DETAILED ACTION
This office action is based on the claim set filed on 08/27/2025.
Claims 1-6 are currently pending and have been examined.
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 08/27/2025 are in accordance with the provisions of 37 CFR 1.97 and are 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.
Claim 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-6, are drawn to a system of which is within the four statutory categories (i.e., a machine and a process). Claims 1-6 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for diagnosing and suggesting a treatment. Collecting image data to analyze and determine a disease and suggest a treatment are steps that could have been performed by a human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Metal Process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 1 recites the steps of:
“a terminal device installed in a hospital;
a trained model connected to the terminal device and configured to assist in determining disease discovery, wherein the terminal device includes a processor configured to:
receive at least numerical data obtained by ophthalmic diagnosis and image data that is a result of image diagnosis of an eyeball;
analyze a disease contained in the image data from similarity between a feature of the image data and a feature of an existing ophthalmic image sample using the trained model;
determine and suggest an optimum treatment for a patient based on the numerical data and an analysis result of the image data”.
Independent Claim 6 recites similar steps as in Claim 1.
These limitations, as drafted, given the broadest reasonable interpretation cover performance of the limitations by a human mind with aid of pen and paper reciting an abstract idea for Mental Process but for the recitation of generic computer components. For example, the limitations encompass a user the ability to collect and analyze images data for diagnosing ophthalmic disease to compare to existing sample(s) and suggest an optimum treatment, which are steps that that could have been performed by a human to implement the abstract idea and are steps reciting mental process that could have been performed using a human mind with aid of pen and paper, but other than the mere nominal recitation of "terminal device, processor, server, trained model", to implement the abstract idea for performing the steps of observing, evaluating, judgment and opinion which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “terminal device, processor, controller, server, trained model” that iteratively takes input data and analyzes said data to determine an output to performing generic computer functions, e.g., using the trained model, such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f), generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and 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. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2-5, include all of the limitations of claim(s) 1 and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 2-5, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process. The claims recite additional elements “machine learning, trained model, processor” that implement the identified abstract idea. These hardware components are recited in the claim(s) at a high level that it amounts to no more than mere instructions to perform the steps of the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, “e.g., alert”, see MPEP 2106.05(f), merely uses the computer as a tool to perform the abstract idea, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.05(d). For example, the claim recites a machine learning model which is recited at a high level of generality and is described in the specification in an arbitrary form without disclosing how a specific algorithm using available data for allowing the model to learn patterns and relationships within the data and implement it to perform the claimed function. Thus, the judicial exceptions recited in claims is/are not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
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)(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.
Claim(s) 1-6 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Singh et al. (US 2022/0313077 A1- “Singh”)
Regarding Claim 1, Singh teaches a suggestion system comprising:
a terminal device installed in a hospital Singh discloses a hospital environment system comprising workstation(s) (Singh: [Fig. 8A-B, 255], [0032]; ... directing the user to capture images and videos of their eyes in a guided manner to support the advanced machine-vision image capture and processing methods enabled in smart phones used by patients, at workstations in the clinic and hospital setting..., [0565], [1614], [2216-2217]);
a trained model connected to the terminal device and configured to assist in determining disease discovery Singh discloses the system is trained to support detection of a disease (Singh: [Fig. 93A-B], [0060]; a novel deep-learning machine-vision ocular image processing and classification system (i.e. “engine”) configured and trained for use and deployment on a system network configured to support and enable automated detection and measurement ..., [1368]; Use the above structures and the trained system for automatic region of interest (ROI), [1614]), wherein the terminal device includes a processor configured to:
receive at least numerical data obtained by ophthalmic diagnosis and image data that is a result of image diagnosis of an eyeball Singh discloses capturing digital images of an eye to be analyzed and determine margin values of a region of interest (Singh: [Fig. 45, 93A-B], [0016]; capturing and processing digital images of human eyes ... automatically recognizing specific ocular disease conditions such as dry eye disease (DED) and other ocular pathologies, [1320], [1343]; [1368]; Measure conjunctival injection in region of interest using methods including, but not limited to, contrast-limited adaptive histogram equalization, hue-saturation-value, product of saturation and hue maps, and maximal values of saturation and hue values, wherein conjunctival injection is measured on scale 0 to 100, [1614]),
analyze a disease contained in the image data from similarity between a feature of the image data and a feature of an existing ophthalmic image sample using the trained model Singh discloses matching the detected eye image data across a series of digital images using the trained system (Singh: [Fig. 46, 50, 54, 93A-B], [0033]; a novel machine-vision based digital image processing system employed within the system network, and which is organized, arranged and configured according to traditional machine-vision models employing feature engineering and feature classifiers with shallow structure, so as to classify input digital images as being representative of a particular ocular disease (OD) condition, from many possible trained ocular disease conditions, including dry eye disease (DED) conditions in the human eye, [0089], [1321], [1368]; Use region of interest matching to measure conjunctival injection across a series of digital images),
determine and suggest an optimum treatment for a patient based on the numerical data and an analysis result of the image data ([Fig. 35A-C], [0058]; a novel dry eye disease (DED) treatment compliance engine ... to provide optimal treatment to the automatically recognized dry eye disease condition recognized by the system, [0643]; in automated response to ocular disease (OD) conditions recognized by the system network, ..., the system network automatically generates treatment recommendations, [0666]).
Regarding Claim 2, Singh teaches the suggestion system according to claim 1, wherein the trained model performs machine learning to determine a possibility of a disease by receiving a relationship between the ophthalmic image sample and the disease as learning data Singh discloses a machine learning used to model probability of a disease from a medical image based on matching across series of training digital images (Singh: [Fig. 38, 50, 53A-B, 54, 57], [1320], [1368]).
Regarding Claim 3, Singh teaches the suggestion system according to claim 1, wherein in the determining and suggesting, the processor is configured to determine and suggest the optimum treatment for the patient by giving highest priority to the analysis result of the image data Singh discloses a diagnostic logic analyzing the image data and a treatment logic that used the analysis results to recommend treatment based on the severity of the analysis (Singh: [Fig. 36A-Z, 49B], [0555]; each rule specifies a recommended course of prescribed treatment for a given set of detected input patient data factors and/or automatically classified OD conditions [0666]; a robust coded list of treatment and management recommendations that the system network of the present invention 10 can automatically prescribe to patients for specific ocular disease conditions [1368]; conjunctival injection is measured on scale 0 to 100, with higher numbers corresponding with greater degrees of conjunctival injection... , [1613]; predicting dry eye disease (DED) class and grade of severity by the automated system).
Regarding Claim 4, Singh teaches the suggestion system according to claim 1,
wherein the image data includes a fundus image (Singh: [Fig. 5], [0011])
wherein in the analyzing, the processor is configured to quantify a specific structure in the fundus image to determine a possibility of a disease Singh discloses using fundus images for diagnosing the eye such as detecting gross macular or optic nerve abnormalities (Singh: [0011]
Regarding Claim 5, Singh teaches the suggestion system according to claim 1,
wherein the image data includes an optical coherence tomography (OCT) image Singh discloses obtaining optical coherence tomography (OCT) image (Singh: [Fig. 5], [0011]),
wherein in the analyzing, the processor is configured to quantify a specific structure in the OCT image to determine a possibility of a disease Singh discloses using OCT images to determine for diagnosing the eye such as angle closure detection (Singh: [0011]).
Regarding Claim 6, Singh teaches a suggestion system comprising:
the claim recites substantially similar limitations to claim 1, as such, are rejected for similar reasons as given above including
Prior Art Cited but not Applied
The following document(s) were found relevant to the disclosure but not applied:
CN116228668A “Sun” discloses obtaining a fundus image of a target user and inputting a target fundus feature data into an image recognition model to obtain a recognition result of a target disease corresponding to the target user trained using training sample set through the deep learning model and labeling data corresponding to the internal image data including disease labeling data of the disease corresponding to the user and at least one pathological type labeling data of the disease corresponding to the user.
Wang et al. “Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images”.
CN118298494A “Xiong” discloses using a pre-trained text encoder to extract text features corresponding to each eye disease type from the text template, wherein the image encoder and the text encoder are trained based on pre-prepared sample images and text information generated based on the sample images.
The references are relevant since it discloses analyzing an ophthalmic image data to determine disease using sample training images.
Conclusion
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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