DETAILED ACTION
Claims 1-20 are currently pending and have been examined.
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 .
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 1-13 are directed to a method (i.e., a process); Claims 14-20 are directed to a system (i.e., a machine). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
14. A system for synthetic echocardiography, comprising:
a wearable monitoring device configured to collect and transmit surface electrocardiography (ECG) signals; and
a computing device comprising processing circuitry configured to:
receive the surface ECG signals obtained from a patient using the wearable monitoring device;
synthesize, through a machine learning model, a 3D model of a heart based upon the surface ECG signals; and
generate a rendering of the heart based upon the synthesized model of the heart.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” receiving ECG data, synthesizing a 3D model of the heart based on the ECG data, and generating a rendering of the heart based on the model are observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or with pen and paper. As an example, a user could practically in their mind analyze anatomical features in the images, patient information, etc., and then generate mentally or with pen and paper a 3D visual representation of the heart.
Accordingly, independent claim 14 and analogous independent claim 1 recite at least one abstract idea.
Furthermore, dependent claims 2-13 & 15-20 further narrow the abstract idea described in the independent claims. Claims 7 & 18 recite the ECG signal data comprising 12-lead ECG data. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 14 and analogous independent claim 1, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
14. A system for synthetic echocardiography, comprising:
a wearable monitoring device configured to collect and transmit surface electrocardiography (ECG) signals; and
a computing device comprising processing circuitry configured to:
receive the surface ECG signals obtained from a patient using the wearable monitoring device;
synthesize, through a machine learning model, a 3D model of a heart based upon the surface ECG signals; and
generate a rendering of the heart based upon the synthesized model of the heart.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the wearable monitoring device, computing device; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of the machine learning model; the Examiner submits that these limitations attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. (see MPEP 2106.05(f)).
Regarding the additional limitation of receiving ECG signals from a patient, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) and is conventional as it merely consists of transmitting data over a network (see MPEP § 2106.05(d)(II)).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 14 and analogous independent claim 1 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recites at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 2 and 15: These claims recite the machine learning model comprising a GAN that synthesizes ECG frames based on ECG data and attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. (see MPEP 2106.05(f)).
Claims 3-5 and 16-18: These claims recite a frame and sequence discriminator that generate a reconstruction of the heart and attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. (see MPEP 2106.05(f)).
Claims 6, 8 & 18: These claims recites to collecting and transmitting data from a mHealth device and therefore merely represent insignificant extra-solution activity (e.g., receiving and transmitting data)(see MPEP § 2106.05(g)) and conventional activities as they merely consist of receiving and transmitting data over a network (see MPEP § 2106.05(d)(II)).
Claims 9-10 & 19-20: These claims recite to a backend server and transmitting data to a user device for display and where the user device is a VR/AR which amounts to using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)). These limitations also do no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the at least one abstract idea is performed (see MPEP § 2106.05(h)).
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 14 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, Regarding the additional limitations of the wearable monitoring device, computing device; the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitations of the machine learning model; the Examiner submits that these limitations attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. (see MPEP 2106.05(f)). Regarding the additional limitation of receiving ECG signals from a patient, the Examiner submits that this additional limitation merely adds insignificant extra-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)) and is conventional as it merely consists of transmitting data over a network (see MPEP § 2106.05(d)(II)).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Regarding the additional limitations of collecting ECG data from a patient and outputting visualizations for display which the Examiner submits merely adds insignificant extra-solution activity to the abstract idea, the Examiner has reevaluated such limitations and determined them to not be unconventional as they merely consist of receiving and transmitting data over a network. See MPEP 2106.05(d)(II).
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is 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. Claim 11 recites that the user device is a virtual reality/augmented reality. The Examiner asserts that is unclear how a user device can be a type of reality. Appropriate clarification and correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 10, & 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Seegerer (US20170185740).
As per claim 1, Seegerer discloses a method for synthetic echocardiography, comprising:
receiving surface electrocardiography (ECG) signals obtained from a patient (para. 22: ECG data received);
synthesizing, through a machine learning model, a 3D model of a heart based upon the surface ECG signals (para. 30, 33: machine learning model used to generate 3D model); and
generating a rendering of the heart based upon the synthesized model of the heart (para. 30, 33: simulation results output and visualized).
As per claim 10, Seegerer discloses the method of claim 1, further comprising transmitting the rendering of the heart to a user device for display (para. 30, 33: simulation results output and visualized on display device).
As per claim 13, Seegerer discloses the method of claim 10, wherein the rendering of the heart is transmitted from a backend server (para. 78: server performs generating of display of heart).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2 & 15 are rejected under 35 U.S.C. 103 as being unpatentable over Seegerer in view of Delaney (“Synthesis of Realistic ECG using Generative Adversarial Networks”).
As per claim 2, Seegerer discloses the method of claim 1, but does not expressly teach wherein the machine learning model comprises a generative adversarial network (GAN) model that synthesizes ECG frames based upon the surface ECG signals.
Delaney, however, teaches to using generative adversarial networks to produce ECG medical time series data using ECG data (pg. 7-8).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Delaney with Seegerer based on the motivation of produce realistic medical time series data which can be used without concerns over privacy (Delaney – abstract).
Claim 15 recites substantially similar limitations as those already addressed in claim 2, and, as such, is rejected for similar reasons as given above.
Claims 6-9, 14, & 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Seegerer in view of Baudenbacher (US20150359489).
As per claim 6, Seegerer discloses the method of claim 1, but does not expressly teach wherein the surface ECG signals are collected and transmitted by a mHealth device worn by the patient.
Baudenbacher, however, teaches to obtaining 12 lead ECG data from a patient via a mobile device (para. 28, 40).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Baudenbacher with Seegerer based on the motivation of providing a proactive, patient-centered and enabling healthcare system via medical equipment for home and ambulatory use (Baudenbacher – para. 3).
As per claim 7, Seegerer and Baudenbacher teach the method of claim 6. Seegerer does not expressly teach wherein the surface ECG signals comprise 12-lead ECG signals obtained from the patient in real time.
Baudenbacher, however, teaches to obtaining 12 lead ECG data from a patient via a mobile device (para. 28, 40).
The motivations to combine the above mentioned references are discussed in the rejection of claim 6, and incorporated herein.
As per claim 8, Seegerer and Baudenbacher teach the method of claim 6. Seegerer does not expressly teach wherein the surface ECG signals are received by a computing device from the mHealth device through a communications network.
Baudenbacher, however, teaches to obtaining 12 lead ECG data from a patient via a mobile device over a network (para. 16, 28, 40).
The motivations to combine the above mentioned references are discussed in the rejection of claim 6, and incorporated herein.
As per claim 9, Seegerer and Baudenbacher teach the method of claim 8. Seegerer wherein the computing device is a backend server (para. 78: server performs generating of display of heart).
As per claim 14, Seegerer teaches a system for synthetic echocardiography, comprising:
a computing device comprising processing circuitry configured to:
receive the surface ECG signals obtained from a patient (para. 22: ECG data received);
synthesize, through a machine learning model, a 3D model of a heart based upon the surface ECG signals (para. 30, 33: machine learning model used to generate 3D model); and
generate a rendering of the heart based upon the synthesized model of the heart (para. 30, 33: simulation results output and visualized).
Seegerer does not expressly teach a wearable monitoring device configured to collect and transmit surface electrocardiography (ECG) signals.
Baudenbacher, however, teaches to obtaining 12 lead ECG data from a patient via a mobile device (para. 28, 40).
The motivations to combine the above mentioned references are discussed in the rejection of claim 6, and incorporated herein.
Claim 18 recites substantially similar limitations as those already addressed in claim 7, and, as such, is rejected for similar reasons as given above.
Claims 19-20 recite substantially similar limitations as those already addressed in claims 9-10, and, as such, are rejected for similar reasons as given above.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Seegerer in view of Rama (US20210287442).
As per claim 11, Seegerer discloses the method of claim 10, but does not expressly teach wherein the user device is a virtual reality/augmented reality (VR/AR).
Rama, however, teaches to generating AR/VR content in real time where ECG data can be used to generate a 3d model in virtual reality and the content is displayed to the user via a head mounted device (para. 25, 27, 42).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Rama with Seegerer based on the motivation of strengthen the visualization of the system dynamics captured using received data (Rama – para. 2).
As per claim 12, Seegerer discloses the method of claim 10, but does not expressly teach wherein the rendering of the heart comprises a cohesive video of the heart.
Rama, however, teaches to generating AR/VR content in real time where ECG data can be used to generate a 3d model in virtual reality and the content is displayed to the user via a head mounted device (para. 25, 27, 42). Rama also teaches to where the content can be a combination of text, audio, images, animations, video, and interactive content (para. 26).
The motivations to combine the above mentioned references are discussed in the rejection of claim 11, and incorporated herein.
Prior Art Rejection
All of the cited references fail to expressly teach or suggest, either alone or in combination, the features found within dependent claims 3-5 & 16-17. In particular, the cited prior art of record fails to expressly teach or suggest the combination of: wherein the machine learning model comprises a frame discriminator and a sequence discriminator configured to generate a reconstruction of the heart based upon the synthesized ECG frames and ground truth frames; wherein the frame discriminator and sequence discriminator produce a cohesive video of the heart that exhibits natural cardiac movements; wherein the rendering comprises the cohesive video.
The most relevant prior art of record includes:
Seegerer (US20170185740) teaches to a patient-specific anatomical heart model is generated from medical image data of a patient. Patient-specific cardiac electrical properties are estimated by simulating cardiac electrophysiology over time in the patient-specific anatomical heart model using a computational cardiac electrophysiology model and adjusting cardiac electrical parameters based on the simulation results and the non-invasive electrocardiography measurements.
Delaney (“Synthesis of Realistic ECG using Generative Adversarial Networks”) teaches to investigate the ability of generative adversarial networks (GANs) to produce realistic medical time series data which can be used without concerns over privacy. The aim is to generate synthetic ECG signals representative of normal ECG waveforms. GANs have been used successfully to generate good quality synthetic time series and have been shown to prevent re-identification of individual records. In this work, a range of GAN architectures are developed to generate synthetic sine waves and synthetic ECG.
Baudenbacher (US20150359489) teaches to a system for smart mobile health monitoring that includes a sensor and a mobile computing device. The sensor is coupled to a patient and configured to detect biometric data associated with the patient. The mobile computing device includes a memory that stores computer-executable instructions and a processor that executes the computer-executable instructions. The execution of the computer-executable instructions allows the mobile computing device to at least receive the biometric data from the sensor; process the biometric data to monitor at least one of diagnose a medical condition of the patient, or diagnose a disease of the patient; and provide therapeutic feedback related to the health status and at least one of an activity of the patient and a body position of the patient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Asirvatham (US20240164688) teaches to generates a generative model as a function of the plurality of ECG images and the ECG data repository wherein generating the generative model includes training the generative model using generative training data and output echocardiogram data as a function of the trained generative model at a viewer.
Awasthi (US20250210158) teaches to a method for synthesizing time series data and diagnostic data is provided. The method includes receiving, by at least a processor, time series data and generating, by the at least a processor, at least one time series label as a function of the time series data, wherein generating at least one time series label as a function of the time series data comprises generating a first time series label using a first label machine-learning model and generating a second time series label using a second label machine learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-7949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan Ng/ Primary Examiner, Art Unit 3619