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 the Claims
Currently, claims 1-16 are pending in the application. Claims 1 & 9 are amended.
Response to Arguments / Amendments
Applicant’s arguments have been fully considered but are rendered moot in view of the new ground of rejection necessitated by amendments initiated by the applicant.
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 of this title, 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 1-5, 7, 9-13 & 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over NAKAMURA et al. (US 20130236081, hereinafter NAKAMURA) in view of Bharti et al. (US 20250095390, hereinafter Bharti) and CHEN et al. (US 20210380910, hereinafter, CHEN).
Regarding Claim 1, NAKAMURA discloses a cell discrimination method using artificial intelligence ([0010], image classification), the method comprising:
an input step of inputting a cell image ([0028] FIG. 1, image classification apparatus with a storage unit 13; Claims 4-5, inputting an input image of an object to be identified, which is an image of a cell mass cultured in a cell culture device);
a discriminating step of discriminating at least one of various cell types, various culturing conditions, and various culturing times corresponding to the input cell image using a deep learning-based discriminating model ([0025] and Claim 1, identifying to which of a plurality of categories the input image belongs on the basis of the feature vector of the input image by using an identification algorithm, which is a linear discriminant analysis, a neural network, a support vector machine, or the like)
wherein the discriminating step includes:
extracting a first feature in the cell image ([0010], a first feature extraction unit configured to segment an input image into a plurality of block images each having a predetermined size, and acquire a feature value of each of the plurality of block images; [0028] FIG. 1, image classification apparatus with a first feature extraction unit 11);
extracting a second feature in the cell image ([0010], a second feature extraction unit calculates the feature vector of a block region; [0028] FIG. 1, image classification apparatus with a first feature extraction unit 11; an area segmentation unit 12; a storage unit 13; a second feature extraction unit 14; and a classification unit 15); and
determining the at least one of the cell types, the culturing conditions, and the culturing times corresponding to the cell image, based on the extracted first feature and second feature ([0010], extraction unit comprises: the first feature extraction unit for extracting a feature amount of each of a plurality of block images and a classification unit configured to classify which of a plurality of categories the input image belongs to, based on the feature vector),
wherein the discriminating model includes:
a first neural network configured to extract the first feature from the cell image; a second neural network configured to extract the second feature from the cell image ([0044], employing a known supervised classification algorithm for numerical vectors, such as linear discriminant analysis, neural network, and support vector machine: the input image I is inputted to a classification apparatus that has undergone a pre-learning process using a previously prepared learning image group with supervised labels attached thereto or the feature vector of each of the learning images is stored in advance in the storage unit 13).
NAKAMURA does not explicitly disclose a fully connected layer configured to determine the at least one of the cell types, the culturing conditions, and the culturing times corresponding to the input cell image, based on the extracted first feature and second feature; and wherein the culturing conditions include at least one of: a culturing condition comprising a medium for maintaining cell characteristics, a culturing condition comprising a medium for inducing cell differentiation, and a culturing condition from which the medium for maintaining cell characteristics is removed
Bharti teaches a fully connected layer configured to determine the at least one of the cell types, the culturing conditions, and the culturing times corresponding to the input cell image, based on the extracted first feature and second feature ([0069] FIGS. 2A and 2B, a selected machine learning framework and a deep neural network framework that might be used individually or together for predicting functions, identity, disease state and health of cells and their derivatives; [0070], FIG. 2A, the machine learning predictive model 204 is trained to correlate the input data 202 [measurements representative of at least one physiological, molecular, cellular, and/or biochemical parameter of a plurality of stem cells or a plurality of stem cell derived cell type] through data driven statistical analysis approaches to generate a new interpretable model; [0074] FIG. 2B, a deep neural network model 212 is trained using a training input array 208 that includes labeled and/or defined images and/or data).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of culturing conditions based on the extracted first feature and second feature as taught by Bharti ([0070]) into the trained machine model system of NAKAMURA in order to eliminate requirement for trained user, increasing throughput and reducing predicting cost and time consumption to remove local background fluorescence and human bias and error and determining a test image and combining output of processing by the trained machine model associated with each of the tiles (Bharti, [0116]).
NAKAMURA & Bharti do not explicitly disclose wherein the culturing conditions include at least one of: a culturing condition comprising a medium for maintaining cell characteristics, a culturing condition comprising a medium for inducing cell differentiation, and a culturing condition from which the medium for maintaining cell characteristics is removed.
CHEN teaches wherein the culturing conditions include at least one of: a culturing condition comprising a medium for maintaining cell characteristics, a culturing condition comprising a medium for inducing cell differentiation, and a culturing condition from which the medium for maintaining cell characteristics is removed ([0033], FIG. 4, culture images of mesenchymal stem cells (MSC) with growth characteristic of mesenchymal stem cells is that they attach the bottom surface of the petri dish 12 and grow along the surface flatly, such that the growth curve of mesenchymal stem cells is proportional to the cell areas, and the cell growth status can be understood by analyzing the culture images).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of culturing condition as taught by CHEN ([0033]) into the trained machine model system of NAKAMURA in order to provide cell counting and culture interpretation that automatically estimate the ratio of the area occupied by cells, and it can timely remind users to replace the culture medium or to harvest the cells at the best timing, such that the cell harvest efficiency is improved and the requirement of advanced labor is reduced (CHEN, [0016]).
Regarding Claim 2, NAKAMURA in view of Bharti and CHEN discloses the cell discrimination method of claim 1,
NAKAMURA discloses wherein the cell image is captured in at least one of following time zones: 1 hour to 1 hour and 30 minutes, 3 hours to 3 hours and 30minutes, 6 hours to 6 hours and 30 minutes, 12 hours to 12 hours and 30 minutes, and 24 hours to 24 hours and 30 minutes after the cell culturing ( Claim 1, comparing and distinguishing images of cells or cell populations, which are obtained by imaging the cells or cell populations in a culture vessel at predetermined time intervals).
Regarding Claim 3, NAKAMURA in view of Bharti and CHEN discloses the cell discrimination method of claim 1,
NAKAMURA discloses wherein the first neural network is implemented as a shallow-structured convolution neural network composed of one convolution layer and one pooling layer, wherein the second neural network is implemented as a deep-structured convolution neural network composed of four convolution layers (the first feature extraction unit for extracting a feature amount of each of a plurality of block images; and the second feature extraction unit for calculating a feature vector of a block region (see paragraph [0010]).
Regarding Claim 4, NAKAMURA in view of Bharti and CHEN discloses the cell discrimination method of claim 1,
NAKAMURA discloses wherein the various cell types include animal cells and human cells including at least one of stem cell lines, human skin fibroblast cell lines, epithelial cell lines, and immune cell lines, wherein the various culturing conditions are different from each other for each cell type (the cell mass disclosed in Dl, which is cultured in a cell culture device (see claim 4).
Regarding Claim 5, NAKAMURA in view of Bharti and CHEN discloses the cell discrimination method of claim 4,
NAKAMURA discloses wherein the stem cell line includes at least one of mouse embryonic stem cell, mouse induced pluripotent stem cell, human embryonic stem cell, human induced pluripotent stem cell, human neural stem cell, human hair follicle stem cell, human mesenchymal stem cell, and human fibroblast cell, wherein the epithelial cell line includes human skin keratinocyte (HaCaT), wherein the immune cell line includes a T cell, wherein the human neural stem cells includes a human somatic cell-derived cell converted neural stem cell or a human brain-derived neural stem cell (cultured in a cell culture device (see claim 4).
Regarding Claim 2, NAKAMURA in view of Bharti and CHEN discloses the cell discrimination method of claim 1,
NAKAMURA discloses wherein the discriminating model includes a data set for image learning, wherein the data set includes each of a set of 1,000 training images, a set of 1,500 training images, and a set of 2,000 training images, and a set of 800 validation images, and a set of 100 test images (using a learning image group with a teacher label prepared in advance (see paragraph [0025]).
Regarding Claims 9-13 & 15-16, Apparatus claims 9-13 & 15-16 of using the corresponding method claimed in claims 1-5 & 7, and the rejections of which are incorporated herein for the same reasons as used above.
Allowable Subject Matter
Claims 6, 8 & 14 are objected to as being dependent upon a rejected base claims, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
7
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
THIS ACTION IS MADE FINAL. 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 Samuel D Fereja whose telephone number is (469)295-9243. The examiner can normally be reached 8AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DAVID CZEKAJ can be reached at (571) 272-7327. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAMUEL D FEREJA/Primary Examiner, Art Unit 2487
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