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
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 October 3, 2025 has been entered.
Status of the Claims
Claims 7-8, 10-11, 14-16, and 28-30 are under examination.
Claims 1-4, 17-19, and 21-27 are withdrawn.
Claim Rejections - 35 USC § 103
2. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 7- 8, 10-11, 14-16 and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Donaldson et al. (Annals of Clinical and Library Science, 2017, vol 47, pages 452-456; IDS filed April 30, 2021) as evidenced by Padmaja et al. (J. Big Data, 2016, 3:24, pages1-15) in view of Meshkin (US 20180137235).
Regarding claims 7, 29, and 30, Donaldson et al. teach a method and system that includes the analyzing a sample of a subject to obtain an SNP profile of specified allelic variants (page 455). In addition, Donaldson et al. discloses building a model that generates a weighted score to predict opioid addiction risk utilizing SNP polymorphism data from 37 patients with opioid/heroin addiction and 30 age and gender matched controls using the TreeNet software (abstract; page 453, right column, last paragraph to page 455, right column, paragraph 4). The weighted score model developed using the TreeNet software is a machine learning ensemble model, as evidenced by Padmaja et al. (J Big Data, 2016, 3:24, pages 1-15). Padmaja et al. discloses that the TreeNet software from Salford Systems (page 7, last para.) uses a stochastic gradient bosting technique that builds several hundred or thousand small trees that each depict a small portion of the overall model and then adds up the individual contributions of each of these small trees (page 9, paragraph 1 to page 10, paragraph 3), which is an ensemble machine learning method as it combines at least two learning models (see the broadest reasonable interpretation of the machine learning ensemble model discussed above in regards to the rejection under 35 U.S.C. 101). In addition, stochastic gradient boosting is known to be a type of ensemble machine learning method. Donaldson et al. also disclose that their prediction algorithm can be used to determine if a patient to start a pain medication or not start one (pg. 456, col. 1, paras. 1-2). Furthermore, the use of software would require a processor and non-transitory machine-readable storage.
While Donaldson et al. teach, “Patients would be able to start a pain medication or not start one, guided by their own genetic composition.” (page 456, column 1), Donaldson et al. does not explicitly teach delivering a low-opioid, non-opioid, or opioid treatment regimen to a subject.
Regarding claims 7, 28, and 29, Meshkin teaches delivering a low-opioid, non-opioid, or opioid treatment regimen to a subject based on a score as well as adjusting the dosage of an opioid (i.e. reducing dosage of hydrocodone, oxycodone, oxymorphone, morphine, codeine, or fentanyl) (paragraphs [0024] and [0054]).
Regarding claim 8, Donaldson et al. disclose where the risk of the opioid use disorder is a value between 0 and 1 (page 455, figure 1); where the machine learning ensemble model includes a pre-determined threshold ( pages 455 and 456); where if the score is greater than the pre-determined threshold indicates the subject has a higher risk for developing an opioid use disorder (page 455; page 456); and where if the score is lower than the pre-determined threshold indicates that the subject has a lower risk of developing an opioid use disorder (page 455; page 456).
Claims 10 and 11, are drawn to a contingent limitation. Thus, the recited elements in 10 and 11 are not required to be met within the metes and bounds of the broadest reasonable interpretation.
Regarding claims 14 and 15, Donaldson et al. teach the SNPs of the listed genes (page 453, Table 1).
Regarding claim 16, Meshkin teaches where the sets of data include clinical data (paragraphs [0015], [0050], [0054] and [0064]).
It would have been obvious to one of ordinary skill in the art, at the time of filing, to combine the methods of Meshkin and Donaldson et al. Meshkin teaches a method of determining opioid dependency based on SNP profiles (abstract). One of ordinary skill in the art would have been motivated to use the machine learning method of determining opioid dependency, such as the one taught by Donaldson et al., to aid in determining patients with a lower risk for opioid addiction (Donaldson et al. abstract) with the method of Meshkin. Furthermore, one of ordinary skill in the art would have had a reasonable expectation of success, because the one of ordinary skill in the art would need only to incorporate the machine learning model into the data analysis of Meshkin.
3. Applicants have responded to this rejection by stating that Donaldson et al. does not teach delivering a low-opioid, non-opioid, or opioid treatment regimen to a subject. However, Meshkin does teach delivering such a treatment as stated above (paragraphs [0024] and [0054]). Thus, the cited prior art does teach this limitation.
Applicants have responded to this rejection by stating that the Meshkin does not teach entering clinical data into a machine learning model. Meshkin teach that clinical data demonstrates a strong association with opioid abuse (paragraph [0064]). In light of this, Meshkin teaches using both clinical data with genetic data to determine the risk of opioid use disorder (paragraph [0085]). Given this knowledge, one ordinary skill in the art would seek to incorporate clinical data with genetic data in the model of Donaldson et al. to more accurately determine the risk of an opioid use disorder. It is the combination of Donaldson et al.’s machine learning model with the data of Meshkin that teaches this limitation. While each reference does not teach this limitation singly, the combination of the references teaches the instant limitation.
This rejection is maintained and modified as necessitated by amendment.
Withdrawn Rejections
4. Applicant’s arguments and amendments, filed October 3, 2025, with respect to the rejection made under 35 U.S.C. §101 have been fully considered and are persuasive. The instant claims have been amended to include delivering a low-opioid, non-opioid treatment based on the score. This provide a particular treatment that integrates the judicial exception into a practical application. This rejection has been withdrawn.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JERRY LIN whose telephone number is (571)272-2561. The examiner can normally be reached T-F 7am-5pm.
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, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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.
/JERRY LIN/Primary Examiner, Art Unit 1685