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 .
Response to Amendment
Regarding the amendment filed 07/07/2025: Claims 2-9 and 18 are pending. Claims 1 and 10-17 have been cancelled. Claim 18 is newly added.
Response to Arguments
Rejection under 35 USC § 112
Applicant’s arguments regarding the rejection to claims 1-17 have been fully considered and are not persuasive. No arguments have been filed. A new 112(a) rejection has been presented for new claim 18.
Rejection Under 35 USC 101
Applicant's arguments regarding the rejection of claims 1-17 under 35 U.S.C. 101 have been fully considered and are not persuasive. Regarding claim 18, Applicant argues: “Claims 1 and 10-17 have been canceled. A new independent claim, claim 18 has been newly added. Claims 2-9 have been amended to depend from claim 18.
Claim 18 is directed to an improved electrocardiogram measuring system. Claim 18 recites, among other things:
* an electrocardiogram measuring device including o a plurality of electrodes configured to detect electrical signals generated by a heart of an object;
o a chip configured to store firmware software for converting the detected signals into electrocardiogram data;
o a shielding layer configured to come into contact with a skin of the object to discharge static electricity, " an electrocardiogram signal processing device communicatively coupled to the electrocardiogram measuring device having a processor, wherein the processor is configured to perform instructions stored in the memory, inter alia, to:
o determine a noise section comprising the third and fourth signal segments;
o generate an analysis target section by excluding the noise section from the electrocardiogram signal; and o transmit the analysis target section with the noise section removed to an external device for analyzing information related to a heart of the object, o wherein the noise decision model is trained using a deep learning model or a machine learning model by taking, as training inputs, the plurality of electrocardiogram signals corresponding to noise sections and the plurality of electrocardiogram signals not corresponding to noise sections.
Claim 18 focuses on a specific means that improves the relevant technology rather than being "directed to a result or effect that itself is the abstract idea and merely invoke generic processes and machinery." McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3de 1299 at 1314 (Fed. Cir. 2016). The specification confirms that claim 18 focuses on a specific means that Page 6 of1
improves the electrocardiogram measuring system. The specification explains defining sections of an electrocardiogram (ECG) signal that do not require analysis as noise sections, and classifying signal segments corresponding to these noise sections through a scanning process in time order of the ECG signal, as shown in the paragraphs reproduced below. The specification, shown below as an example, further supports that claim 18 provides an improved technique for determining noise sections by allowing such sections to be identified through a single scan, without requiring multiple rounds of scanning, and by determining the analysis target data while excluding the noise sections. The specification also supports that claim 18 automatically executes noise detection rules such as a division rule based on data in which ECG signal segments are classified using machine learning.
[0151] The electrocardiogram signal processing device 100 may divide the electrocardiogram signal obtained by measuring the object obj into preset signal segments and group or cluster the signal segments. The electrocardiogram signal processing apparatus 100 may divide the electrocardiogram signal into signal segments by dividing the electrocardiogram signal according to a division rule determined by R interval or R peak. The electrocardiogram signal processing device 100 may group or cluster signal segments having a similar pattern to each reference signal segment based on one or more reference signal segments. The electrocardiogram signal processing device 100 may group or cluster signal segments using an electrocardiogram classification model. The electrocardiogram classification model may be learned by deep learning or machine learning. The electrocardiogram classification model may be learned by using measured electrocardiogram signals as input. The electrocardiogram signal processing device 100 may group or cluster signal segments corresponding to each reference value based on one or more reference values.
[0183] The signal processor 150 may generate data by listing group information of signal segments of an electrocardiogram signal in a time dimension. The signal processor 150 may scan group information of signal segments to detect the first signal segment classified as the abnormal group and may determine whether the second signal segment subsequent to the first signal segment corresponds to the abnormal group. When both the first signal segment and the second signal segment correspond to the abnormal group, the first signal segment and the second signal segment may be classified as a noise section. When the second signal segment corresponds to the one or more group, the first signal segment may not be classified as an abnormal section.
[0184] The electrocardiogram signal processing device 100 may generate output data for displaying the electrocardiogram signal excluding the noise section. The electrocardiogram signal processing apparatus 100 may generate data for signal segments belonging to the one or more groups. The electrocardiogram signal processing apparatus 100 may generate data for signal segments belonging to the abnormal group. The
electrocardiogram signal processing apparatus 100 may input signal segments to a machine learning model and/or a deep learning model related to electrocardiogram signal processing in order to train models related to electrocardiogram signal processing. The electrocardiogram signal processing device 100 may analyze each signal segment of the electrocardiogram signal from which a noise section is removed. The electrocardiogram signal processing device 100 may analyze each signal segment as one of a normal beat (N) or bundle branch block), a supraventricular ectopy beat (S, SVEB) and a ventricular ectopic beat (V, VEB). The electrocardiogram signal processing device 100 may generate data related to the analysis target section for each signal segment.
The electrocardiogram signal processing device 100 may analyze the electrocardiogram signal measured by the electrocardiogram measurement device T and classify signal segments corresponding to a noise section. The electrocardiogram signal processing device 100 may classify the electrocardiogram signal excluding the noise section into an analysis target section. By preferentially classifying the electrocardiogram signal in the noise section and analyzing only the electrocardiogram signal in the analysis target section, time and/or resource for analyzing the electrocardiogram signal in the noise section can be saved.
[0225] Accordingly, the electrocardiogram signal may be measured by a single channel measuring device. The electrocardiogram signal may have different characteristics depending on an attachment position, a heart state of an object, or a heart movement of the object. The electrocardiogram signals measured from the same object may have different characteristics depending on the measurement point or the measurement time. The electrocardiogram signal measured by a single-channel measuring device individually includes signal patterns that change each time measurement is performed. In order to determine a noise section in the measured electrocardiogram signal, a process of determining a reference value or reference segment for each electrocardiogram signal may be required.
As shown above, the applicant has discovered a simple and automated detection rule for identifying noise sections-i.e., sections of ECG data where analysis is unnecessary-based on classification results generated using machine learning. Thus, claim 18 is directed to a specific means that improves the relevant technology for detecting noise sections rather than generic processes and machinery. Thus, claim 18 is not directed to an abstract idea. Even for the sake of arguments that claim 18 recites an abstract idea, the claimed features of claim 18 are significantly more for the reasons discussed above.
Claims 2-9 depend from claim 18 and at least for the reasons discussed above, claims 2-9 are not directed to an abstract idea and claim 2-9 recite significantly more.
Reconsideration and withdrawal of this rejection are respectfully requested.”.
The Examiner respectfully disagrees, as shown in the specification in Paragraph [0110]-[0111], the limitations “acquire an electrocardiogram data of the object in real-time from the electrocardiogram measuring device" are obtained from routine data gathering. The other limitations of “a plurality of electrodes configured to detect electrical signals generated by a heart of an object; a chip configured to store firmware software for converting the detected electrical signals into electrocardiogram data; and a shielding layer configured to come into contact with a skin of the object to discharge static electricity… an electrocardiogram signal processing device communicatively coupled to the electrocardiogram measuring device, the electrocardiogram signal processing device comprising: a processor; a communication unit; and a memory” are merely mathematical flow charts, see figs. 1, 9, and 14-19, and a data processing device such as a processor, controller, etc to perform a set of computations. Furthermore, while the claims do provide an improvement, the claims merely recite the generic limitation “an electrocardiogram signal processing device including at least one processor”, without providing for any limiting structure. Accordingly, the claim recites an abstract idea and therefore, the rejection stands.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2-9 and 18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
With regards to claim 18, claim 1 recites the limitation " generate first signal segments by dividing an electrocardiogram signal according to a division rule; classify the first signal segments into one or more preset groups using an electrocardiogram classification model, and classifying second signal segments, which do not belong to the one or more preset groups, into an abnormal group, wherein the electrocardiogram classification model is a model trained using a plurality of electrocardiogram signals and corresponding feature data; detect, using a noise decision model a third signal segment classified into the abnormal group and a fourth signal segment subsequent to the third signal segment that also corresponds to the abnormal group; determine a noise section comprising the third and fourth signal segments; generate an analysis target section by excluding the noise section from the electrocardiogram signal; and transmit the analysis target section with the noise section removed to an external device for analyzing information related to a heart of the object, wherein the noise decision model is trained using a deep learning model or a machine learning model by taking, as training inputs, the plurality of electrocardiogram signals corresponding to noise sections and the plurality of electrocardiogram signals not corresponding to noise sections" As best understood, this is a computer-implemented function (e.g., by processing unit 150 in fig. 11B).
When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008) (internal citation omitted). It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.
However, in the case of the aforementioned signal processing algorithm, the specification merely states "using a deep learning method or a machine learning model to classify the first signal segments into the one or more preset groups and classifying the second signal segments into the abnormal group using a degree of complexity" ([0012] as-published), "the division rule is to divide into signal segments based on time intervals or QRS time intervals of peaks of the electrocardiogram signal." ([0013] as-published), "the noise decision model is to determine signal segments continuously generated more than a reference number of times as the noise section among the second signal segments" ([0014] as-published), and "The deep learning model or machine learning model is learned with signal segments including a noise section and signal segments not including a noise section. The electrocardiogram classification model includes a plurality of models and is operated by selecting a model according to a measurement position." ([0015] as-published). These are all examples of what the algorithm could be or how one could develop such an algorithm. There is no description of any signal processing algorithm described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. Claim 1 is thus found to fail to comply with the written description requirement.
Claims 2-9 are rejected for depending from rejected claim 18.
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 2-9 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. as set forth below.
The following analysis is performed as set forth in the 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter 2019 PEG), as set forth in MPEP § 2106. (Note: the claim limitations below considered to fall within an abstract idea are highlighted in bold font; the remaining features are "additional elements.")
Step 1
Step 1 of the 2019 PEG asks whether a claim is directed to a process, machine, manufacture, or composition of matter.
Claims 2-9 and 18 are directed to a machine, and therefore fall within a statutory category.
Claim 18 recites:
" An electrocardiogram measuring system comprising: an electrocardiogram measuring device comprising:
a plurality of electrodes configured to detect electrical signals generated by a heart of an object;
a chip configured to store firmware software for converting the detected electrical signals into electrocardiogram data; and
a shielding layer configured to come into contact with a skin of the object to discharge static electricity,
wherein the electrocardiogram measuring device is configured to:
output the electrocardiogram data;
segment the electrocardiogram data based on predetermined criteria; and
normalize the segmented electrocardiogram data;
an electrocardiogram signal processing device communicatively coupled to the electrocardiogram measuring device, the electrocardiogram signal processing device comprising:
a processor;
a communication unit; and
a memory;
wherein the processor is configured to perform instructions stored in the memory to:
acquire an electrocardiogram data of the object in real-time from the electrocardiogram measuring device;
generate first signal segments by dividing an electrocardiogram signal according to a division rule;
classify the first signal segments into one or more preset groups using an electrocardiogram classification model, and classifying second signal segments, which do not belong to the one or more preset groups, into an abnormal group,
wherein the electrocardiogram classification model is a model trained using a plurality of electrocardiogram signals and corresponding feature data;
detect, using a noise decision model a third signal segment classified into the abnormal group and a fourth signal segment subsequent to the third signal segment that also corresponds to the abnormal group;
determine a noise section comprising the third and fourth signal segments;
generate an analysis target section by excluding the noise section from the electrocardiogram signal; and
transmit the analysis target section with the noise section removed to an external device for analyzing information related to a heart of the object,
wherein the noise decision model is trained using a deep learning model or a machine learning model by taking, as training inputs, the plurality of electrocardiogram signals corresponding to noise sections and the plurality of electrocardiogram signals not corresponding to noise sections."
The highlighted portion of claim 18 comprises subject matter that falls within the abstract idea judicial exception. Specifically, "… generate first signal segments by dividing an electrocardiogram signal according to a division rule;
classify the first signal segments into one or more preset groups using an electrocardiogram classification model, and classifying second signal segments, which do not belong to the one or more preset groups, into an abnormal group,
wherein the electrocardiogram classification model is a model trained using a plurality of electrocardiogram signals and corresponding feature data;
detect, using a noise decision model a third signal segment classified into the abnormal group and a fourth signal segment subsequent to the third signal segment that also corresponds to the abnormal group;
determine a noise section comprising the third and fourth signal segments;
generate an analysis target section by excluding the noise section from the electrocardiogram signal" is a series of abstract process steps that, under a broadest reasonable interpretation, covers mathematical concepts/mental processes performed in the human mind and/or with pen and paper.
Nothing in the claim, other than the generically recited computer elements, precludes the identified abstract process steps from practically being performed in the mind and/or with pen and paper.
Step 2A, Prong Two
Step 2A, Prong Two of the 2019 PEG asks whether a claim recites additional elements that integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claims 2-9 and 18 do not include additional elements that integrate the recited abstract idea into a practical application. Based on the individual and collective limitations of claims 2-9 and 18 applying a broadest reasonable interpretation, the most significant of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); adding a specific limitation other than what is well-understood, routine, conventional activity in the field (MPEP § 2106.05(d)); and applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)).
Regarding improvements to the functioning of a computer or other technology or technical field, claims 2-9 and 18 do not include any such improvements.
Regarding adding a specific limitation other than what is well-understood, routine, conventional activity in the field, claims 2-9 and 18 do not appear to contain such a limitation.
Regarding applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (MPEP 2106.05(e)), claims 2-9 and 18 do not apply or use the judicial exception in some other meaningful way.
claims 2-9 and 18 thus require further analysis under Step 2B.
Step 2B
Step 2B of the 2019 PEG asks whether the claim recites additional elements that amount to significantly more than the judicial exception.
With regards to claim 18: the additional element of acquiring an electrocardiogram signal of an object merely relates to insignificant extra-solution activity (data gathering is to be performed). The additional element of transmitting the analysis target section to an external device to analyze information related to a heart of the object merely relates to insignificant extra-solution activity (data is transmitted). These elements thus fail the "significantly more" test under Step 2B. Claim 1 therefore constitutes ineligible subject matter.
Claims 2-9 are rejected for depending from rejected claim 18 and only amounting to further limit the abstract idea of claim 18.
Comments
The prior art of record found as a result of the search, does not teach alone or in combination all of the elements recited in claims 2-9 and 18. Therefore, no prior art rejection for claim 18 is presented in this action. However, Claims 2-9 and 18 are rejected under 35 U.S.C. 112 and 35 U.S.C. 101. It is suggested to contact the Examiner for any clarification with respect the rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Gill et al teaches a measuring and processing method for a heart rate sensor.
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 ADAM S CLARKE whose telephone number is (571)270-3792. The examiner can normally be reached M-F 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Judy Nguyen can be reached on (571)272-2258. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADAM S CLARKE/Examiner, Art Unit 2858
/JUDY NGUYEN/Supervisory Patent Examiner, Art Unit 2858