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 Claims
Claims 1-20 are pending.
This communication is in response to the communication filed March 18, 2025.
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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 30 of U.S. Patent No. 12,254,987. Although the claims at issue are not identical, they are not patentably distinct from each other because the pending claims recite the limitations of the patented claims. Pending claims 1 and 20 recite the limitations of patented claims 1 and 30. Pending claim 2 maps to patented claim 3. Pending claim 3 maps to patented claim 6. Pending claim 4 maps to patented claim 7. Pending claim 5 maps to patented claim 8. Pending claim 6 maps to pending claim 9. Pending claim 7 maps to patented claims 11 and 12 . Pending claim 8 maps to patented claim 15. Pending claim 9 maps to patented claim 16. Pending claim 10 maps to patented claim 17. Pending claim 11 maps to patented claim 18. Pending claim 12 maps to patented claim 19. Pending claim 13 maps to patented claim 20. Pending claim 14 maps to patented claim 21. Pending claim 15 maps to patented claim 22. Pending claim 16 maps to patented claim 23. Pending claim 17 maps to patented claim 26. Pending claim 18 maps to patented claim 28. Pending claim 19 maps to patented claim 29. The dependent claims are also rejected for being dependent on rejected claims.
Pending claim 1: A method of using at least one hardware processor to train at least one machine learning algorithm being in a prediction model for drug response of a plurality of drugs to a cancer in a human patient diagnosed with the cancer, the method comprising:
Pending claim 30: A system comprising: at least one hardware processor; and at least one memory storing instructions that cause the at least one hardware processor to perform operations comprising:
Patented claim 1: A method of using at least one hardware processor to train at least one machine learning algorithm being in a prediction model for drug response of a plurality of drugs to a cancer in a human patient diagnosed with the cancer, the method comprising:
Patented claim 30: A method of using at least one hardware processor to train at least one machine learning algorithm being in a prediction model for drug response of a plurality of drugs to a cancer in a human patient diagnosed with the cancer, the method comprising:
using at least one cancer drug discovery data set comprising sub-structural features of the plurality of drugs comprising a descriptor associated with a sub-structural feature of a drug from externally sourced specification of the plurality of drugs, cancer biomarker data comprising gene expression data, and externally sourced drug response training dataset associated with the plurality of drugs and the cancer, to train the at least one machine learning algorithm in the prediction model,
using at least one cancer drug discovery data set comprising sub-structural features of a plurality of drugs comprising a descriptor associated with a sub-structural feature of a drug from externally sourced specification of the plurality of drugs, cancer biomarker data comprising gene expression data, and externally sourced drug response training dataset associated with the plurality of drugs and the cancer, to train at least one machine learning algorithm in a drug prediction model,
using at least one cancer drug discovery data set comprising sub-structural features of the plurality of drugs comprising a descriptor from SMILES specification of the plurality of drugs, cancer biomarker data comprising gene expression data, and externally sourced drug response training dataset associated with the plurality of drugs and the cancer, to train the at least one machine learning algorithm in the prediction model,
using at least one cancer drug discovery data set comprising sub-structural features of the plurality of drugs comprising a descriptor from SMILES specification of the plurality of drugs, cancer biomarker data comprising gene expression data, and externally sourced drug response training dataset associated with the plurality of drugs and the cancer, the drug response training dataset comprising data associated with in vitro monotherapy drug screening data sets, in vitro cell line combination data sets, in vivo drugs screening experiments of the plurality of drugs, and clinical study data set, to train the at least one machine learning algorithm comprising a feature selection algorithm to refine the sub-structural features of the plurality of drugs to a smaller subset of drug molecular features and the gene expression data to a smaller subset of gene expression data in the prediction model,
using the prediction model to qualify a subset of drugs or drug combinations from the plurality of drugs in terms of their predicted biological response to a cancerous tissue of the human patient exhibiting gene expressions of a set of cancer biomarkers;
using the prediction model to qualify a subset of drugs or drug combinations from the plurality of drugs in terms of their predicted biological response to a cancerous tissue of a human patient exhibiting gene expressions of a set of cancer biomarkers;
using the prediction model to qualify a subset of drugs or drug combinations from the plurality of drugs in terms of their predicted biological response to a cancerous tissue of the human patient exhibiting gene expressions of a set of cancer biomarkers, wherein the cancerous tissue of the human patient is in the form of tumor fragments and not cancer cell lines;
using the prediction model after training the at least one machine learning algorithm, qualifying a subset of drugs or drug combinations from the plurality of drugs in terms of their predicted biological response to a cancerous tissue of the human patient exhibiting gene expressions of a set of cancer biomarkers, wherein the cancerous tissue of the human patient is in the form of tumor fragments and not cancer cell lines;
implanting, in parallel, the cancerous tissue of the human patient exhibiting the gene expressions of the set of cancer biomarkers into multiple immune-deficient mice each subsequently administered a treatment with one of the subset of drugs or drug combinations; and
implanting, in parallel, the cancerous tissue of the human patient exhibiting the gene expressions of the set of cancer biomarkers into multiple immune-deficient mice each subsequently administered a treatment with one of the subset of drugs or drug combinations; and
implanting, in parallel, the cancerous tissue of the human patient exhibiting the gene expressions of the set of cancer biomarkers into multiple immune-deficient mice each subsequently administered a treatment with one of the subset of drugs or drug combinations; and
implanting, in parallel, the cancerous tissue of the human patient exhibiting the gene expressions of the set of cancer biomarkers into multiple immune-deficient mice each subsequently administered a treatment with one of the subset of drugs or drug combinations; and
validating the prediction model by feeding back data corresponding to biological response of the treatment from the multiple immune-deficient mice, to the prediction model to further train the machine learning algorithm.
validating the prediction model by feeding back data corresponding to biological response of the treatment from the multiple immune-deficient mice, to the prediction model to further train the machine learning algorithm.
validating the prediction model by feeding back data corresponding to biological response of the treatment from the multiple immune-deficient mice, to the prediction model to further train the machine learning algorithm.
validating the prediction model by feeding back data corresponding to biological response of the treatment from the multiple immune-deficient mice, to the prediction model to further train the machine learning algorithm.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY M. PATEL whose telephone number is (571)272-6793 and email is jay.patel2@uspto.gov. The examiner can normally be reached on Monday-Friday 8AM-4:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H. Choi can be reached on (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JAY M. PATEL/Primary Examiner, Art Unit 3686
Prosecution Insights