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 .
Claim Objections
Claims 22, 12-14 and 17 are objected to because of the following informalities:
In line 2 of claim 22 the word “comprising” is repeated.
The word “normalisation” is misspelled in claim 12 as it is the British spelling of the word.
The word “utilised” is misspelled in claim 13 as it is the British spelling of the word.
The word “glycaemic” is misspelled in claim 14 as it is the British spelling of the word.
The word “standardisation” is misspelled in claim 22 as it is the British spelling of the word.
Appropriate correction is required.
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.
Claims 1-18 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning (hereinafter “Poplin”) in view of Hart et al. (US 11,138,732 B2 hereinafter “Hart”).
As to claim 21, Poplin teaches a method of predicting a risk of cardiovascular disease (CVD) from one or more fundus images (Prediction of cardiovascular risk factors from retinal fundus photographs; Title), the method performed by one or more processors (computers; page 6 Section Code availability), the method comprising: processing one or more fundus images associated with an individual to determine whether the one or more fundus images are of sufficient quality for further processing (Images
of poor quality were filtered out before training and validation; Page 5, Col 2, paragraph 2);
Poplin does not explicitly teach using of CNN to determine the quality of the fundus images are sufficient quality for further processing. Hart teaches assessment of fundus images for the purposes of estimating disease state (Abstract), wherein CNN is used to rate fundus images as “readable” or “unreadable” (Col. 14, lines 4-7). It would have been obvious for one ordinary skilled in the art before the effective filing date to have combined the cardiovascular risk factor method of Poplin with the fundus image assessment method of Hart in order to better analyze the input fundus images.
Note, the rest of the claim limitations in this claim as well as all dependent claims from claim 21 are considered contingent limitations as they are not required after the quality assessment of the fundus images since if there are no images that are deemed to be sufficient quality, then none of the following steps are performed (MPEP 2111.04 II).
As to claim 22, Poplin teaches a system that performs the prediction of the cardiovascular risk factors (computers; page 6 Section Code availability).
As to claim 23, Poplin teaches a computer readable medium performing the method of the prediction of the cardiovascular risk factors (computers; page 6 Section Code availability).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. (WO 2022/261513 A1) teaches a method and a system using fundus images and metadata and deep learning models to determine disease.
Conclusion
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CLAIRE X. WANG
Supervisory Patent Examiner
Art Unit 1774
/CLAIRE X WANG/Supervisory Patent Examiner, Art Unit 1774
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