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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 19, 2025 has been entered.
Claim Rejections - 35 USC § 112
Claims 1-6, 8, 9, 11-16, 18, 19, 21 and 22 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.
In claims 1 and 11, the phrase, “associated with a deep learning architecture” makes it unclear whether the act of identifying, monitoring and classifying the at least one cardiac signal using a detection module configured to execute an object detection technique, is merely loosely associated with deep learning, or if deep learning is actually performed and includes the functions of the down-sampling layer, the bottleneck layer and the up-sampling layer. By analogy, one could say that a human driving a vehicle executes object detection associated with a deep learning architecture because the human uses visual processing, memory, spatial awareness, etc. –features that autonomous driving systems also require.
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 1-6, 8, 9, 11-16, 18, 19, 21 and 22 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 acts of receiving electrocardiogram (ECG) signal data comprising at least a cardiac signal from a sensor configured to detect an input and transmit data associated with the detected input; at least one of identifying, monitoring and classifying the detected at least a cardiac signal using a detection module configured to execute an object detection technique associated with a deep learning architecture including decoding an input waveform segment, compressing an output, decoding an output and generating an output waveform associated with the detected cardiac signal; labeling the ECG signal data as a function of an ECG machine learning model, wherein training the ECG machine learning model comprises: receiving a plurality of de-identified medical data from a medical database; generating ECG training data as a function of the plurality of de-identified medical data, wherein the ECG training data comprises the plurality of de-identified medical data correlated to a plurality of signal labels; training the ECG machine learning model as a function of the ECG training data; and labeling the ECG signal data as a function of the trained ECG machine learning model. The above processing involves observation, analysis, judgement and opinion. It is noted that the act of receiving ECG signal data or the act of receiving de-identified medical data is distinguished from the act of collecting data and therefore considered mentally performable (e.g., visually reviewing a printout of ECG signal data or de-identified medical data). Whether the source of the data comes from a sensor, a database, a memory, a printout, etc., is immaterial. Even if the claim were amended to include the act of collecting data from a sensor and thus be considered an additional element, such an act would be considered insignificant data gathering as all implementations of the abstract idea would require such data gathering. A human is capable of at least one of identifying, monitoring or classifying executing an object detection technique (e.g., visual perception, decision-making) associated with a deep learning architecture. References to aspects of deep learning serve to merely generally link the judicial exception to a particular technological environment.
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) because the processor, memory and GUI operate in their usual capacity; there is no application or use of a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, but only visualization of data – 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) because the processor, memory and GUI are cited with a high level of generality and form the basis of any computerized system; there is no transformation or reduction of a particular article to a different state or thing, but only data manipulation, as discussed in MPEP 2106.05(c); 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 ECG monitoring, 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 processor, memory and GUI are generically recited and function in their usual capacity, alone and in combination, to process, store and display data. Their presence is insignificant as they merely function as the tool upon which the abstract idea is executed. The sensor alluded to is not positively recited. As argued above, the act of receiving ECG signal data or the act of receiving de-identified medical data is distinguished from the act of collecting data and therefore considered mentally performable (e.g., visually reviewing a printout of ECG signal data or de-identified medical data). Whether the source of the data comes from a sensor, a database, a memory, a printout, etc., is immaterial. Even if the claim were amended or interpreted to include a sensor for collecting data and thus be considered an additional element, such an act would be considered insignificant data gathering as all implementations of the abstract idea would require such data gathering. Further, note the comments above regarding the reference to “deep learning architecture.” The examiner does not consider this term, or the actions associated therewith, to represent an additional element, as decoding and compression of data is considered mentally performable (or with pen and paper). In addition, as stated above, the reference to deep learning represents an attempt to generally link the judicial exception to a particular technological field.
The act of presenting a visualization output through a GUI represents insignificant extra-solution activity related to data outputting. The applicant discloses that a “visualization output” refers to graphical visualization of cardiac signals and associated signal labels, and that such visualization may include without limitation, bar charts, line graphs, pie charts, histograms, scatter plots, heat maps, box plots, tree maps, network graphs, two-dimensional charts, three-dimensional charts and the like (par. 0056). Clearly the manner in which the data is presented to a user does not convey any technological advantage, other than a means to simply provide a human perceivable output for the intended result of allowing identification of pulmonary vein potential. The applicant further teaches that persons of ordinary skill in the art will be aware of various ways in which data entries in databases may store, retrieve, organize, and/or reflect data and/or records (par. 0028). Par. 0060 discloses that persons skilled in the art, upon reviewing the entirety of the disclosure, will be aware of various ways in which a GUI and/or elements thereof may be implemented and/or used as described in the disclosure. As such, the reference to a GUI is only nominally related to the judicial exception in an insignificant way and fails to provide the necessary integration into a practical application. There are no improvements to the functioning of a computer, or to any other technology or technical field represented by the additional elements; there is no application or use of a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, but only visualization of data; there is no application of the judicial exception with, or by use of, a particular machine; there is no transformation or reduction of a particular article to a different state or thing, 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 ECG monitoring and artificial intelligence, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Furthermore, individually and in combination, the processor, memory, GUI and/or sensor (note the comments above regarding interpretation of the sensor) are WURC in the medical field as they form the basic building blocks of any computerized system. The applicant discloses that the processor may be any device capable of executing instructions and performing calculations (pars. 0023, 0064, 0114, etc.), the memory may be any variety of known storage devices (par. 0115), the GUI may include any variety of items allowing users to interact with electronic devices through visual representations (pars. 0060, 0062). Even if the sensor were to be considered an additional element, the sensor is generic and would be required in all implementations of a system for visualizing ECG signals. It is further disclosed that those of ordinary skill would recognize that different combinations of elements and/or functions than those explicitly described may be utilized, and that the terms employed within the present disclosure are used in a generic and descriptive sense only and not for purposes of limitation (par. 0117). Again, the limitations are described by the applicant as being known to those of ordinary skill in the art as elaborated in the preceding paragraph.
Claims 2-6, 8, 9 and 12-16, 18, 19, 21 and 22 contain no new additional elements. It is noted that new claims 21 and 22 include mentally performable tasks that are merely linked to the technological field of deep learning (see MPEP 2106.05(h)).
Response to Arguments
Applicant's arguments filed August 19, 2025 have been fully considered but they are not persuasive.
Regarding the rejection under §101, the applicant concludes that the various actions referenced as mentally performable in the previous Office Action, cannot be performed mentally.
The examiner is not convinced because the actions listed such as identifying, monitoring and classifying involve observation, evaluation, judgement and/or opinion. An object detection technique can simply involve a cardiologist visually recognizing a particular feature of the signal data and labeling the feature accordingly. The various actions associated with deep learning are patterned after human intelligence and involve algorithmic operations. Data decoding, data compression, data labeling and training using ECG data to learn, can take place within the mind or with pen and paper. Reference to deep learning architecture and machine learning generally links the invention to the field of artificial intelligence as a more efficient way to process the data and does not provide a practical application because they consist of mere instructions to apply the abstract idea on a general computer suitable for such processing. As stated above, the applicant discloses that the computer/processor may be any device capable of executing instructions and performing calculations. According to MPEP 2106.05(b):
It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17, 112 USPQ2d 1750, 1755-56 (Fed. Cir. 2014). See also TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) (noting that Alappat’s rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim was superseded by the Supreme Court’s Bilski and Alice Corp. decisions). If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)
The mere presence of a “specially programmed” generic computer –in this case, a generic computer programmed for deep learning-- is insufficient to overcome an eligibility rejection.
The receipt of data from a sensor configured to detect an input and transmit data associated with the detected input is at best insignificant data gathering because all implementations of the abstract idea would require such data receipt. Further, as argued above, the sensor itself does not appear to be positively recited –it is only used to describe the source of the information that is received.
The applicant refers to Example 39 of the 2019 PEG in an effort to show that the present invention cannot be performed within the human mind, but the examiner does not see the parallel between the present invention and Example 39 which requires applying transformations to digital face images including mirroring, rotating, smoothing and contrast reduction to create a modified set of digital facial images. In the present case, the claims simply require receiving data, identifying, monitoring or classifying, decoding, compression (e.g., removing less important data), labeling, generating training data by correlating it to the labeling, and labeling further data as a function of the training. Furthermore, the examiner considers the citation of mathematical relationships, formulas or calculations to be a separate issue from the class of abstract ideas associated with mental processes (with abstract ideas including: mathematical concepts, methods of organizing human activity, and mental processes). The absence of any specific mathematical recitation does not suggest that the abstract idea cannot be performed within the human mind because the analysis of each category is independent of the other (e.g., one may have a mental process with no mathematical recitations, or a method of organizing human activity with no mental process involved, etc.).
The applicant further argues that the invention represents a practical application because the specification teaches a technological improvement, and because the claimed invention reflects the improvement. The applicant refers to Example 47 of the 2019 PEG in an effort to support this position.
The eligible claims of Example 47, however, do not appear to be analogous to the present invention. Eligible Claim 1 cites no judicial exception under Prong One. The applicant’s present claims, to the contrary, recite mentally performable actions as discussed above. Example Claim 3 included the additional elements of dropping the one or more malicious network packets in real time and blocking future traffic from the source address. These actions made use of the output of the ANN to provide security solutions to the detected anomalies and thus represented an integrated improvement. The applicant’s claimed invention is more analogous to ineligible Example Claim 2, which included the additional step of outputting the anomaly data. The outputting of anomaly data was insufficient to convey eligibility because it merely required a generic output using the trained ANN. The claim did not impose any limits on how the data was output, or require any particular components to output the anomaly data. Like Example Claim 2, the present invention uses a generic output in the form of a GUI to present the result of the performance of the abstract idea in human perceivable form. Any alleged improvements in accuracy reside solely within the performance of the abstract idea. Like Example Claim 2 which output more accurate anomaly data –disclosed to be an important task for any industry that benefits from identifying abnormal data—the present invention’s mere output of more accurate cardiac signal visualizations, in the absence of any additional element representing an integration of the improvement into a practical application, is insufficient to convey eligibility.
The applicant further argues that, by providing specific training data and incorporating it into the machine learning model, these additions result in optimal accuracy and efficiency.
Ineligible Claim 2 of Example 47, however, also includes training the ANN based on specific training data and incorporating it into the machine learning model to improve its performance. Clearly eligibility of AI inventions under §101 requires more than claims to accuracy and efficiency enhancements. Any asserted advantages in accuracy and efficiency appears to reside within the abstract idea itself (i.e., the mentally performable steps as elaborated above) and is not reflected in the additional elements. As stated in MPEP 2106.05, I., one must look beyond the judicial exception for the inventive concept:
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).
Regarding Step 2B analysis, the applicant asserts that at least the limitations of claim 1 as amended recite additional elements that amount to significantly more than the judicial exception.
The examiner notes, however, that the newly amended material introduces no new additional elements beyond the processor, memory and GUI, with the concept of receiving ECG signal data merely indicating that a sensor was the source of the received data (even if one arguably considers the sensor to be an additional element, the sensor represents insignificant data collection as discussed in the rejection above). The detection module configured to execute an object detection technique and the deep learning layers reside within the abstract idea, and represent subroutines of the mentally performable algorithm. They serve to generally link the invention to the field of AI.
Contrary to the applicant’s assertion, said additional elements are WURC, whether considered individually or in combination. The applicant argues that no court cases, literature, or references of record indicate that the additional elements are WURC. Evidence of WURC additional elements can be found within the specification of the present invention as stated in MPEP 2106.05(d), I:
A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d 1307, 1317; 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
The relevant portions of the specification supporting the examiner’s determination have already been cited above. Furthermore, in Section II of 2106.05(d), the courts have recognized that receiving or transmitting information, storing information, and performing repetitive calculations is WURC. The present claim’s generic processor for performing repetitive calculations, the generic memory for storing instructions, the receipt of ECG information, and the transmission of information for display are considered analogous.
Regarding assertions that method claim 11 contains limitations that are non-conventional and non-generic, the applicant appears to be relying upon the abstract idea itself and not the additional elements to provide the asserted non-conventionality. Similar to claim 1, the use of a generic processor to receive information, generate a visualization and present it to a GUI are considered WURC in the ECG monitoring art as every ECG system would inherently require such elements.
Regarding the rejection under §102 and §103, the claims as amended appear to be allowable over the prior art of record as the examiner has found no motivation or teaching to modify the Lyman et al. reference to include the recited object detection and deep learning architecture features.
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
January 24, 2026