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 .
DETAILED ACTION
In the response filed 17 August 2026, the following has occurred: Claims 1, 14 and 20 have been amended; claim 27 is newly added.
Now claims 1, 5-9, 13-14, 18 and 20-27 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 17 August 2026 has been entered.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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, 5-9, 13-14, 18 and 20-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 14 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite method, system and non-transitory computer readable medium (CRM) for assessing a representation of a physiological system of a subject for a disease state. The limitations of:
Claim 1, which is representative of claims 14 and 20
[… obtaining …], a plurality of biophysical signal data sets of a subject; wherein the plurality of biophysical signal data sets include wide-band phase gradient biopotential signal data, wherein the plurality of biophysical signal data sets comprise unfiltered electrophysiological signals [… collected …], and wherein the plurality of biophysical signal data sets are simultaneously [… collected in …] less than about 1 microsecond to prevent non-linear phase distortions in the wide-band phase gradient biopotential signal data; generating outputs of feature sets for use in a trained machine learning operation by: generating, […], a decomposed and reconstructed model from a […] analysis of the wide-band phase gradient biopotential signal data; subtracting, […], the decomposed and reconstructed model from the wide-band phase gradient biopotential signal data to generate a residue data set; generating, […] using only data from the residue data set, a residue model comprising a point-cloud residue; performing a […] analysis of the frequency components of the wideband phase gradient biopotential signal data by analyzing, […], only the residue model and not the decomposed and reconstructed model; generating, […], a three-dimensional volumetric object from only the point-cloud residue and without using the subtracted signal data or the decomposed and reconstructed model, wherein the point-cloud residue comprises a plurality of vertices defined in a three-dimensional phase space, by performing a [… mathematical …] operation on the point-cloud residue, wherein the [… mathematical …] operation is selected from the group consisting of Delaunay [… mathematical operation …], Mesh generation, Alpha Hull [… mathematical operation …], and Convex Hull [… mathematical operation …]; and determining, […], values for machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object, wherein the machine extractable features are selected from the group consisting of a 3D object volume value, a void volume value, a surface area value, a principal curvature direction value, and a Betti number value; automatically executing, […], a [… model …] on the determined values for the corresponding machine extractable features to calculate a classification value representing the presence and/or non-presence of significant coronary artery disease; and automatically [… providing …], the calculated classification value and the values determined for the corresponding machine extractable features to a [… output …] of a healthcare provider portal configured to [… output …] a dynamic, three-dimensionally rotatable anatomical visualization of the three-dimensional volumetric object showing regions of affected myocardium.
, as drafted, is a method, which under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). That is, by a human user inter and equipment configured to acting with one or more processors (claim 1), a system comprising a processor, memory and equipment (claim 14) and a non-transitory CRM with a processor (claim 20), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, by a human user interacting with one or more processors (claim 1), a system comprising a processor, memory and equipment (claim 14) and a non-transitory CRM with a processor (claim 20), the claim encompasses collection of data for organization of the collected data into models to be used to further organize the data into determinations about coronary artery disease that are provides to a human user via interaction with the generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of one or more processors (claim 1), a system comprising a processor and a memory (claim 14) and a non-transitory CRM with a processor (claim 20), which implements the abstract idea. The one or more processors (claim 1), a system comprising a processor and a memory (claim 14) and a non-transitory CRM with a processor (claim 20) are recited at a high level of generality (i.e., a general purpose computer; See Applicant’s specification Figure 17, paragraphs [0174]-[0184]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “equipment configured to measure electrical properties of a subject… captured at microvolt or sub-microvolt resolutions… sampled with a temporal skew of”, “obtaining, by one or more processors… automatically transmitting”, “a Fast Fourier Transform analysis”, “a chaotic analysis”, a “triangulation operation”, “automatically executing… a trained neural network classifier” and “a graphical user interface… configured to render and display a dynamic, three-dimensionally rotatable… visualization”. The “equipment configured to measure electrical properties of a subject… captured at microvolt or sub-microvolt resolutions… sampled with a temporal skew of” steps are recited at a high-level of generality (i.e., an off the shelf lead for and ECG; See Applicant’s specification paragraphs [0068]-[0069]), and amounts to generally linking the abstract idea to a particular technological environment. The “obtaining, by one or more processors… automatically transmitting” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “a Fast Fourier Transform analysis” is recited at a high level of generality (i.e., off the shelf FFT and inverse FFT) and amounts to generally linking the abstract idea to a particular technological environment. The “a chaotic analysis” is recited at a high level of generality (i.e., off the shelf algorithms for analysis of chaotic datasets) and amounts to generally linking the abstract idea to a particular technological environment. The “triangulation operation” is recited at a high level of generality (i.e., off the geometry/mathematical operations) and amounts to generally linking the abstract idea to a particular technological environment. The “automatically executing… a trained neural network classifier” is recited at a high level of generality (i.e., off the shelf training of a model for use in making determinations) and amounts to generally linking the abstract idea to a particular technological environment. The “a graphical user interface… configured to render and display a dynamic, three-dimensionally rotatable… visualization” is recited at a high level of generality (i.e., as a generic display interface for presentation of information to a user) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed toward an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of one or more processors (claim 1), a system comprising a processor and a memory (claim 14) and a non-transitory CRM with a processor (claim 20), to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “equipment configured to measure electrical properties of a subject… captured at microvolt or sub-microvolt resolutions… sampled with a temporal skew of”, “obtaining, by one or more processors… automatically transmitting”, “a Fast Fourier Transform analysis”, “a chaotic analysis”, a “triangulation operation”, “automatically executing… a trained neural network classifier” and “a graphical user interface… configured to render and display a dynamic, three-dimensionally rotatable… visualization” were considered extra-solution activity and/or generally linking to a particular technological environment. The “equipment configured to measure electrical properties of a subject… captured at microvolt or sub-microvolt resolutions… sampled with a temporal skew of” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Burton: Figure 1, paragraphs [0047]-[0052]; Upton: Figure 1, paragraph [0098]; Gupta (20170119272): paragraph [0006]; Cohen (20040097802): paragraph [0030], [0082]; use of noninvasive equipment to collect biophysical signals with a microvolt resolution and temporal skew is well-understood, routine, and conventional. The “obtaining, by one or more processors… automatically transmitting” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The “a Fast Fourier Transform analysis” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Burton: paragraphs [0058]; use of FFT is well-understood, routine and conventional. The “a chaotic analysis” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Chen: page 3; Bukhman (20100274303): paragraph [0095]; Duan (20080183093): paragraph [0062]; use of chaotic analysis is well-understood, routine and conventional. The a “triangulation operation” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Burton (20160378936): paragraphs [0019], [0098]-[0100]; Upton (20200388037): paragraphs [0028], [0047]; Frieman (20200375564): paragraph [0027]; Stevens (20110130543): paragraph [0166]; Wang (20110044524): paragraph [0903]; use of math/geometry to process data is well-understood, routine and conventional. The “automatically executing… a trained neural network classifier” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Burton: paragraph [0089], [0103]; Choi (20200202527): Figure 1, paragraphs [0007]; training a neural network model is well-understood, routine and conventional. The “a graphical user interface… configured to render and display a dynamic, three-dimensionally rotatable… visualization” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Burton: Figure 12-14, paragraph [0053]; Upton: Figure 5, paragraph [0150]; display of generated data objects is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 5-9, 13, 18 and 21-27 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claim 5 further recites generation of contour data but does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claims 6 recite the additional element of noninvasive equipment. However, this additional element was already considered above and the same motivation is incorporated herein.
Claim 7 further recites the removal of a baseline wandering trend but does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claim 8 recites the additional elements of a computing device. The computing device is recited at a high level of generality (i.e., a general purpose computer; See Applicant’s specification Figure 17, paragraphs [0174]-[0184]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed toward an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing device to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Claim 9, further recites use of a sampling rate in collection of data but does not recite any additional elements, and therefore cannot provide a practical application and/or significantly more.
Claims 13, further recites analysis of biophysical signal but does not recite any additional elements, and therefore cannot provide a practical application and/or significantly more.
Claim 21 recites the additional element of using a neural network, however was already considered above and is incorporated herein.
Claim 22 recites generation of values with an intended use of coloring an output, however the claim does not actually display any data (i.e., an additional element), nevertheless, arguendo display of data was already considered above and is incorporated herein.
Claim 23 recites mathematical concepts (i.e., an abstract idea) a fractional derivate is math and is directed to an abstract idea and is not an additional element, capable of providing an abstract idea and/or significantly more.
Claim 24 recites the additional element of “using a… filter”, however this is recited at a high level of generality (i.e., using a generic off-the shelf filter to remove labeled data) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “using a… filter” were considered generally linking the abstract idea to a particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Burton (20160378936): paragraphs [0056]-[0059]; Upton (20200388037): paragraphs [0141]; Bukhman (20100274303): paragraph [0093]; use of a filter is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 25 recites the labels of data collected, and elements of how the data is collected, however collection of data was already considered above and is incorporated herein.
Claim 26 recites determination and assessment of data (i.e., organization of data), and does not recite any additional elements capable of providing a practical application and/or significantly more.
Claim 27 recites the additional elements of “wherein the trained neural network classifier is trained by automatically adjusting network weights of a plurality of hidden layers of the neural network”, however this is recited at a high level of generality (i.e., off the shelf training of a model for use in making determinations) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “wherein the trained neural network classifier is trained by automatically adjusting network weights of a plurality of hidden layers of the neural network”, were considered generally linking the abstract idea to a particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Choi (20200202527): paragraph [0246]; Upton (20200388037): paragraphs [0141]; Baumeister (20210204857): paragraph [0033]; adjusting weights of layers to train a neural network is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Response to Arguments
Applicant's arguments filed on 17 August 2026 have been fully considered but they are not persuasive. Applicant's arguments will be addressed below in the order in which they appear in the response filed on 17 August 2026.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 11, 5-9, 13-14, 18 and 20-26, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Applicant respectfully submits that the amended claims are not directed to a "method of organizing human activity." The Examiner's characterization of the claims is an unsupported oversimplification of the technical limitations… The claims have been further amended to eliminate any human diagnostic steps. There is no human clinical activity, human decision-making, or human data-sorting step… Such interactive computerized rendering of a physical system's phase states in space-time is completely distinct from organizing human activity. Because the claims as a whole are directed to an automated, hardware-constrained signal processing and graphical rendering method, they do not recite 'methods of organizing human activity' or any other judicial exception. Under Step 2A, Prong One, the claims do not recite an abstract idea and are patent eligible… Even if the claims are directed to an Abstract idea (which Applicant does not concede), the claims are patent eligible under Step 2A, Prong Two because they integrate any such concept into a concrete, practical application that provides a specific technological improvement to the fields of electrocardiography and interactive medical data visualization… One such improvement is resolving non-linear phase distortion. Standard clinical electrocardiography filters out high-frequency and low-resolution phase information, introducing non-linear distortions that mask fine phase-gradient properties (Specification Paragraph [0004]). The claims solve this physical hardware problem… Another improvement is interactive 3D phase space visualization… This is an improvement in computer graphics interfaces and data-visualization techniques. It allows clinical operators to interactively rotate, pan, and scale high-dimensional phase-space representations of the heart's electrical conduction anomalies, making the spatial topologies (such as void volumes or Betti numbers) visually accessible and interpretable… by incorporating the microvolt sampling, sub-microsecond skew limit, automatic neural network execution, and dynamic rotatable 3D graphics transmission into the independent claims, the claims are no longer generic data organization or generic computer instructions. They recite a specific, physical-graphical instrument pipeline.
The Examiner respectfully disagrees.
It is respectfully submitted, that the claims under the broadest reasonable interpretation amount to a human user collecting data via human interaction using generic off-the-shelf equipment, and organizing the collected data into a visualization for the human user to use in their workflow for managing a patient (i.e., human activity), via a human user interacting with generic off-the-shelf computer components, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. The claim is directed toward collecting, organizing and providing a human user a result for the human user to use, and is directed toward certain methods of organizing human activity” grouping of abstract ideas.
It is respectfully submitted, none of the claimed additional elements recite a technical problem recited in Applicant’s specification, in particular paragraph [0004], at best describes a human activity problem of human error in probe placement, and provides no technical details of any “non-linear phase distortion” problems, and furthermore the equipment is described as generic off-the-shelf probes (see above but at least Applicant’s specification paragraph [0069]), performing well-understood, routine and conventional activity to gather a signal. Additionally, it is not a technical problem rooted in computer hardware technology to analyze biophysical signals, biophysical signal analysis is a human activity problem performed by a healthcare provider in the treatment of their patient and is not rooted in computer hardware technology. Finally, no technical problems with existing displays are described, merely using mathematical operations to better organize a result for a human user to interact with is not an improvement in the functionality of the computer, at best it is a human activity solution to a non-technical problem to allow a human user to better interact with the organized data, which may improve upon the abstract idea, nevertheless an improved abstract idea is still an abstract idea. The claims use generic off-the-shelf equipment to collect and organize data using generic machine learning models for a human user to interact with, which generally links the abstract idea to a particular technological environment but does not recite any additional elements which recite a technical solution to a technical problem recited in Applicant’s specification, as such the argument is not persuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew E Lee whose telephone number is (571)272-8323. The examiner can normally be reached M-Th 9-5:00 PM.
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/A.E.L./ Examiner, Art Unit 3684
/Shahid Merchant/ Supervisory Patent Examiner, Art Unit 3684
