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 .
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.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 7 is objected to because of the following informalities: the limitation “real of fake” appears to contain a clerical error. For the purpose of further examination, the limitation has been interpreted as “real [[of]] or fake.” Appropriate correction is required.
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).
Claims 11-12 are 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 ([0041] of the specification recites that “[t]he computer-readable recording medium may include all types of recording devices in which data that is readable by a computer system is stored. Examples of the computer-readable recording medium may include read-only memory (ROM), random access memory (RAM), compact-disc ROM (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.,” indicating that the transitory media are not excluded). Transitory signals are not within one of the four statutory categories (i.e. non-statutory subject matter). See MPEP 2106(I). 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.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “a feature extraction unit configured to extract,” “a reconstruction unit configured to generate,” “a first training unit configured to train,” “a second training unit configured to train,” and “a discriminator configured to discriminate” in claims 8-10.
One of ordinary skill in the art would understand that the said units have sufficient structure, materials, or acts to perform the function because they belong to a machine learning system using at least a CPU, GPU, memory, etc.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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) 1-5 and 8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kang et al. (“Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing,” arXiv:2103.14255v2 [eess.IV] 31 Jul 2021), hereinafter referred to as Kang.
Regarding claim 1, Kang teaches a method, performed by an image reconstruction apparatus, of reconstructing an image, the method comprising:
a. extracting, through an encoder configured to extract a structure and a texture form an image, a first structure and a first texture form a first medical image (Kang Abstract: “in medical images”; Kang Fig. 1; pg. 4: “Given a chest CT dataset … feed searched pairs x1i, x2j to an encoder network E that outputs structure and texture features used as input for a generator network G; pg. 5: “To generate a de-biased and texture mixed image, an encoder network E takes as input the sampled image pairs to produce texture and structure features … G takes both features for an image generation and texture transfer”);
b. extracting, through the encoder, a second structure and a second texture of a second medical image (Kang Fig. 1 & pg. 4-5 discussed above); and
c. inputting the first structure of the first medical image and the second texture of the second medical image to a decoder configured to reconstruct an image based on a structure and a texture and generate a third medical image (Kang Fig. 1 & pg. 4-5 discussed above).
Regarding claim 2, Kang teaches the method as claimed in claim 1, wherein the encoder comprises an artificial neural network configured to output a structure comprising a two-dimensional (2D) vector of N channels and a texture comprising one-dimensional (1D) vector of 1 channel, wherein N is a natural number of at least 2, and the decoder comprises an artificial neural network configured to generate an image based on the structure of the 2D vector of the N channels and the texture of the 1D vector of the 1 channel (Kang pg. 5: “the structure feature is a feature with spatial dimensions pooled at an earlier stage of E, whereas the texture feature is an embedding vector obtained following 1 x 1 convolution in the later stages of E … G takes both features for an image generation and texture transfer”).
Regarding claim 3, Kang teaches the method as claimed in claim 1, wherein the first medical image and the second medical image are X-ray images (Kang pg. 4: “Given a chest CT dataset” – CT images are computed x-ray images).
Regarding claim 4, Kang teaches a method of generating an image reconstruction model, the method comprising:
inputting a first medical image to an image reconstruction model comprising an encoder configured to extract a structure and a texture from an image and a decoder configured to reconstruct the image based on the structure and the texture and generate a second medical image (Kang Fig. 1 & pg. 4-5 discussed above);
primarily training the image reconstruction model based on a first loss function indicating an error between the first medical image and the second medical image (Note that the term “primarily” has been interpreted as “firstly” to be consistent with claim 6. Kang pg. 5: “To train the entire framework, we follow the arbitrary style transfer training procedure described in [10]. A VGG19 pre-trained model is used to extract features from the input pairs which is then used in the style Lstyle and content Lcontent losses, respectively”).
Regarding claim 5, Kang teaches the method as claimed in claim 4, wherein the primarily training comprises:
discriminating, through a discriminator configured to distinguish between real or fake, whether the second medical image is real or fake; and training the image reconstruction model by using a discrimination result output by the discriminator and the first loss function (Kang pg. 4: “following standard practice for adversarial-based methods, we also employ a discriminator network D to improve the quality of generation”; Kang pg. 5: “we use an adversarial loss to improve the image generation quality via LGAN(G,D) …The final loss function is defined”; Kang pg. 6: “COVID-19 classification”; Kang pg. 8: “To verify … 1110 CT scans from COVID-10 positive patients”).
Regarding claim 8, Kang teaches an image reconstruction apparatus comprising the units configured to perform the method described in claim 1 (Kang pg. 6 & refer to the rejection of claim 1 above). Therefore, claim 8 is rejected using the same rationale as applied to claim 1 discussed above.
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.
Claim(s) 6, 7, and 9-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (“Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing,” arXiv:2103.14255v2 [eess.IV] 31 Jul 2021), in view of Munawar et al. (“Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks,” IEEE Access Vol. 8, 19 August 2020), hereinafter referred to as Kang and Munawar, respectively.
Regarding claim 6, Kang teaches the method as claimed in claim 4, further comprising:
extracting, through the encoder, a first structure and a first texture of the first medical image; extracting, through the encoder, a second structure and a second texture of a third medical image; inputting the first structure of the first medical image and the second texture of the third medical image to the decoder to generate a fourth medical image (Kang Fig. 1 & pg. 4-5 discussed above); and
secondarily training the image reconstruction model by using a second loss function indicating an error between the third medical image and the fourth medical image (Kang pg. 5 discussed above).
However, Kang does not appear to explicitly each using a patch-based error.
Pertaining to the same field of endeavor, Munawar teaches using a patch-based error (Munawar pg. 153538 right column: “The optimization problem then relies on reducing the L1 loss”; Munawar pg. 153539 right column: “Discriminator’s configurations as either image discriminator or patch discriminator are utilized … the patch discriminator divides the input image to a set of patches and maps each patch to a single scalar output”).
Kang and Munawar are considered to be analogous art because they are directed to medical image processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for constructing a de-biased dataset using adaptive structural instance normalization and texture mixing (as taught by Kang) to use a patch-based method (as taught by Munawar) because the combination can discriminate different regions of an image (Munawar pg. 153539 right column).
Regarding claim 7, Kang, in view of Munawar, teaches the method as claimed in claim 6, wherein the secondarily training comprises:
discriminating whether the fourth medical image is real of fake through a discriminator configured to distinguish between real or fake; and training the image reconstruction model by using a discrimination result output by the discriminator and the second loss function (Kang pg. 4-6, & 8 discussed above).
Claim 9 recites the same subject matter as claims 4 and 6, and therefore is rejected using the same rationale as applied to claims 4 and 6 discussed above.
Claim 10 recites the same subject matter as claims 5 and 7, and therefore is rejected using the same rationale as applied to claims 5 and 7 discussed above.
Regarding claim 11, Kang teaches all corresponding limitations of claim 1 (refer to the rejection of claim 1 discussed above), but does not appear to explicitly teach a computer-readable recording medium having recorded thereon a computer program for executing the method.
Pertaining to the same field of endeavor, Munawar teaches a computer-readable recording medium having recorded thereon a computer program for executing the method (Munawar pg. 153540 right column: “All the experiments were performed using a standard PC with Intel core i7 CPU and GeForce GTX 1080 GPU with 8 GB memory”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for constructing a de-biased dataset using adaptive structural instance normalization and texture mixing (as taught by Kang) to use a computer-readable medium (as taught by Munawar) because the combination is more convenient and allows the algorithm to be stored on a memory and automatically executed by a processor of the computer rather than being manually written each time.
Claim 12 is rejected using the same rationale as applied to claims 4 and 11 discussed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOO J SHIN whose telephone number is (571)272-9753. The examiner can normally be reached M-F; 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Soo Shin/Primary Examiner, Art Unit 2667 571-272-9753
soo.shin@uspto.gov