ¶[0143], where “The electronic device according to various embodiments may be one of various types of electronic devices. The electronic devices may include, for example, a portable communication device (e.g., a smart phone)”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Seo, which teaches that the cardiac current curve data is acquired by the implantable medical device at predetermined intervals and/or on request, and wherein the cardiac current curve data is transmitted to a central server via a patient communication device or smartphone, with the modified invention of Sipe in order to identify a change in the plurality of parameter sets (Seo ¶[0098]) and to communicate the data with another device.
Regarding claim 11, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Seo teaches that a beginning of the first time interval differs from a beginning of the second time interval (¶[0098], where “the processor 210 according to an embodiment may obtain a plurality of ECG waveforms for the user periodically or with a designated time interval or a time difference and may identify changes between the plurality of electrocardiogram waveforms.” Examiner interprets that since there is a designated time interval or time difference for each ECG waveform that the intervals between a first and second ECG waveform differ from one another.) and/or the first time interval ends before a beginning of the second time interval.
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Seo, which teaches that a beginning of the first time interval differs from a beginning of the second time interval, with the modified invention of Sipe in order to identify a change in the plurality of parameter sets (Seo ¶[0098]).
Regarding claim 13, the claim is directed to a system comprising substantially the same subject matter of claim 1 and is rejected under substantially the same sections of Sipe in combination with Sullivan and Seo. However, claim 13 adds “System for classification of a medical relevance of a deviation between cardiac current curves”, “means for applying a machine learning algorithm”, and “means for outputting a second data set”.
Sipe teaches a system for classification of a medical relevance of a deviation between cardiac current curves (Abstract, where “Data samples derived from raw physiological data captured by sensors of a wearable medical device monitoring a patient's heart, such as a first ECG channel data sample, a second ECG channel data sample, and/or a cardio-vibrational data sample, are applied to a multi-tier machine learning engine to determine a cardiac condition measure … A first tier of the multi-tier machine learning engine may analyze the physiological data sample(s) using first machine learning classifier(s) to obtain a first result. The first result may then be applied to second machine learning classifier(s) of a second tier of the machine learning engine, along with physiological metrics and/or patient clinical information, to determine the cardiac condition measure,” ¶[0014], where “the present disclosure relates to a system for determining a cardiac condition measure of a patient wearing a wearable medical device based on machine learning analysis”), comprising:
means for applying a machine learning algorithm (¶[0014], which teaches “operations stored as computer executable instructions to a non-transitory computer readable media and/or encoded in hardware logic. The operations may be configured to apply the physiological data to a machine learning engine to determine the cardiac condition measure of the patient, where applying includes applying the ECG data and/or the cardio-vibrational data to a first tier of the machine learning engine including one or more first machine learning classifiers to obtain a first result”); and
means for outputting a second data set (¶[0078], where “As illustrated in FIG. 1A, in some embodiments, the output of each of the machine learning classifiers 124a, 124b, and 124c is provided to a classification analysis engine 120 to produce a first result 110. The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure”).
Regarding claim 14, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Sipe teaches a computer program with program code to perform the method of claim 1 when the computer program is executed on a computer (¶[0154], where “Reference has been made to illustrations representing methods and systems according to implementations of this disclosure. Aspects thereof may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer”).
Regarding claim 15, Sipe in combination with Sullivan and Seo teaches all limitations of claim 14 as described in the rejection above.
Sipe teaches a computer readable data carrier storing a computer program (¶[0156], where “software, stored as instructions to a non-transitory computer-readable medium such as a memory device, on-chip integrated memory unit, or other non-transitory computer-readable storage, may be used to perform at least portions of the herein described functionality”).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sipe in view of Sullivan and Seo as applied to claim 1 above, and further in view of Sirendi et al. (hereinafter “Sirendi”) (WO 2020/049267 A1). For the purpose of prior art rejections, U.S. Pub. No. 2021/0353166 A1, IDS Reference No. 2 from IDS dated 07/08/2024, has been used as an equivalent document for Sirendi reference.
Regarding claim 7, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
None of Sipe, Sullivan, nor Seo teaches that the first data set further comprises third cardiac current curve data not originating from the patient from which the first cardiac current curve data and the second cardiac current curve data are collected.
Sirendi teaches a method of analysing cardiac data relating to a patient to indicate a probability of the patient experiencing a cardiac event (Abstract) utilizing at least one machine learning algorithm with classifiers (¶[0040]), and further teaches that the first data set further comprises third cardiac current curve data not originating from the patient from which the first cardiac current curve data and the second cardiac current curve data are collected (¶[0142], where “The features derived from the RR interval data are input into an Artificial Intelligence Based Classifier (the AI classifier). The AI classifier can comprise a pre-trained classifier, or preferably multiple pre-trained classifiers combined into a hybrid classifier, that has been trained (as described below) to identify abnormal beats in the physiological data,” ¶[0153], where “The AI classifier, and more specifically each of the beat-level classifier, patient-level classifier, and decision-level classifier, can be trained by a machine learning system receiving as input examples of heartbeats from a training dataset comprising known normal and abnormal heartbeats from which the system can learn to predict whether an arrhythmia is going to occur. Each heartbeat in the training data set is represented as a real-valued vector containing values for features that describe the specific heartbeat, and enable a classification to be made. The training data is pre-processed in the same way as described above in relation to FIG. 2b, providing the same features that will be used in the prediction method for use in the training process.” Examiner interprets that the training data is not patient-derived data since the training data is utilized to train the classification model.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sirendi, which teaches that the first data set further comprises third cardiac current curve data not originating from the patient from which the first cardiac current curve data and the second cardiac current curve data are collected, with the modified invention of Sipe in order to train the AI classifier to predict whether an arrhythmia is going to occur (Sirendi ¶[0153]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sipe in view of Sullivan and Seo as applied to claim 1 above, and further in view of Felix et al. (hereinafter “Felix”) (U.S. Pub. No. 2021/0000418 A1).
Regarding claim 9, Sipe in combination with Sullivan and Seo teaches all limitations of claim 1 as described in the rejection above.
Although Sipe teaches acquiring first and second cardiac current curve data of a patient (¶[0073]), none of Sipe, Sullivan, nor Seo explicitly teach that the first cardiac current curve data and the second cardiac current curve data comprise a subcutaneous ECG, in particular a wide-field ECG between electrodes and a housing of the implantable medical device, a pseudo-ECG between a shock coil and the implantable medical device and/or intracardiac current waveforms.
Felix teaches a P-wave centric subcutaneous insertable cardiac monitor (ICM) for use in performing long term electrocardiographic (ECG) monitoring (Abstract) that includes firmware with programming instructions, including machine learning and other forms of artificial intelligence-originated instructions (¶[0060]), and further teaches that the first cardiac current curve data and the second cardiac current curve data comprise a subcutaneous ECG (¶[0036], where “FIG. 1 is a diagram showing, by way of example, a subcutaneous P-wave centric ICM 12 for long term electrocardiographic monitoring”), in particular a wide-field ECG between electrodes and a housing of the implantable medical device (¶[0044], where “Physically, the ICM 12 is constructed with a hermetically sealed implantable housing 15 with at least one ECG electrode forming a superior pole on the proximal end 13 and at least one ECG electrode forming an inferior pole on the distal end 14,” ¶[0046], where “Physically, the ICM 12 has four ECG electrodes 16, 17, 18, 19. There could also be additional ECG electrodes, as discussed infra. The ECG electrodes include two ventral (or dorsal) ECG electrodes 18, 19 and two wraparound ECG electrodes 16, 17,” ¶[0047], where “The sensing circuitry can be programmed, either pre-implant or in situ, to use different combinations of the available ECG electrodes (and thereby changing electrode surface areas, shapes, and inter-electrode spacing), including pairing the two ventral ECG electrodes 16, 17, the two wraparound ECG electrodes 18, 19, or one ventral ECG electrode 16, 17 with one wraparound ECG electrode 18, 19 located on the opposite end of the housing 15.” Examiner interprets that the ECG data is collected between the electrodes and housing of the ICM, which is an implantable medical device.), a pseudo-ECG between a shock coil and the implantable medical device and/or intracardiac current waveforms.
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Felix, which teaches that the first cardiac current curve data and the second cardiac current curve data comprise a subcutaneous ECG, in particular a wide-field ECG between electrodes and a housing of the implantable medical device, with the modified invention of Sipe in order to facilitate the recording of high quality ECG data with a good delineation of the P-wave (Felix ¶[0037]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sipe in view of Sullivan, Seo, and Sirendi.
Regarding claim 12, Sipe teaches a computer-implemented method for providing a trained machine learning algorithm configured to classify a medical relevance of a deviation between cardiac current curves (Abstract, where “Data samples derived from raw physiological data captured by sensors of a wearable medical device monitoring a patient's heart, such as a first ECG channel data sample, a second ECG channel data sample, and/or a cardio-vibrational data sample, are applied to a multi-tier machine learning engine to determine a cardiac condition measure … A first tier of the multi-tier machine learning engine may analyze the physiological data sample(s) using first machine learning classifier(s) to obtain a first result. The first result may then be applied to second machine learning classifier(s) of a second tier of the machine learning engine, along with physiological metrics and/or patient clinical information, to determine the cardiac condition measure,” ¶[0003], where “the present disclosure relates to a method for determining a cardiac condition measure of a patient wearing a wearable medical device based on machine learning analysis”), comprising the steps of:
receiving a first training data set comprising first cardiac current curve data of a patient acquired during a first time interval (¶[0073], where “Turning to FIG. 2A, example training data 200 illustrates first ECG (e.g., 15-second data) samples 202a through 202j … Each data sample may represent a different discrete timeframe of capture”) and at least second cardiac current curve data of the patient acquired during a second time interval (¶[0073], where “Turning to FIG. 2A, example training data 200 illustrates … second ECG (e.g., 4-second data) samples 204a through 204j … Each data sample may represent a different discrete timeframe of capture”), said first time interval and said second time interval differing from each other (¶[0073], where “Each data sample may represent a different discrete timeframe of capture”); and
receiving a second training data set comprising at least a first class between the first cardiac current curve data and the second cardiac current curve data and/or a second class between the first cardiac current curve data and the second cardiac current curve data (¶[0078], where “As illustrated in FIG. 1A, in some embodiments, the output of each of the machine learning classifiers 124a, 124b, and 124c is provided to a classification analysis engine 120 to produce a first result 110. The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure.” Examiner interprets the output representative of one or more features as a second training data set, where the features are further classifications that are a first or second class.).
Although Sipe teaches that “The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure” (¶[0078]), where such classification output based on relevant cardiac features by the machine learning model is interpreted as a second training data set, Sipe does not teach an implantable medical device, nor explicitly teach a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data nor training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification of the first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or the second class representing a medically not relevant deviation between the first cardiac current curve data and the second cardiac current curve data from the first cardiac current curve data and the second cardiac current curve data.
Sullivan teaches an implantable medical device (¶[0234], where “Parameters or metrics of a subject that may be monitored include various parameters of the subject's ECG,” ¶[0254], where “Disclosed embodiments, however, are not limited to wearable medical devices, and a medical device for monitoring any one or more of the parameters of a subject listed herein may include an implantable medical device”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sullivan, which teaches an implantable medical device, with the invention of Sipe in order to monitor any one or more of the parameters of a subject (Sullivan ¶[0254]).
Although Sipe teaches that “The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure” (¶[0078]), where such classification output based on relevant cardiac features by the machine learning model is interpreted as a second data set, neither Sipe nor Sullivan explicitly teach a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data nor training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification of the first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or the second class representing a medically not relevant deviation between the first cardiac current curve data and the second cardiac current curve data from the first cardiac current curve data and the second cardiac current curve data.
Seo teaches a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data (¶[0096], where “the processor 210 may compare the first ST interval and first QRS interval of the measured first ECG waveform and the second ST interval and second QRS interval of the previously obtained second ECG waveform (e.g., normal ECG waveform) and, if the difference between the first ST interval and the second ST interval and the difference between the first QRS interval and the second QRS interval are a designated threshold variation or more, identify that the suspected disease is hypoglycemia,” ¶[0098], where “the processor 210 according to an embodiment may obtain a plurality of ECG waveforms for the user periodically or with a designated time interval or a time difference and may identify changes between the plurality of electrocardiogram waveforms … The processor 210 according to an embodiment may provide information regarding the suspected disease.” Examiner interprets identification of changes or differences between electrocardiogram waveforms as deviation between said waveforms, where a deviation will either be present or not be present. Furthermore, such deviation will be medically relevant since it identifies a disease.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Seo, which teaches a first class representing a medically relevant deviation between the first cardiac current curve data and the second cardiac current curve data and/or a second class representing a medically not relevant deviation or no deviation between the first cardiac current curve data and the second cardiac current curve data, with the modified invention of Sipe in order to identify changes between the ECG waveforms and determine disease presence (Seo ¶[0098]).
Although Sipe teaches that “The classification analysis engine 120, for example, may represent an additional one or more CNN stages of the CNN model, designed to combine the output of the classifiers 124 into an output representative of one or more features most relevant to the determination of the cardiac measure” (¶[0078]), where such classification output based on relevant cardiac features by the machine learning model is interpreted as a second data set, none of Sipe, Sullivan, nor Seo explicitly teach training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification.
Sirendi teaches training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification (¶[0050], where “Optionally, training at least two classifiers comprises using a genetic algorithm,” ¶[0148], where “the hyperparameters used within one or more of the classifiers are optimised using evolutionary algorithms, preferably genetic algorithms,” ¶[0181], where “The feature vectors are given as input to an artificial neural network consisting of three layers … The network may be optimised … Parameters may be updated based on mean squared error as the loss function” Examiner interprets a genetic algorithm as a type of optimization algorithm that calculates an extreme value).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Sirendi, which teaches training the machine learning algorithm by an optimization algorithm which calculates an extreme value of a loss function for classification, with the modified invention of Sipe in order to improve the performance of the classifier (Sirendi ¶[0050]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Masuda et al. (U.S. Pub. No. 2022/0028060 A1), which teaches systems and methods are provided for monitoring progression of a cardiac disease in a patient by providing cardio-vibrational image matrixes and/or ECG image matrices generated using sensor data supplied by a medical device (See Abstract).
Carr, J. (2014). An Introduction to Genetic Algorithms. https://www.whitman.edu/Documents/Academics/Mathematics/2014/carrjk.pdf, which teaches genetic algorithms that are a type of optimization algorithm that find the maximum or minimum of a function.
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/SEFRA D. MANOS/Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792
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