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 .
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1. Claims presented for examination: 1-20
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 07/15/2024, 10/11/2024 and 01/29/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-20 are directed to a method, a system and a tangible , non-transitory computer readable medium which belongs to a statutory class.
Step 2A, Prong One:
Claims recites limitation:
“verifying a diagnostic prediction model of the one or more diagnostic prediction model for an application consumer based upon receiving information corresponding to an application” is a mental process” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two:
Claims recites processor and memories including instructions to process. These are generic computer components and program which use to perform abstract ideas.
“Receive from an application provider: one or more diagnostic prediction models, each model trained to generate a respective diagnostic prediction based upon genomic data; and for the each model, adaptation factors used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the each model” is the process of obtaining information processing.
“Provide for display via a graphical user interface, one or more representations corresponding to the one or more diagnostic prediction models” is the process providing information to the user using the display.
“Authorize the diagnostic prediction model for distribution to the application consumer” is the process approving information to be provided to user.
“Provide the diagnostic prediction model and the corresponding adaptation factors to the application consumer” is the process of computer providing information to user.
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As to claim 2, the limitation:
“Determine at least one relevant label in common, between the application consumer genomic dataset and the reference genomic dataset, for a gene and associated outcome” is a mental process.
As to claim 3, the limitation:
“The gene and associated outcome is based upon risk genes and protective gene” is the process of analyzing information based on given value.
As to claim 4, the limitation:
“Determine pairs of genes which have at least one covariance between the application consumer” is a mental process.
As to claim 5, the limitation
“Determine at least one relevant label is not in common, between the application consumer genomic dataset and the reference genomic dataset, for a gene and associated outcome” is a mental process.
As to claim 6, the limitation:
“Generate a cosine similarity between the application consumer genomic dataset and the reference genomic dataset” is a mathematical calculation.
As to claim 7, the limitation:
“Verify the diagnostic prediction model of the one or more diagnostic prediction models for a second application consumer based upon receiving information corresponding to a second application consumer genomic dataset” is a mental process.
“Authorize the diagnostic prediction model for distribution to the second application consumer” is the process providing information to the user using the display.
“Provide the diagnostic prediction model and the corresponding adaptation factors to the second application consumer” is the process of computer providing information to user.
As to claim 8, the limitation:
“One or both of the application provider and the application consumer are affiliated with a biotechnology entity, an educational entity, a collaborator of a provider of the diagnostic prediction models, or a pharmaceutical entity” is only further defined what application provide and application consumer are and insignificantly to amount significantly more.
As to claim 9, the limitation:
“The reference genomic dataset is generated by a first set of sequencing equipment and the application consumer genomic dataset is generated by a second set of sequencing equipment” are obtained using equipment which process the data.
As to claim 10, the limitation:
The reference genomic dataset and the application consumer genomic dataset have differences in characteristics due to their respective sets of sequencing equipment” are further defined what genomic dataset and the application consumer genomic dataset are and insignificantly to amount significantly more.
As to claim 11, the limitation:
“The reference genomic dataset is a public dataset or a laboratory-specific dataset, and the application consumer genomic dataset is the public dataset or the laboratory-specific dataset” are further defined what genomic dataset and application consumer genomic are and insignificantly to amount significantly more.
As to claim 13, the limitation:
The diagnostic prediction model is at least one of a diagnosticator model, a prognosticator model, a subtyping model, or a predictive model” is only further defined what diagnostic prediction model is insignificantly to amount significantly more.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
4. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Musso et al. (Pub. No. US 2024/0020576 A1) in view of CHOI et al. (Pub. No. US 2023/0316230 A1).
As to claim 1, Musso discloses a computing system for distributing diagnostic prediction models comprising: one or more memories (memory) (paragraph 0053) having stored thereon computer-executable instructions (instructions) (paragraph 0053) that, when executed by one or more processors (CPUs) (paragraph 0051), cause the computing system to:
receive from an application (users 312…) (paragraph 0012) provider:
one or more diagnostic prediction models (prediction model) (paragraph 0012), each model trained to generate a respective diagnostic prediction based upon genomic data (machine learning, artificial intelligent) (paragraph 0003).
provide for display via a graphical user interface, one or more representations corresponding to the one or more diagnostic prediction models (the preprocessing module can also be configured to re-encode certain feature values into a different data representation…) (paragraph 0121);
verify a diagnostic prediction model of the one or more diagnostic prediction models for an application consumer based upon receiving information corresponding to an application consumer genomic dataset (computing an accuracy score for each of the at least one combination(s) based on accuracy of prediction of the prediction model) (paragraph 0010);
authorize the diagnostic prediction model for distribution to the application consumer (the cluster manager 309 also receives an output form the overlay network… send the outputs to each of the user…) (paragraph 0074); and
provide the diagnostic prediction model and the corresponding adaptation factors to the application consumer (the post analysis and visualization 814 of the results are sent as to the output stage 815) (paragraph 0100).
Musso does not for the each model, adaptation factors used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the each model.
However, discloses for the each model, adaptation factors used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the each model (the personality prediction model is a transformed-based on pretrained language model, and the personality prediction model is configured to output prediction points for top five adaptive factors and top five maladaptive factors) (claim 10).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify Musso to include for the each model, adaptation factors used to transform a target genomic dataset to conform to a dataset-specific nature of a reference genomic dataset of the each model as disclosed by
As to claims 2 and 14, Musso discloses the computing system of claim 1, the one or more memories having stored thereon instructions that, when executed, cause the computing system further to: determine at least one relevant label in common, between the application consumer genomic dataset and the reference genomic dataset, for a gene and associated outcome (the outcome of the predicted model) (paragraph 0062)
As to claims 3 and 15, Musso discloses the computing system of claim 2, wherein the gene and associated outcome is based upon risk genes and protective genes (the outcome of the predicted model) (paragraph 0062).
As to claims 4 and 17, Musso discloses the computing system of claim 1, the one or more memories having stored thereon instructions that, when executed, cause the computing system further to: determine pairs of genes which have at least one covariance between the application consumer genomic dataset and the reference genomic dataset (correlated features between dataset) (paragraph 0178).
As to clams 5 and 18, discloses the computing system of claim 4, wherein to determine pairs of genes which have at least one covariance, the one or more memories have stored thereon instructions that, when executed, cause the computing system further to:
determine at least one relevant label is not in common, between the application consumer genomic dataset and the reference genomic dataset, for a gene and associated outcome (label for different classification) (paragraph 0095).
As to clams 6 and 19, CHOI discloses the computing system of claim 4, the one or more memories having stored thereon instructions that, when executed, cause the computing system to: generate a cosine similarity between the application consumer genomic dataset and the reference genomic dataset (similarity) (claim 10).
As to claim 7, Musso discloses the computing system of claim 1, the one or more memories having stored thereon instructions that, when executed, cause the computing system further to:
verify the diagnostic prediction model of the one or more diagnostic prediction models for a second application consumer (plurality of users) (paragraph 0069) (other user includes second application) based upon receiving information corresponding to a second application consumer genomic dataset (computing an accuracy score for each of the at least one combination(s) based on accuracy of prediction of the prediction model) (paragraph 0010);
authorize the diagnostic prediction model for distribution to the second application consumer (the post analysis and visualization 814 of the results are sent as to the output stage 815) (paragraph 0100).; and
provide the diagnostic prediction model and the corresponding adaptation factors to the second application consumer (the post analysis and visualization 814 of the results are sent as to the output stage 815) (paragraph 0100).
As to claim 8, Musso discloses computing system of claim 1, wherein one or both of the application provider and the application consumer are affiliated with a biotechnology entity, an educational entity, a collaborator of a provider of the diagnostic prediction models, or a pharmaceutical entity (client) (paragraph 0230) (client is one of biotechnology, educational entity or other entity).
As to claim 9, Musso disclose the computing system of claim 1, wherein the reference genomic dataset is generated by a first set of sequencing equipment and the application consumer genomic dataset is generated by a second set of sequencing equipment (Datasets that have same format, but are gathered using different machines of the same type can be considered different types of data) (paragraph 0123) (different machines are sequencing equipments).
As to claim 10, Musso discloses the computing system of claim 9, wherein the reference genomic dataset and the application consumer genomic dataset have differences in characteristics due to their respective sets of sequencing equipment (different dataset) (paragraph 0004) (each data set includes differences in characteristics).
As to claim 11, Musso discloses the computing system of claim 1, wherein the reference genomic dataset is a public dataset (biomedical data) (paragraph 0010) or a laboratory-specific dataset, and the application consumer genomic dataset is the public dataset or the laboratory-specific dataset (different dataset) (paragraph 0004).
As to claim 12, discloses the computing system of claim 1, wherein the diagnostic prediction model is at least one of a diagnosticator model, a prognosticator model, a subtyping model, or a predictive model (predictive model) (paragraph 0012)
Claim 13 is rejected under the same reason as to claim 1, Musso discloses computer-implemented method (method) (paragraph 0010)for distributing diagnostic prediction models (predictive model) (paragraph 0010)
Claim 14 is rejected under the same reason as to claim 2.
Claim 15 is rejected under the same reason as to clam 3.
Claim 16 is rejected under the same reason as to claim 4.
Claim 17 is rejected under the same reason as to claim 5.
Claim 18 is rejected under the same reason as to claim 6.
Claim 20 is rejected under the same reason a to claim 1, Musso discloses a non-transitory, tangible computer-readable medium (memory) (paragraph 0053) storing machine-readable instructions (instructions) (paragraph 0053)for distributing diagnostic prediction models that, when executed by one or more processors (CPUs) (paragraph 0051).
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BAOQUOC N TO whose telephone number is (571)272-4041. The examiner can normally be reached Mon-Fri 9AM - 6PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
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