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 .
Status of Claims
This reply in response to the application filed on 21 January 2025.
Claims 1-10 and 12-21 are currently pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 01/21/2025. The
submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the
information disclosure statement has been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-10 and 12-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-23 of App# 18/994,127 in view of Solanki et al. (US20150110368A1). Although the claims at issue are not identical, they are not patentably distinct from each other because recite substantially similar limitations.
This is a provisional nonstatutory double patenting rejection.
The table/chart below exhibits the similarity* between the independent claims where claims 1 and 20 of the current application is a narrower variation of the claims of the reference patent.
* Similarities highlighted in BOLD
App# 18/811,687
App# 18/994,127
Claim 1:
performing a pairwise comparison of the fundus image against each example fundus image in a reference image set using one or more machine learning algorithms to determine a difference in severity of the fundus image compared to each example fundus image in the reference image set; and
flagging the fundus image as requiring referral for investigation for the disease based on results of the pairwise comparisons,
wherein the reference image set includes a plurality of example fundus images, the plurality of example fundus images ranked according to their degree of severity of disease
Claim 1:
performing a pairwise comparison of the medical data sample against each example of medical data in a reference data set using one or more machine learning algorithms to determine a difference in severity of the medical data compared to each example medical data in the reference data set; and
flagging the medical data sample as requiring referral for investigation for the disease based on results of the pairwise comparisons, wherein the reference data set includes a plurality of medical data examples, the plurality of medical data examples ranked according to their degree of severity of disease
Claim 20:
receiving the reference image set;
performing pairwise comparison of each image of the plurality of images within the reference image set against every other image of the plurality of images within the image set; and
ranking the plurality of example fundus images according to a degree of severity of disease based on the pairwise comparisons
Claim 22:
receiving the reference data set; and
performing pairwise comparison of each medical data example within the reference data set against every medical data example within the reference data set; and
ranking the plurality of medical data examples according to a degree of severity of disease based on the pairwise comparisons
The difference between the present application and App# 18/994,127 is that the present application discloses the data being fundus image(s) which is obvious over Solanki et al. (US20150110368A1) ([0046] & [0120]) with the motivation for timely treatment and better quality of life (See Solanki, Background).
Dependent claim 2 is an obvious variant of claim 2 of App# 18/994,127 for recitation of determining a position of the fundus image against the example fundus images within the reference image set; and comparing the position of the fundus image with a threshold for referral, wherein the flagging the fundus image as requiring referral comprises flagging the fundus image as requiring referral if the position of the fundus image is above the threshold for referral. Dependent claim 3 is an obvious variant of claim of App# 18/994,127 for reciting wherein the one or more machine learning algorithms comprises one or more neural networks. Dependent claim 4 is an obvious variant of claim 4 of App# 18/994,127 for reciting wherein the or each neural network is a convolutional neural network. Dependent claim 5 is an obvious variant of claim 5 of App# 18/994,127 for reciting wherein the one or more convolutional neural networks is a plurality of convolutional neural networks. Dependent claim 6 is an obvious variant of claim 6 of App# 18/994,127 for reciting wherein each of the plurality of convolutional neural networks is trained on a different data set. Dependent claim 7 is an obvious variant of claim 7 of App# 18/994,127 for reciting amalgamating the results of the pairwise comparisons from each convolutional neural network, and wherein the sending the fundus image for referral for investigation for the disease is based on the amalgamated pairwise comparisons. Dependent claim 8 is an obvious variant of claim 8 of App# 18/994,127 for reciting wherein the amalgamating of the results of the pairwise comparisons comprises supplying the results of the pairwise comparison as inputs to one or more lasso regression models, the or each lasso regression model having a decision boundary associated with a threshold of disease severity. Dependent claim 9 is an obvious variant of claim 9 of App# 18/994,127 for reciting wherein the or each lasso regression model is a plurality of lasso regression models and wherein the threshold of disease severity for each model is different. Dependent claim 10 is an obvious variant of claim 10 of App# 18/994,127 for reciting performing bootstrapping to estimate a certainty value of a probability of needing referral. The remaining dependent claims in the present application also recite substantially similar limitations to of App# 18/994,127 such as comparing accumulated results to the threshold for referral, determining a frequency of occurrence, thresholds corresponding to one of the plurality of thresholds for disease severity, performing linear discriminant analysis, using a selecting algorithm, and using an ensemble process.
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 1-10 and 12-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claim 1-10 and 12-19), process (claim 20), and article of manufacture (claim 21).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
performing a pairwise comparison of the fundus image against each example fundus image in a reference image set using one or more machine learning algorithms to determine a difference in severity of the fundus image compared to each example fundus image in the reference image set; and
flagging the fundus image as requiring referral for investigation for the disease based on results of the pairwise comparisons,
wherein the reference image set includes a plurality of example fundus images, the plurality of example fundus images ranked according to their degree of severity of disease.
Claim 20 recites steps of
receiving the reference image set;
performing pairwise comparison of each image of the plurality of images within the reference image set against every other image of the plurality of images within the image set; and
ranking the plurality of example fundus images according to a degree of severity of disease based on the pairwise comparisons.
These steps for determining if a fundus image requires referral for investigation for a disease, as drafted, under the broadest reasonable interpretation, includes performance of the limitations in the mind. That is, nothing in the claim element precludes the italicized portions from practically being performed in the mind through the evaluation and determination of a degree of severity of disease based on the pairwise comparisons. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis (MPEP 2106). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements, non-italicized portions identified above for claims 1 and 20, do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of using one or more machine learning algorithms amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving the reference image set amounts to mere data gathering since it does not add meaningful limitations to the receiving action performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity and add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as an using one or more machine learning algorithms, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, see Intellectual Ventures I LLC v. Capital One Bank, MPEP 2106.05(f)
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving the reference image set amounts; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-10, 12-19, and 21 reciting particular aspects for determining if a fundus image requires referral for investigation for a disease such as
[Claim 2] determining a position of the fundus image against the example fundus images within the reference image set; and
comparing the position of the fundus image with a threshold for referral,
wherein the flagging the fundus image as requiring referral comprises flagging the fundus image as requiring referral if the position of the fundus image is above the threshold for referral;
[Claim 3] wherein the one or more machine learning algorithms comprises one or more neural networks;
[Claim 4] wherein the or each neural network is a convolutional neural network;
[Claim 5] wherein the one or more convolutional neural networks is a plurality of convolutional neural networks;
[Claim 6] wherein each of the plurality of convolutional neural networks is trained on a different data set;
[Claim 7] amalgamating the results of the pairwise comparisons from each convolutional neural network, and wherein the sending the fundus image for referral for investigation for the disease is based on the amalgamated pairwise comparisons;
[Claim 8] wherein the amalgamating of the results of the pairwise comparisons comprises supplying the results of the pairwise comparison as inputs to one or more lasso regression models, the or each lasso regression model having a decision boundary associated with a threshold of disease severity;
[Claim 9] wherein the or each lasso regression model is a plurality of lasso regression models and wherein the threshold of disease severity for each model is different;
[Claim 10] performing bootstrapping to estimate a certainty value of a probability of needing referral;
[Claim 12] wherein the amalgamating the results of the pairwise comparison comprises accumulating results from each convolutional neural network, and comparing the accumulated results to the threshold for referral;
[Claim 13] wherein the amalgamating the results comprises setting a plurality of thresholds of disease severity within the reference image set, and determining a frequency of occurrence of the fundus image above each of the thresholds of disease severity;
[Claim 14] wherein the threshold for referral corresponds to one of the plurality of thresholds of disease severity;
[Claim 15] wherein the amalgamating the results of the pairwise comparison comprises fitting an s-curve to the results of each convolutional neural network; determining a probability of requiring referral based each fitted s-curve; and performing linear discriminant analysis on the determined probabilities;
[Claim 16] comprising selecting a subset of the plurality of convolutional neural networks using a selecting algorithm;
[Claim 17] wherein the selecting algorithm comprises a lasso regression model, the lasso regression model having a decision boundary associated with the threshold for referral, the selecting comprising applying the results from each convolutional neural network into the lasso regression model, ordering the convolutional neural networks in terms of accuracy at predicting referral, and selecting a predetermined number of the highest ranked convolutional neural networks as the subset;
[Claim 18] receiving the reference image set;
performing pairwise comparison of each image of the plurality of images within the reference image set against every other image of the plurality of images within the image set; and
ranking the plurality of example fundus images according to a degree of severity of disease based on the pairwise comparisons;
[Claim 21] A non-transitory computer-readable medium including instructions stored thereon that when executed by one or more processors cause the processor to perform the method of claim 1;
these italicized portions are mental since they merely describe types of data and determinations that can be performed by humans. In addition, then claims also recite mathematical calculations such as s-curve fitting, bootstrapping, and linear discriminant analysis. The italicized portions containing the recitation of the training step has also been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance
Step 2A Prong 2
Dependent claims 3-9, 12, 15-18, and 21 recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claim 3 (wherein the one or more machine learning algorithms comprises one or more neural networks); claim 4 (wherein the or each neural network is a convolutional neural network); claim 5 (wherein the one or more convolutional neural networks is a plurality of convolutional neural networks); claim 6 (convolutional neural networks); claim 7 (each convolutional neural network); claim 8 (one or more lasso regression models, the or each lasso regression model); claim 9 (wherein the or each lasso regression model is a plurality of lasso regression models); claim 12 (each convolutional neural network); claim 15 (each convolutional neural network); claim 16 (the plurality of convolutional neural networks using a selecting algorithm); claim 17 (wherein the selecting algorithm comprises a lasso regression model, the lasso regression model; applying the results from each convolutional neural network into the lasso regression model; and, the convolutional neural networks); and, claim 21 (A non-transitory computer-readable medium including instructions stored thereon that when executed by one or more processors cause the processor) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)); and, add insignificant extra-solution activity to the abstract idea (such as recitation of receiving the reference image set amounts to mere data gathering since it does not add meaningful limitations to the receiving action performed, see MPEP 2106.05(g))). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 3-9, 12, 15-17, and 21 recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Dependent claim 18 recites additional subject matter which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of receiving the reference image set amounts; e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
Therefore, in consideration of all the facts, the present invention is not a patent-eligible invention under USC 101. Additionally, it is evident that the present claims monopolize every possible application of pairwise comparison to rank fundus images, restricting further innovation in this area without offering a specific, technical improvement to how the computer actually operates. Improved predictive accuracy or using AI tools is generally not enough to transform an abstract idea into patent-eligible subject matter if the core of the invention is still a method of determination; “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)).
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 10-15, and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Solanki et al. (US20150110368A1) in view of Burlina et al. (US20150265144A1).
Regarding claim 1, Solanki discloses performing a pairwise comparison of the fundus image against each example fundus image in a reference image set using one or more machine learning algorithms to determine a difference in severity of the fundus image compared to each example fundus image in the reference image set ([0337] “The first image in the longitudinal series is referred to as the baseline image Ib and any other registered longitudinal image is denoted as I1.” [0324] “In one embodiment, a support vector machine (SVM) is used for lesion classification. In other embodiments, classifiers such as k-nearest neighbor, naive Bayes, Fisher linear discriminant, deep learning, or neural networks can be used.” [0341] “In one embodiment, these pixels are then classified using lesion classifier […] Newly appearing lesions can be found by labeling image I and comparing those regions to corresponding regions in Ib to identify newly appearing lesions.”)
and flagging the fundus image as requiring referral for investigation for the disease based on results of the pairwise comparisons ([0246] “whereas images with hazy media are flagged as being of insufficient quality for effective grading. Quality assessment can allow the clinician to determine whether he needs to immediately reimage the eye or refer the patient to a clinician”)
wherein the reference image set includes a plurality of example fundus images ([0340] “In one embodiment, binary images Bb and Bl with lesions of interest marked out are created for the baseline and longitudinal images.”)
Solanki does not explicitly disclose however Burlina teaches the plurality of example fundus images ranked according to their degree of severity of disease ([0014] “FIG. 1 shows examples of four fundus images with increasing AMD severity. Upper left: an AMD category 1 (no AMD). Upper right: an AMD category 2 (early AMD); Lower left: an AMD category 3 (intermediate AMD) with geographic atrophy not involving the center of the retina; Lower right: an AMD category 4 with evidence of both neovascularization and geographic atrophy (advanced AMD)”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki the plurality of example fundus images ranked according to their degree of severity of disease as taught by Burlina since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 2, Solanki discloses determining a position of the fundus image against the example fundus images within the reference image set; and comparing the position of the fundus image with a threshold for referral ([0016] “identifying pixels in the first image as landmarks; identifying pixels in the second image as landmarks; computing descriptors at landmark pixels; matching descriptors across the first image and the second image; and estimating a transformation model to align the first image and the second image; compute changes in lesions and anatomical structures in registered images; and quantify the changes in terms of statistics” [0392] “DR screening block 114 determines whether a particular fundus image includes abnormalities indicative of diabetic retinopathy such that the patient should be referred to an expert”)
wherein the flagging the fundus image as requiring referral comprises flagging the fundus image as requiring referral if the position of the fundus image is above the threshold for referral ([0022] “In one embodiment of the system, this lesion information is further processed to generate a prediction score indicating the severity of retinopathy in the patient, which provides context determining potential further operations such as clinical referral or recommendations for the next screening date.”)
Regarding claim 3, Solanki discloses wherein the one or more machine learning algorithms comprises one or more neural networks ([0014] “using one or more of: […] neural network”)
Regarding claim 4, Solanki discloses wherein the or each neural network is a convolutional neural network ([0014] “using one or more of: […] convolution networks”)
Regarding claim 5, Solanki discloses wherein the one or more convolutional neural networks is a plurality of convolutional neural networks ([0387] “In one embodiment, the system uses convolution networks, sometimes referred to as cony-nets, based classifiers, which are deep architectures that have been shown to generalize well for visual inputs.”)
Regarding claim 6, Solanki discloses wherein each of the plurality of convolutional neural networks is trained on a different data set ([0120] “using algorithms that learn, directly or indirectly, from a set of examples of already graded fundus images. This training data could have a key influence on the sensitivity and specificity of the algorithm. […] in some embodiments, the computerized screening algorithm performs well on cross-dataset testing, that is, the algorithm generalizes well, when trained on one dataset and tested on another.”)
Regarding claim 7, Solanki discloses amalgamating the results of the pairwise comparisons from each convolutional neural network, and wherein the sending the fundus image for referral for investigation for the disease is based on the amalgamated pairwise comparisons ([0021] “using the descriptors to classify image suitability for grading comprising one or more of: […] convolution networks” [0022] “A joint segmentation-recognition method can be used to recognize and localize the lesions and retinal structures. In one embodiment of the system, this lesion information is further processed to generate a prediction score indicating the severity of retinopathy in the patient, which provides context determining potential further operations such as clinical referral or recommendations for the next screening date.”)
Regarding claim 10, Solanki discloses performing bootstrapping to estimate a certainty value of a probability of needing referral ([0359] “Classifying encounters using encounter-level descriptors computed in (f) as normal or abnormal (one classifier each for each abnormality, lesion, or disease) using one or more of supervised learning techniques including but not limited to: […] (viii) convolution networks, […] with […] bootstrap aggregation.”)
Regarding claim 12, Bowers discloses wherein the amalgamating the results of the pairwise comparison comprises accumulating results from each convolutional neural network, and comparing the accumulated results to the threshold for referral ([0013] “normal or abnormal using supervised learning utilizing the computed descriptors, using one or more of: […] convolution networks [0335] “The results of the analysis are provided to the mobile device 289008, which performs an initial interpretation of the results and presents a diagnosis report to the patient.” [0094] “FIGS. 41C and 41D show embodiments of Cytomegalovirus retinitis screening results using one embodiment of the Cytomegalovirus retinitis detection module for “retina with CMVR” category screened as “refer”.”)
Regarding claim 13, Solanki does not explicitly disclose however Burlina teaches wherein the amalgamating the results comprises setting a plurality of thresholds of disease severity within the reference image set ([0091] “A set of fundus photographs were digitized from 595 of these 600 patients forming a set of over 72,000 images graded for AMD severity.” [0092] “Specifically, each image is assigned an AMD category from 1 to 4, with 1 representing images showing minimal to no evidence of AMD; category 2 corresponding to the early stage of AMD25; category 3 corresponding to the intermediate stage of AMD; and category 4 representing images from patients with the advanced stage of AMD.”)
and determining a frequency of occurrence of the fundus image above each of the thresholds of disease severity [0095]
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Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki wherein the amalgamating the results comprises setting a plurality of thresholds of disease severity within the reference image set, and determining a frequency of occurrence of the fundus image above each of the thresholds of disease severity as taught by Burlina since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 14, Solanki does not explicitly disclose however Burlina teaches wherein the threshold for referral corresponds to one of the plurality of thresholds of disease severity ([0014] “[…] (no AMD)[…] (early AMD) […] (intermediate AMD) […] (advanced AMD)”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki wherein the threshold for referral corresponds to one of the plurality of thresholds of disease severity as taught by Burlina since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 15, Solanki discloses wherein the amalgamating the results of the pairwise comparison comprises fitting an s-curve to the results of each convolutional neural network ([0387] “In one embodiment, the system uses convolution networks” [0405] “The ROC curves for this example implementation, shown in FIG. 40, demonstrate an impressive cross-dataset testing performance, especially for detecting DR onset (AUROC of 0.91).”)
determining a probability of requiring referral based each fitted s-curve ([0325] “the class probability for lesion class and non-lesion class are computed and are used as the decision statistic.”)
and performing linear discriminant analysis on the determined probabilities ([0325] “A decision statistic for a particular pixel is generated by computing the distance of the given element from the given lesion classification hyper plane defined […] using Fisher linear discriminant or the like.”)
Regarding claim 18, Solanki discloses receiving the reference image set ([0244] “On a dataset of 10,104 images with over 2000 lens shot images on 50-50 train-test splits area under receiver operating characteristics (ROC) curve (AUROC) of over 0.998 were obtained.”)
performing pairwise comparison of each image of the plurality of images within the reference image set against every other image of the plurality of images within the image set ([0337] “The first image in the longitudinal series is referred to as the baseline image Ib and any other registered longitudinal image is denoted as I1.” [0341] “In one embodiment, these pixels are then classified using lesion classifier […] Newly appearing lesions can be found by labeling image I and comparing those regions to corresponding regions in Ib to identify newly appearing lesions.”)
Solanki does not explicitly disclose however Burlina teaches and
ranking the plurality of example fundus images according to a degree of severity of disease based on the pairwise comparisons ([0014] “FIG. 1 shows examples of four fundus images with increasing AMD severity. Upper left: an AMD category 1 (no AMD). Upper right: an AMD category 2 (early AMD); Lower left: an AMD category 3 (intermediate AMD) with geographic atrophy not involving the center of the retina; Lower right: an AMD category 4 with evidence of both neovascularization and geographic atrophy (advanced AMD)”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki ranking the plurality of example fundus images according to a degree of severity of disease based on the pairwise comparisons as taught by Burlina since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 19, Solanki discloses wherein the disease is diabetic retinopathy ([0105] “Methods and systems are disclosed that provide automated image analysis allowing detection, screening, and/or monitoring of retinal abnormalities, including diabetic retinopathy”)
Regarding claim 20, Solanki discloses receiving the reference image set ([0244] “On a dataset of 10,104 images with over 2000 lens shot images on 50-50 train-test splits area under receiver operating characteristics (ROC) curve (AUROC) of over 0.998 were obtained.”)
performing pairwise comparison of each image of the plurality of images within the reference image set against every other image of the plurality of images within the image set ([0337] “The first image in the longitudinal series is referred to as the baseline image Ib and any other registered longitudinal image is denoted as I1.” [0341] “In one embodiment, these pixels are then classified using lesion classifier […] Newly appearing lesions can be found by labeling image I and comparing those regions to corresponding regions in Ib to identify newly appearing lesions.”)
Solanki does not explicitly disclose however Burlina teaches and
ranking the plurality of example fundus images according to a degree of severity of disease based on the pairwise comparisons ([0014] “FIG. 1 shows examples of four fundus images with increasing AMD severity. Upper left: an AMD category 1 (no AMD). Upper right: an AMD category 2 (early AMD); Lower left: an AMD category 3 (intermediate AMD) with geographic atrophy not involving the center of the retina; Lower right: an AMD category 4 with evidence of both neovascularization and geographic atrophy (advanced AMD)”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki ranking the plurality of example fundus images according to a degree of severity of disease based on the pairwise comparisons as taught by Burlina since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 21, Solanki discloses A non-transitory computer-readable medium including instructions stored thereon that when executed by one or more processors cause the processor to perform the method of claim 1 ([0021] “In another embodiment, non-transitory computer storage that stores executable program instructions is disclosed.”)
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Solanki et al. (US20150110368A1) in view of Burlina et al. (US20150265144A1) and further in view of Oh et al. (Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study).
Regarding claim 8, Solanki in view of Burlina does not explicitly disclose however Oh teaches wherein the amalgamating of the results of the pairwise comparisons comprises supplying the results of the pairwise comparison as inputs to one or more lasso regression models ([pg. 5] “When the optimal values of λ were applied for training the penalized logistic regression including […] LASSO, the resulting coefficients models that we obtained are given in Additional file 1. The coefficients of OLR and LR-BS were also calculated.”)
the or each lasso regression model having a decision boundary associated with a threshold of disease severity ([pg. 5] “We generated the ROC curves and selected cut-off points that maximized Youden's index [32]. Participants above the cut-off points were classified as being at high risk in each prediction model.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki and Burlina wherein the amalgamating of the results of the pairwise comparisons comprises supplying the results of the pairwise comparison as inputs to one or more lasso regression models, the or each lasso regression model having a decision boundary associated with a threshold of disease severity as taught by Oh since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Regarding claim 9, Solanki in view of Burlina does not explicitly disclose however Oh teaches wherein the or each lasso regression model is a plurality of lasso regression models and wherein the threshold of disease severity for each model is different ([pg. 4] “The form of logistic regression was used for all prediction models due to dichotomous clinical outcome […]
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[pg. 5] “selected cut-off points that maximized Youden's index [32].”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki and Burlina wherein the or each lasso regression model is a plurality of lasso regression models and wherein the threshold of disease severity for each model is different as taught by Oh since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable over Solanki et al. (US20150110368A1) in view of Burlina et al. (US20150265144A1) and further in view of Xia et al. (CN112686902A).
Regarding claim 16, Solanki in view of Burlina does not explicitly disclose however Xia teaches selecting a subset of the plurality of convolutional neural networks using a selecting algorithm ([pg. 4] “FCNN Representing 128-dimensional convolutional neural network features, Fradiomics Representing the image omics characteristics on four sequences of 4 × 104 dimensions, F comprises 544-dimensional characteristics in total; step 5.4: l1 regularization feature selection, wherein feature selection is performed by adopting an L1 regularization Lasso algorithm (Lasso)”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki and Burlina selecting a subset of the plurality of convolutional neural networks using a selecting algorithm as taught by Xia since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Solanki et al. (US20150110368A1) in view of Burlina et al. (US20150265144A1), Xia et al. (CN112686902A), and further in view of Bu et al. (Ensemble of Deep Convolutional Learning Classifier System Based on Genetic Algorithm for Database Intrusion Detection).
Regarding claim 17, Solanki in view of Burlina does not explicitly disclose however Xia teaches wherein the selecting algorithm comprises a lasso regression model ([pg. 4] “wherein feature selection is performed by adopting an L1 regularization Lasso algorithm (Lasso)”)
the lasso regression model having a decision boundary associated with the threshold for referral ([pg. 5] “according to the classification mark obtained by each pixel point, summarizing the pixel points (x, y) obtaining the same classification result to form the tumor boundary and the labels of different tumor tissues […] determining the classification label of the region so as to determine the label of the central pixel of the region”)
the selecting comprising applying the results from each convolutional neural network into the lasso regression model ([pg. 4] “FCNN Representing 128-dimensional convolutional neural network features, Fradiomics Representing the image omics characteristics on four sequences of 4 × 104 dimensions, F comprises 544-dimensional characteristics in total; step 5.4: l1 regularization feature selection, wherein feature selection is performed by adopting an L1 regularization Lasso algorithm (Lasso)”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki and Burlina wherein the selecting algorithm comprises a lasso regression model, the lasso regression model having a decision boundary associated with the threshold for referral, the selecting comprising applying the results from each convolutional neural network into the lasso regression model as taught by Xia since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Solanki in view of Burlina and Xia does not explicitly disclose however Bu teaches ordering the convolutional neural networks in terms of accuracy at predicting referral ([pg. 3] “It is known that the intrusion detection system accuracy can be enhanced by utilizing the ensemble of multiple models [25,26]. […] finally, the ensemble collection component uses the results of each generated models to select and combine relevant models into an ensemble with respect to accuracy and diversity”)
and selecting a predetermined number of the highest ranked convolutional neural networks as the subset ([pg. 9] “Afterward, starting with the model with the highest accuracy, a set of models are collected such that Qav of the ensemble is reduced until the number of models within the ensemble is more or equal to the minimum number of required models, and no other models can be added to the set without increasing the ensemble’s Qav […] The details of the ensemble collection process for selecting the heterogeneous model considering diversity are described in Algorithm 1.
S ← {}, P ← Collection of generated models;
x ← best accuracy model in P;”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Solanki, Burlina, and Xu ordering the convolutional neural networks in terms of accuracy at predicting referral, and selecting a predetermined number of the highest ranked convolutional neural networks as the subset as taught by Bu since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art.
Prior Art Cited but Not Relied Upon
Halimi, I., Piffo, E., Boudersa, O., Vilmorin, Y. M. C., Ait-ikhlef, M., Kone, K., ... & Dorcelus, G. (2026). A Latent Risk-Aware Machine Learning Approach for Predicting Operational Success in Clinical Trials based on TrialsBank. arXiv preprint arXiv:2603.29041.
This reference is relevant because it conceptually discloses the applicant’s invention.
Conclusion
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/WINSTON R FURTADO/Examiner, Art Unit 3687
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