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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/05/26 has been entered.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-4, 7-14, 17-24, and 27-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over U. S. Publication No. 2010/0036217 to Choi et al in view of U. S. Publication No. 2018/0204111 to Zadeh et al.; U. S. Publication No. 2008/0139920 to Bigilieri et al.; and further in view of U. S. Publication No. 2011/0246413 to Shibayama et al.
Regarding Claims, 1, 11, and 21, Choi teaches a system, method and program comprising a computer system having one or more processors: receiving time series data of fluorescence from tissue of the subject, the time series data related to perfusion of the tissue and the time series data being or having been captured by an image capture system (abstract and claim 1 teaches time series data of fluorescence imaging representing perfusion for tissue diagnosis) .
Choi does not expressly teach classifying a plurality of regions in the time series data, wherein the plurality of regions are classified using one or more machine learning models; and plurality of regions are classified by providing a numerical value representing a condition of the tissue.
Zadeh teaches classifying a plurality of regions in the time series data, wherein the plurality of regions are classified using one or more machine learning models (abstract; para 0114, 0125 and 02431 teaches classifying images using machine learning models).
It would be obvious to one of ordinary skill in the art at the time of filing to modify Choi with a setup such that the plurality of regions are classified using machine learning models as taught by Zadeh, since such a setup would result in a more precise and fast analysis of the images.
Choi and Zadeh teaches all of the above claimed limitations but does not expressly teach plurality of regions are classified by providing a numerical value representing a condition of the tissue.
Bigilieri teaches plurality of regions are classified by providing a numerical value representing a condition of the tissue (para 0051 teaches assigning numerical values to inflammatory condition of tissues on a numeric scale).
It would be obvious to one of ordinary skill in the art at the time of filing to modify Choi and Zadeh with a numerical value representing a condition of tissue as taught by Bigilieri, since such a setup would result in easy observation of different tissue conditions by the doctor. Furthermore, it would also speed up the time spent by doctors on evaluating images.
Choi, Zadeh, and Bigilieri teaches all of the above claimed limitations but does not expressly teach classifying the plurality of regions in the time series data comprises providing a risk estimate for the plurality of regions.
Shibayama teaches classifying the plurality of regions in the time series data comprises providing a risk estimate for the plurality of regions (para 0050 teaches a recurrence risk score).
It would be obvious to one of ordinary skill in the art at the time of filing to modify Choi, Zadeh, and Bigilieri with a setup to provide a risk estimate, as taught by Shibayama, since such a setup would result in easy and faster diagnosis.
Regarding Claim 2-4, 12-14, and 22-24, Zadeh teaches that the predicted tissue condition comprises inflammation, malignancy, abnormality or disease (para 02431 teaches caner/tumor detection).
Regarding Claim 7, 17, and 27, Choi teaches that the time series data comprises raw data, pre-processed data, or a combination thereof (abstract; para 0046 and claim 1 teaches time series data is processed data).
Regarding Claim 8, 18, and 28, Zadeh teaches that the time series data comprises pre-processed data that has been pre-processed by applying data compression, principal component analysis, autoencoding, or a combination thereof (para 02267 teaches principal component analysis).
Regarding Claim 9, 19, and 29, Zadeh teaches that the plurality of regions are classified using an unsupervised clustering algorithm (para 02100 teaches classification using clustering algorithm).
Regarding Claim 10, 20, and 30, Choi teaches that the time series data of fluorescence from tissue of the subject comprises fluorescence intensity data (para 0046 teaches fluorescence intensity data).
Response to Arguments
Applicant’s arguments, see Remarks, filed 11/04/25, with respect to the rejection(s) of claim(s) 1-4, 7-14, 17-24 and 27-30 under 103 have been fully considered but are moot in view of new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANJAY CATTUNGAL whose telephone number is (571)272-1306. The examiner can normally be reached M-F 9-5 EST.
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/SANJAY CATTUNGAL/Primary Examiner, Art Unit 3793