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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/25/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 10/02/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
35 U.S.C. 101 requires that a claimed invention must fall within one of the four eligible categories of invention (i.e. process, machine, manufacture, or composition of matter) and must not be directed to subject matter encompassing a judicially recognized exception as interpreted by the courts. MPEP 2106. The four eligible categories of invention include: (1) process which is an act, or a series of acts or steps, (2) machine which is an concrete thing, consisting of parts, or of certain devices and combination of devices, (3) manufacture which is an article produced from raw or prepared materials by giving to these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery, and (4) composition of matter which is all compositions of two or more substances and all composite articles, whether they be the results of chemical union, or of mechanical mixture, or whether they be gases, fluids, powders or solids. MPEP 2106(I).
Claim 10 is rejected under 35 U.S.C. 101 as not falling within one of the four statutory categories of invention because the broadest reasonable interpretation of the instant claims in light of the specification encompasses transitory signals. But, transitory signals are not within one of the four statutory categories (i.e. non-statutory subject matter). See MPEP 2106(I). However, claims directed toward a non-transitory computer readable medium may qualify as a manufacture and make the claim patent-eligible subject matter. MPEP 2106(I). Therefore, amending the claims to recite a “non-transitory computer-readable medium” would resolve this issue.
Response to Preliminary Amendment
The preliminary amendment filed 10/02/2024 has been entered.
Claims 1, 2, 9 and 10 have been amended.
Claim 8 has been cancelled.
Claims 1-7 and 9-10 are pending.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-2, 4-5, 7 and 9-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Seo et al (“Seo” hereinafter, “A learning-based method for solving ill-posed nonlinear inverse problems: a simulation study of lung EIT”, see IDS filed 03/25/2026).
As per claim 1, Seo discloses an image reconstruction method (abstract and figure 1: “reconstruct time-difference conductivity images”), comprising: acquiring actually measured data of a target (section 2.1: “electrodes are placed around the human thorax”); constructing, according to the actually measured data, an actually measured data inversion objective function (section 3.1: equation 13 is an inversion objective function) which takes latent space parameter of a variational auto-encoder deep neural network (section 3.4.1: autoencoder is deep learning network) as an unknown number (section 3.1: “unknown constraint M”); minimizing the actually measured data inversion objective function by using the variational auto-encoder deep neural network (section 3.4.1: equation 14), so as to obtain a target latent space parameter (section 3.4.1: encoder’s output h the target latent space parameter); and decoding the target latent space parameter by using the variational auto-encoder deep neural network, to obtain a target reconstructed image (section 3.4.1: equation 17: the decoder converts h to an image; sections 3.4.2 & 3.5: “reconstruct the conductivity from the latent variable by applying the decoder” in equation 17).
As per claim 2, Seo discloses wherein the variational auto-encoder deep neural network comprises a decoder and an encoder; the minimizing the actually measured data inversion objective function by using the variational auto-encoder deep neural network, to obtain a target latent space parameter comprises: setting an initial model according to the target; encoding the initial model by using the encoder to obtain a code of the initial model; decoding the code by the decoder and performing calculation to obtain simulated data; determining whether a difference between the simulated data and the actually measured data is greater than a first threshold; determine an update quantity of the code in a case where the difference is greater than the first threshold; updating the code according to the update quantity, and continuously performing the step of decoding the code by the decoder and performing calculation to obtain simulated data; and outputting the code as the target latent space parameter in a case where the difference is smaller than or equal to the first threshold (section 3.4.2 and section 4, as explained in section 3.4.1, the variational autoencoder is trained with low dimensional EIT image data as well as high dimensional EIT image data and the loss by the deep learning network is minimized by an equation as shown in equation 14, and the reconstruction loss is continuously minimized throughout autoencoder as shown in equation 20).
As per claim 4, Seo discloses wherein training of the variational auto-encoder deep neural network comprises: acquiring training data, and constructing a training set according to the training data; constructing the variational auto-encoder deep neural network according to the training set; constructing a training function of the variational auto-encoder deep neural network according to the training set; and training the variational auto-encoder deep neural network by using the training function (see sections 3.4; 3.4.1;3.5 and 4.2 for training procedure and reconstruction result for training variational autoencoder).
As per claim 5, Seo discloses wherein the training data comprises image data, and the acquiring training data and constructing a training set according to the training data comprises: segmenting the image data to obtain an interested target in the image data; distributing a training parameter to the interested target to form an initial training model; and adjusting different orientations of the initial training model to obtain a plurality of deformation training models, so as to obtain the training set (as shown in figures 1, 4-6, the lung images for training data are segmented and can be adjusted for different orientations for standard training input image data).
As per claim 7, Seo discloses wherein the actually measured data comprise one of: temporal differential electrical impedance data, absolute electrical impedance data and microwave data (abstract: “lung electrical impedance tomography”).
As per claim 9, see explanation in claim 1. The examiner notes Seo’s system is a computer-like system, which inherently includes a memory and a processor.
As per claim 10, see explanation in claim 1. The examiner notes Seo’s system is a computer-like system, which inherently includes a non-transitory computer readable medium.
Allowable Subject Matter
Claims 3 and 6 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOM Y LU whose telephone number is (571)272-7393. The examiner can normally be reached Monday - Friday, 9AM - 5PM.
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/TOM Y LU/Primary Examiner, Art Unit 2667
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