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 .
Preliminary Amendment
The Preliminary Amendment submitted on 08/30/2024 has been entered and made of record.
Status of Claims
This communication is in response to the Application Filed on 08/30/2024
Claims 1–15 are pending in this application.
Drawings
The drawing(s) filed on 08/30/2024 are accepted by the Examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/30/2024 and 11/06/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Positive Statement for Eligibility under 35 USC § 101
An abstract idea rejection under 35 USC 101 was considered but it was determined that the claims are patent eligible. Claims 1–9 are patent eligible as they recite combinations of elements which as a whole amount to significantly more than an abstract idea. Each of the claims, 1, on its surface, may seem to comprise a mental process of comparison of received data with additional elements amount to no more than insignificant extra-solution activities, but Examiner has determined that the claimed elements recited in each of the claims 1 as a whole reflect an improvement to mobile automatic ROP detection in order to reduce the cost of ROP detection for examining large populations in different geographical locations (Specification, ¶ [0013], It may be noted that, particularly having temporal view image helps in making the process computationally efficient and accurate. ¶ [0021], In an example, the categorical classification model may be used only during training to supplement in the accuracy of detection of ROP by the binary classification model). The claimed elements recite an abstract idea that appear to add significantly more such as using a machine learning model using temporal view images to increase the accuracy of ROP detection and allow the ability to detect the ROPs in regular equipment with no skill required. (Specification, ¶ [0022], The present approaches overcome the above-mentioned technical advantages. For example, the above-mentioned approaches may be implemented in a single device for effective ROP screening. Since no specialized equipment or skill is required, a system implementing the present approaches is mobile, cost-effective, and accurate for the purposes of ROP detection. For example, an implementing system allows for screening without expert knowledge and is performable on portable retinal camera itself, while ensuring a desired and functional level of accuracy). Thus, the claims cover a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of the solution or outcome. This would be considered a practical and useful application in the field of Retinopathy of Prematurity. Claims 1 and their respective dependent claims are therefor considered to be patent eligible.
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(s) 10, 11, 13 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea 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, organizing human activity and mathematical concepts and calculations). The independent claim(s) 10 recite(s) a system and a method respectively. 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 claim(s) does/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 except for the generic computer elements at high level of generality (i.e., processor, memory).
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 the independent claims 10 are directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Independent claims 10 are directed to a system and a method, respectively.
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).
Independent claim 10 comprise a mental process that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding independent claim(s) 10: the limitations recite:
obtaining a training information comprising a training eye image and training attribute information corresponding to plurality of eye image characteristics, wherein the training eye image is associated with Retinopathy of prematurity (ROP) (data gathering); and
training a ROP detection model pipeline based on the training information comprising training eye image, training attribute information, and a ROP category of the training eye image, wherein the training attribute information corresponds to a plurality of training eye image attributes (mental processes including a mental process including observation and evaluation, and can be done mentally in the human mind).
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).
As such, a person could mentally 10 could be performed mentally by observing an image and evaluating the eye images by correlating the attributes and images. The mere nominal recitation that the various steps are being executed by a processor does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
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.
Independent claims 10 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Independent claims 10 discloses a processor, which are generic computer components and/or insignificant pre/post-solution extra activity that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea in a system.
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? No, the claims do not 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.
Independent claim(s) 10 do not recite any additional elements that are not well-understood, routine or conventional. The use of a generic computer elements are routine, well-understood and conventional process that is performed by computers.
Thus, since independent claims 10 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, it is clear that independent claims 10 are not eligible subject matter under 35 U.S.C 101.
Regarding claim 11, 13 and 14: the additional limitations do not integrate the mental process into practical application or add significantly more to the mental process. The limitation(s) are mental processes including a mental process including observation and evaluation, and can be done mentally in the human mind.
Regarding claim 12 and 15: the additional limitations do integrate the mental process into practical application or add significantly more to the mental process.
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.
Claim(s) 9 and 11 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.
Regarding claim(s) 9 and 11, the phrase “and many more" renders the claim(s) indefinite because the claim(s) include(s) elements not actually disclosed (those encompassed by "and many more"), thereby rendering the scope of the claim(s) unascertainable. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 103
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 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 non-obviousness.
Claim(s) 1 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") further in view Katuwal et al. (US 20150104087 A1, hereafter, "Katuwal").
Regarding claim 1, Shen teaches a system (See Shen, [Abstract], The invention claims a detecting method and device for retinopathy of prematurity, device) comprising:
a processor (See Shen, ¶ [0020], the third aspect, the invention further claims a detecting device for retinopathy of prematurity, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor); and
an analysis engine coupled to the processor, wherein the analysis engine (See Shen, ¶ [0026], FIG. 1 is a schematic flow diagram of the detection method of retinopathy of prematurity of the embodiment of the application embodiment) is for:
obtaining an input eye image, wherein the input eye image corresponds to a subject eye which is under evaluation for detecting presence of Retinopathy of Prematurity (ROP) (See Shen, ¶ [0027], step 101, obtaining the data set of early fundus image of premature infant, and dividing the data set into a training set and a test set);
[using a ROP detection model pipeline, wherein the ROP detection model pipeline is trained based on a training dataset comprising training images associated with the ROP, and training attribute information which corresponds to a plurality of training eye image attribute, wherein the ROP detection model pipeline is for:
determining a type of view of the input eye image];
on performing a plurality of processing steps on the input eye image to make it compatible for further processing (See Shen, ¶ [0034], In this embodiment, after the trained MN-ROP model in step 103, then pre-processing the early fundus image in the test set, and then the pre-processed early fundus image input to the trained MN-ROP model) [upon determining the type of view as a temporal view;
identifying an attribute information from the input eye image, wherein the attribute information corresponds to a plurality of input eye image attributes; and
determining a detection result indicating presence of ROP within the subject eye based on the attribute information].
However, Shen fail(s) to teach using a ROP detection model pipeline, wherein the ROP detection model pipeline is trained based on a training dataset comprising training images associated with the ROP, and training attribute information which corresponds to a plurality of training eye image attribute, wherein the ROP detection model pipeline is for: identifying an attribute information from the input eye image, wherein the attribute information corresponds to a plurality of input eye image attributes; and determining a detection result indicating presence of ROP within the subject eye based on the attribute information.
Ranjana, working in the same field of endeavor, teaches: using a ROP detection model pipeline, wherein the ROP detection model pipeline is trained based on a training dataset comprising training images associated with the ROP, and training attribute information which corresponds to a plurality of training eye image attribute (See Ranjana, [Pg. 936, Col. 2, ln. 5–10], total of 10,000 Retcam images (left and right eye) of 900 patients were collected with gestation ages of 26–60 weeks and birth weight < 3000 g. The images were annotated in detail, specifying zones and stages by a team of 5 trained and experienced ROP specialists. [Pg. 939, Col. 1, ln. 3-5], As shown in Fig. 8, the zone detection system consists of two separate U-networks for the segmentation (Fig. 9) of the optic disc and blood vessels See also [Fig. 8]. Note: Examiner is interpreting blood vessel segmentation as the attribute information), wherein the ROP detection model pipeline is for:
identifying an attribute information from the input eye image, wherein the attribute information corresponds to a plurality of input eye image attributes (See Ranjana, [Pg. 939, Col. 1, ln. 3–5], As shown in Fig. 8, the zone detection system consists of two separate U-networks for the segmentation (Fig. 9) of the optic disc and blood vessels); and
determining a detection result indicating presence of ROP within the subject eye based on the attribute information (See Ranjana, [Pg. 938, Col. 2, ln. 18–21], We have divided the work into two parts: (a) zones I, II, and III detection and (b) detection of stages 1, 2, 3 from fundus images. The results will be combined to explain a class of ROP).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to using a ROP detection model pipeline, wherein the ROP detection model pipeline is trained based on a training dataset comprising training images associated with the ROP, and training attribute information which corresponds to a plurality of training eye image attribute, wherein the ROP detection model pipeline is for: identifying an attribute information from the input eye image, wherein the attribute information corresponds to a plurality of input eye image attributes; and determining a detection result indicating presence of ROP within the subject eye based on the attribute information based on the method of Ranjana’s reference. The suggestion/motivation would have been to increase the accuracy of detecting ROPs (See Ranjana, [Fig. 17]).
However, Shen and Ranjana fail(s) to teach determining a type of view of the input eye image; upon determining the type of view as a temporal view.
Katuwal, working in the same field of endeavor, teaches: determining a type of view of the input eye image (See Katuwal, ¶ [0022], The set of three image fields typically includes, for example, a disc centered field, macula centered field, and temporal to macula field. As these three image fields are acquired by the camera operator, it is desirable to confirm the identity of the image fields prior to assessing the image quality);
upon determining the type of view as a temporal view (See Katuwal, ¶ [0022], The set of three image fields typically includes, for example, a disc centered field, macula centered field, and temporal to macula field. As these three image fields are acquired by the camera operator, it is desirable to confirm the identity of the image fields prior to assessing the image quality. Note: Katuwal is determining the identity of the image field prior to processing which the examiner is interpreting as determining that the image is a temporal view).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to determining a type of view of the input eye image; upon determining the type of view as a temporal view based on the method of Ranjana’s reference. The suggestion/motivation would have been to increase the quality of the eye images (See Katuwal, ¶ [0003]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Ranjana and Katuwal with Shen to obtain the invention as specified in claim 1.
Regarding claim 4, Shen in view of Ranjana further in view of Katuwal teaches the system as claimed in claim 1, wherein the analysis engine is to use the ROP detection model pipeline (See Shen, ¶ [0039], In this embodiment, based on MN-ROP model, the first detection result and the second detection result output by the MN-TR-ROP model, can be diagnosing whether the fundus image has ROP, TRROP disease provides some reference effect and auxiliary effect, so as to improve the accuracy of detecting whether the eye-ground image has ROP, TR-ROP disease) for:
[discarding the input eye image when the type of view of the input eye image is other than temporal view].
However, Shen fail(s) to teach discarding the input eye image when the type of view of the input eye image is other than temporal view.
Ranjana, working in the same field of endeavor, teaches: discarding the input eye image when the type of view of the input eye image is other than temporal view (See Ranjana, [Pg. 936, Col. 2, ln. 10–12], We needed only posterior and temporal views of the image for this work, thus excluding 8100 images from the dataset).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to discarding the input eye image when the type of view of the input eye image is other than temporal view based on the method of Ranjana’s reference. The suggestion/motivation would have been to increase the accuracy of detecting ROPs (See Ranjana, [Fig. 17]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Ranjana with Shen and Katuwal to obtain the invention as specified in claim 4.
Claim(s) 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") further in view Katuwal et al. (US 20150104087 A1, hereafter, "Katuwal") further in view of Feng et al. (US 20230379579 A1, hereafter, "Feng") and further in view of . Freedman (US 20210080565 A1, hereafter, "Freedman").
Regarding claim 2, Shen in view of Ranjana further in view of Katuwal teaches the system as claimed in claim 1, [wherein the analysis engine is to: performing a plurality of pre-processing step on the input eye image to make the input eye image compatible for the view assessment, wherein the plurality of pre-processing steps comprises cropping, padding, resizing, and sharpening the edges of the input eye image].
However, Shen, Ranjana and Katuwal fail(s) to teach wherein the analysis engine is to: performing a plurality of pre-processing step on the input eye image to make the input eye image compatible for the view assessment, wherein the plurality of pre-processing steps comprises cropping, padding, resizing, and sharpening the edges of the input eye image.
Feng, working in the same field of endeavor, teaches: wherein the analysis engine is to: performing a plurality of pre-processing step on the input eye image to make the input eye image compatible for the view assessment, wherein the plurality of pre-processing steps comprises cropping, padding, resizing (See Feng, ¶ [0063], As another example, the preprocessing may include determining a ROI in the scene, cropping the image data to the ROI, and then resizing and/or padding the cropped image data to match the trained size for inputs to the machine learning algorithm).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to wherein the analysis engine is to: performing a plurality of pre-processing step on the input eye image to make the input eye image compatible for the view assessment, wherein the plurality of pre-processing steps comprises cropping, padding, resizing based on the method of Feng’s reference. The suggestion/motivation would have been to capture quality images focused on the area of interest (See Feng, ¶ [0002–0004]).
However, Shen, Ranjana and Katuwal fail(s) to teach resizing, and sharpening the edges of the input eye image.
Freedman, working in the same field of endeavor, teaches: resizing, and sharpening the edges of the input eye image (See Freedman, ¶ [0030], For example, if one of the submodules 315 is a CNN, SPR images may be preprocessed by resizing to the input size of the CNN, denoising, edge smoothing, sharpening, etc).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to resizing, and sharpening the edges of the input eye image based on the method of Freedman’s reference. The suggestion/motivation would have been to preprocess the image for more accurate detection (See Freedman, ¶ [0030]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Feng and Freedman with Shen, Ranjana and Katuwal to obtain the invention as specified in claim 2.
Regarding claim 3, Shen in view of Ranjana further in view of Katuwal teaches the system as claimed in claim 1, [wherein the plurality of processing steps comprises cropping, padding, resizing, and sharpening the edges of the input eye image].
However, Shen, Ranjana and Katuwal fail(s) to teach wherein the plurality of processing steps comprises cropping, padding, resizing, and sharpening the edges of the input eye image.
Feng, working in the same field of endeavor, teaches: wherein the plurality of processing steps comprises cropping, padding, resizing (See Feng, ¶ [0063], As another example, the preprocessing may include determining a ROI in the scene, cropping the image data to the ROI, and then resizing and/or padding the cropped image data to match the trained size for inputs to the machine learning algorithm).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to wherein the plurality of processing steps comprises cropping, padding, resizing based on the method of Feng’s reference. The suggestion/motivation would have been to capture quality images focused on the area of interest (See Feng, ¶ [0002–0004]).
However, Shen, Ranjana and Katuwal fail(s) to teach resizing, and sharpening the edges of the input eye image.
Freedman, working in the same field of endeavor, teaches: resizing, and sharpening the edges of the input eye image (See Freedman, ¶ [0030], For example, if one of the submodules 315 is a CNN, SPR images may be preprocessed by resizing to the input size of the CNN, denoising, edge smoothing, sharpening, etc).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference resizing, and sharpening the edges of the input eye image based on the method of Freedman’s reference. The suggestion/motivation would have been to preprocess the image for more accurate detection (See Freedman, ¶ [0030]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Feng and Freedman with Shen, Ranjana and Katuwal to obtain the invention as specified in claim 3.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") further in view Katuwal et al. (US 20150104087 A1, hereafter, "Katuwal") further in view of Ranchod (US 20130271728 A1, hereafter, "Ranchod").
Regarding claim 5, Shen in view of Ranjana and further in view of Katuwal teaches the system as claimed in claim 1, wherein the analysis engine (See Shen, ¶ [0026], FIG. 1 is a schematic flow diagram of the detection method of retinopathy of prematurity of the embodiment of the application embodiment) is to:
[obtaining a set of input eye images, wherein each of the images of the set of input eye images corresponds to different views of the user’s eye, wherein the views comprises a temporal view, nasal view, disc centered view, macula centered view, inferior view, and superior view];
using the ROP detection model pipeline (See Shen, ¶ [0039], In this embodiment, based on MN-ROP model, the first detection result and the second detection result output by the MN-TR-ROP model, can be diagnosing whether the fundus image has ROP, TRROP disease provides some reference effect and auxiliary effect) for:
[determining the view of each of the images of the set of input eye images;
selecting an image from the set of input eye image having temporal view to be designated as input eye image based on the determined view].
However, Shen and Ranjana fail(s) to teach obtaining a set of input eye images, wherein each of the images of the set of input eye images corresponds to different views of the user’s eye, wherein the views comprises a temporal view, nasal view, disc centered view, macula centered view, inferior view, and superior view; determining the view of each of the images of the set of input eye images; selecting an image from the set of input eye image having temporal view to be designated as input eye image based on the determined view.
Katuwal, working in the same field of endeavor, teaches: obtaining a set of input eye images, wherein each of the images of the set of input eye images corresponds to different views of the user’s eye, wherein the views comprises a temporal view, disc centered view, macula centered view (See Katuwal, ¶ [0022], The set of three image fields typically includes, for example, a disc centered field, macula centered field, and temporal to macula field. As these three image fields are acquired by the camera operator, it is desirable to confirm the identity of the image fields prior to assessing the image quality);
determining the view of each of the images of the set of input eye images (See Katuwal, ¶ [0022], The set of three image fields typically includes, for example, a disc centered field, macula centered field, and temporal to macula field. As these three image fields are acquired by the camera operator, it is desirable to confirm the identity of the image fields prior to assessing the image quality);
selecting an image from the set of input eye image having temporal view to be designated as input eye image based on the determined view (See Katuwal, ¶ [0022], The set of three image fields typically includes, for example, a disc centered field, macula centered field, and temporal to macula field. As these three image fields are acquired by the camera operator, it is desirable to confirm the identity of the image fields prior to assessing the image quality).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to obtaining a set of input eye images, wherein each of the images of the set of input eye images corresponds to different views of the user’s eye, wherein the views comprises a temporal view, disc centered view, macula centered view; determining the view of each of the images of the set of input eye images; selecting an image from the set of input eye image having temporal view to be designated as input eye image based on the determined view based on the method of Katuwal’s reference. The suggestion/motivation would have been to increase the quality of the eye images (See Katuwal, ¶ [0003]).
However, Shen, Ranjana and Katuwal fail(s) to teach nasal view, inferior view, and superior view.
Ranchod, working in the same field of endeavor, teaches: nasal view, inferior view, and superior view (See Ranchod, ¶ [0005], The image set generally includes an external photograph, a retinal image centered on either the optic nerve 7 or macula 8 (the central retina 10), and four mid-peripheral retinal images centered superior, inferior, nasal, and temporal, respectively, to the disc and macula 8);
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to nasal view, inferior view, and superior view based on the method of Ranchod’s reference. The suggestion/motivation would have been providing a wide field image of the eye (See Ranchod, ¶ [0002–0008]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Katuwal and Ranchod with Shen and Ranjana to obtain the invention as specified in claim 5.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") further in view Katuwal et al. (US 20150104087 A1, hereafter, "Katuwal") further in view of Villard et al. (US 20220405927 A1, hereafter, "Villard") and further in view of Helmie et al. (See NPL attached, "Efficient and Robust Method to Detect the Location of Macular Center Based on Optimal Temporal Determination", hereafter, "Helmie").
Regarding claim 6, Shen in view of Ranjana further in view of Katuwal teaches the system as claimed in claim 1, wherein the ROP detection model pipeline comprises a plurality of deep learning models selected from a group comprising (See Shen, ¶ [0032], In this embodiment, training the MS-ROP model through the training set in the data set, namely the training set early fundus image pre-processing the image, then input to the MS-ROP model, the MSROP model for training, so as to obtain the trained MS-ROP model, then training the MN-ROP model through the trained MS-ROP model and the training set) [a view assessment model, a quality assessment model, and a categorization model].
However, Shen fail(s) to teach a view assessment model, a quality assessment model, and a categorization model.
Ranjana, working in the same field of endeavor, teaches: a categorization model (See Ranjana, [Pg. 938, Col. 2, ln. 18-21], We have divided the work into two parts: (a) zones I, II, and III detection and (b) detection of stages 1, 2, 3 from fundus images. The results will be combined to explain a class of ROP. Note: Examiner is interpreting this as the categorization model).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to a categorization model based on the method of Ranjana’s reference. The suggestion/motivation would have been to increase the accuracy of detecting ROPs (See Ranjana, [Fig. 17]).
However, Shen, Ranjana and Katuwal fail(s) to teach a view assessment model, a quality assessment model.
Villard, working in the same field of endeavor, teaches: a quality assessment model (See Villard, ¶ [0011], Each of the machine learning models can be a single model or a number of machine learning models, which can be combined to assess either the presence of the at least one disease or the quality of an image. Note: Examiner is interpreting this as the quality assessment model).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to a quality assessment model based on the method of Villard’s reference. The suggestion/motivation would have been to improve the accuracy of medical diagnoses (See Villard, ¶ [0003]).
However, Shen, Ranjana, Katuwal and Villard fail(s) to teach a view assessment model.
Helmie, working in the same field of endeavor, teaches: a view assessment model (See Helmie, [Pg. 3, 2.2 Methods, ln. 27-31], The method used for identifying the macular center consists of several main steps, which include optic disc localization, determining temporal area direction, identifying the macular region of interest (ROI), and macular center point coordinates extraction. Figure 2 shows the flow of macular center point detection. See also [Figure 2]. Note: Examiner is interpreting this as the view assessment model).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to a view assessment model based on the method of Helmie’s reference. The suggestion/motivation would have been to increase the accuracy of detection by providing quality input (See Helmie, [Table 1]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Ranjana, Villard and Helmie with Shen and Katuwal to obtain the invention as specified in claim 6.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") further in view Katuwal et al. (US 20150104087 A1, hereafter, "Katuwal") further in view of Villard et al. (US 20220405927 A1, hereafter, "Villard").
Regarding claim 7, Shen in view of Ranjana further in view of Katuwal teaches the system as claimed in claim 1, wherein the analysis engine is to use the ROP detection model pipeline (See Shen, ¶ [0039], In this embodiment, based on MN-ROP model, the first detection result and the second detection result output by the MN-TR-ROP model, can be diagnosing whether the fundus image has ROP, TRROP disease provides some reference effect and auxiliary effect, so as to improve the accuracy of detecting whether the eye-ground image has ROP, TR-ROP disease) to:
[assessing quality of the input eye image to generate a quality score];
extracting the attribute information from the input eye image upon determining quality score to be greater than the threshold quality score (Note: Examiner is not teaching this because the Examiner only needs to teach the below the threshold); or
[discarding the input eye image upon on determining the quality score to be less than a threshold quality score; and
prompting a user to obtain or capture new input eye image].
However, Shen, Ranjana and Katuwal fail(s) to teach assessing quality of the input eye image to generate a quality score; discarding the input eye image upon on determining the quality score to be less than a threshold quality score; and prompting a user to obtain or capture new input eye image.
Villard, working in the same field of endeavor, teaches: assessing quality of the input eye image to generate a quality score (See Villard, ¶ [0011], Each of the machine learning models can be a single model or a number of machine learning models, which can be combined to assess either the presence of the at least one disease or the quality of an image);
discarding the input eye image upon on determining the quality score to be less than a threshold quality score (See Villard, ¶ [0010], The electronic processing circuitry can be configured to, responsive to a determination that the quality score does not satisfy a quality threshold, discard the at least one image); and
prompting a user to obtain or capture new input eye image (See Villard, ¶ [0011], Discarding the at least one image can cause the at least one image to be retaken).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference assessing quality of the input eye image to generate a quality score; discarding the input eye image upon on determining the quality score to be less than a threshold quality score; and prompting a user to obtain or capture new input eye image based on the method of Villard’s reference. The suggestion/motivation would have been to improve the accuracy of medical diagnoses (See Villard, ¶ [0003]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Villard with Shen, Ranjana and Katuwal to obtain the invention as specified in claim 7.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") further in view Katuwal et al. (US 20150104087 A1, hereafter, "Katuwal") further in view of Baiying et al. (See NPL attached, "Automated detection of retinopathy of prematurity by deep attention network", hereafter, "Baiying").
Regarding claim 8, Shen in view of Ranjana further in view of Katuwal teaches the system as claimed in claim 1, wherein the analysis engine (See Shen, ¶ [0026], FIG. 1 is a schematic flow diagram of the detection method of retinopathy of prematurity of the embodiment of the application embodiment) is for further:
[generating an activation map depicting salient regions within the input eye image which triggered the detection of ROP within the input eye image of the subject eye].
However, Shen, Ranjana and Katuwal fail(s) to teach generating an activation map depicting salient regions within the input eye image which triggered the detection of ROP within the input eye image of the subject eye.
Baiying, working in the same field of endeavor, teaches: generating an activation map depicting salient regions within the input eye image which triggered the detection of ROP within the input eye image of the subject eye (See Baiying, [Pg. 36344, ln. 9-17], We take advantage of the residual learning and attention mechanism to train a DCNN with a set of fundus images in which the attention mechanism is used to strengthen the feature representation ability of DCNN, making it focuses more on semantically meaningful parts (i.e., demarcation lines or ridges) in fundus images. Except for classification, another task of our study is to locate the pathological structures. This is similar to weakly supervised localization.We apply a class-discriminative localization technique named gradient- weighted class activation mapping (Grad-CAM) [41] to localize the pathological structure of ROP, which is generally a demarcation line or ridge).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to generating an activation map depicting salient regions within the input eye image which triggered the detection of ROP within the input eye image of the subject eye based on the method of Baiying’s reference. The suggestion/motivation would have been to increase the accuracy and performance of detecting ROP (See Baiying, [Table 7]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Baiying with Shen, Ranjana and Katuwal to obtain the invention as specified in claim 8.
Claim(s) 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana").
Regarding claim 10, Shen teaches a method (See Shen, [Abstract], The invention claims a detecting method and device for retinopathy of prematurity, device) comprising:
[obtaining a training information comprising a training eye image and training attribute information corresponding to plurality of eye image characteristics, wherein the training eye image is associated with Retinopathy of prematurity (ROP); and
training a ROP detection model pipeline based on the training information comprising training eye image, training attribute information, and a ROP category of the training eye image, wherein the training attribute information corresponds to a plurality of training eye image attributes].
However, Shen fail(s) to teach obtaining a training information comprising a training eye image and training attribute information corresponding to plurality of eye image characteristics, wherein the training eye image is associated with Retinopathy of prematurity (ROP); and training a ROP detection model pipeline based on the training information comprising training eye image, training attribute information, and a ROP category of the training eye image, wherein the training attribute information corresponds to a plurality of training eye image attributes.
Ranjana, working in the same field of endeavor, teaches: obtaining a training information comprising a training eye image and training attribute information corresponding to plurality of eye image characteristics, wherein the training eye image is associated with Retinopathy of prematurity (ROP) (See Ranjana, [Pg. 936, Col. 2, ln. 5-10], total of 10,000 Retcam images (left and right eye) of 900 patients were collected with gestation ages of 26–60 weeks and birth weight < 3000 g. The images were annotated in detail, specifying zones and stages by a team of 5 trained and experienced ROP specialists. [Pg. 939, Col. 1, ln. 3-5], As shown in Fig. 8, the zone detection system consists of two separate U-networks for the segmentation (Fig. 9) of the optic disc and blood vessels See also [Fig. 8]. Note: Examiner is interpreting blood vessel segmentation as the attribute information); and
training a ROP detection model pipeline based on the training information comprising training eye image, training attribute information, and a ROP category of the training eye image, wherein the training attribute information corresponds to a plurality of training eye image attributes (See Ranjana, [Pg. 936, Col. 2, ln. 5-10], total of 10,000 Retcam images (left and right eye) of 900 patients were collected with gestation ages of 26–60 weeks and birth weight < 3000 g. The images were annotated in detail, specifying zones and stages by a team of 5 trained and experienced ROP specialists. [Pg. 939, Col. 1, ln. 3-5], As shown in Fig. 8, the zone detection system consists of two separate U-networks for the segmentation (Fig. 9) of the optic disc and blood vessels See also [Fig. 8]. Note: Examiner is interpreting blood vessel segmentation as the attribute information).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference obtaining a training information comprising a training eye image and training attribute information corresponding to plurality of eye image characteristics, wherein the training eye image is associated with Retinopathy of prematurity (ROP); and training a ROP detection model pipeline based on the training information comprising training eye image, training attribute information, and a ROP category of the training eye image, wherein the training attribute information corresponds to a plurality of training eye image attributes based on the method of Ranjana’s reference. The suggestion/motivation would have been to increase the accuracy of detecting ROPs (See Ranjana, [Fig. 17]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Ranjana with Shen to obtain the invention as specified in claim 10.
Regarding claim 12, Shen teaches the method as claimed in claim 10, wherein the ROP detection model pipeline when trained based on the training eye image and corresponding training attribute information is to determine a detection result indicating presence of ROP within the within an input eye image of a subject eye which is under evaluation (See Shen, ¶ [0034], In this embodiment, after the trained MN-ROP model in step 103, then pre-processing the early fundus image in the test set, and then the pre-processed early fundus image input to the trained MN-ROP model).
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") further in view of Villard et al. (US 20220405927 A1, hereafter, "Villard") and further in view of Helmie et al. (See NPL attached, "Efficient and Robust Method to Detect the Location of Macular Center Based on Optimal Temporal Determination", hereafter, "Helmie").
Regarding claim 13, Shen in view of Ranjana teaches the method as claimed in claim 10, wherein the ROP detection model pipeline comprises a plurality of deep learning models selected from a group comprising (See Shen, ¶ [0032], In this embodiment, training the MS-ROP model through the training set in the data set, namely the training set early fundus image pre-processing the image, then input to the MS-ROP model, the MSROP model for training, so as to obtain the trained MS-ROP model, then training the MN-ROP model through the trained MS-ROP model and the training set) [a view assessment model, a quality assessment model, and a categorization model].
However, Shen fail(s) to teach a view assessment model, a quality assessment model, and a categorization model.
Ranjana, working in the same field of endeavor, teaches: a categorization model (See Ranjana, [Pg. 938, Col. 2, ln. 18-21], We have divided the work into two parts: (a) zones I, II, and III detection and (b) detection of stages 1, 2, 3 from fundus images. The results will be combined to explain a class of ROP. Note: Examiner is interpreting this as the categorization model).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to a categorization model based on the method of Ranjana’s reference. The suggestion/motivation would have been to increase the accuracy of detecting ROPs (See Ranjana, [Fig. 17]).
However, Shen and Ranjana fail(s) to teach a view assessment model, a quality assessment model.
Villard, working in the same field of endeavor, teaches: a quality assessment model (See Villard, ¶ [0011], Each of the machine learning models can be a single model or a number of machine learning models, which can be combined to assess either the presence of the at least one disease or the quality of an image. Note: Examiner is interpreting this as the quality assessment model).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to a quality assessment model based on the method of Villard’s reference. The suggestion/motivation would have been to improve the accuracy of medical diagnoses (See Villard, ¶ [0003]).
However, Shen, Ranjana and Villard fail(s) to teach a view assessment model.
Helmie, working in the same field of endeavor, teaches: a view assessment model (See Helmie, [Pg. 3, 2.2 Methods, ln. 27-31], The method used for identifying the macular center consists of several main steps, which include optic disc localization, determining temporal area direction, identifying the macular region of interest (ROI), and macular center point coordinates extraction. Figure 2 shows the flow of macular center point detection. See also [Figure 2]. Note: Examiner is interpreting this as the view assessment model).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to a view assessment model based on the method of Helmie’s reference. The suggestion/motivation would have been to increase the accuracy of detection by providing quality input (See Helmie, [Table 1]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Ranjana, Villard and Helmie with Shen to obtain the invention as specified in claim 13.
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") and further in view of Villard et al. (US 20220405927 A1, hereafter, "Villard").
Regarding claim 14, Shen in view of Ranjana teaches the method as claimed in claim 10, wherein the ROP detection model pipeline (See Shen, ¶ [0034], In this embodiment, after the trained MN-ROP model in step 103, then pre-processing the early fundus image in the test set, and then the pre-processed early fundus image input to the trained MN-ROP model) when trained is to [assess quality of the input eye image to discard low quality images].
However, Shen and Ranjana fail(s) to teach assess quality of the input eye image to discard low quality images.
Helmie, working in the same field of endeavor, teaches: assess quality of the input eye image to discard low quality images (See Villard, ¶ [0010], The electronic processing circuitry can be configured to, responsive to a determination that the quality score does not satisfy a quality threshold, discard the at least one image).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to a view assessment model based on the method of Helmie’s reference. The suggestion/motivation would have been to increase the accuracy of detection by providing quality input (See Helmie, [Table 1]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Helmie with Shen and Ranjana to obtain the invention as specified in claim 14.
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (CN 114881927 A, hereafter, "Shen") in view of Ranjana et al. (See NPL attached, "Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning", hereafter, "Ranjana") further in view of Katuwal et al. (US 20150104087 A1, hereafter, "Katuwal") and further in view of Ranchod (US 20130271728 A1, hereafter, "Ranchod").
Regarding claim 15, Shen in view of Ranjana teaches the method as claimed in claim 10, [wherein the ROP detection model pipeline when trained is to determine type of view of the input eye image to accept only temporal view image, wherein the input eye image have one of a temporal view, nasal view, disc centered view, macula centered view, inferior view, and superior view].
However, Shen fail(s) to teach wherein the ROP detection model pipeline when trained is to determine type of view of the input eye image to accept only temporal view image, wherein the input eye image have one of a temporal view, nasal view, disc centered view, macula centered view, inferior view, and superior view.
Ranjana, working in the same field of endeavor, teaches: wherein the ROP detection model pipeline when trained is to determine type of view of the input eye image to accept only temporal view image (See Ranjana, [Pg. 936, Col. 2, ln. 10-12], We needed only posterior and temporal views of the image for this work, thus excluding 8100 images from the dataset).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference wherein the ROP detection model pipeline when trained is to determine type of view of the input eye image to accept only temporal view image based on the method of Ranjana’s reference. The suggestion/motivation would have been to increase the accuracy of detecting ROPs (See Ranjana, [Fig. 17]).
However, Shen and Ranjana fail(s) to teach wherein the input eye image have one of a temporal view, nasal view, disc centered view, macula centered view, inferior view, and superior view.
Katuwal, working in the same field of endeavor, teaches: wherein the input eye image have one of a temporal view, disc centered view, macula centered view (See Katuwal, ¶ [0022], The set of three image fields typically includes, for example, a disc centered field, macula centered field, and temporal to macula field. As these three image fields are acquired by the camera operator, it is desirable to confirm the identity of the image fields prior to assessing the image quality).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference wherein the input eye image have one of a temporal view, disc centered view, macula centered view based on the method of Katuwal’s reference. The suggestion/motivation would have been to increase the quality of the eye images (See Katuwal, ¶ [0003]).
However, Shen, Ranjana and Katuwal fail(s) to teach nasal view, inferior view, and superior view.
Ranchod, working in the same field of endeavor, teaches: nasal view, inferior view, and superior view (See Ranchod, ¶ [0005], he image set generally includes an external photograph, a retinal image centered on either the optic nerve 7 or macula 8 (the central retina 10), and four mid-peripheral retinal images centered superior, inferior, nasal, and temporal, respectively, to the disc and macula 8).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Shen’s reference to nasal view, inferior view, and superior view based on the method of Ranchod’s reference. The suggestion/motivation would have been providing a wide field image of the eye (See Ranchod, ¶ [0002–0008]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Katuwal and Ranchod with Shen and Ranjana to obtain the invention as specified in claim 15.
Allowable Subject Matter
Claim(s) 9 and 11 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.
Claim(s) 9 and 11 contain subject matter that is not disclosed or made obvious in the cited art.
In regard to claim 9, when considering claim 9 as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“The system as claimed in claim 1, wherein the plurality of input eye image attributes comprises retinal blood vessel location, retinal blood vessel dimension, retinal blood vessel architecture, demarcation line presence, demarcation line location, demarcation line dimension, presence of ridge, location of ridge, dimension of ridge, indicators indicating partial retinal detachment, and indicators indicating total retinal detachment and many more”.
In regard to claim 11, when considering claim 11 as a whole, prior art of record fails to disclose or render obvious, alone or in combination:
“The method as claimed in claim 10, wherein the plurality of training eye image attributes comprises retinal blood vessel location, retinal blood vessel dimension, retinal blood vessel architecture, demarcation line presence, demarcation line location, demarcation line dimension, presence of ridge, location of ridge, dimension of ridge, indicators indicating partial retinal detachment, indicators indicating total retinal detachment and many more”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Trese et al. (US 20160213242 A1, hereafter, “Trese”) teaches an automated method for diagnosing and evaluating severity of retinopathy of prematurity in a retina of a patient is provided that is superior to conventional techniques. A graphical user interface (GUI) is provided for receiving biographical information for the patient creating a patient record in a database via the GUI. A photograph of the retina of the patient is collected and placed in the patient record via the GUI. The photograph is then analyzed to determine vascular distributions within the retina. A zone 1 boundary is assigned to the retina based on a set of threshold levels with respect to the determined vascular distributions. A system for performing the automated method is also provided.
Fukushima et al. (US 20240274295 A1, hereafter, “Fukushima”) teaches there are provided a highly versatile method for screening for retinopathy of prematurity, a screening apparatus, and a trained model that are capable of accurately predict the progression of retinopathy of prematurity at appropriate timing. There is provided a method for screening for retinopathy of prematurity to predict the progression of retinopathy of prematurity, the method including a treatment determination step of determining whether or not treatment is indicated for retinopathy of prematurity after a predetermined number of days after birth based on premature infant information including postnatal time-series data on weight, height, and vital signs of a premature infant whose gestational age is less than a predetermined week.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST.
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/DION J SATCHER/Patent Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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