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 .
Applicant’s amendment
Applicant’s amendment filed 5/4/2026 has been received and entered. Claims 1-11, 13-22 have been amended.
Claims 1-22 are pending.
Priority
This application filed 8/18/2022 is a 371 National stage filing of PCT/US2021/018589 filed 2/28/2021, which claims benefit to US Provisional application 62/978070 filed 2/18/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/5/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-22 stand 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.
Claim analysis
The method of claim 1 has been amended and still is directed to a method of receiving a CFP image and classifying each image by extracting deep features, grading drusen and pigmentary abnormalities and then predicting risk of AMD using Cox proportional hazard model. More specifically, the claims has been amended to provide two wherein clauses describing the images as comprising macular drusen and pigmentary abnormalities and prediction automatically based on where the images are classified. Dependent claims have been amended to be additional ‘further’ comprising estimation steps without any specific indication of how they are estimated or performed, and providing relative to the data provided predictions steps and the use of a Cc proportional hazard model without any specific indication of how it is applied to the data or estimation.
In view of the specification and art of record, color fundus photography is a non-invasive imaging technique that provides high resolution photographs of the fundus, which includes the retina, optic nerve, and choroid. This method provides a true color representation of the eye's internal structures, which the art suggests can be used for diagnosing and documenting retinal diseases and conditions. In evaluating the photo the specification at [0045] teaches that “In some examples, the deep features may include drusen and/or pigmentary abnormalities.” and appears to provide for observable features relative to extracting and using any observed abnormality in classification of AMD status in the patient.
As amended, independent claim 11 provides more broadly any type of image of both eyes of a patient, where dependent claim 12 sets forth CFP and other known imaging processes, and more broadly ‘classifying’ and ‘grading’ but dependent claim 15 provides that Cox proportional hazard model be used. Claim 16 provides for a device that receives the images, and sets forth the same analysis steps of the method. With respect to processing and normalizing images, the specification for both the methods and device use ‘deep learning’ which the specification at [0041] teaches “Deep learning is a branch of machine learning that allows computers to learn by example; in the case of image analysis, it involves training algorithmic models on images with accompanying labels such that they can perform classification of novel images according to the same labels.” With respect to automatically providing classification, in [0043] it teaches “The prediction method delivers autonomous predictions of a higher accuracy than those from retinal specialists using two existing clinical standards. Hence, the predictions are closer to the ground truth of actual time-based progression to late AMD than when retinal specialists are grading the same bilateral CFP and entering these grades into the SSS or the online calculator. In addition, deep feature extraction may generally achieve higher accuracy than DL grading of traditional hand-crafted features.” however this is not a definition or requirement of ‘automatic’ nor is there any requirement of what standards are used in classifying and predicting broadly required of the claims. Dependent claims provide for creating or using a standard or normalized score, and considering additional information such as genotype and patient age, smoking status and AMD genotype (claims 5-7), as well as associating observed abnormalities with BA or NV (claims 2-3).
For step 1 of the 101 analysis, the claims are found to be directed to a statutory category of a process and a product.
For step 2A of the 101 analysis, the judicial exception of the claims are the steps of accessing an image of an eye for abnormalities. In review of the specification, there is no teaching of new abnormalities or unique or different image types, and appear to provide for assessing abnormalities generally used in assessing AMD, GA or NV status. Similar for predicting, scoring, automatically or using known correlative data such as age, smoking or any genotype associated with AMD, there does not appear to be any specific process of grading or predicting required by the claim as set forth in the specification. The step of receiving, classifying and predicting appear to be instructional steps of observing a photo and assessing any abnormalities that might be present. The claims set forth the use of a Cox proportional hazard model, which was known in the art broadly as a class of survival models in statistics. The judicial exception is a set of instructions for analysis of images and appear to fall into the category of Mental Processes, that is concepts performed in the human mind (including an observation, evaluation, judgment, opinion) for observing and noting abnormalities in a photo of the eye and into the category of Mathematical Concepts, that is mathematical formulas or equations to the extent that Cox statistical analysis is used to assess data.
In view of the claim breadth and requirements of the specification, the breadth of “receiving”, “classifying”, and “predicting” encompasses non-transformative visual assessment of a image of an eye coupled with prior knowledge of the correlation of said phenotype(s) with the presence of particular genotype(s); for example, using known genotypes to be informative, or knowing that smoking status has a positive correlation with AMD. This breadth does not impose a meaningful limit on the claim scope, such that all others are not precluded from using the natural principle of observing abnormalities and using them to diagnose AMD. Although the claims encompass “automatically’, the courts have also identified limitations that did not integrate a judicial exception into a practical application; for example, merely including instructions to implement an abstract idea on a computer device, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). As the steps of “receiving”, “classifying”, and “predicting” are very generally recited, the combination of “receiving”, “classifying”, and “predicting” is together reasonably interpreted as mere data gathering and observation of photos. Computing, constructing datasets and using statistical models was well understood, conventional, and routinely performed in the art at the time the application was filed, to the extent that a Cox model is to be applied in the assessment of AMD from the image. Furthermore, the limitation of genotyping breeding individuals at a plurality of markers or alleles does not change the steps to be performed. See MPEP § 2106.05(g) for a discussion on adding insignificant extra-solution (both pre-solution and post-solution) activity to the judicial exception. See also MPEP § 2106.05(h) for a discussion on generally linking the use of a judicial exception to a particular technological environment or field of use. The claims appear to fall into the category of Mathematical Concepts, as it applies the use of statistics and mathematical relationships in analyzing probabilities, and also into the category of mental processes, as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because there is no apparent complexity to or amount of data that is collected and analyzed as presently claimed which was not previously performed by a physician for assessing AMD.
Recent guidance from the office requires that the judicial exception be evaluated under a second prong to determine whether the judicial exception is practically applied. In the instant case, the claims do not have an additional element to which the assessment is applied and as a final step only provides a prediction based on the observation. This judicial exception requires steps recited at high level of generality and for a device/computer are only stored on a non-transitory memory, and is not found to be a practical application of the judicial exception as broadly set forth.
For step 2B of the 101 analysis, to the extent ‘receiving’ can be interpreted as a physical requirement of the claims, each of the independent claims recites additional elements and are found to be generic instruction and steps of obtaining image data. As such, the claims do not provide for any additional element to consider under step 2B. For the product or processing the method using a computer, it is noted that in explaining the Alice framework, the Court wrote that "[i]n cases involving software innovations, [the step one] inquiry often turns on whether the claims focus on the specific asserted improvement in computer capabilities or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool." The Court further noted that "[s]ince Alice, we have found software inventions to be patent-eligible where they have made non-abstract improvements to existing technological processes and computer technology." Moreover, these improvements must be specific -- "[a]n improved result, without more stated in the claim, is not enough to confer eligibility to an otherwise abstract idea. [t]o be patent-eligible, the claims must recite a specific means or method that solves a problem in an existing technological process.”
As indicated in the summary of the judicial exception above and in view of the teachings of the specification, the steps are drawn to analysis of image data. While the instruction for the method can be stored on a medium and could be implemented on a computer device, together the steps do not appear to result in significantly more than a means to assess or compare images of the eye where observed abnormalities can be associated with AMD. The judicial exception of the method as claimed can be performed by hand and in light of the previous claims to a computer medium and in light of the teaching of the specification on a computer. In review of the instant specification the methods do not appear to require a special type of processor and can be performed on a general purpose computer. Further, ‘deep features’ do not appear to require any new or unique images, or any new or unique types of abnormalities, and appear to encompass features that are observable by the human eye. Based upon an analysis with respect to the claim as a whole, claims 1-22 do not recite something significantly different than a judicial exception. Dependent claims set forth additional steps which are more specifically define the considerations and steps of calculating, and comparing, and do not add additional elements which result in significantly more to the claimed method for the analysis. The Step 2 analysis is a two-part analysis, Step 2A and Step 2B, with the first part Step 2A requiring a determination of whether the claims are directed towards a judicial exception, i.e. a law of nature, natural phenomenon, or an abstract idea. In the instant case, the claims comprise steps of comparing images or observing in images and is considered the judicial exception. As such, the instant claims set forth an inventive concept that are drawn only to an abstract process that only manipulates data with respect to grading and predicting using statistical models such as Cox model. Therefore the result of Step 2A analysis is that the claims are directed towards a judicial exception, i.e. an abstract idea. The second part, Step 2B of the two step analysis is to determine whether any element or combination of elements, in the claim is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception. No additional steps are recited in the instantly claimed invention that would amount to significantly more than the judicial exception for having an image and reporting what is observed relative to it be indicative of AMD. Furthermore, if a claim is directed essentially to a method of calculating, using a mathematical formula, even if the solution is for a specific purpose, the claimed method is non-statutory. In other words, patenting abstract idea (observing abnormalities known and associated with AMD in a photo) cannot be circumvented by attempting to limit the use to a particular technological environment or purpose and desired result.
Response to Applicants arguments
Applicants provide an overview of guidance of the courts and the MPEP for the evaluation of claims under the 101 analysis, and argue the analysis is based on the original claim language and as amended for ‘extracting deep features, grading drusen and pigmentary abnormalities and then predicting risk of AMD using Cox proportional hazard model’ is not manual grading and a physician providing the analysis.
In response, the amendments to the claims are noted, but ‘deep features’ appears relative to the data image provided and is not specifically defined, and broadly appear to encompass any observable feature. Indicating ‘deep feature’ is a possible description of the image and does not limit the image or ability to observe in an image by a physician. Using a Cox proportional hazard model encompasses mathematical assessment and is possible to perform with ones mind using pen and paper. Arguments related to the use of AI do not appear to be consistent with the claim requirements. The amendment to add ‘automatically’ is noted, however the claims do not indicate the method requires a computer nor how such steps are automatically performed. The art of record clearly indicates that a physician can analyze images for macular drusen and pigmentary abnormalities and based on what is observed, diagnose and prognose based on what is observed. Arguments that ‘deep features’ and ‘automatic’ are steps that cannot be interpreted as abstract concepts and instructions a physician can perform are inconsistent with the reasonable interpretation of the claim requirements. Claims directed to a device, where a processor is recited fails to integrate the instructions, and appear to encompass that the device is directed to broadly practicing the steps of method claims with a device/processor, steps which can be performed by a physician.
Accordingly, for the reasons above and of record, the rejection is maintained.
As noted previously, one way to overcome a rejection for non-patent-eligible subject matter is to persuasively argue that the claimed subject matter is not directed to a judicial exception. Another way for the applicants to overcome the rejection is to persuasively argue that the claims contain elements in addition to the judicial exception that either individually or as an ordered combination are not well understood, routine, or conventional. Another way for the applicants to overcome the rejection is to persuasively argue that the claims as a whole result in an improvement to a technology. Persuasive evidence for an improvement to a technology could be a comparison of results of the claimed subject matter with results of the prior art, or arguments based on scientific reasoning that the claimed subject matter inherently results an improvement over the prior art. The applicants should show why the claims require the improvement in all embodiments.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-22 are rejected under 35 U.S.C. 103 as being unpatentable over Phan et al. (2016), Sleiman et al. (2017) and Zhu et al. (2019).
Response to Applicants arguments
Applicants provide an overview of the amended claims and a summary of the rejection, and argue that the references fail to provide requirements of the claims. Specifically, it is argued Phan fails to provide ‘deep learning or deep feature extraction automatically and only provides classification of AMD; Sleiman fails to teach classification of CFP images and provides a different qualitative and quantitative analysis of biomarkers not of CFP images; and Shu only provides for Cox proportional hazard regression without guidance of applying it to AMD.
In response, the amendments are noted, however the arguments appear to address the references individually and not what they provide as a whole. Clearly Phan provides the basis that images can be analyzed and used to classify automatically AMD in an automatic screening and grading of Age-Related Macular Degeneration from fundus images. The teachings of Phan clearly provide the basis of image analysis and necessary guidance of examining images for AMD diagnosis and prognosis. The claim language and as amended for ‘extracting deep features, grading drusen and pigmentary abnormalities and then predicting risk of AMD’ appears to be obvious over Phan. Using a ‘Cox proportional hazard model’ is not manual grading however is well known in medical analysis and is provided by Shu. Sleiman is provided as another reference for analyzing images and using predictors in the estimation of prognosis. Together, the references appear to make obvious the generic steps of ‘extracting’ features of an image and then ‘predicting’ using Cox proportional analysis as broadly required of the claims. Review of the specific fails to require or define any specificity to these steps and given that a physician can perform these steps manually, and that image analysis has been provided for a more automatic assessment of images, the claims as a whole are found obvious.
Accordingly, for the reasons above and of record, the rejection is maintained.
Rejection of record
Claim 1 has been amended and comprises receiving a CFP image and classifying each image by extracting deep features, grading drusen and pigmentary abnormalities and then predicting risk of AMD using Cox proportional hazard model. Independent claim 11 provides more broadly any type of image of the eyes of a patient, where dependent claim 12 sets forth CFP and other known imaging processes, and more broadly ‘classifying’ and ‘grading’ but dependent claim 15 provides that Cox proportional hazard model be used consistent with claim 1. Claim 16 provides for a device that receives the images, and sets forth the same analysis steps of the method. Dependent claims provide for creating or using a standard or normalized score, and considering additional information such as genotype and patient age, smoking status and AMD genotype (claims 5-7), as well as associating observed abnormalities with BA or NV (claims 2-3).
Analyzing and assessing CFP images for abnormalities, in particular grading drusen and pigmentary abnormalities for an indication of AMD were known. For example, Phan et al. teach Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images. Similarly, Sleiman et al. teach Optical Coherence Tomography Predictors of Risk for Progression to Non-Neovascular Atrophic Age-Related Macular Degeneration. Each provide analysis steps and guidance for consideration in assessing, confirming and predicting AMD in a patient’s eye. However neither specifically state to use Cox proportional hazard model, but Zhu et al. who teach Association of Age-Related Macular Degeneration With Risk of All-Cause and Specific-Cause Mortality provide the use of Cox modeling in the analysis and associate the analysis. Each provide that other conditions are used in the assessment such as age or genetic predisposition. Given the detailed guidance on assessing and using CFP images to determine AMD and provide an assessment of the severity, it would have been prima facie obvious to one having ordinary skill in the art at the time the invention was made to provide the art as a whole in assessing CFP for diagnosis AMD. One having ordinary skill in the art would have been motivated to combine the various teachings because each provide details of AMD diagnosis and a more complete ability to assess the state and progression of AMD. Given the successful results of each, there would have been a reasonable expectation of success of evaluating CFP images and staging AMD based on druen grading and pigmentary abnormalities that could be observed.
Thus, the claimed invention as a whole was clearly prima facie obvious.
Conclusion
No claim is allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph T Woitach whose telephone number is (571)272-0739. The examiner can normally be reached Mon-Fri; 8:00-4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz R Skowronek can be reached at 571 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Joseph Woitach/Primary Examiner, Art Unit 1687
Prosecution Insights