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.
Claim 12 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11, 969, 289. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claim discloses the claimed features as follow:
Claim 12,
a method of identifying a beneficial therapy based on medical predictions, the method comprising:
determining a quantitative prediction of a future medical condition
the quantitative prediction including a probability that the medical condition will occur within a future time range and being based on analysis of medical images of a patient
providing a candidate therapy for the medical condition to the patient
repeating the steps of determining the quantitative prediction of the future medical condition
providing the candidate therapy for a plurality of other patients
identifying the candidate therapy as the beneficial therapy based on repeating the steps of determining the quantitative prediction [see column 24 lines 5-20].
Claim 25 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 21 of U.S. Patent No.11, 969, 289. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claim discloses the claimed features as follow:
Claim 25,
a method of generating a quantitative prediction of a future medical condition, the method comprising:
obtaining a set of medical images
analyzing the medical images using a machine learning system to produce the quantitative prediction
the quantitative prediction including
(a)an estimate of a time until the future medical condition occurs
or
(b) a probability that the future medical condition will occur within a future time range
providing the quantitative prediction to a user [see column 25 lines 14-18, column 26 lines 1-4].
Claim 33 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 22 of U.S. Patent No. 11, 969, 289. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claim discloses the claimed features as follow:
Claim 33,
method of training a medical prediction system, the method comprising:
receiving a plurality of medical images
classifying the images according to views or features included within the images
training a neural network to provide a quantitative prediction regarding a future medical condition
the quantitative prediction including an estimate of a time until the future medical condition occurs
testing the trained neural network, to determine accuracy of the quantitative prediction [see column 26 lines 5-18].
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 12-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a method of identifying a beneficial therapy without significantly more.
Regarding claims 1, 25, 33, The claim(s) recite(s) determining a quantitative prediction of a future medical condition based on analysis of medical image. This judicial exception is not integrated into a practical application because the recitation of "using a learning machine system" merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element "a learning machine system" limits the identified judicial exceptions "determining a
quantitative prediction using the learning machine system" this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because Additional elements (regression algorithm); (ultrasound machine) were both found to be insignificant extra-solution activity in Step 2A,
Prong Two, because they were determined to be insignificant limitations as necessary data gathering
and outputting. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-
solution activity, which do not provide an inventive concept. (Step 2B: NO).
As discussed above, the broadest reasonable interpretation of steps (b) is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
Specifically, step (b) recites "determining a quantitative prediction " which may be practically performed in the human mind using observation, evaluation, judgment, and opinion. Under its broadest reasonable interpretation when read in light of the specification, the "determining" encompasses mental observations or evaluations that are practically performed in the human mind.
The Step of "providing" to generate predictive; encompasses performing evaluation, judgment, and opinion to make a determination about candidate therapy. Under its broadest reasonable interpretation when read in light of the specification, the "selecting" encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
As discussed above, the broadest reasonable interpretation of dividing also encompasses mathematical concepts that can be performed mentally. The limitations providing" and "identifying" are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) ("whether the limitation is significant"). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 12-33 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lapointe et al (Pub. No.: US 2003/0105731)
Regarding claim 12, Lapointe et al disclose a method of identifying a beneficial therapy based on medical predictions, the method comprising:
determining a quantitative prediction of a future medical condition [see 0021, 0025, 0035 and fig 5];
the quantitative prediction including a probability that the medical condition will occur within a future time range and being based on analysis of medical images of a patient [see 0419] by disclosing one of the indicators is indicative of a risk of preterm delivery. The other is an indicator of the absence of such risk and while the output pair A, B provide generally valid indication of risk, a consensus network of trained neural networks provides a higher confidence index. [see 0419];
providing a candidate therapy for the medical condition to the patient [see 0036] by disclosing a method of selecting a course of treatment to reduce the risk of delivery within a selected period of time or preterm by predicting the outcome of various possible treatments [see 0036];
repeating the steps of determining the quantitative prediction of the future medical condition [see 0158] by disclosing This test is preferably administered to women at about 12 weeks gestation and repeated at each perinatal visit (every two to four weeks) until at least week 37, preferably until delivery [see 0158];
providing the candidate therapy for a plurality of other patients [see 0034, 0066, 0107, 0158] by disclosing the systems can be used to select subpopulations of patients for whom a particular drug or therapy is effective. Thus, methods for expanding the indication for a drug or therapy and identifying new drugs and therapies are provided [see 0034];
identifying the candidate therapy as the beneficial therapy based on repeating the steps of determining the quantitative prediction [see 0020, 0036] by disclosing a method of selecting a course of treatment to reduce the risk of delivery within a selected period of time or preterm by predicting the outcome of various possible treatments [see 0036]
Regarding claim 13, Lapointe et al disclose wherein the medical images include ultrasound images [see 0131-0133].
Regarding claim 14, Lapointe et al disclose providing a user with feedback regarding acquisition of the medical images based on a quality (effectiveness, emphasis added) of the quantitative prediction or a subject classification of images already acquired [see 0022].
Regarding claim 15, Lapointe et al disclose balancing (by aligning, emphasis added) the images based on a subject matter classification of views or features of the medical images [see 0131-0133].
Regarding claim 16, Lapointe et al disclose wherein the candidate therapy is only provided to patients having a quantitative prediction of greater than 50%, 66% or 75% that the medical condition will occur within the future time range [see 0020, 0036] by disclosing not only for diagnosing the presence of a condition or disorder, but also the severity of the disorder [see 0036].
As disclosing herein, by determining the severity of the disorder consequently confirms the presence of the medical condition; therefore, once confirmed, the prediction reaches 100% (emphasis added).
Regarding claim 17, Lapointe et al disclose wherein the medical condition is premature birth of a fetus [see abstract].
Regarding claim 18, Lapointe et al disclose balancing quantities (via portioning and processing by different sets of inputs, emphasis added) of the images based on a gestational age of the fetus at birth [see 0131-0135].
Regarding claim 19, Lapointe et al disclose wherein the medical condition is not present or apparent in the medical images at a time the quantitative prediction is determined [see 0036, 0060, 0419] by disclosing one of the indicators is indicative of a risk of preterm delivery. The other is an indicator of the absence of such risk and while the output pair A, B provide generally valid indication of risk, a consensus network of trained neural networks provides a higher confidence index. [see 0419].
Regarding claim 20, Lapointe et al disclose wherein the candidate therapy includes a pharmaceutical or physical treatment [see 0034]
Regarding claim 21, Lapointe et al disclose wherein the candidate therapy is provided to the plurality of patients prior to the patients exhibiting any symptoms of the medical condition [see 0034]
Regarding claim 12, Lapointe et al disclose wherein a time between determining the quantitative prediction and the future time range is at least one month [see 0029, 0080, 0158] by disclosing This test is preferably administered to women at about 12 weeks gestation and repeated at each perinatal visit (every two to four weeks) until at least week 37, preferably until delivery [see 0158]
Regarding claim 23, Lapointe et al disclose wherein the medical condition comprises cancer [see 0106].
Regarding claim 24, Lapointe et al disclose generating the medical images with an ultrasound machine [see 0163].
Regarding claim 25, Lapointe et al disclose a method of generating a quantitative prediction of a future medical condition, the method comprising:
obtaining a set of medical images [see 0131-0133, fig 5];
analyzing the medical images using a machine learning system to produce the quantitative prediction [see 0021, 0025, 0035 and fig 5];
the quantitative prediction including
(a)an estimate of a time until the future medical condition occurs [see 0021, 0025, 0035, 0080, 0082 and fig 5];
or
(b) a probability that the future medical condition will occur within a future time range [see 0325];
providing the quantitative prediction to a user [see 0043].
Regarding claim 26, Lapointe et al disclose wherein a time between determining the quantitative prediction and the future time range is at least one month [see 0029, 0080, 0082, 0158] by disclosing This test is preferably administered to women at about 12 weeks gestation and repeated at each perinatal visit (every two to four weeks) until at least week 37, preferably until delivery [see 0158]
Regarding claim 27, Lapointe et al disclose wherein the future medical condition comprises cancer [see 0106].
Regarding claim 28, Lapointe et al disclose generating the medical images with an ultrasound machine [see 0163].
Regarding claim 29, Lapointe et al disclose wherein the future medical condition comprises an estimated time until birth of a fetus [see 0029, 0080, 0082, 0158] by disclosing This test is preferably administered to women at about 12 weeks gestation and repeated at each perinatal visit (every two to four weeks) until at least week 37, preferably until delivery [see 0158]
Regarding claim 30, Lapointe et al disclose training a machine learning system to estimate a time until birth of the fetus, a gestational age of the fetus at birth, and/or a current gestational age of the fetus [see 0029, 0080, 0082, 0158] by disclosing This test is preferably administered to women at about 12 weeks gestation and repeated at each perinatal visit (every two to four weeks) until at least week 37, preferably until delivery [see 0158]
Regarding claim 31, Lapointe et al disclose wherein the quantitative prediction is further based on clinical data related to a mother of a fetus [see 0095].
Regarding claim 32, Lapointe et al disclose using a quantile regression algorithm or a classification algorithm [see 0052] to provide the prediction as a prediction that a fetus will be born prematurely [see 0080, 0082, 0158].
Regarding claim 33, Lapointe et al disclose method of training a medical prediction system, the method comprising:
receiving a plurality of medical images [see 0131-0133];
classifying the images according to views or features included within the images [see 0052, 0131-0133];
training a neural network to provide a quantitative prediction regarding a future medical condition [see 0021, 0025, 0035 and fig 5];
the quantitative prediction including an estimate of a time until the future medical condition occurs [see 0080, 0082, 0158];
testing the trained neural network, to determine accuracy of the quantitative prediction [see 0419].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOEL F BRUTUS whose telephone number is (571)270-3847. The examiner can normally be reached Mon-Sat, 11:00 AM to 7:00 PM.
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/JOEL F BRUTUS/ Primary Examiner, Art Unit 3797