DETAILED ACTION
Applicant's response, filed 12/18/2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 filed 10/28/2021 claims foreign priority to Korean application 10-2021-0074803, filed 06/09/2021. The claims are therefore examined as filed on 06/09/2021, the effective filing date. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further review of the priority application(s).
Claim Status
Claims 1-12 and 14-15 are pending.
Claim 13 is cancelled.
Claims 1-12 and 14-15 are examined.
Claims 1-12 and 14-15 are rejected.
Withdrawn Rejections
The rejection of claims 1-5 and 7-14 under 35 U.S.C. §103 over MCCARTER in view of LIU and LEE, in the Office action mailed 09/18/2025 is withdrawn in view of the amendments filed 12/08/2025, and persuasive argument that the claims do not teach the added limitations of the graph-based SSL using a graph Laplacian matrix derived from a intra pseudo matrix and a inter pseudo matrix, specifically where the intra pseudo matrix includes a disease-disease intra matrix, a gene-gene intra matrix and an SNP-SNP intra matrix, and the inter pseudo matrix includes a disease-gene inter matrix and a gene-SNP inter matrix.
The rejection of claims 6 and 15 under 35 U.S.C. §103 over MCCARTER in view of LIU, LEE, and KONG, in the Office action mailed 09/18/2025 is withdrawn in view of the amendments filed 12/08/2025 and withdrawal of the rejection of claims 1-5 and 7-14 above.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-12 and 14-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental processes and mathematical concepts, without significantly more.
The MPEP at MPEP 2106 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of matter?
(2A)(1) Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea?
(2A)(2) Do the claims recite additional elements that integrate the judicial exception into a practical application?
(2B) If the claims recite a judicial exception and do not integrate the judicial exception, do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?
With regard to step (1) (Are the claims directed to a process, machine, manufacture or composition of matter?): Yes. The claims are directed to one of the statutory classes. Claims 1-9 are directed to a process (a method performed by a computer-executable program), and claims 10-12 and 14-15 are also directed to a process (a method performed by a computer-executable program).
With regard to step (2A)(1) (Do the claims recite a judicially recognized exception?): Yes. The claims recite the abstract ideas of processing data using mental steps and mathematical concepts, and observing the processed data. Claims that recite nothing more than abstract ideas, natural phenomena, or laws of nature are not eligible for patent protection (see MPEP 2106.04).
Abstract ideas include mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (See MPEP 2106.04(a)(2)). In particular, these abstract ideas include but are not limited to:
Creating a layered network based on other networks/information (mental process/mathematical concept; the specification describes the process of creating the network as setting values of gene-SNP edges between networks based on interrelation data, where values are obtained based on odds ratios in a logistic regression model [0008-9], the human mind is capable of drawing a network and setting values within the network based on logistic regression, and setting values based on data using a regression is a mathematical process; claims 1-4, 10-12)
Identifying a candidate gene for a genetic disease using the layered network by calculating a score for each of the genes, and identifying a candidate gene for the genetic disease based on the calculated score (mental process/mathematical concept; the human mind is capable of using a network of nodes and edges, with numerical values, to make associations and identify a variable based on those associations the human mind is also capable of calculating a score, and making a determination based on the score; these are also mathematical concepts; claims 1, 5-6, 10, 14-15)
Setting a value of a gene-SNP edge between the first node and the second node to 1 when a first SNP corresponding to a first node among nodes of the SNP network belongs to a first gene corresponding to a second node among nodes of the gene network; and when the first SNP does not belong to the first gene, setting the value of the gene-SNP edge between the first node and the second node to 0 (mental process/mathematical concept; the human mind is capable of setting a value to 1 or 0 based on whether a criterion is met, setting values is a mathematical concept; claims 4, 12)
Setting a label of nodes corresponding to genes and SNPs already known to be related to the genetic disease among nodes of the layered network to 1 and a label of the other nodes to 0 (mental process/mathematical concept; the human mind is capable of setting labels to numerical values; claims 5, 14)
Identifying at least one gene with the calculated score higher than a reference score as the candidate gene (mental process/mathematical concept; the human mind is able to compare scores to identify a gene, comparing scores is a mathematical process; claims 6, 15)
Setting values of disease-gene edges between the disease network and the gene network based on the disease-gene association information, and wherein the setting of the values of disease-gene edges comprises: when a first gene corresponding to a first node among nodes of the gene network is identified based on the disease-gene association information as being related to causing a first disease corresponding to a second node among nodes of the disease network, setting a value of a disease-gene edge between the first node and the second node to 1; and when the first gene is not identified as being related to causing the first disease, setting the value of the disease-gene edge between the first node and the second node to 0 (mental process/mathematical concept; the human mind is capable of setting a value to 1 or 0 based on whether a criterion is met, setting values is a mathematical concept; claim 9)
Dependent claims 7-8 further limit the abstract ideas recited in the independent claims, and do not change their characterization as abstract ideas.
Therefore, the claims recite elements that constitute one or more judicial exceptions.
With regard to step (2A)(2) (Do the claims recite additional elements that integrate the judicial exception into a practical application?): No. The claims recite the additional elements of the methods being performed by a computer-executable program (performed on the computer), and the use of semi-supervised learning (a type of machine-learning) to perform part of the abstract idea. Claim 1 and its dependents also recite the limitation of obtaining a disease network, obtaining disease-gene association information, obtaining a gene network, and obtaining an SNP network and inter-relation data between genes and SNPs. Claim 8 further recites obtaining a degree of association between connected genes from a database.
While the claims recite the additional element of obtaining data, such necessary data gathering steps, without any technical details of how the data is obtained, are insignificant extrasolution activities that do not add a meaningful limitation to the claims (see MPEP 2106.05(g)). As a result, the judicial exception is not integrated into a practical application. Similarly, while the claims recite additional elements related to the use of computers, they do not provide any specific details by which the computer program performs or carries out the judicial exception listed in step (2A)(1), nor do they provide any details of how specific structures of the computer are used to implement these functions. The judicial exception is therefore not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea, as they amount to simply implementing the abstract idea of analysis on a computer (see MPEP 2106.05(f)). This also applies to the use of semi-supervised learning/machine learning algorithms to process data, as this is also analogous to implementing an abstract idea of data analysis on a computer. Because the claims do not recite any additional elements that integrate the judicial exception into a practical application, the claims as a whole are directed to an abstract idea.
With regard to step (2B) (Do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?): No.
The claims recite an abstract idea with additional elements; however, these additional elements are general computer elements added to abstract ideas, and non-particular instructions to apply the abstract idea by linking it to a field of use or extrasolution activity (see MPEP 2106.05(f-h)). General computer elements used to perform an abstract idea do not provide an inventive concept, and similarly, non-particular instructions to gather or produce data do not provide an inventive concept. Non-particular instructions to gather or output data are also considered well-understood, routine and conventional activities (see MPEP 2106.05(d), which indicates that limitations such as “Receiving or transmitting data over a network” from Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, and “Storing and retrieving information in memory” from Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 are recognized as conventional activities). The claims therefore do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As a result, the claims as a whole do not provide an inventive concept.
Response to Arguments – Rejections Under 35 USC § 101
In the reply filed 12/18/2025, Applicant asserts that claim 1 does not recite any of the three groupings of abstract ideas (remarks pg 11). However, claim 1 recites both mental process and mathematical concepts, including creating a network by labeling nodes and setting values for edges, calculating a score, and identifying a gene using the network/analyzed data as explained in the rejection above.
The Applicant also asserts that the additional element in the claims transform the idea into patent eligible subject matter and that the ordered combination of operations in claim 1 are not purely conventional and are therefore a practical application (remarks pg 11). However, the ordered combination of operations, when these operations are abstract ideas, cannot provide a practical application. In the MPEP, a “practical application” must be found among the non-abstract additional elements - section 2106.05(a), specifically states that “the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)).” The only additional elements in the claims are the extrasolution activities of gathering data for the method, and using general computer/machine learning elements to apply the abstract ideas; as explained in the rejection above, these alone are insufficient for integrating the abstract ideas into a practical application. The MPEP section 2106.04(d) provides some examples of what may constitute a practical application.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim Rejection
Claims 1-12 and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by DONG-GI LEE 2020 “Dementia key gene identification with multi-layered SNP-gene-disease network”.
Claim Interpretation and Scope and Contents of Prior Art
Claim 1 recites a method of identifying a candidate gene for a genetic disease performed by a computer-executable program, the method comprising: obtaining a disease network, disease-gene association information, and a gene network; and obtaining a single nucleotide polymorphism (SNP) network based on intra-relation data between a plurality of SNPs, and inter-relation data between genes and SNPs. Claim 10 similarly recites the limitation of creating a single nucleotide polymorphism (SNP) network based on intra-relation data between a plurality of SNPs. With respect to these limitations, DONG-GI LEE teaches a method of identifying candidate genes for dementia using a machine learning algorithm (Abstract) involving obtaining a disease network, gene network, disease-gene association information, and an SNP network based on intra-relation SNP data and inter relation gene-SNP data (pg 832, Table 1).
Claim 1 also recites the limitation of creating a disease-gene-SNP layered network based on the disease network, the disease-gene association information, the gene network, the SNP network, and the interrelation data between genes and SNPs; and identifying a candidate gene for a genetic disease using the layered network. Claim 10 similarly recites creating a gene-SNP layered network based on a gene network, the SNP network, and intra- relation data between SNPs; and identifying a candidate gene for a genetic disease using the layered network and creating a disease-gene-SNP layered network based on the gene-SNP layered network, a disease network, and disease-gene association information. With respect to these limitations, DONG-GI LEE teaches creating an SNP-gene-disease multilayered network based on the obtained information, and using the network to identify candidate genes for dementia (Abstract, Fig 1, Table 3).
Claims 1 and 10 also recite the limitations of calculating a score for each of the genes using graph-based semi-supervised learning (SSL), and identifying a candidate gene for the genetic disease based on the calculated score, wherein the graph-based SSL uses a graph Laplacian matrix, the graph Laplacian matrix is derived from a intra pseudo matrix and a inter pseudo matrix, the intra pseudo matrix includes a disease-disease intra matrix, a gene-gene intra matrix and a SNP-SNP intra matrix, and the inter pseudo matrix includes a disease-gene inter matrix and a gene-SNP inter matrix. With respect to these limitations, DONG-GI LEE teaches calculating scores for each of the genes using graph-based SSL and ranking them to determine candidate genes (pg 832 par 5, pg 833 col 2, Fig 1), where the SSL uses a Laplacian matrix derived from a disease-disease intra matrix, SNP-SNP intra matrix, disease-gene inter matrix, and a gene-SNP inter matrix (pg 833 col 2 and 834 col 1, Fig 2)
Claims 2 and 11 recite the limitations wherein the SNP network comprises a plurality of nodes each corresponding to an SNP type; and at least one edge representing connection between the plurality of nodes, and wherein each of the at least one edge represents a degree of similarity/value based on the intra- relation between the connected SNPs. With respect to these limitations, DONG-GI LEE teaches that the SNP network comprises nodes each corresponding to an SNP type and edges representing connections between nodes, where each edge represents a degree of similarity based on interactions between the SNPs (pg 833 par 1 Fig 2).
Claim 3 recites the limitation wherein each of the at least one edge has a value obtained based on odds ratios in a logistic regression model based on allele dosage of the connected SNPs. With respect to this limitation, DONG-GI LEE teaches that the edges represent interactions computed based on the odds ratio from a fitted logistic regression model that classifies case on control groups based on allele dosage from each SNP (832 last par, 833 par 1).
Claims 4 and 12 recite the limitation of setting values of gene-SNP edges between the gene network and the SNP network based on the interrelation data between genes and SNPs, and wherein the setting of the values of gene-SNP edges comprises: when a first SNP corresponding to a first node among nodes of the SNP network belongs to a first gene corresponding to a second node among nodes of the gene network, setting a value of a gene-SNP edge between the first node and the second node to 1; and when the first SNP does not belong to the first gene, setting the value of the gene-SNP edge between the first node and the second node to 0. With respect to this limitation, DONG-GI LEE teaches setting values of gene-SNP edges where the value is 1 if a relation exists between two nodes and 0 otherwise (pg 833 par 2).
Claims 5 and 14 recite the limitations of setting a label of nodes corresponding to genes and SNPs already known to be related to the genetic disease among nodes of the layered network to 1 and a label of the other nodes to 0. With respect to this limitation, DONG-GI LEE teaches setting a label of nodes so that genes and SNPs already known to be involved with dementia are assigned 1, and others are assigned 0 (pg 834 col 1, col 2 par 1).
Claims 6 and 15 recite the limitations of identifying at least one gene with the calculated score higher than a reference score as the candidate gene. With respect to this limitation, DONG-GI LEE teaches identifying the candidate genes that are higher than a reference score (Fig 1).
Claim 7 recites the limitation wherein the disease network comprises a plurality of nodes each corresponding to a disease; and at least one edge representing connection between the plurality of nodes, wherein each of the at least one edge represents a degree of association between the connected diseases, and wherein the degree of association is calculated based on a number or a rate of genes common between the connected diseases. With respect to this limitation, DONG-GI LEE teaches that the disease network comprises nodes representing a disease and edges representing similarity between two diseases calculated based on the number of related genes shared by the diseases (pg 832 par 3).
Claim 8 recites the limitation wherein the gene network comprises: a plurality of nodes each corresponding to a gene; and at least one edge representing connection between the plurality of nodes, wherein each of the at least one edge represents a degree of association between the connected genes, and wherein the degree of association is obtained from a database in which pieces of protein- protein interaction information are integrated. With respect to this limitation, DONG-GI LEE teaches that the gene network comprises nodes representing genes and edges representing association scores between two genes, and where the scores are from the STRING database which contains available protein-protein interaction information (pg 832 col 2 par 5-6).
Claim 9 recites the limitation wherein the disease-gene association information comprises information about genes related to causing each disease, wherein the creating of the layered network comprises setting values of disease-gene edges between the disease network and the gene network based on the disease-gene association information, and wherein the setting of the values of disease-gene edges comprises: when a first gene corresponding to a first node among nodes of the gene network is identified based on the disease-gene association information as being related to causing a first disease corresponding to a second node among nodes of the disease network, setting a value of a disease-gene edge between the first node and the second node to 1; and when the first gene is not identified as being related to causing the first disease, setting the value of the disease-gene edge between the first node and the second node to 0. With respect to this limitation, DONG-GI LEE teaches that the disease-gene association information includes information about genes related to diseases (Abstract, pg 832), and where the network comprises setting values of disease-gene edges between the disease network and the gene network based on the disease-gene relation (pg 832 col 2 par 3), where the value is 1 if a relation exists and 0 if it does not (pg 833 col 1 par 2).
Examiner Comment Regarding Potential 102/103 Exception
The current amendments have resulted in a new search and grounds of rejection, but it is recognized that the prior art now cited above has several common inventors with the claimed invention, published within a year before the effective filing date. The Office is currently unable to consider it exempt as prior art under a “grace period inventor-originated disclosure exception” because of the presence of an additional author on the cited publication (Myungjun Kim) who is not listed as an inventor. However, this rejection may be withdrawn if proper evidence is submitted showing that an exception under AIA 35 U.S.C. 102(b)(1) applies (see MPEP 2153.01(a)) and/or if Myungjun Kim is added to the application as an inventor.
Conclusion
No claim is allowable.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/M.C.L./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687