Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
The Applicant’s election of Species I: claims 1-5 and 11-15 without traverse is acknowledged; claims 6-10 and 16-20 are withdrawn from consideration.
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 1-3 and 11-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas under the mental processes and mathematical concepts groupings, without significantly more.
The framework for establishing a prima facie case of lack of subject matter eligibility requires that the Examiner determine: (1) Does the claim fall within the four categories of patent eligible subject matter; (2a) prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon and (2a) prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application; and (2b) Does the claim recite additional elements that amount of significantly more than the judicial exception.
Under Step (1): Independent claims 1 and 11 are each directed to a system and a method for generating imaging information, and thus, the claims fall under one of the four patent eligible categories.
To Step 2(a) prong 1:
Independent claim 1 recites
A system for generating imaging information based on at least a signal comprising:
at least a processor; and
a non-transitory computer executable storage medium communicatively connected to the at least a processor, wherein the non-transitory computer executable storage medium contains instructions configuring the at least a processor to:
receive at least an ECG waveform and a trained neural network;
input the at least an ECG waveform into the trained neural network;
output, using the trained neural network, simulated echocardiogram data as a function of the trained neural network and the at least an ECG waveform; and
display, at a viewer, the simulated echocardiogram data.
Claim 1, when considered as a whole, is directed to an abstract idea under the mental processes and mathematical concepts/relationships grouping, because the claim involves receiving data, processing data via a non-specific trained neural network and outputting data. Claim 1 recites a system, which solely comprises the structural features: a processor and a non-transitory computer executable memory (bolded and underlined above).
The recited processor and non-transitory computer executable memory of claim 1 appear to be generic computer components. When the claims, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it is still in the mental processes grouping unless the claim limitation cannot practically be performed in the mind. Likewise, performance of a claim limitation using generic computer components does not preclude the claim limitation from being in the mathematical concepts grouping or the certain methods of organizing human activity grouping. A person of ordinary skill in the art would have been able to use a generic computer having a memory, to perform the recited limitations of receiving data, generating a model, and producing data output. As for the recited generative model, this falls under mathematical concepts/relationships.
Claim 11 is directed a method performed by a processor to carry out the recited steps analogous to claim 1. Similarly, claim 11 is also wholly directed to an abstract idea under the same rational.
As for dependent claims 2-3 and 12-13, the limitations are given its plain meaning, a person can perform manual and manual processes to transform a (single) ECG waveform into a numeric token, token is an individual digits (0–9). Autoregressive model predicts each token sequentially, conditioning on all previous tokens.
Under Step 2(a) prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
This judicial exception is not integrated into a practical application because the claims, do not disclose using the output, i.e. echocardiogram data, for an evaluation of particular treatment for a disease or medical condition under MPEP 2106.05(e). in fact, none of the independent claims positively recite outputting the calculated results in a human perceivable manner.
This judicial exception is not integrated into a practical application because the claims, do not provide improvements to the functioning of a computer or to any the technical field under MPEP 2106.05(a). Currently, the claims recite various generative model(s), but these models do not appear to improve the functioning of the computer, or the analysis of ECG or echocardiogram data.
Under Step 2b: The claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation “non-transitory computer executable memory” and “processor” are well known and routinely use in the analysis of ECG and/or echocardiogram data.
Under the Berkheimer guidance, the following reference sets the standard for well-understood, routine and conventional activities:
Peterson et al. US 11,445,918 B2 discloses a machine learned computational modles of electrocardiogram and electrocardiogram derived features to compute quantitative diastolic indicators including echocardiography parameters; see Fig. 2.
Ramachandra Iyer US 2021/0287442 A1 discloses a method and system for generating AR/VR content including a generative adversarial network (GAN) model to generate multimedia content to view blood flow in the heart of a person, through echocardiogram; see Figs. 1 and 2.
Accordingly, claims 1-3 and 11-13 are thus rejected under 35 USC 101 for reciting abstract ideas-which are patent-ineligible subject matter.
Note: claims 4-5 and 14-15 are not rejected under 35 USC 101 because these claims recite using additional features of training the “autoregressive model” using specific data.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1 and 11 are under 35 U.S.C. 102(a)(2) as being anticipated by Peterson et al. US 2021/0059540 A1 (hereinafter “Peterson”).
Regarding claim 1, Peterson discloses a system (Fig.1: system 100, [0018]) for generating imaging information based on at least a signal (Abstract: using ECG signals to generating estimates of echocardiography parameter) comprising:
at least a processor (Fig.1: processing facility 104; [0018-0019]); and
a non-transitory computer executable storage medium ([0019] processor 104 has appropriate software and memory) communicatively connected to the at least a processor (Fig.1 and [0019]), wherein the non-transitory computer executable storage medium contains instructions configuring the at least a processor to:
receive at least an ECG waveform (electrodes interface 106 receives ECG signals from electrodes, and [0018-0020]. Also see Fig. 2: 202, 206) and a trained neural network (machine-learned computational models 116 and training engine 120, [0020-0021]);
input the at least an ECG waveform into the trained neural network (Fig. 2: 210 and 212, [0027-0028]);
output, using the trained neural network, simulated echocardiogram data as a function of the trained neural network and the at least an ECG waveform (Fig. 2:214 and [0027-0028] echocardiogram parameter estimates ); and
display, at a viewer, the simulated echocardiogram data (Fig.1: display 108; [0030] echocardiogram parameters are outputted via a display screen).
Claim 11 is rejected by Peterson under the same rationale as discussed to claim 1 above.
Allowable Subject Matter
Claims 4-5 and 14-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Note to Applicant: there are no art rejections to claims 2-5 and 12-15, the Applicant is invited to contact the Examiner to expedite prosecution.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Skandarani can be applied as a 102 reference to reject claims 1 and 11, this reference teaches using GAN models to simulate echocardiogram, but does not mention using generative autoregressive models.
Skandarani et al. “Generative Adversarial Networks in Cardiology” Canadian Journal of Cardiology 38 (2022) 196-203 Received 17 September 2021, Accepted 8 November 2021, Available online 13 November 2021, Version of Record 28 January 2022 https://www.sciencedirect.com/science/article/pii/S0828282X21008606 accessed on February 9, 2024
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY X JIAN whose telephone number is (571)270-7374. The examiner can normally be reached M-F 8:00-4:00.
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/SHIRLEY X JIAN/Primary Examiner, Art Unit 3792
September 29, 2026
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