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 .
Information Disclosure Statement
The IDS filed 9/19/2025 and 7/17/2025 have been considered by the Examiner.
Priority
Priority to a US application has not been filed. The instant filing date of 12/23/2021 is acknowledged for search and consideration.
Status of Claims
Amendments to the claims filed 9/08/2025 are acknowledged.
Claims 17-20 are cancelled. Claims 21-24 are new.
Claims 1-16 and 21-24 are under consideration.
Claim Rejections - 35 USC § 101
The instant rejection is maintained from the Office Action filed 6/17/2025 and modified in view of amendments filed 9/8/2025. The rejection over claim 16 for being drawn to a signal or program per se and also maintained from the previous Office Action.
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 1-16 and 21-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-7 and 21-24 are to a system comprising a processor, so a machine.
Claims 8-15 are to a method, so a process.
Claims 16 is to a computer product comprising a computer readable storage medium. A review of the specification does not show a definition of computer readable media that excludes an embodiment that is information in a signal. As such, an embodiment of the claims read on non-statutory subject matter (In re Nuijten 84 USPQ2d 1495 (2007)). The applicants may overcome the rejection by 1) amendment of the claims to be limited to physical forms of computer readable storage media described in the specification or 2) by amending the claimed subject matter to be limited to “non-transitory”, see the notice regarding Computer Readable Media (1351 OG 212 (23 February 2010)).
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. training a cr-GAN model with a first data set of a first pharmaceutical and a second data set of a second pharmaceutical.
This step reads on an abstract idea because the cr-GAN is recited at a high level on generality. The step reads on training two neural networks, which comprise the structure of a typical GAN. Training neural networks with a first and second data set representing a pharmaceutical can be accomplished with mathematics alone.
2. conditioning said cr-GAN model with at least one condition variable wherein said at least one conditional variable includes a therapeutic target for a specific condition.
This step reads on math. The step broadly recites conditioning a cr-GRAN, which according to the specification means that the GAN generator is conditioned on auxiliary observed data where the generative model is formulated according to equations in par. 0022-0043. While the set does not recite math, according to the specification, the step encompasses performing only math. The limitation describing the variable describes the meaning attributed to the variable as represent a “therapeutic target” which is a mathematical relationship.
3. generating, with said cr-GAN, patient parameters such that said patient parameters replicate a set of patient data of a patient.
Generating patient parameters such that said patient parameters replicate a set of patient data of a patient can be performed by the human mind and is therefore an abstract idea. The cr-GAN is recited at a high level of generality. See USPTO Example 47 Anomaly Detection (Claim 2, step (e) analysis). The step provides nothing more than mere instructions to implement an abstract idea on a generic computer, as described in MPEP 2106.05(f). The limitation reciting that patient parameters replicate a set of patient data of a patient is an intended use of the cr-GAN but does not further limit or describe the structure of the cr-GAN.
5. calculating dosage data with said patient parameters based on said therapeutic target.
This step reads on a process that can be performed with math or the human mind and is therefore an abstract idea.
Dependent claims 2-7, 9-15 and 21-24 further describe the abstract idea performed and are therefore also drawn to the abstract idea.
The recited pharmacokinetic model and pharmacology model read on mathematical concepts.
Training the second cr-GAN with a distribution of efficacy measures and tumor sizes reads on mathematical concepts. A distribution and tumor sizes are mathematical concepts. Here the mathematical concepts are applied to mathematics on a tangential computer. See MPEP 2106.05(f).
Claims 21-24 recite abstract idea steps performed with “said cr-GAN” such that the cr-GAN is recited at a high level of generality. See USPTO Example 47 Anomaly Detection (Claim 2, step (e) analysis). The step provides nothing more than mere instructions to implement an abstract idea on a generic computer, as described in MPEP 2106.05(f).
Step 2A Prong Two: Consideration of Practical Application
The claimed process results in determining dosage data, and displaying the dosage data which includes a recommended dosage. The claims do not recite additional elements that integrate the abstract idea into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
1. a memory
2. a processor
3. a processor configured to train a cr-GAN
4. displaying dosage data to a user.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because training a cr-GAN with a processor is routine, conventional and well understood. The computer and cr-GAN are recited at a high level of generality and amount to the terms “apply it” as described in MPEP 2106.05(g).
Displaying dosage data to a user is extra solution activity as described in MPEP 2106.05(g).
Other elements of the method include the memory and processor which is a recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 9/8/2025 have been fully considered but they are not persuasive.
Applicant’s arguments are directed to the newly introduced limitations reciting “conditioning said cr-GAN with at least one conditional variable, wherein said at least one conditional variable includes a therapeutic target for a specific condition.”
In response, conditioning a generative adversarial network with a conditional variable, without further limitations about how the GAN is conditioned or how the structure of the GAN is effected, reads on performing mathematics. Conditioning a GAN broadly reads on providing information to a neural network of the GAN to guide its output. This reads on the abstract idea of providing data to a mathematical relationship. The new limitation reciting “wherein said at least one conditional variable includes a therapeutic target for a specific condition” is also an abstract idea describing the conditional variable as representing a therapeutic target.
Applicant’s arguments are directed to the new limitation reciting displaying dosage data which includes recommended dosage.
In response, displaying the results of implemented calculations by way of abstract ideas is extra solution activity as discussed in MPEP 2106.05(g).
Applicants are further guided to the USPTO 2024 Subject Matter Eligibility Examples in the Guidance on Artificial Intelligence: https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf s
Claim Rejections - 35 USC § 112-2nd paragraph
The rejection of Claims 1-20 under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph are withdrawn in view of Applicant’s amendments filed 9/08/2025.
Claim Rejections - 35 USC § 103
The following rejection is necessitated by Applicant’s amendments filed 9/08/2025.
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 1-16 and 21-24 are rejected under 35 U.S.C. 103(a) as being unpatentable over Tang et al. (US 6,658,396) in view of Peck et al. (US 2022/0142480) and further in view of Ge et al. ("Conditional generative adversarial networks for individualized treatment effect estimation and treatment selection." Frontiers in genetics vol. 11 (2020), 585804)
Tang et al. teach training a neural network based on a first and second drug of interest (Figure 11b and 11d) where the optimal dosage of the first drug (drug #1) and second drug (drug #2) for a Particular Patient is predicted (col. 9, lines 41-52); Tang et al. teach training the neural network with historical medical records of persons administered the two drugs (col. 9, lines 54-63)(i.e. training a model with a first and second data set of a first and second pharmaceutical), as in claims 1, 8 and 16.
Tang et al. teach training the model based on individual patient characteristics specific for drug #1 and drug #2 (Figure 11b) and training the neural network based on Particular Patient’s diagnostic indications (col. 9, lines 14-24)(i.e. conditioning the model with at last one conditional variable, wherein the conditional variable includes a therapeutic target for a specific condition), as in claims 1, 8 and 16.
Tang et al. teach predicting drug side effects and drug efficacy (col. 10, lines 8-15) where the side effects include health characteristics during treatment (col. 16, lines 5-9) and efficacy measures symptom levels and pharmacokinetic peptide levels (col. 16, lines 5-9)(i.e. generating with said model patient parameters such that said patient parameters replicate a set of patient data of a patient), as in claims 1, 8 and 16.
Tang et al. teach outputting the predicted optimal dosage for the Particular Patient (col. 9, lines 22-24)(i.e. calculating dosage data with said patient parameters based on said therapeutic target), as in claims 1, 8 and 16.
Tang et al. teach determining pharmacokinetics drugs (col. 2, lines 13-15) based on age, gender, ethnicity, weight, diagnosis and diet (i.e. generating a first data set with a pharmacokinetic model) and modeling pharmacokinetics for individuals (i.e. quantitative systems)(col. 2-3, connecting), as in claims 2-3 and 9-10.
Tang et al. teach determining and interaction neural network and drug interaction in a Particular Patient (col. 9, line 40 to col. 10, line 6) and determining drug efficacy and side effects (col. 10, lines 6-24)(i.e. interaction between pharmaceutical and patient), as in claims 4 and 11.
Tang et al. teach that the model recognizes patient characteristics including drug reaction associations and sensitivity of both effective responses and undesired side effects to drug dosage levels (col. 20, lines 18-26)(i.e. which suggests interaction describes absorption and metabolism), as in claim 12.
Tang et al. teach calculating recommended optimal patient dosage for patient characteristics (Abstract)(i.e. recommended dosage was calculated to achieve therapeutic target), as in claim 21.
Tang et al. teach determining optimal dosage of a first drug and optimal dosage of a second drug (col. 4, lines 57-65 and col. 9, lines 41-53)(i.e. determining a combination therapy); Tang et al. teach that using concentrations is known (col. 3, lines 17-20), as in claim 22.
Tang et al. teach training a neural network with pharmacodynamic parameters of a particular drug to predict optimal dosage of the drug or drug combinations (col. 8, lines 39-47 and lines 61-62)(i.e. sampling pharmacokinetic model parameters wherein parameters are conditioned by pharmaceutical identity), as in claim 23.
Tang et al. teach effect of one drug on another determined with a neural network (col. 7-8, connecting) and accounting for multiple drug interactions (col. 26, lines 29-32)(i.e. combination therapy); Isobole contour plots are well known to those of ordinary skill as visual representations of drug interactions showing combinations of two drugs (A and B) that produce an effect with axes representing individual drug doses. It would be obvious to one or ordinary skill to combine the teachings of Tang et al. with what is well known, the plotting of isobole contour plots, to arrive at accessing a desired isobole by a recommended combination of drugs, as in claim 24
Tang et al. do not specifically teach using a trained and conditioned cr-GAN model, as in claims 1, 8, 16 and 22-23.
Tang et al. does not teach a cr-GAN model where the conditional variable and therapeutic target is a tumor, as in claims 6 and 14.
Tang et al. does not teach training a second cr-GAN with distribution of efficacy measures and tumor sizes from the dosage data to identify a recommended dosage.
Peck et al. teach predicting pharmacokinetics of a therapeutic radiopharmaceutical on a subject (Abstract) using Generative Adversarial Networks (GANs); GANs are used to predict the uptake to radiopharmaceuticals (i.e. training a GAN with pharmaceuticals and conditioning a GAN with a conditional variable), as in claims 1, 8 and 16.
Peck et al. teach that radiopharmaceutical therapy involves the targeted delivery of radiation to tumor cells (par. 0003); Peck teach tracking tumor change by measuring the relative change of the tumors and using the relationship between change and patient characteristics to determine adjustment to the dosage (par. 0008); Peck teaches machine learning engine (MLE) to track tumor size over time to adjust treatment (Figure 13 and par. 0078-79)(i.e. wherein the conditional variable includes an observed tumor size change), as in claims 5 and 13.
Regarding claims 6 and 14, Peck et al. teach dosimetry reports correlating dose, absorbed radiation dose and clinical results on tumors (i.e. efficacies)(par. 0003); Isobole contour plots are well known to those of ordinary skill as visual representations of drug interactions showing combinations of two drugs (A and B) that produce an effect with axes representing individual drug doses. It would be obvious to one or ordinary skill to combine the teachings of Peck et al. with what is well known, the plotting of Isobole contour plots to arrive at efficacies of two pharmaceuticals plotted on an isobole plot.
Peck et al. teach predicting efficacy of radiopharmaceuticals over future time intervals using machine learning by predicting SPECT scans (par. 0005); Peck et al. teach tumor tracking and dosage recommendations (par. 0008 and 0038) resulting from training a machine learning model with SPECT scans to arrive at predicted SPECT scans (par. 0035), as in claims 7 and 15.
Neither Tang et al. or Peck et al. teach cr-GANs (conditional generative adversarial networks) as recited by claims 1, 8, 16 and 22-23.
However, Ge et al. teach using conditional generative adversarial networks for inference of individual treatment effects (Abstract); Ge et al. teach selection of biomarkers for predicting the best treatment for each patient (Abstract). Ge et al. teach conditional generative adversarial networks for generative potential outcomes of individualized treatment (page 11-12).
It would be obvious to one of ordinary skill in the art at the time the invention was made to have combined the drug dosage prediction with neural networks as taught by Tang et al. with applications for radiopharmaceutical dosage prediction with GANs as taught by Peck et al. Peck provide motivation by teaching that their process allows for dynamic treatment of a patient with radiopharmaceuticals (Abstract) and GANs are useful for generating new realistic data samples (par. 0093). One of skill in the art would have had a reasonable expectation of success at combining Tang et al. with Peck et al. because GANS are comprised from neural networks and both Tang et al. and Peck et al. teach therapeutic dosage prediction.
It would be obvious to one of ordinary skill in the art at the time the invention was made to have combined the radiopharmaceutical dosage prediction with neural network based GANs as made obvious by Tang et al. in view of Peck et al. with the implementation of conditional GANs as taught by Ge et al. Ge et al. provide motivation by teaching that their conditional GANs allow estimation of individualized effects of any type of treatments including binary or continuous treatments (Abstract). Furthermore, substituting the neural network of Tang et al or GAN for the conditional GAN of Ge et al. would be an obvious substitution of one known equivalent element for another to obtain predictable results. Tang et al., Peck et al. and Ge et al. all teach treatment prediction using neural networked based models and therefore substitution with the conditional GAN of Ge et al. would be predictable with a reasonable expectation of success.
Relevant Prior Art
Kearney, Vasant, et al. "DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation." Scientific reports 10.1 (2020): 11073.
Murakami, Yu, et al. "Fully automated dose prediction using generative adversarial networks in prostate cancer patients." PloS one 15.5 (2020): e0232697.
Hibbard US 2019/0333623
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
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Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna Skibinsky whose telephone number is (571) 272-4373. The examiner can normally be reached on 12 pm - 8:30 pm.
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/Anna Skibinsky/
Primary Examiner, AU 1635
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