SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES FOR GENERALIZED DISEASE DETECTION

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. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 1-7 and 9-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7, 9-14, and 16-20 of U.S. Patent No. 12,217,483 to Dogdas et al. Although the claims at issue are not identical, they are not patentably distinct from each other because Dogdas et al. claims the claimed invention as follows: Claim 1 Dogdas et al. 1. A computer-implemented method for processing electronic images, the method comprising: receiving a generalized machine learning model; receiving a plurality of training images, a quantity of training images being insufficient to generate a machine learning model that meets a threshold; receiving a plurality of target specialized attributes each related to a respective training image of the plurality of training images, wherein the plurality of target specialized attributes are based on at least one of drug response information, cancer recurrence prediction information, or toxicity assessment information; generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of training images and respective target specialized attributes, the specialized machine learning model meeting the threshold; receiving a target image corresponding to a target specimen; applying the specialized machine learning model to the target image to determine at least one characteristic of the target image; and outputting the at least one characteristic of the target image. 1. A computer-implemented method for processing electronic images, the method comprising: receiving a generalized machine learning model; receiving a plurality of training images, a quantity of training images being insufficient to generate a machine learning model that meets a threshold; receiving a plurality of target specialized attributes each related to a respective training image of the plurality of training images (i.e., claim 1 at Col. 20 lines 37-45); wherein the plurality of target specialized attributes are based on at least one of drug response information, cancer recurrence prediction information, or toxicity assessment information (i.e., claim 7 at Col. 21 lines 19-23) generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of training images and respective target specialized attributes, the specialized machine learning model meeting the threshold …; receiving a target image corresponding to a target specimen; applying the specialized machine learning model to the target image to determine at least one characteristic of the target image; and outputting the at least one characteristic of the target image (i.e., claim 1 at Col. 20 lines 46-60). Claim 2 Dogdas et al. 2. The computer-implemented method of claim 1, further comprising: determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target image; and outputting the prediction of the specimen type of the target specimen. 2. The computer-implemented method of claim 1, further comprising: determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target image; and outputting the prediction of the specimen type of the target specimen (i.e., claim 2 at Col. 20 lines 61-67. Claim 3 Dogdas et al. 3. The computer-implemented method of claim 1, wherein the plurality of target specialized attributes are one or more biomarkers present within each respective training image. 3. The computer-implemented method of claim 1, wherein the plurality of target specialized attributes are one or more biomarkers present within each respective training image (i.e., claim 3 at Col. 21 lines 1-4). Claim 4 Dogdas et al. 4. The computer-implemented method of claim 1, wherein the generalized machine learning model comprises a plurality of layers and modifying the generalized machine learning model further comprises modifying one or more outer layers of the generalized machine learning model. 4. The computer-implemented method of claim 1, wherein the generalized machine learning model comprises a plurality of layers and modifying the generalized machine learning model further comprises modifying one or more outer layers of the generalized machine learning model (i.e., claim 4 at Col. 21 lines 5-9). Claim 5 Dogdas et al. 5. The computer-implemented method of claim 1, wherein modifying the generalized machine learning model further comprises removing an output layer of the generalized machine learning model. 5. The computer-implemented method of claim 1, wherein modifying the generalized machine learning model further comprises removing an output layer of the generalized machine learning model (i.e., claim 5 at Col. 21 lines 10-13). Claim 6 Dogdas et al. 6. The computer-implemented method of claim 1, wherein the plurality of target specialized attributes are one or more indications of characteristic outputs selected from a disease presence, staging variable presence, drug response, toxicity, or cancer classification. 6. The computer-implemented method of claim 1, wherein the plurality of target specialized attributes are one or more indications of characteristic outputs selected from a disease presence, staging variable presence, drug response, toxicity, or cancer classification (i.e., claim 6 at Col. 21 lines 14-18). Claim 7 Dogdas et al. 7. The computer-implemented method of claim 1, wherein the specialized machine learning model is generated in accordance with a large-margin scheme built over one or more features of the generalized machine learning model, wherein the large-margin scheme reduces generalization. …the specialized machine learning model being generated in accordance with a large-margin scheme built over one or more features of the generalized machine learning model (i.e., claim 1 at Col. 20 lines 50-53) …wherein the large-margin scheme reduces generalization (i.e., claim 8 at Col. 21 lines 24-25) Claim 9 Dogdas et al. 9. The computer-implemented method of claim 1, wherein each of the training images are generated based on a same category of pathology specimens and wherein a category of pathology specimens is selected from histology, cytology, immunohistochemistry, or a combination thereof. 9. The computer-implemented method of claim 1, wherein each of the training images are generated based on a same category of pathology specimens and wherein a category of pathology specimens is selected from histology, cytology, immunohistochemistry, or a combination thereof (i.e., claim 9 at Col. 21 lines 26-30). Claim 10 Dogdas et al. 10. The computer-implemented method of claim 1, wherein modifying a generalized machine learning model further comprises adjusting the generalized machine learning model to have outputs based on the target specialized attributes. 10. The computer-implemented method of claim 1, wherein modifying a generalized machine learning model further comprises adjusting the generalized machine learning model to have outputs based on the target specialized attributes (i.e., claim 10 at Col. 21 lines 31-35). Claim 11 Dogdas et al. 11. The computer-implemented method of claim 1, wherein generalized machine learning model is generated by processing a plurality of first training images to predict at least one cancer characteristic. 11. The computer-implemented method of claim 1, wherein generalized machine learning model is generated by processing a plurality of first training images to predict at least one cancer characteristic (i.e., claim 11 at Col. 21 lines 36-39). Claim 12 Dogdas et al. 12. The computer-implemented method of claim 1, wherein the at least one characteristic of the target image is one of a cancer diagnosis, a tumor characterization, or biomarker detection. 12. The computer-implemented method of claim 1, wherein the at least one characteristic of the target image is one of a cancer diagnosis, a tumor characterization, or biomarker detection (i.e., claim 12 at Col. 21 lines 40-43). Claim 13 Dogdas et al. 13. A system comprising: at least one memory storing instructions; and at least one processor executing the instructions to perform operations comprising: receiving a generalized machine learning model; receiving a plurality of training images, a quantity of training images being insufficient to generate a machine learning model that meets a threshold; receiving a plurality of target specialized attributes each related to a respective training image of the plurality of training images, wherein the plurality of target specialized attributes are based on at least one of drug response information, cancer recurrence prediction information, or toxicity assessment information; generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of training images and the respective target specialized attributes, the specialized machine learning model meeting the threshold; receiving a target image corresponding to a target specimen; applying the specialized machine learning model to the target image to determine at least one characteristic of the target image; and outputting the at least one characteristic of the target image. 13. A system comprising: at least one memory storing instructions; and at least one processor executing the instructions to perform operations comprising: receiving a generalized machine learning model; receiving a plurality of training images, a quantity of training images being insufficient to generate a machine learning model that meets a threshold; receiving a plurality of target specialized attributes each related to a respective training image of the plurality of training images (i.e., claim 13 at Col. 21 lines 44-54); wherein the plurality of target specialized attributes are based on at least one of drug response information, cancer recurrence prediction information, or toxicity assessment information (i.e., claim 1 at Col. 20 lines 37-67 and claim 7 at Col. 21 lines 19-23) generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of training images and the respective target specialized attributes, the specialized machine learning model meeting the threshold …; receiving a target image corresponding to a target specimen; applying the specialized machine learning model to the target image to determine at least one characteristic of the target image; and outputting the at least one characteristic of the target image (i.e., claim 13 at Col. 21 line 55 through Col. 22 line 7). claim 14 Dogdas et al. 14. The system of claim 13, the operations further comprising: determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target image; and outputting the prediction of the specimen type of the target specimen. 14. The system of claim 13, the operations further comprising: determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target image; and outputting the prediction of the specimen type of the target specimen (i.e., claim 14 at Col. 22 lines 8-14). Claim 15 Dogdas et al. 15. The system of claim 13, wherein the specialized machine learning model is generated in accordance with a large-margin scheme built over one or more features of the generalized machine learning model, wherein the large-margin scheme reduces generalization. … the specialized machine learning model being generated in accordance with large-margin scheme built over one or more features of the generalized machine learning model (i.e., claim 13 at Col. 21 lines 59-63) … wherein the large-margin scheme reduces generalization (i.e., claim 15 at Col. 22 lines 15-16). Claim 16 Dogdas et al. 16. The system of claim 13, wherein the generalized machine learning model comprises a plurality of layers and modifying the generalized machine learning model further comprises modifying one or more outer layers of the generalized machine learning model. 16. The system of claim 13, wherein the generalized machine learning model comprises a plurality of layers and modifying the generalized machine learning model further comprises modifying one or more outer layers of the generalized machine learning model (i.e., claim 16 at Col. 22 lines 17-21). Claim 17 Dogdas et al. 17. The system of claim 13, wherein modifying the generalized machine learning model further comprises removing an output layer of the generalized machine learning model. 17. The system of claim 13, wherein modifying the generalized machine learning model further comprises removing an output layer of the generalized machine learning model (i.e., claim 17 at Col. 22 lines 23-25). Claim 18 Dogdas et al. 18. A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform operations comprising: receiving a generalized machine learning model; receiving a plurality of training images, wherein a quantity of training images being insufficient to generate a machine learning model that meets a threshold; receiving a plurality of target specialized attributes each related to a respective training image of the plurality of training images, wherein the plurality of target specialized attributes are based on at least one of drug response information, cancer recurrence prediction information, or toxicity assessment information; generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of training images and the respective target specialized attributes, the specialized machine learning model meeting the threshold; receiving a target image corresponding to a target specimen; applying the specialized machine learning model to the target image to determine at least one characteristic of the target image; and outputting the at least one characteristic of the target image. 18. A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform operations comprising: receiving a generalized machine learning model; receiving a plurality of training images, wherein a quantity of training images being insufficient to generate a machine learning model that meets a threshold; receiving a plurality of target specialized attributes each related to a respective training image of the plurality of training images (i.e., claim 18 at Col. 22 lines 26-35); wherein the plurality of target specialized attributes are based on at least one of drug response information, cancer recurrence prediction information, or toxicity assessment information (i.e., claim 1 at Col. 20 lines 37-67 and claim 7 at Col. 21 lines 19-23) generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of training images and the respective target specialized attributes, the specialized machine learning model meeting the threshold …; receiving a target image corresponding to a target specimen; applying the specialized machine learning model to the target image to determine at least one characteristic of the target image; and outputting the at least one characteristic of the target image (i.e., claim 18 at Col. 22 lines 36-51). Claim 19 Dogdas et al. 19. The non-transitory computer-readable medium of claim 18, the operations further comprising: determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target image; and outputting the prediction of the specimen type of the target specimen. 19. The non-transitory computer-readable medium of claim 18, the operations further comprising: determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target image; and outputting the prediction of the specimen type of the target specimen (i.e., claim 19 at Col. 22 lines 52-59). Claim 20 Dogdas et al. 20. The non-transitory computer-readable medium of claim 18, wherein the plurality of target specialized attributes are one or more biomarkers present within each respective training image. 20. The non-transitory computer-readable medium of claim 18, wherein the plurality of target specialized attributes are one or more biomarkers present within each respective training image (i.e., claim 20 at Col. 22 lines 59-62). Claim 8 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 7 and 8 of U.S. Patent No. 12,217,483 to Dogdas et al. in view of Medela et al. (“Few Shot Learning in Histopathology Images: Reducing the Need of Labeled Data on Biological Datasets”, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1860-1864). The teachings of Dogdas et al. have been discussed above. As to claim 8, Dogdas et al. does not explicitly claim wherein the generalized machine learning model is optimized to generate a specialized machine learning model by implementing low-shot learning techniques. Medela et al. teaches a generalized machine learning model (i.e., “Deep Siamese Neural Network”, 4.1. Histology Siamese Network, pp. 1861-1862) is optimized to generate a specialized machine learning model (i.e., “shallow classifier”, 4.1. Histology Siamese Network, pp. 1861-1862) by implementing low-shot learning techniques (See for example, “In this work we validate that the use of few shot learning techniques can transfer knowledge from a well defined source domain from Colon tissue into a more generic domain composed by Colon, Lung and Breast tissue by using very few training images”, ABSTRACT, p. 1860; and “This shallow classifier is trained from a reduced number of sample images Xs from the target domain Dt”, 4.1.3. Shallow Classifier, p. 1862). Dogdas et al. and Medela et al. are analogous art because they are from the field of digital image processing for specimen analysis. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Dogdas et al. by incorporating the generalized machine learning model is optimized to generate a specialized machine learning model by implementing low-shot learning techniques, as taught by Medela et al. The suggestion/motivation for doing so would have been to transfer knowledge from a well defined source domain into a more generic domain. Therefore, it would have been obvious to combine Medela et al. with Dogdas et al. to obtain the invention as specified in claim 8. Allowable Subject Matter Claims 1-20 would be allowable upon the filing of a Terminal Disclaimer in order to obviate the nonstatutory double patenting rejection as set forth above. The following is a statement of reasons for the indication of allowable subject matter: the closest prior art made of record fails to disclose, teach, and/or suggest, inter alia, the generation of a specialized machine learning model for image-based specimen classification, by at least, receiving a generalized machine learning model, receiving a plurality of training images, a quantity of training images being insufficient to generate a machine learning model that meets a threshold, receiving a plurality of target specialized attributes each related to a respective training image of the plurality of training images, wherein the plurality of target specialized attributes are based on at least one of drug response information, cancer recurrence prediction information, or toxicity assessment information, generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of training images and respective target specialized attributes, the specialized machine learning model meeting the threshold, receiving a target image corresponding to a target specimen, applying the specialized machine learning model to the target image to determine at least one characteristic of the target image, and outputting the at least one characteristic of the target image, as claimed. 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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. /JOSE M TORRES/Examiner, Art Unit 2664 09/30/2026