DETAILED ACTION
Notice of 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 Objections
Claims 8, 10-11, 17, and 19-20 are objected to because of the following informalities:
In claim 8 Line 1, the term “wherein the trained model for determining;” should be changed to, “wherein a trained model for determining” in order to avoid typographical/grammar error and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 10 Line 5, the term “a classifier module configured to” should be changed to, “a binary classifier configured to” in order to avoid typographical/grammar error and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, since the applicant disclosure, the specification dated 12/20/2023 do not have the term “classifier module” but do have support for the term binary classifier.
In claim 11, Line 1, the term “wherein the classification module comprises” should be changed to, “wherein a classification module comprises;” in order to avoid typographical/grammar error and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 11, Line 5, the term “a binary classifier configured to” should be changed to “the binary classifier configured to” in order to avoid typographical/grammar error and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 17, Line 1, the term “the trained model for determining” should be changed to “a trained model for determining” in order to avoid typographical/grammar error and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 19, Line 9, the term “a classifier” should be changed to “a binary classifier” in order to have consistency in terminology in keeping with the disclosure dated 12/20/2023 and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 20, Line 1, the term “a classifier” should be changed to “the binary classifier” in order to have consistency in terminology in keeping with the disclosure dated 12/20/2023 and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 20, Line 6, the term “a classifier” should be changed to “the binary classifier” in order to have consistency in terminology in keeping with the disclosure dated 12/20/2023 and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. Appropriate correction is required.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, receiving a source image of the blank test card; generating one or more synthetic images by applying an image-to-image translation model to the source image; and applying a classifier to the one or more synthetic images to determine a classification of the blank test card must be shown or the feature(s) canceled from the claim(s). Currently the flow chart of figure 6, representative of the methodology performed in claim 1, does not show the steps as being traversed in the claim. In the event said steps are present examiner would respectfully request a clear mapping of the claim(s) in view of the flow chart in figure 6. No new matter should be entered.
Corrected drawing sheets, in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitations are:
Claims 10-11, 17, and 19 recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 10; recites the limitation, “an image-to-image translation model configured to ….” [Line 2].
Claim 11; recites the limitation, “a test result identification subsystem configured to ….” [Line 2].
Claim 17; recites the limitation, “trained model for determining …….” [Line 1].
Claim 19; recites the limitation, “a diagnostic system, ……configured to: …….” [Line 4].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The specification discloses the limitations in claim 10-11, 17, and 19 as follows:
(i) “an image-to-image translation model” (Fig. 8, #582. Paragraph [0013 and 0045]- an image-to-image translation model is described as FIG. 8 illustrates an example of a system using multiple image-to-image translation models, according to one embodiment. the blank card testing subsystem 580 generates synthetic images from the received image of the blank test card using a trained image-to-image translation model 582. (Wherein the image-to-image translation model do have sufficient structure associated with it a known algorithm with an image from one domain is converted to a corresponding image in another domain.).
(iii) “test result identification subsystem” (Fig. 5, #540. Paragraph [0040 and 0025]- test result identification subsystem is described as the result identification subsystem 540 includes a reaction classification module, for classifying whether an image depicts a specific biological reaction corresponding to a test card. For example, if the test card is a gel card, the reaction classification module of the result identification subsystem 540 determines if images of the wells of the gel card show that specific biological reactions have taken place. as used herein, the term "module" refers to computer program instructions or other logic used to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, or software, or a combination thereof. In one embodiment, program modules formed of executable computer program instructions are stored on the storage device 208, loaded into the memory 206, and executed by the processor 202. Fig. 5, illustrates the result identification subsystem as a black box. (Wherein the result identification subsystem does have sufficient structure associated with it, processors and memory.).
(iv) “trained model” (Fig. 5. Paragraph [0048]- trained model is described as includes the image-to-image translation model 582 may be trained using a generative adversarial network (GAN) or a conditional adversarial network (cGAN). The GAN architecture may have a generator model for generating a synthetic image from a source image, and a discriminator for determining whether an image is a real image or a synthetic image. (Wherein the trained model does have sufficient structure associated with it, processor, memory discriminator, and generator.).
(v) “diagnostic system” (Fig. 1, #130. Paragraph [0048]- diagnostic system is described as includes the diagnostic system 130 can be formed of multiple computers 200 operating together to provide the functions described. (Wherein the trained model does have sufficient structure associated with it, computers.).
If applicant does not intend 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 avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process (concept performed in a human mind, including as observation, evaluation, judgment, opinion). The claims recite a method of diagnostic testing and classifying blank test cards. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g., an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories?
YES. Claim 1 is directed to a method, i.e., process, and claims 10 and 19 are directed to a device and a system, i.e., an apparatus.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES, the claims are directed toward a mental process (i.e., abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The method in claim 1 comprise a mental process that can be practicably performed in the human mind therefore, an abstract idea.
Claim 1 recites:
generating one or more synthetic images mental process as an abstract idea).
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Because both product and process claims may recite a "mental process", the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim. The courts have identified numerous product claims as reciting mental process-type abstract ideas, for instance the product claims to computer systems and computer-readable media in Versata Dev. Group. v. SAP Am., Inc., 793 F.3d 1306, 115 USPQ2d 1681 (Fed. Cir. 2015).
As such, a person could perform diagnostic testing and classifying blank test cards, either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a computing device (e.g. processing unit) (see claims 10- 11 and 14- 18) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with a simple tool such as a pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses 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 is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1- 20 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Claims 1, 10 and 18 recite:
“receiving a source image of the blank test card” (adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea);
“applying a classifier to the one or more synthetic images to determine a classification of the blank test card” is an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea); and
The “applying an image-to-image translation model” (see the crossed-out portion) is an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
Claims 2 and 11 recite: “a trained model”.
The “trained model” is an additional element that does not integrate the abstract idea into a practical application - that the "data model" are just mere instructions to apply an exception as in MPEP 2106.05(f)
claims 6 and 15 recite: “generative adversarial network”.
The “generative adversarial network” represents no more than mere instructions to apply the judicial exception on a computer OR merely uses the computer as a tool to perform an abstract idea. See MPEP 2106.05(f)).
The “generative adversarial network” further stands in to automate a human mental process using a generic machine learning engine that has not been invented, nor improved by the applicant.
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
With regard to (2b) the Guidance provided the following examples of limitations that may be enough to qualify as “significantly more" when recited in a claim with a judicial exception:
Improvement to another technology or technical field
Improvement to functioning of computer itself and/or applying the judicial exception with, or by use of, a particular machine
Effecting a transformation or reduction of a particular article to a different state or thing.
Adding a specific limitation other that what is well understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application
Meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment.
The Guidance further set forth limitations that were found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
Adding words to “apply it” (or an equivalent) with the judicial exception or mere instructions to implement abstract ideas on a computer
Simply appending well-understood, routine and conventional activities previously known to the industry specified at a high level of generality to the judicial exception, e.g. a claim to an abstract idea requiring no more than a generic
Computer to perform generic computer functions that are well -understood, routine and conventional activities previously known to the industry.
Adding insignificant extra-solution activity to the judicial exception, e.g. mere data gathering in conjunction with a law of nature or abstract idea
Generally linking the use of the judicial exception to a particular technological environment or field of use.
Claims 1- 20 do not recite any additional elements that are not well-understood, routine or conventional.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The identified additional computer components/ computing device are merely generic computer components that are well-known, routine, and conventional as is evidenced by Bancorp Services v. Sun Life (Fed. Cir. 2012) and Alice Corp. v. CLS Bank (2014).
Claims 1- 20 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Claims 1, 10 and 18 recite:
“receiving a source image of the blank test card” (adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea);
“applying a classifier to the one or more synthetic images to determine a classification of the blank test card” is an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea); and
The “applying an image-to-image translation model” (see the crossed-out portion) is an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea)
Claims 2 and 11 recite: “a trained model”.
The “trained model” is an additional element that does not integrate the abstract idea into a practical application - that the "data model" are just mere instructions to apply an exception as in MPEP 2106.05(f)
claims 6 and 15 recite: “generative adversarial network”.
The “generative adversarial network” represents no more than mere instructions to apply the judicial exception on a computer OR merely uses the computer as a tool to perform an abstract idea. See MPEP 2106.05(f)).
The “trained model” and the “generative adversarial network” which appear as recited are routine, well-understood and conventional by the virtue of the factual evidences as follows:
Paragraph [0144] of 2023/ 0032472 discloses “This embodiment of the present application provides the method for training the medical image reconstruction network that fuses the variational autoencoder and the generative adversarial network. Compared with the conventional generative adversarial network, the method of the present application introduces prior knowledge guidance originated from the real images through the variational autoencoder, thus solving the problem of difficult training of the generative adversarial network”.
Paragraph [0002] of 2022/ 0245932 discloses “CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training”, arXiv preprint arXiv: 1703.10155, 2017, Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, and Gang Hua provides an overview of conventional generative methods such as variational autoencoders and generative adversarial networks”.
Paragraph [0041] of 2022/ 0188602 discloses “FIG. 4 is a PRIOR ART schematic that illustrates a conventional Generative Adversarial Network (GAN) including a generator, a discriminator, and a training set”.
Paragraph [0041] of 2020/ 0358212 discloses “Differential rendering or “DR” as it is referred to herein is an example of one conventional approach that attempts to boost reconstruction accuracy through prior knowledge. DR is a type of reverse rendering of the 3D model from a 2D image and has become widely used in deep learning systems used for face reconstruction. One conventional approach to applying DR to 3D face reconstruction trains a progressive generative adversarial network (GAN) to learn the highly nonlinear texture representation of the face as opposed to using the traditional linear principal component analysis (“PCA”) model”.
Column 16 lines 10- 20 of the US Patent 11074507 discloses “The target function described above may be basically similar to that of a conventional Generative Adversarial Network (GAN), but there may be a difference in that the first low-dimensional distribution features for training and the second low-dimensional distribution features for training are inputted into the discriminator 200 to cope with the sub-database or the existing database. Also, in the training processes as above, the distribution analyzer 110 may participate in the training processes of the data generator 140 and the discriminator 200”.
Thus, since Claims 1, 10 and 19 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, claims 1, 10 and 19 are not eligible subject matter under 35 U.S.C 101. Similar analysis is made for the dependent claims 2- 9, 11- 18 and 20 and the dependent claims are similarly identified as: being directed towards an abstract idea, not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception.
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 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- 5, 7- 8, 10- 14,16- 17, and 19- 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by BERMUDEZ ARIANA ET AL, "A First Glance to the Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning”, “D1” (IDS).
As per claims 1, 10 and 19, D1 teaches receiving a source image of the blank test card (i.e., Blank images of PSP plates. (Carestream, CS 7600) were acquired from the oral radiology clinic at the Faculty of Dentistry”, see section III- A, first paragraph; “More concretely, we selected 25 PSP plates: 15 and 5 plates presented 7 severe and intermediate damage, respectively, while 4 plates are new, and the last one is a blank mask image”;
generating one or more synthetic images by applying an image-to-image translation model to the source image (i.e., for the interpretation of the term “image-to-image translation" see item 1.11 above; “In short, 160 CMOS cases were combined with 25 PSP plate images to augment the data up to .2500 images. The remaining 431 images superimposed with a blank plate correspond to non-disposable samples (see Figure 3 (reproduced below)). The dataset generation pipeline is depicted in Figure 4 (reproduced below). Pixel addition was performed to create these 2500 samples, where each pixel was weighted and summed with the according pixels’ intensities, producing a new image”, section H-A, third paragraph: see also Fig. 4); and
applying a classifier to the one or more synthetic images to determine a classification of the blank test card (i.e., we implemented three state-of-the-art deep CNN architectures (i.e., ResNet118, ResNet50 and InceptionV3) to positively or negatively assess PSP plates [...] : These models were trained with the aforementioned superimposed images”, see section Il-C, first and second paragraphs; when training a classification model, the model implicitly classifies each training image, therefore, the above-mentioned classifiers are applied on ; the synthetic images of D1; "We performed a 5-fold cross validation. A total of 732 images (20%) were used for model evaluation, while the remaining 2199 images (80%) were used for training”; Some of the synthetic images of D1 are used for testing/evaluation, this corresponds to the step of “applying a classifier to the one or more synthetic images” of claim 1 as well. Finally, the resulting classification, to dispose a PSP card or not - see section III-A, last paragraph - refers to the blank PSP cards themselves used as initial images for generating _ the synthetic images of D1, see section III- A, first and second paragraphs. Therefore, the neural networks of D1 are trained by classifying the synthetic images in order to finally classify the quality of the blank PSP cards).
PNG
media_image1.png
612
358
media_image1.png
Greyscale
As per claims 2, 11 and 20, D1 teaches determining one or more test results for the one or more synthetic images by applying a trained model to the one or more synthetic images; and determining a classification for the received source image of the blank test card by applying a binary classifier based on the determined one or more test results for the one or more synthetic images (i.e., These models were trained with the aforementioned super-imposed images (see Section III-A). Additionally, pretrained weights obtained from the ImageNet Large Scale Visual Recognition Challenge [11] were loaded and tuned for binary PSP plate classification. Our input samples were standardized and resized to 299×299 pixels for its usage in InceptionV3and to 224×224 to feed the ResNet architectures) see for example section III- C.
As per claims 3 and 12 and under the BRI (broadest reasonable interpretation), D1 teaches determining whether each test result is an invalid test result; and responsive to determining that at least one test result is an invalid test result, assigning a fail classification to the received source image (i.e., dispose PSP plate classes, broadly interpreted as “invalid” ("fail classification")) see for example !!!-A.
As per claims 4 and 13, D1 teaches determining whether each test result is a valid test result; and responsive to determining that every test result is a valid test result, assigning a pass classification to the received source image (i.e.., no PSP plate disposal, broadly interpreted as “valid” ("pass classification")) see for example !!!-A.
As per claims 5 and 14, D1 teaches comparing each determined test result to a corresponding expected test result; and responsive to at least one determined test result not matching the corresponding expected test result, assigning a first classification to the received source image, and responsive to every determined test result matching the corresponding expected test result, assigning a second classification to the received source image, different than the first classification (i.e., comparison of the three models using the Resnet50 and Resnet18) see for example section V and table 4 (reproduced below).
PNG
media_image2.png
142
422
media_image2.png
Greyscale
As per claims 7 and 16, D1 teaches the image-to-image translation model is trained using a training set including source images of a plurality of test cards before being used and target images of the plurality of test cards after being used (i.e., for the interpretation of the term “image-to-image translation" see item 1.11 above; “In short, 160 CMOS cases were combined with 25 PSP plate images to augment the data up to .2500 images. The remaining 431 images superimposed with a blank plate correspond to non-disposable samples (see Figure 3). The dataset generation pipeline is depicted in Figure 4. Pixel addition was performed to create these 2500 samples, where each pixel was weighted and summed with the according pixels’ intensities, producing a new image”, section H-A, third paragraph: see also Fig. 4)
As per claims 8 and 17, D1 teaches the trained model for determining the one or more test results is a reaction classification model for classifying a biological reaction depicted in an image (I.e., Our input samples were standardized and resized to 299×299 pixels for its usage in InceptionV3 and to 224×224 to feed the ResNet architectures) see for example section III-C.
Claims 1, 9, 10, 18 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO 2020/ 192972.
As per claims 1, 10 and 19, WO 2020/ 192972 teaches receiving a source image of the blank test card see for example fig. 9 (reproduced below) and image 42;
PNG
media_image3.png
198
324
media_image3.png
Greyscale
generating one or more synthetic images by applying an image-to-image translation model to the source image (i.e., mirror image of said set of original pictures) see for example [0023, 39 and 66]; and
applying a classifier to the one or more synthetic images to determine a classification of the blank test card (i.e., a centrifugation apparatus arranged for centrifugation of at least one container; an imaging device for generating images of the centrifuged container (for example images of each well of the centrifuged gel card); and an apparatus for classifying a picture of a reaction of reactants in a predetermined container as detailed above, arranged for receiving in input each image of the centrifuged container) see for example [0026 and 81].
As per claims 9 and 18 the blank test card is a blank gel card including a plurality of wells, and wherein each well of the plurality of wells contains a gel and a reticulation agent see [003] and fig. 1 (reproduced below).
PNG
media_image4.png
258
352
media_image4.png
Greyscale
Allowable Subject Matter
Claim 6 and 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.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Manuchehr Rahmjoo whose telephone number is 571-272- 7789. The examiner can normally be reached on 8 AM- 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached on 571-272- 9752. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Manuchehr Rahmjoo
/Manuchehr Rahmjoo/
Primary Examiner, AU 2673
Manuchehr.Rahmjoo@uspto.gov