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 .
Specification
The disclosure is objected to because of the following informalities:
Section 3. Neural Implicit Representation discloses a method for reconstruction of 3D structure and discloses a pipeline for the reconstruction process that is shown in Fig. 1. However, several components of the pipeline (i.e., the pose network, flow network, and density network) are not clearly defined in the specification nor are they clearly labeled or notated within Fig. 1. Furthermore, the specification does not provide sufficient detail such that one skilled in the art can understand how each subnetwork interacts, and how the inputs and outputs of each network are associated with each other. Independent claim 1 recites an “encoding” step and sections 3.1-3.2 disclose an encoding layer and encoder, respectively, however there is no notation of an encoding layer/encoder in Fig. 1 submitted. The Examiner recommends that the Applicant thoroughly review the submitted specification and drawings to ensure that the claimed matter is clearly disclosed and understandable based on the disclosure present in the specification, and that the drawings correctly correspond to what is disclosed in the specification.
Appropriate correction is required.
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.
Claims 1-15 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.
Claim 1 recites “the pose network is configured to map an image to a rotation and a translation”, however prior to that limitation there is only discussion of a “plurality of images”. It is unclear what “an image” is referring to and how the “plurality of images” is associated with the model (as there is no language regarding each image from the plurality of images being input into the model). Additional clarification is needed to connect how the “plurality of images” are used by the claimed machine learning model.
Claim 1 recites a plurality of networks (pose, flow, etc.), but it is unclear what the inputs and outputs are from each of these models, and furthermore how each network interacts with each other (i.e., how do the inputs and outputs from the pose network interact with the inputs/outputs of the flow network and density network?).
Claim 1 recites that the density network generates a “projection image” and that the CTF network generates a “rendered image”. However, both of these are written as singular “image” and it is unclear how this is associated with the “plurality of images”.
Claim 1 recites “training the machine learning model using the plurality of images”, where it is unclear which plurality of images the machine learning model is training on (i.e., the initial plurality of images, the plurality of images after the various vectors have been assigned, or the projection and/or rendered images through the encoding process)? If the limitation is referring to the original plurality of images, then it is unclear how/why the “encoding” step and generation of projection and/or rendered images is relevant.
The Examiner recommends that the Applicant review the language of the entire claim set to ensure that the claims clearly recite the claimed invention.
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.
Claims 1, 6, and 9 are rejected as being unpatentable over Zhong et al. (“CryoDRGN: Reconstruction of Heterogenous Cryo-EM Structures using Neural Networks”, DOI: 10.1038/s41592-020-01049-4, Publication Year: 2021, hereinafter “Zhong”) in view of Punjani and Fleet (US 2023/0335216; hereinafter “Punjani”).
Regarding Claim 1, Zhong discloses a computer-implemented method comprising (Supplementary Table 1, Zhong discloses a software used to reconstruct three-dimensional models, and specifically notes that the software includes a model trained on a computer machine containing a CPU and GPU card.):
obtaining a plurality of images representing projections of an object placed in a plurality of poses and a plurality of translations (Methods, Datasets, Zhong discloses utilizing datasets of simulated particle images which applied a random rotation and in-plane translation to the images.);
encoding, by a computer device, a machine learning model comprising a pose network, a flow network, a density network, and a CTF network, wherein the pose network is configured to map an image to a rotation and a translation via the pose embedding vector, the flow network is configured to concatenate a spatial coordinate with the flow embedding vector, the density network is configured to derive a density value in accordance with the spatial coordinate and to generate a projection image, and the CTF network is configured to modulate the projection image appended with the CTF embedding vector to generate a rendered image (Fig. 1, Methods, Zhong discloses a process of taking an input image X from a random unknown orientation (i.e., pose), inputting through a positionally encoded MLP to output reconstructed image (i.e., projection image), from which a CTF function and translation are applied to generate a final image (i.e., rendered image).);
training the machine learning model using the plurality of images (Methods, Training system, Zhong discloses training the cryoDRGN model using different datasets of images.); and
reconstructing a 3D structure of the object based on a trained machine learning module (Figs. 1-2, Zhong discloses a trained cryoDRGN model which can generate a 3D reconstruction of an object.).
Zhong does not disclose assigning a pose embedding vector, a flow embedding vector, and a Contrast Transfer Function (CTF) embedding vector to each of the plurality of images.
Punjani discloses assigning a pose embedding vector, a flow embedding vector, and a Contrast Transfer Function (CTF) embedding vector to each of the plurality of images (Fig. 3, [0061], Punjani discloses obtaining experimental images along with CTF parameters, pose parameters, and flow generator parameters which are fit to the experimental image.);
Zhong and Punjani are considered to be analogous to the claimed invention as they are in the same field of 3D image reconstruction utilizing machine learning techniques. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Zhong by incorporating the various parameters disclosed by Punjani in order to aid the machine learning model in the reconstruction process. The motivation for this combination being the ability to incorporate tunable/adjustable parameters which can be used to aide in reconstruction.
Regarding Claim 6, Zhong in view of Punjani teaches the computer-implemented method of claim 1, further comprising: prepending a positional encoding layer to map the spatial coordinate to a high-frequency representation (Methods, Zhong discloses modifying positional encoding by increasing all wavelengths by a factor of 2π.).
Regarding Claim 9, Zhong in view of Punjani teaches the computer-implemented method of claim 1, wherein each of the plurality of images is a cryogenic electron microscopy (cryo-EM) image (Datasets, Zhong discloses utilizing cryo-EM images.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PROMOTTO TAJRIAN ISLAM whose telephone number is (703)756-5584. The examiner can normally be reached Monday - Friday 8:30 am - 5:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at (571) 272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PROMOTTO TAJRIAN ISLAM/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669