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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 20, 2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 4-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) the mentally performable actions of dividing an egm into a plurality of sections that includes specified sections and candidate sections, accepting a first user command to upload the egm to the egm analyzer, accepting a second user command to select a non-analysis section from the candidate sections, such that the second user command causes the candidate sections to be divided into analysis sections that are not selected by the second user command and non-analysis sections that are selected by the second user command, transmitting an instruction to the egm analyzer (the egm analyzer being that portion of the mind of the cardiologist(s) tasked with analyzing egms) to initiate a secondary analysis to analyze the egm using the analysis sections; wherein, prior to the secondary analysis, the plurality of sections are analyzed in a primary analysis using as teaching data egm data exhibiting a disease and egm data which does not exhibit a disease to identify the specified sections as indicating a waveform caused by the disease; automatically excluding the specified sections from the plurality of sections and simultaneously analyze multiple analysis sections in the secondary analysis using, as teaching data, first egm data and second egm data each of which does not exhibit the disease or the test abnormalities, wherein the first egm data is acquired from humans having the disease and the second egm data is acquired from humans who do not have the disease.
Regarding the reference as to how the egm was acquired (i.e., over a time period that is equal or longer than 1 hour, and wherein the durations of each section are as recited), such time constraints do not remove the invention from that which is achievable by humans. There are no limitations regarding the speed of processing/analyzing. While a computer may inherently speed up the process, a human mind is still capable of processing such information regardless of the time and tediousness involved. It is further argued that a team of humans cooperatively processing and analyzing the data makes such analysis entirely feasible. Additionally, given that a human user may select only a limited number of candidate sections for analysis, and exclude a large number of specified sections within the data, the entire data set need not be analyzed.
Regarding the use of a supervised learning model prepared by machine learning using egm teaching data which does not exhibit the disease to identify the specified sections as indicating a waveform caused by the disease, the broad reference to supervised learning models and machine learning is insufficient to convey eligibility. By definition, supervised learning models use labeled datasets to learn patterns and make predictions about new datasets based on the learned patterns, in a similar manner to how a human would learn. A human is capable of learning and recognizing patterns from labeled datasets. In this instance, cardiologists would prepare themselves to identify waveforms caused by disease by drawing on past experiences, education and training, where they have been exposed to waveforms labeled as representing a disease, or waveforms labeled as representing sinus activity, so as to recognize the same patterns in future situations. The reference to machine learning merely invokes computers or other machinery as a tool to perform an existing process. There are no details as to how the supervised learning model works, other than generalities that would apply to all supervised learning models (i.e., training a model to recognize a critical feature based on labeled data, where the data contains labeled examples representing both the critical feature and the absence of the critical feature; see for example par. 0030 where the presence of noise can be determined by inputting noise-containing egms and noise-free egms into a supervised learning model), with reference to identifying diseased egm waveforms amounting to a field-of-use limitation. There are no limitations on the complexity of the model (the applicant discloses that many different algorithms may be used for supervised learning (par. 0025)), the accuracy of the model, the amount of information processed (note the argument above regarding time constraints), the number of iterations required, or the speed and time within which such analysis is made.
Regarding the reference to simultaneous analysis of multiple analysis sections, such an operation can be performed by humans collaborating on the analysis, where each person is designated a section to analyze so as to speed up the process. Furthermore, claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate the judicial exception into a practical application or provide significantly more (MPEP 2106.05(f)(2)).
Regarding the use of a supervised learning model prepared in advance by machine-learning characteristics of diseases or test abnormalities, which cannot be identified by the user, using, as teaching data, first electrocardiogram data and second electrocardiogram data each of which does not exhibit the disease or the test abnormalities, wherein the first electrocardiogram data is acquired from humans having the disease and the second electrocardiogram data is acquired from humans who do not have the disease, note the comments made above regarding use of supervised learning models.
Specifically regarding the limitation “which cannot be identified by the user,” such a feature is subjective and may depend on the training and skill of the user. A novice user may have little experience and training in interpreting egms, but may rely upon an expert who does and who can accurately determine the presence of a disease condition, despite the novice being unable to identify the disease characteristics. Furthermore, there are no constraints on the accuracy or success of analysis, such that one could still attempt to analyze sections for diseases or abnormalities which cannot be identified by the user.
The act of accepting a command itself is considered mentally performable. In any event, even if one would not consider the acceptance of commands or the transmission of an instruction to be mentally performable, the actual issuance of commands and the transmission of an instruction is only nominally related to the applicant’s abstract idea. Such commands or instructions merely serve as a tool to initiate data uploading or selection of non-analysis sections from candidate sections and, at best, constitute insignificant additional elements related to necessary data gathering or inherent computer control (e.g., a limitation relating to the transmission of an instruction to a computer to add two numbers is not deemed significantly different from a limitation simply drawn to adding two numbers by a computer since the transmission of the instruction is implicit in the latter case).
This judicial exception is not integrated into a practical application because there are no improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), as the generic support device, egm analyzer, computing device and display function in their usual capacity of analyzing, computing and displaying, without any improvement in their operation; there is no application or use of a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; there is no application of the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), but only generic computing/analyzing and displaying structure; there is no transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), but only data manipulation; and there is no application or use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to the particular technological environment of electrocardiogram analysis support using machine learning, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the egm analyzer and the computing and display devices of the support device, along with the act of uploading the egm into the egm analyzer, alone and in combination, merely act as the tool upon which the abstract idea is executed and thus represent insignificant extra-solution activity. As stated above, the acceptance of first command and subsequently uploading the egm to the egm analyzer is insignificant because it merely initiates execution of the abstract idea which would inherently be necessary in any computerized system requiring data uploading. Likewise the transmission of an instruction is insignificant as it would be required in any computerized system attempting to perform the mentally performable process of analyzing the egm using analysis sections.
The egm analyzer, the computing device and the display device, alone and in combination, are also considered WURC in the art. The applicant discloses that a wide variety of known devices may be employed in the invention. Any electrocardiograph device may be used to record the data (p. 6, 2nd full paragraph); the egm analyzer may be a personal computer or an application server (p. 7, 1st full paragraph); the support device including a computing device and display device, may reside in a physician terminal and can comprise a general-purpose computer, smartphone, tablet, etc., all of which are well-known to include a display device (p. 7, 2nd full paragraph). The combination would further be required in any implementation of the abstract idea as a means to input, process/analyze and display data.
Claim 4 contains no new additional elements.
Regarding the additional element of display set forth in claims 5-8, 10-13 and 15-18, the act of displaying is considered insignificant post-solution activity, as the display of the various sections on the screen merely provides the necessary human perceivable output in order to allow execution of the abstract idea.
Response to Arguments
Applicant's arguments filed June 20, 2025 have been fully considered but they are not persuasive.
The objection to claim 7 has been overcome by the applicant’s amendment.
Regarding the rejection under §101, the applicant argues that the independent claims contain limitations that cannot be performed within the mind because a human cannot practically examine one or more hours’ worth of egm data and make an analysis based on this data, such that a supervised learning model is required.
As argued above, the time constraints set forth in claims 1, 9 and 14 (i.e., over a time period that is equal or longer than 1 hour, and wherein the durations of each section are as recited) do not remove the invention from that which is achievable by humans. There are no limitations regarding the speed of processing/analyzing. While a computer may inherently speed up the process, a human mind is still capable of processing such information regardless of the time and tediousness involved. It is further argued that a team of humans cooperatively processing and analyzing the data makes such analysis entirely feasible. Additionally, given that a human user may select only a limited number of candidate sections for analysis, and exclude a large number of specified sections within the data, the entire data set need not be analyzed.
The applicant’s argument that the word “automatically” somehow takes the task of excluding specified sections from the plurality of sections out of the realm of human capabilities is respectfully not understood. The applicant appears to be alluding to a computerized process or structure, but merely associating the task with a computer is insufficient to integrate the abstract idea into a practical application, because at best, mere instructions to implement the abstract idea on a generic computer amount to nothing more than to “apply it” (see MPEP 2103.05(f)).
The applicant’s argument concerning the use of the word “simultaneously” is similarly considered ineffective as such an operation can be performed by humans collaborating on the analysis, where each person is designated a section to simultaneously analyze so as to speed up the process. Furthermore, claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate the judicial exception into a practical application or provide significantly more (MPEP 2106.05(f)(2)).
Regarding analysis of multiple analysis sections in the secondary analysis using a supervised learning model prepared in advance by machine-learning characteristics of diseases or test abnormalities, which cannot be identified by the user, using, as teaching data, first electrocardiogram data and second electrocardiogram data each of which does not exhibit the disease or the test abnormalities, wherein the first electrocardiogram data is acquired from humans having the disease and the second electrocardiogram data is acquired from humans who do not have the disease, the applicant argues that such a feature provides an improvement to the technical field of electrocardiogram analysis. It is stated that the secondary analysis requires the generation of a new data set and is not merely an existing data set. By way of example, the applicant refers to the detection of noise unrelated to the diseases or the test abnormalities to identify the section containing the noise as the specified section, such that the user can eliminate candidate sections containing artifacts as a non-analysis section.
As stated in MPEP 2106.05:
An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, "we then ask, ‘[w]hat else is there in the claims before us?") (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"). Instead, an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
Here the applicant is relying upon the abstract idea itself to provide the improvement instead of an element or combination of additional elements beyond the judicial exception. The act of generating a new data set is mentally performable. A human can visually inspect the egm and choose to eliminate sections that contain noise. The applicant states in par. 0030 of the present application that there are no restrictions on the method for determining the presence or absence of noise, and that any known method or algorithm may be applied. The use of a supervised learning model is only discussed in an exemplary fashion. Such a model employs mathematical algorithms (see par. 0025) and thus can be performed by the human mind.
The applicant additionally argues that the subject matter of claims 1, 9 and 14 represent an improvement in the technical field of egm analysis because the function of the analyzer is being improved upon to provide more accurate detections of various diseases, their signs, and test abnormalities, again arguing that a new data set is created, and that the supervised learning model is prepared by machine learning that has been trained on input data that is difficult for the user to judge if a disease or a test abnormality exists. It is asserted that this not only provides faster results, but also more precise results than human or untrained egm analysis.
Once again, the applicant appears to be relying upon the abstract idea itself for any alleged improvements. A human learning to distinguish between egms with disease or abnormalities and those without, likely wouldn’t be able to initially identify which sections of the egm exhibited the diseases or abnormalities, until after a sufficient training period (i.e., after reviewing egm sections labeled “diseased/abnormal” and those labeled “normal/sinus” in order to recognize a pattern). A cardiologist, for example, through the course of training and experience would be able to recognize clinically significant patterns by drawing on their knowledge bank of previously recognized/labeled arrhythmias. There are no restrictions on the precision or speed of the diagnosis. There are no details as to how the supervised learning model works, other than generalities that would apply to all supervised learning models (i.e., training a model to recognize a critical feature based on labeled data, where the data contains labeled examples representing both the critical feature and the absence of the critical feature), with reference to identifying diseased egm waveforms amounting to a field-of-use limitation. There are no limitations on the complexity of the model (the applicant discloses that many different algorithms may be used for supervised learning (par. 0025)), the accuracy/precision of the model, the amount of information processed (note the argument above regarding time constraints), the number of iterations required, or the speed and time within which such analysis is made. The additional elements of the analyzer, computing device and display of the support device operate in their usual capacity unaffected by the performance of the abstract idea. The applicant is effectively saying, “…apply machine learning to the problem of diagnosing cardiac disease.”
The performance of a mental process in a computer environment is insufficient to convey eligibility (MPEP 2106.04(a)), as are mere claims of improved speed or efficiency inherent with applying the abstract idea on a computer (MPEP 2106.05(f)). As discussed in Example 47 of the 2024 guidance update on patent subject matter eligibility including artificial intelligence, the presence of a trained artificial neural network (ANN) and using the ANN to detect one or more anomalies in the physiological data was found to be insufficient to convey eligibility, despite the claim that the use of a trained ANN resulted in more accurate detection of anomalies over traditional methods.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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 KENNEDY SCHAETZLE whose telephone number is (571)272-4954. The examiner can normally be reached 2nd Monday of the biweek and W-F.
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/KENNEDY SCHAETZLE/Primary Examiner, Art Unit 3796
KJS
November 10, 2025