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 .
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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 23-42 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6, 9-12, and 21 of Bharti et al. (USPN 12,020,494). Although the claims at issue are not identical, they- are not patentably distinct from each other.
For example, regarding claim 23, Bharti discloses: A method for non-invasively predicting retinal pigment epithelial (RPE) maturation of one or more cells and cell derivatives (see Bharti claim 1, “A method for non-invasively predicting retinal pigment epithelial (RPE) maturation of one or more cells and cell derivatives”), the method comprising:
obtaining a trained machine learning model previously trained using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells (see Bharti claim 1, “obtaining a trained machine learning model previously trained using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells”);
receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired noninvasively (see Bharti claim 1, “receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired noninvasively”);
providing the at least one test cell image to the trained machine learning model (see Bharti claim 1, “providing the at least one test cell image to the trained machine learning model”);
predicting, using machine learning based on the trained machine learning model, transepithelial resistance of the at least one test cell (see Bharti claim 1, “predicting, using machine learning based on the trained machine learning model, transepithelial resistance of the at least one test cell”); and
generating, by the trained machine learning model, release criteria based on the predicted transepithelial resistance of the at least one test cell (see Bharti claim 1, “generating, by the trained machine learning model, release criteria […] based on the predicted transepithelial resistance of the at least one test cell”).
Subject matter recited in claims 24-42 can also be found in Bharti claims 1-6, 9-12, and 21.
Allowable Subject Matter
No prior art of record discloses the subject matter recited in claims 23-42, however, these claims are rejected on the ground of nonstatutory double patenting as being unpatentable over Bharti. These claims would be allowable upon approval of a terminal disclaimer. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 23, Fan et al. (“A machine learning assisted, label-free, non-invasive approach for somatic reprogramming in induced pluripotent stem cell colony formation detection and prediction”) discloses:
obtaining a trained machine learning model previously trained using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells (see 4th paragraph on p2 (“Here, we report a label-free […]”) and fig 1, a model is trained with training iPSC (induced pluripotent stem cell) images to recognize their corresponding iPSC characteristics);
receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired noninvasively (see 4th paragraph on p2, a test iPSC image is a bright field microscopic image of iPSC colonies acquired noninvasively);
providing the at least one test cell image to the trained machine learning model (see 4th paragraph on p2, providing the test iPSC image to the trained model);
predicting, using machine learning based on the trained machine learning model, [characteristics] of the at least one test cell (see 4th paragraph on p2 and 2nd paragraph on p4 (“First, an individual colony was […]”), the model determines iPSC characteristics of the iPSC colonies in the test iPSC image, such as texture, the growth phase, and maturation time window); and
generating, by the trained machine learning model, release criteria based on the predicted [characteristics] of the at least one test cell (see 1st paragraph on p1 (“Stem cells are a kind of self-replenishing […]”) and 4th paragraph on p2, the model generating the best selection time window for the iPSC colonies based on the iPSC characteristics, to be used in cell replacement therapy in clinical applications).
However, Fan does not disclose transepithelial resistance. Similar reasons apply to claim 33.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SJ PARK whose telephone number is (571)270-3569. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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/SJ Park/Primary Examiner, Art Unit 2675