DETAILED ACTION
Claim Status
Claims 1-25 are rejected.
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 .
Priority
This application does not make any claim of priority. The effective filing date of claims 1-25 is 03/01/2023.
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 03/01/2023, 03/01/2023, 07/23/2024, 12/31/2025, 02/02/2026 are in compliance with the provisions of 37 CFR 1.97 and have been considered in full. A signed copy of list of references cited from each IDS is included with this Office Action.
Drawings
The drawings filed on 03/01/2023 are accepted.
Claim Objections
Specification (¶ 31) objected to because of the following informalities: hyperlinks are included in the specification. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “high-ranking” in claims 1 and 6 is a relative term which renders the claim indefinite. The term “high-ranking” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The “high-ranking perturbation responses” are meant to be used for a combination therapy, but without a standard for what is high-ranking, the limitation for the high-ranking perturbation responses is indefinite. Claims 2-5 and 7-10 inherit the indefiniteness of their parent claims without resolving the issues, and are thus additionally rejected.
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 21-25 are non-statutory as they recite “a system” comprising two data sets and an AI platform, defined by the specification as a computer algorithm (specification ¶ 24). The claims as instantly recited read on data per se, which are a form of stored information and therefore are not proper patentable subject matter because they do not fit within any of the four statutory categories of invention (In re Nuijten, Federal Circuit, 2007).
Claims 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1).
While claims 21-25 do not recite a statutory category of invention (see above), the analysis of these claims under the remaining steps of the subject matter eligibility analysis is continued in the interest of compact prosecution. In the instant application, the claims recite the following limitations that equate to an abstract idea:
1. identifying at least one cell line comprising characteristics similar to the clone of interest and compiling a data set comprising perturbation data for the at least one cell line; inputting the data set into an artificial intelligence (AI) platform, wherein the data set trains the Al platform to predict responses to perturbations included in the perturbation data; entering information relating to the clone of interest into the trained Al platform and obtaining as output a ranking of the predicted perturbation responses of the clone of interest to the perturbations included in the perturbation data, wherein the predicted perturbation responses are ranked from highest perturbation response to lowest perturbation response; and developing a combination therapy for the clone of interest comprising perturbations from at least two of the high-ranking perturbation responses.
2. The method of claim 1, wherein the characteristics of the at least one cell line that are similar to the clone of interest are selected from the group consisting of tumor type, perturbation data, genomics, transcriptomics, proteomics, microbiomics, metabolomics, lipidomics, epigenomics, and combinations thereof.
3. The method of claim 1, wherein the Al platform is selected from the group consisting of machine learning, deep learning, artificial neural networks, convolution neural networks, generative adversarial networks, and combinations thereof.
4. The method of claim 1, wherein the information relating to the clone of interest is selected from the group consisting of tumor type, tumor location, lesion location, genomics, transcriptomics, proteomics, microbiomics, metabolomics, lipidomics, epigenomics, and combinations thereof.
5. The method of claim 1, wherein the perturbations for the combination therapy are selected from the group consisting of environmental stimuli, drug inhibition, gene editing, disease treatment, and combinations thereof.
6. identifying at least two existing cell lines comprising characteristics similar to the clone of interest and compiling a first data set comprising perturbation data for the at least two existing cell lines; inputting the first data set into an artificial intelligence (AI) platform, wherein the first data set trains the Al platform to generate at least one synthetic cell line comprising perturbation data unified from the at least two existing cell lines, wherein the perturbation data for the at least one synthetic cell line is compiled into a second data set; applying the first and second data sets as training data for the Al platform to learn how to predict responses to perturbations included in the perturbation data of the first and second data sets; entering information relating to the clone of interest into the trained Al platform and obtaining as output a ranking of the predicted perturbation responses of the clone of interest to the perturbations included in the perturbation data of the first and second data sets, wherein the predicted perturbation responses are ranked from highest perturbation response to lowest perturbation response; and developing a combination therapy for the clone of interest comprising perturbations from at least two of the high-ranking perturbation responses.
7. The method of claim 6, wherein the characteristics of the at least two existing cell lines that are similar to the clone of interest are selected from the group consisting of tumor type, perturbation data, genomics, transcriptomics, proteomics, microbiomics, metabolomics, lipidomics, epigenomics, and combinations thereof.
8. The method of claim 6, wherein the Al platform is selected from the group consisting of machine learning, deep learning, artificial neural networks, convolution neural networks, generative adversarial networks, and combinations thereof.
9. The method of claim 6, wherein the information relating to the clone of interest is selected from the group consisting of tumor type, tumor location, lesion location, genomics,transcriptomics, proteomics, microbiomics, metabolomics, lipidomics, epigenomics, and combinations thereof.
10. The method of claim 6, wherein the perturbations for the combination therapy are selected from the group consisting of environmental stimuli, drug inhibition, gene editing, disease treatment, and combinations thereof.
11. A computer program product for ranking tumor clone perturbation responses comprising: program instructions on one or more computer readable storage media for training an artificial intelligence (AI) platform to predict responses to perturbations included in a data set comprising perturbation data for at least one cell line having characteristics similar to a clone of interest; program instructions on one or more computer readable storage media for inputting information relating to the clone of interest into the trained Al platform, wherein the trained Al platform predicts perturbation responses for the clone of interest to the perturbations included in the perturbation data of the data set; and program instructions on one or more computer readable storage media for outputting from the Al platform a ranking of the predicted perturbation responses for the clone of interest from highest perturbation response to lowest perturbation response.
14. The computer program product of claim 11, wherein the information relating to the clone of interest is selected from the group consisting of tumor type, tumor location, lesion location, genomics, transcriptomics, proteomics, microbiomics, metabolomics, lipidomics, epigenomics, and combinations thereof.
15. The computer program product of claim 11, wherein the perturbations included in the data set are selected from the group consisting of environmental stimuli, drug inhibition, gene editing, disease treatment, and combinations thereof.
16. A computer program product for ranking tumor clone perturbation responses comprising: program instructions on one or more computer readable storage media for training an artificial intelligence (AI) platform to generate at least one synthetic cell line comprising perturbation data unified from perturbation data for at least two existing cell lines having characteristics similar to a clone of interest, wherein the perturbation data for the at least two existing cell lines is compiled in a first data set and the perturbation data for the at least one synthetic cell line is compiled in a second data set; program instructions on one or more computer readable storage media for training the Al platform to predict responses to the perturbations included in the perturbation data for the first and second data sets; program instructions on one or more computer readable storage media for inputting information relating to the clone of interest into the trained Al platform, wherein the trained Al platform predicts perturbation responses for the clone of interest to the perturbations included in the perturbation data of the first and second data sets; and program instructions on one or more computer readable storage media for outputting from the Al platform a ranking of the predicted perturbation responses for the clone of interest from highest perturbation response to lowest perturbation response.
19. The computer program product of claim 16, wherein the information relating to the clone of interest is selected from the group consisting of tumor type, tumor location, lesion location, genomics, transcriptomics, proteomics, microbiomics, metabolomics, lipidomics, epigenomics, and combinations thereof.
20. The computer program product of claim 16, wherein the perturbations included in the first and second data are selected from the group consisting of environmental stimuli, drug inhibition, gene editing, disease treatment, and combinations thereof.
21. A system comprising: a first data set for computer input comprising perturbation data relating to at least two existing cell lines that have characteristics similar to a sequence from a clone of interest obtained from at least one tumor lesion; a second data set for computer input comprising perturbation data relating to a synthetic cell line, wherein the at least one synthetic cell line comprises perturbation data unified from the at least two existing cell lines; and an artificial intelligence (AI) platform that accepts the first data set, the second data set, and information relating to the clone of interest as input and provides as output a ranking of predicted perturbation responses for the clone of interest to perturbations included in the perturbation data of the first and second data sets, wherein, the first data set trains the Al platform to generate the perturbation data for the at least one synthetic cell line, the first and second data sets train the Al platform to predict the perturbation responses of the clone of interest to the perturbations included in the perturbation data of the first and second data sets, and the predicted perturbation responses for the clone of interest are ranked from highest perturbation response to lowest perturbation response.
24. The system of claim 21, wherein the information relating to the clone of interest is selected from the group consisting of tumor type, tumor location, lesion location, genomics, transcriptomics, proteomics, microbiomics, metabolomics, lipidomics, epigenomics, and combinations thereof.
25. The system of claim 21, wherein the perturbations included in the first and second data sets are selected from the group consisting of environmental stimuli, drug inhibition, gene editing, disease treatment, and combinations thereof.
The claims disclose steps for “identifying,” “inputting,” “applying,” “entering,” and “developing,” that amount to instructions to train and run a machine learning model, and then interpret the results. The language of an “AI platform” that can consist of “machine learning” is so broad that it includes embodiments that can be practically performed by a human being using pen and paper, such as a linear regression. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 13, 16-25 recite performing some aspects of the analysis with a “neural/generative adversarial network” or “computer”, there are no additional limitations that indicate that this “neural/generative adversarial network” or “computer” require anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-25 recite an abstract idea ( Step 2A, Prong 1 : YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to effect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment. Specifically, the claims recite the following additional elements:
1, 6. sequencing a clone of interest obtained from at least one tumor lesion;
12. The computer program product of claim 11, wherein the Al platform is selected from the group consisting of machine learning, deep learning, artificial neural networks, convolution neural networks, generative adversarial networks, and combinations thereof.
17. The computer program product of claim 16, wherein the Al platform is selected from the group consisting of machine learning, deep learning, artificial neural networks, convolution neural networks, generative adversarial networks, and combinations thereof.
13, 18, 23. wherein the Al platform comprises a generative adversarial network.
22. The system of claim 21, wherein the Al platform is selected from the group consisting of machine learning, deep learning, artificial neural networks, convolution neural networks, generative adversarial networks, and combinations thereof.
23. The system of claim 21, wherein the Al platform comprises a generative adversarial network.
The step for sequencing is mere data gathering, similar to Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989). There are no limitations that indicate that the claimed “neural/generative adversarial network”, “computer” or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-25 are directed to an abstract idea ( Step 2A, Prong 2 : NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The instant claims recite the following additional elements:
1, 6. sequencing a clone of interest obtained from at least one tumor lesion;
12. The computer program product of claim 11, wherein the Al platform is selected from the group consisting of machine learning, deep learning, artificial neural networks, convolution neural networks, generative adversarial networks, and combinations thereof.
17. The computer program product of claim 16, wherein the Al platform is selected from the group consisting of machine learning, deep learning, artificial neural networks, convolution neural networks, generative adversarial networks, and combinations thereof.
13, 18, 23. wherein the Al platform comprises a generative adversarial network.
22. The system of claim 21, wherein the Al platform is selected from the group consisting of machine learning, deep learning, artificial neural networks, convolution neural networks, generative adversarial networks, and combinations thereof.
23. The system of claim 21, wherein the Al platform comprises a generative adversarial network.
Sequencing is well-understood, routine and conventional, as it is a form of analyzing DNA to provide sequence information or detect allelic variants, Genetic Techs. Ltd., 818 F.3d at 1377; 118 USPQ2d at 1546. As discussed above, there are no additional limitations to indicate that the claimed “computer” or “neural/generative adversarial network” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No). As such, claims 1-25 are not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Garana et al. (BioRxiv Jan 2023) in view of Hansen (https://developer.ibm.com/articles/generative-adversarial-networks-explained/, 2022) and Jones et al. (PNAS, 2008, 105 (11) 4283-4288, henceforth “Jones”).
Regarding claim 1, in Garana’s method, a data set of perturbation data for a cell line is compiled and entered into an algorithm, and the perturbation responses are ranked from highest to lowest (pg 12 ¶ 2-3). A combination therapy was developed based on the high-ranking responses (pg 31 ¶ 1).
Garana is silent as to an AI platform.
Hansen teaches a type of AI called a GAN (¶ 1).
Garana is silent as to sequencing a clone of interest and similar cells.
Jones teaches sequencing a clone of interest and similar cells from tumor lesions (pg 2 ¶ 4).
Regarding claim 2, Garana uses perturbation data (pg 12 ¶ 2-3) and RNAseq data in the cell line data (pg 10 ¶ 2).
Regarding claim 3, Hansen teaches the GAN (¶ 1).
Regarding claim 4, the individual cell data can come from tumor clones (fig. 4). the clone data includes cancer type (fig. 6).
Regarding claim 5, the perturbation is drug inhibition (pg 12 ¶ 2-3, fig. 6).
Regarding claim 6, 200 synthetic cell lines are formed from an existing database of cell lines (pg 12 ¶ 2-3). In Garana’s method, a data set of perturbation data for a cell line is compiled and entered into an algorithm, and the perturbation responses are ranked from highest to lowest (pg 12 ¶ 2-3). A combination therapy was developed based on the high-ranking responses (pg 31 ¶ 1).
Garana is silent as to sequencing a clone of interest and similar cells.
Jones teaches sequencing a clone of interest and similar cells from tumor lesions (pg 2 ¶ 4).
Regarding claim 7, Garana uses perturbation data (pg 12 ¶ 2-3) and RNAseq data (pg 10 ¶ 2).
Regarding claim 8, Hansen teaches the GAN (¶ 1).
Regarding claim 9, the individual cell data can come from tumor clones (fig. 4). the clone data includes cancer type (fig. 6).
Regarding claim 10, the perturbation is drug inhibition (pg 12 ¶ 2-3, fig. 6).
Claims 11-15 set forth the same limitations as claims 1-5, only differing in that they are directed to a computer program product rather than a method. The arguments against claims 1-5 apply, mutatis mutandis.
Claims 16-20 set forth the same limitations as claims 6-10, only differing in that they are directed to a computer program product rather than a method. The arguments against claims 6-10 apply, mutatis mutandis.
Claims 21-25 set forth the same limitations as claims 6-10, only differing in that they are directed to a system rather than a method. The arguments against claims 6-10 apply, mutatis mutandis.
Regarding claims 1-25, An invention would have been prima facie obvious to one of ordinary
skill in the art at the time of the effective filing date of the invention if some teaching, suggestion, or motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. There is a teaching to use GANs in the text of Hansen, because they can generate synthetic data that can make an algorithm more useful (§ “Data Generation”, ¶ 1). Jones teaches sequencing a clone of interest and similar cells from tumor lesions (pg 2 ¶ 4). There would be a
reasonable expectation of success in making this combination to a person of ordinary skill in the art, as a GAN could be implemented instead of the mathematical ranking used in Garana, and the tumor clone data could be sequenced as in Jones (pg 2 ¶ 4) rather than from an external dataset as in Garana (Garana pg 2 ¶ 2). One of ordinary skill in the art would use a GAN, as taught by Hansen, instead of mathematical ranking in Garana, because Hansen teaches that GANs generate synthetic data that can make an algorithm more useful (§ “Data Generation”, ¶ 1). Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time to modify the method of Garana by adding a GAN, in order to generate synthetic data that could make the algorithm more useful.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACELYN M HILL whose telephone number is (571)272-9871. The examiner can normally be reached Monday-Friday 8:30-5pm.
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/G.M.H./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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