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 .
This application has been reassigned to AU 1686. Please see Examiner information at the end of this action.
Applicant’s election without traverse of Invention I, claims 1-18, in the reply filed on 7/6/2026 is acknowledged.
Applicant’s election without traverse of the species of “genes” as the target biomolecule, and MS data as the multimodal Omics Data in the reply filed on 7/6/2026 is acknowledged.
Claim 19 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 7/6/2026.
Claims 1-18 are under examination to the extent the claims read on a gene as the target biomolecule, and using mass spectrometry data as the multimodal omics data.
The effective filing date for the claims under examination is 12/4/2023.
The IDS filed 12/4/2023 has been entered and considered.
The drawings are objected to because Figure 5 is illegible. None of the text within the graph structure is legible. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.
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 1-18 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES, the claims are drawn to statutory categories: Claims 1-18 are drawn to a computer system.
With respect to step (2A) (1): YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
Claim 1. A medical information processing apparatus comprising a processing circuitry configured to:
(Preamble identifying an apparatus using an EIA- a general-purpose computer. MPEP 2106.05h)
receive drug target information based on a medical condition, the drug target information comprising at least one target biomolecule, each of the at least one target biomolecule associated with a respective drug suitable for the treatment of said medical condition;
(EIA- data gathering step, and a description of the data gathered. MPEP 2106.05g)
receive protein-protein interaction information based on domain knowledge,
(EIA- data gathering step, and a description of the data gathered. MPEP 2106.05g)
receive multimodal omics data for at least one subject;
(EIA- data gathering step, and a description of the data gathered. MPEP 2106.05g)
construct a graph based upon the drug target information, the protein-protein interaction information and the multimodal omics data; and
(Mental Process: observing the sets of information, analyzing the information for related concepts, and making a judgment as to how the information should be connected in a graph, and drawing the graph. MPEP 2106.04(a)(2))
generate, for each of the at least one drug, a score for each of the at least one subject based on the graph and the multimodal omics data.
(Mathematic concept of calculating a score. MPEP 2106.04(a)(2).)
2. The medical information processing apparatus of claim 1, wherein the processing circuitry is configured to construct the graph by being further configured to, for each of the at least one drug:
add a node to the graph corresponding to the respective target biomolecule;
expand the graph by connecting, with an edge, the node corresponding to the respective target biomolecule to at least one additional node corresponding to a gene or protein, wherein the edge corresponds to a first or second degree connection between the target biomolecule and the gene or protein based upon the protein-protein interaction information; and
further expand the graph by connecting, with an edge, one or more nodes of the graph to an additional node corresponding to a biomolecule based upon the multimodal omics data.
(Mental Process of observing the graph and adding nodes based on certain information.)
3. The medical information processing apparatus of claim 2, wherein the multimodal omics data comprises gene expression data, the gene expression data comprising gene expression levels for a plurality of genes normalized with respect to a plurality of subjects.
(EIA- data gathering step, and a description of the data gathered. MPEP 2106.05g)
4. The medical information processing apparatus of claim 3, wherein said score is generated based upon assigning the gene expression levels to each of the nodes of the graph corresponding to a target biomolecule and gene or protein.
(Mathematic concept of calculating a score)
5. The medical information processing apparatus of claim 4, wherein the contribution of a gene expression level assigned to a node to the score is weighted based upon the degree of separation of the node from the node representing the target biomolecule.
(Mathematic concept of calculating a score)
6. The medical information processing apparatus of claim 5, wherein the weighting is inversely proportional to the degree of separation.
(Mathematic concept of calculating a score)
7. The medical information processing apparatus of claim 4, wherein the score is calculated based on absolute gene expression levels.
(Mathematic concept of calculating a score)
8. The medical information processing apparatus of claim 2, wherein the multimodal omics data comprises gene variant information indicating the presence of gene mutations associated with the at least one target biomolecule and/or at least one gene or protein represented by the graph; wherein the graph is further expanded based upon the gene variant information.
(EIA- a step of data gathering AND a mental process, observing and expanding the graph.)
9. The medical information processing apparatus of claim 1, wherein the graph comprises: at least one starting node representing a respective drug; at least one 0th order node representing a respective target biomolecule, wherein each of the 0th order nodes are connected to a respective starting node by an edge based on the drug target information; at least one 1st order node representing a respective gene or protein, wherein each of the 1st order nodes are connected to a 0th order node by an edge based on the protein-protein interaction information; at least one 2nd order node representing a respective gene protein, wherein each of the 2nd order nodes are connected to a 1st order node by an edge based on the protein-protein interaction information; any number of nodes representing a gene variant connected to a respective 0th order, 1st order or 2nd order node by a gene variant edge based on the gene variant information; wherein a first value is assigned to each of the 0th order, 1st order and 2nd order nodes based on the gene expression levels; wherein a second value is assigned to each of the 0th order, 1st order and 2nd order nodes based on the presence of a gene variant; and wherein the score for each drug for each subject is calculated by summing the values for said subject assigned to the nodes originating from the starting node representing said drug, wherein the values are weighted based on the distance of each corresponding node from the respective 0th order node.
(Mental process- a description of the construction of the graph, AND mathematic concept of calculating a weighted score.)
10. The medical information processing apparatus of claim 1, wherein the multimodal omics data is normalized with respect to a plurality of subjects stratified by the medical condition and/or a side effect from a treatment for the medical condition.
(EIA- a description of the data gathered.)
11. The medical information processing apparatus of claim 1, wherein each of the target biomolecules correspond to a gene, RNA or protein that is targeted and/or modulated by a respective drug.
(EIA- a description of the data gathered.)
12. The medical information processing apparatus of claim 1, wherein each of the target biomolecules are a primary target of a respective drug.
(EIA- a description of the data gathered.)
13. The medical information processing apparatus of claim 1, wherein the multimodal omics data further comprises at least one of DNA methylation, single-cell sequencing and mass spectrometry for at least one subject.
(EIA- a description of the data gathered.)
14. The medical information processing apparatus of claim 1, wherein the processing circuitry is further configured to: receive health economics data relating to the at least one drug wherein said score is further based on the health economics data.
(EIA- a description of the data gathered AND mathematic concept of using the health economics data in the calculation of the score.)
15. The medical information processing apparatus of claim 1, wherein the processing circuitry is further configured to: receive IC50 or other response information for at least one drug and associated omics data for a plurality of samples; generate, for each of the at least one drug, a score for each of the plurality of samples based on a graph constructed based upon the target biomolecule associated with said drug, the protein-protein interaction information and the omics data for said sample; construct a mapping for each of the at least one drug defining a relationship between a score and the associated IC50 or other response information; predict a response metric for each of the at least one subject for each of the at least one drug based on the respective score for that subject and the mapping.
(EIA- data gathering, mathematic concept of calculating a score and mental process of mapping and predicting a result.)
16. The medical information processing apparatus of claim 1, wherein the drug target information relates to a plurality of drugs, and wherein the processing circuitry is further configured to: for each of the at least one subject, rank each of the plurality of drugs based on the score generated for the respective drug.
(EIA- describing data gathered, AND mathematic concept of ranking scores)
17. The medical information processing apparatus of claim 1, wherein the processing circuitry is further configured to: select, based on the score for each drug for at least one subject, a suitable line of treatment for the respective subject.
(Mental Process of observing the score and making a judgement as to what treatment to select.)
18. The medical information processing apparatus of claim 1, further comprising a display circuitry configured to: display the graph and the at least one generated score.
(EIA- adding routine display to the general-purpose computer and routine output of results)
With respect to step 2A (2): NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)):
“Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).”
Claim(s) 1, 3, 8, 10-16, 18 recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The data gathering steps constitute a general link to a technological environment: the trait prediction methods are intended to be applied to plant populations. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide integration into a practical application. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.).
Claim(s) 1 recites the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Dependent claim(s) 2, 4-9, 14-17 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO, the claims do not recite a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05).
“… an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. Alice Corp…”
With respect to claim(s) 1, 3, 8, 10-16, 18: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
The data required includes: drug target information, protein-protein information, and multimodal omics data for at least one subject. The elected target is a gene, the multimodal omics data comprises gene expression data and further comprises mass spectrometry data.
Kim (2021; PTO-1449) provides target genes, protein-protein information, gene expression information, and drug related genes.
Zhong (2022: PTO-1449) provides drug target gene information, protein-protein information, gene expression information and omics data.
Drews (US 2023/0253070 A1) provides drug target gene information, protein-protein information and gene expression data as well as omics data.
Nothaft (US 2023/0187074 A1) provides drug target gene information, protein-protein information and gene expression data as well as omics data.
Ong (US 2021/0238171 A1) provides drug target gene information, protein-protein information and gene expression data as well as omics data.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art. MPEP 2106.05(d): “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide an inventive concept. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.)
The data gathering steps constitute a general link to a technological environment: the trait prediction methods are intended to be applied to plant populations. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
Therefore, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claim(s) 1: the limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
Each of Kim, Zhong, Drews, Nothaft and Ong disclose computer systems or computing elements which meet the BRI of the claimed computer system or computer system elements, comprising input, output/ display, a processor, and memory.
As such, the prior art recognizes that these computing elements are routine, well understood and conventional in the art.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not provide significantly more.
Dependent claim(s) 2, 4-9, 14-17 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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(s) 1-2, 8-12, 14, 16 and 18 is/are rejected under 35 U.S.C. 102a2 as being anticipated by Li et al (2023).
Li, V. et al. DeepDrug: an expert-led directed graph neural networking drug-repurposing framework for identification of a lead combination of drugs protecting against Alzheimer’s disease and related disorders. (US 2023/0098833 A1, published 3/30/2023, having priority to at least 9/2022)
Li is directed to: “A three-step methodology is used. First, a heterogeneous biomedical graph is constructed comprising complex and interconnected genes, proteins, and drug information to capture the network characteristics of the AD pathology, considering the expert known associations between different AD pathways and utilizing node weighting and edge weighting and direction. Second, the curated graph is taken as an input to an artificial intelligence (AI)-driven graphical neural network (GNN) framework, with embeddings of drug and gene nodes as the outputs. Third, a drug scoring and selection analysis is conduced to generate the drug-gene scores and identify a lead combination of repurposed AD drug candidates for clinical verification.”
With respect to claim 1 and: “Claim 1. A medical information processing apparatus comprising a processing circuitry configured to: receive drug target information based on a medical condition, the drug target information comprising at least one target biomolecule, each of the at least one target biomolecule associated with a respective drug suitable for the treatment of said medical condition;”
All of Li’s methods are computer implemented using computer systems with processors and memory.
Li obtains drug target information, based on the medical condition Alzheimer’s disease (AD) [0008]. The target biomolecule (genes such as APP, PSEN1, PSEN2, APOE-4 etc [0008]) are associated with a drug suitable for treating AD [0008, 0025-0028, Tables 1-3, 00033-0034].
“[0033] Drug Nodes: Based on FDA-approved drugs, drug nodes are selected using the top co-morbidities and risk factors related to AD as well as expert-led insights. The drug nodes are weighted according to the type and number of AD co-morbidities and risk factors.”
“[0034] Data on the drugs was obtained from DrugBank [18], an online database containing information on drugs and drug targets…”
With respect to claim 1 and: “receive protein-protein interaction information based on domain knowledge, receive multimodal omics data for at least one subject;”
Li collects protein-protein interaction data, and multimodal omics data, that can be gene expression data.
“[0029] … Specifically, a multimodal graph is constructed comprising four types of nodes (genes, proteins, drugs, and drug targets) and four types of associations between these nodes (gene-protein, protein-protein, drug-target, and drug-drug edges).”
“[0030] Gene Nodes: Two types of gene mutations relevant to AD, namely, somatic mutations and germline mutations, are considered. For somatic mutations, gene nodes are obtained from long genes [11] and somatic mutation markers are identified in the blood. [14] For germline mutations, gene nodes are obtained from the high-risk genes identified from GWAS data. [15] Other important genes relevant to (1) AD pathology (such as APP, APOE, Tau, PSEN1, PSEN2, PAX6, and ACT1), (2) healthy aging (such as SIRT1, FOXO3, and IGF1), or (3) inflammation, immunological, and aging pathways, are also selected from expert-led insights.”
“[0036] Protein Nodes: Protein nodes are initially obtained according to the gene and target nodes, as each gene resulted in one protein and all targets were proteins in this study. Then, the first-order neighbors of the initial protein nodes are included in the graph according to the human protein-protein interaction (PPI) data. The protein nodes are weighted by the same method as that of the target nodes.
[0037] Data on the proteins are obtained from BioGRID [21], an online curated biological database…”
“[0043] Protein-Protein Edges: A mixture of unidirectional and bidirectional relationships is established between proteins, according to the bait-hit concept, [39] allowing the PPI data to have direction, essentially leading to some edges being unidirectional. Other edges that do not have bait-hit information, are set as bidirectional by default. The protein-protein edges are weighted by calculating the protein sequence similarity of the two involved proteins, using the standard Smith-Waterman algorithm, without being normalized. A total of 89,289 protein-protein edges, either unidirectional, when the bait-hit information is available, or bidirectional, when no direction information is available, were established.”
With respect to claim 1 and: “construct a graph based upon the drug target information, the protein-protein interaction information and the multimodal omics data; and”
Li generates a multimodal graph, comprising drug-target information, protein-protein information and gene data. See at least Fig 1B, 1C, 2A, 2B and their description.
“[0035] Target Nodes: Target nodes are obtained along with the drug nodes, as each drug has one or more specific targets. There were four types of targets in the data obtained from the DrugBank, including targets, enzymes, carriers, and transporters, resulting in 1,361 drug targets. The target nodes are weighted only by their involvement in the top AD co-morbidities and not risk factors, as it is difficult to relate proteins to risk factors. The weights of the target nodes were normalized to [1, 2].
[0036] Protein Nodes: Protein nodes are initially obtained according to the gene and target nodes, as each gene resulted in one protein and all targets were proteins in this study. Then, the first-order neighbors of the initial protein nodes are included in the graph according to the human protein-protein interaction (PPI) data. The protein nodes are weighted by the same method as that of the target nodes.”
“[0038] Next, the edges between the nodes and the associated weights are determined. There are four types of edges, namely, gene-protein, drug-drug, drug-target, and protein-protein. Note that except for drug-drug edges, all other edges are directed. Descriptions on how the weights of these edges are determined can be found below, including the list and the directions of these edges, plus their associated weights.”
“[0042] Drug-Target Edges: A unidirectional relationship between drugs and targets is established directly, as each drug has its own (one or more) specific targets. The drug-target edges are weighted uniformly, with options to further weight when drug-target efficacy data becomes available. Specifically, the drug-target edges are weighted uniformly with a default weight of 1.5, as drug-target effectiveness data was unavailable. A total of 5,310 drug-target edges were identified.
[0043] Protein-Protein Edges: A mixture of unidirectional and bidirectional relationships is established between proteins, according to the bait-hit concept, [39] allowing the PPI data to have direction, essentially leading to some edges being unidirectional. Other edges that do not have bait-hit information, are set as bidirectional by default. The protein-protein edges are weighted by calculating the protein sequence similarity of the two involved proteins, using the standard Smith-Waterman algorithm, without being normalized. A total of 89,289 protein-protein edges, either unidirectional, when the bait-hit information is available, or bidirectional, when no direction information is available, were established.
[0044] Based on the expert-led domain-specific knowledge, the weights of some protein-protein edges are further increased according to the enrichment value of the pathways if they are part of an AD-associated pathway. Specifically, pathways that overlap across multiple datasets covering different AD populations are assigned a higher score, i.e., given additional weight, with the assumption that these edges are likely to be the common denominators of AD. All genes (long, GWAS, and expert-led genes) are used to generate the AD-related pathways, using the STRING pathway analysis. [40-42].”
Li constructs multi-level, multi-modal graphs with nodes and edges, as in Fig 3A, Fig 6.
With respect to claim 1 and: “generate, for each of the at least one drug, a score for each of the at least one subject based on the graph and the multimodal omics data.”
Li scores drug-gene interactions, as shown at least at [0019, 0025-0026, Tables 1-3, 0044, 0063-0077, 0081 et al.].
“[0063] After model training and validation, all data are fed into the trained model to obtain an embedding vector for drug and gene nodes. The drug-gene score is calculated, and top drug candidates and a lead combination are selected from the top candidates (FIG. 2C). Specifically, the closer a drug’s node is to the AD-risk gene nodes, the more likely it is a higher-ranking AD drug candidate.”
As such, claim 1 is anticipated.
With respect to claim 2, nodes are added by expanding from the initial node, connecting to genes, proteins, or other biomolecules based on the gene data (omics). Nodes are connected with edges. [0029-0038]
[0029] “Specifically, a multimodal graph is constructed comprising four types of nodes (genes, proteins, drugs, and drug targets) and four types of associations between these nodes (gene-protein, protein-protein, drug-target, and drug-drug edges). After the graph is construction isolated nodes are removed from the graph. The detailed graph construction procedures are as follows.
[0030] Gene Nodes: Two types of gene mutations relevant to AD, namely, somatic mutations and germline mutations, are considered. For somatic mutations, gene nodes are obtained from long genes [11] and somatic mutation markers are identified in the blood. [14] For germline mutations, gene nodes are obtained from the high-risk genes identified from GWAS data. [15] Other important genes relevant to (1) AD pathology (such as APP, APOE, Tau, PSEN1, PSEN2, PAX6, and ACT1), (2) healthy aging (such as SIRT1, FOXO3, and IGF1), or (3) inflammation, immunological, and aging pathways, are also selected from expert-led insights.
“[0032] Additional nodes can be added at will, for example, pathways associated with post-traumatic stress syndrome (PTSD), a major risk for AD as well as pathways associated with intellectual disabilities, autism spectrum disorders and cytoskeletal function.”
“[0033] Drug Nodes: Based on FDA-approved drugs, drug nodes are selected using the top co-morbidities and risk factors related to AD as well as expert-led insights. The drug nodes are weighted according to the type and number of AD co-morbidities and risk factors.”
“[0036] Protein Nodes: Protein nodes are initially obtained according to the gene and target nodes, as each gene resulted in one protein and all targets were proteins in this study. Then, the first-order neighbors of the initial protein nodes are included in the graph according to the human protein-protein interaction (PPI) data. The protein nodes are weighted by the same method as that of the target nodes.”
“[0038] Next, the edges between the nodes and the associated weights are determined. There are four types of edges, namely, gene-protein, drug-drug, drug-target, and protein-protein. Note that except for drug-drug edges, all other edges are directed. Descriptions on how the weights of these edges are determined can be found below, including the list and the directions of these edges, plus their associated weights.”
With respect to claim 8, the gene information includes variant information [0012, 0029-0032, 0078-0084].
With respect to claim 9, the graph contains 0th, 1st and 2nd order nodes what are connected with edges, and the edges represent the interaction information as set forth above [0029-0038]. Scoring the graph comprises summing the weighted edge values and distance information.
With respect to claim 10, the gene information can be normalized with respect to the disorder, or any desired parameter. [0031, 0034, 0035, 0037, 0041, 0045]
With respect to claim 11, the target molecule can be a gene, protein as set forth above.
With respect to claim 12, primary targets of drugs can be selected as set forth above.
With respect to claim 14, “heath economics” data is met by the information regarding each drug obtained from the various databases cited.
With respect to claim 16, ranking is provided at least at [0063, 0080].
With respect to claim 18, display of the graph and scores are provided throughout.
Claim(s) 1-14, 16-18 is/are rejected under 35 U.S.C. 102a2 as being anticipated by Ghiassian et al. (2024).
Ghiassian, S. et al. Methods and systems for personalized therapies. US 2024/0153580 A1, 5/9/2024 having priority to at least 6/21/2022.
Ghiassian’s methods are all carried out on computer systems which comprise processors and memory. [0133 et al.]
With respect to claim 1 and: “Claim 1. A medical information processing apparatus comprising a processing circuitry configured to: receive drug target information based on a medical condition, the drug target information comprising at least one target biomolecule, each of the at least one target biomolecule associated with a respective drug suitable for the treatment of said medical condition;”
Ghiassian obtains drug target information based on a medical condition, which can be a gene, and the gene can be associated with a drug suitable for the treatment. See at least [0007, 0009, 0011, 0039, 0101 et al.]
“[0007] In an aspect, the present disclosure provides a method of determining or validating a target for therapy for treating a subject suffering from a disease, disorder, or condition, the method comprising: receiving a set of response genes corresponding to a disease gene expression signature, wherein the disease gene expression signature comprises one or more genes that, when expression is reversed in whole or in part, resembles gene expression of a non-diseased subject; receiving a plurality of interactions between one or more potential therapies and a plurality of gene expressions;”
“[0039] In some embodiments, the one or more potential targets are selected from JAK1, JAK2, JAK3, IL23A, ITGA4, ITGB7, IL2RA, IL12A, IL12B, TNF, IL12RB1, IL23R, IL12RB2, and MADCAM1.”
With respect to claim 1 and “receive protein-protein interaction information based on domain knowledge, receive multimodal omics data for at least one subject;”
Ghiassian obtains PPI information [0008, 0012, 0060, 0078 et al.] and gene expression data [0011, 0018, 0045, 0078 et al] for at least one subject.
“[0008] … In some embodiments, the biological network is a human protein-protein interactome.”
“[0078] … In some embodiments, a biological network is a protein-protein interaction network (PPI), representing interactions among proteins present in a cell, where proteins are nodes and their interactions are edges… In some embodiments, a biological network is a human interactome (a network of experimentally derived interactions that occur in human cells, which includes protein-protein interaction information as well as gene expression and co-expression, cellular co-localization of proteins, genetic information, metabolic and signaling pathways, etc.). In some embodiments, a biological network is a gene regulatory network, a gene co-expression network, a metabolic network, or a signaling network.”
“[0011] In another aspect, the present disclosure provides a method of treating a subject suffering from a disease, disorder, or condition, wherein the subject exhibits a disease gene expression signature associated with the disease, disorder, or condition,”
With respect to claim 1 and “construct a graph based upon the drug target information, the protein-protein interaction information and the multimodal omics data; and”
Ghiassian constructs graphs and networks which comprise nodes and edges based on the obtained information at least at: [0008, 0012, 0019, Fig 2A, 0059, Fig 6, 0078, 0097, 0107 et al.]
“[0078] Biological Network: As used herein, the term “biological network” generally refers to any network that applies to biological systems, having sub-units (e.g., “nodes”) that are linked into a whole, such as species units linked into a whole web. In some embodiments, a biological network is a protein-protein interaction network (PPI), representing interactions among proteins present in a cell, where proteins are nodes and their interactions are edges. In some embodiments, connections between nodes in a PPI are experimentally verified. In some embodiments, connections between nodes are a combination of experimentally verified a mathematically calculated. In some embodiments, a biological network is a human interactome (a network of experimentally derived interactions that occur in human cells, which includes protein-protein interaction information as well as gene expression and co-expression, cellular co-localization of proteins, genetic information, metabolic and signaling pathways, etc.). In some embodiments, a biological network is a gene regulatory network, a gene co-expression network, a metabolic network, or a signaling network.”
With respect to claim 1 and “generate, for each of the at least one drug, a score for each of the at least one subject based on the graph and the multimodal omics data.”
Ghiassian scores based on the graph and the subject specific data, throughout. [0016, 0022, 0041, 0045, 0060, 0104, 0106-0109, 0111, 0112, 0180-0182, et al.] Ghiassian utilizes Z-scores, Weighted connectivity scores, enrichment scores, proximity and selectivity scores, Diffusion State Distance scores, Impact Similarity scores, etc. As such claim 1 is anticipated.
With respect to claim 2, Ghiassian creates graphs with nodes and edges, where the graph begins with the gene targets from the disease gene expression profile, and is expanded based on gene information, PPI information and gene expression data. [Fig 6, Fig 7, 0078 et al.]
With respect to claim 3, Ghiassian obtains gene expression information from the individual, subjects having the same medical condition, and healthy subjects. [0005, 0011, 0013, 0020, 0032, 0046, 0078, 0093 et al.]
With respect to claim 4-7, scoring and weighting of the information is based on the applied gene expression data on the network. [0016, 0022, 0041, 0045, 0060, 0104, 0106-0109, 0111, 0112, 0180-0182, et al.] Ghiassian utilizes Z-scores, Weighted connectivity scores, enrichment scores, proximity and selectivity scores, Diffusion State Distance scores, Impact Similarity scores, etc.
With respect to claim 8, gene variant information can be provided. [0092, 0195, 0203, 0225, 0227 et al.]
With respect to claim 9, nodes are added using edges, and are expanded utilizing drug-target information, protein-protein information, gene-protein information, gene expression information and gene variant information as provided above.
With respect to claim 10, gene expression data can be normalized [0100, 0233] by stratified patient populations. [0013, 0020, 0032, 0044, 0046, 0095, 0177 et al.].
With respect to claim 11-12, target protein information is provided as set forth above.
With respect to claim 13, single cell sequencing can be performed and added. [0100].
With respect to claim 14, “health economic data” is met by the drug information provided throughout.
With respect to claim 16, Ranking of the drugs is based on the scoring, throughout. [0018, 0045, 0060, 0115, 0126, 0130, 0182, 0201 et al.]
With respect to claim 17, the highest ranked drug can be selected for treatment, throughout. [0101, 0107 et al.]
With respect to claim 18, the display can display the graph and scores as set forth in the figures and tables, throughout.
Claim(s) 1-9, 11, 12, 14, 16-18 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Morselli-Gysi et al. (2022).
Morselli-Gysi, D. et al. Network medicine framework for identifying drug repurposing opportunities. US 2022/0165352 A1, 5/26/2022, having priority to at least 11/2021. Herein: MG.
MG is directed to:
“A multi-modal system includes a protein-protein interaction network (PPI), a graph neural network (GNN), a diffusion module, a proximity module, and an aggregation module. The GNN is configured to predict new edges between candidate drug nodes and disease nodes in an embedded representation of the PPI to produce a decoded embedding space. The diffusion module is configured to determine a proximity distance for pairs of nodes in the PPI, and the proximity module is configured to determine a proximity distance for pairs of nodes in the PPI, each pair comprising a pathogen-protein node and a drug-protein node. A ranked list of candidate drugs predicted to be effective in treatment of the disease based on candidate drug lists generated by the other modules is generated by the aggregation module.” Abstract.
With respect to claim 1 and: “A medical information processing apparatus comprising a processing circuitry configured to: receive drug target information based on a medical condition, the drug target information comprising at least one target biomolecule, each of the at least one target biomolecule associated with a respective drug suitable for the treatment of said medical condition;”
All MG’s methods are computer-implemented using computer systems which comprise processors and memory, throughout. [0050-0051]
MG obtains drug target information, which can be a gene that encodes a target protein, and a medical condition can be infection with a pathogen, and associated drug information. See at least [0015, Fig 1, 0016, fig 2, 0017 Fig 3]
“[0057] … To search for diseases whose molecular mechanisms overlap with the COVID-19 disease, we first mapped the experimentally identified 332 host protein targets of the SARS-CoV-2 proteins to the human interactome, a collection of 332,749 pairwise binding interactions between 18,508 human proteins (see Example 7, herein)…”
“[0060] 1) An artificial intelligence-based algorithm maps drug protein targets and disease-associated proteins to points in a low-dimensional vector space, resulting in four predictive pipelines A1-A4, that rely on different drug-disease embeddings. The AI module is further described in Example 11, herein.”
With respect to claim 1 and “receive protein-protein interaction information based on domain knowledge, receive multimodal omics data for at least one subject;”
MG receives protein-protein interaction data, and omics data for at least one subject. [0006, 0010, 0016, 0036, 0062, 0083-0084, 0102, 0109]
“[0006] A multi-modal system for generating drug repurposing predictions for a disease caused by a pathogen includes a protein-protein interaction network defining pathogen-protein interactions for the pathogen and drug-protein interactions for a plurality of candidate drugs.”
“[0010] The protein-protein interaction network can be a human interactome”
“[0036] An example method 100 is shown in FIG. 15. A protein-protein interaction network 110 includes a model of pathogen-protein interactions 102 for a pathogen and a model of drug-protein interactions 104 for a plurality of candidate drugs. The protein-protein interaction network can be, for example, a human interactome. An example of a protein-protein interaction network (PPI) and human interactome is further described in Example 7, herein.”
With respect to claim 1 and “construct a graph based upon the drug target information, the protein-protein interaction information and the multimodal omics data; and”
MG constructs graphs comprising nodes and edges, using the drug target information, the PPI information and the gene expression data. [0006, 0012-0013, 0038, 0040-0041, 0087-0092 et al.]
“The system further includes a graph neural network comprising an embedded representation of the protein-protein interaction network, the embedded representation including candidate drug nodes and disease nodes. The graph neural network is configured to predict new edges between the candidate drug nodes and disease nodes to produce a decoded embedding space. A first list comprising a subset of the plurality of candidate drugs is identifiable from the decoded embedding space. The system further includes a diffusion module and a proximity module. The diffusion module is configured to determine a diffusion metric for pairs of nodes in the protein-protein interaction network, each pair comprising a pathogen-protein node and a drug-protein node. A second list comprising a subset of the plurality of candidate drugs is identifiable from the determined diffusion metrics. The proximity module is configured to determine a proximity distance for pairs of nodes in the protein-protein interaction network, each pair comprising a pathogen-protein node and a drug-protein node. A third list comprising a subset of the plurality of candidate drugs is identifiable from the determined proximity distances. The system further includes an aggregation module configured to generate a ranked list of candidate drugs predicted to be effective in treatment of the disease based on the first, second, and third lists.”
With respect to claim 1 and “generate, for each of the at least one drug, a score for each of the at least one subject based on the graph and the multimodal omics data.”
MG generates scores for each drug as set forth at least at [0016, 0017, 0078, 0087, 0105, 0117, 0126 et al.] MG discloses Z-scores, proximity scores, “drug scores”, pairwise similarity scores, ranking scores, AUC scores, Borda scores, Dowdall scoring, Kemeny scoring, etc.
“[0087] … The algorithm generates embedding vectors of drug and disease nodes, which are then used to predict drug scores, representing how promising a given drug is for COVID-19”
As such claim 1 is anticipated.
With respect to claim 2, MG creates graphs with nodes and edges, where the graph begins with the gene / protein targets from the PPI, and is expanded based on gene information, PPI information and gene expression data. [0006, 0012-0013, 0038, 0040-0041, 0087-0092 et al.]
With respect to claim 3, MG obtains gene expression information from the individual, subjects having the same medical condition, and healthy subjects. [0006, 0010, 0016, 0036, 0062, 0083-0084, 0102, 0109]
With respect to claim 4-7, scoring and weighting of the information is based on the applied gene expression data on the network. [0016, 0017, 0078, 0087, 0105, 0117, 0126 et al.] MG discloses Z-scores, proximity scores, “drug scores”, pairwise similarity scores, ranking scores, AUC scores, Borda scores, Dowdall scoring, Kemeny scoring, etc.
With respect to claim 8, gene variant information can be provided. [0002]
With respect to claim 9, nodes are added using edges, and are expanded utilizing drug-target information, protein-protein information, gene-protein information, gene expression information and gene variant information as provided above. [0021-22, 0038 et al]
With respect to claim 11-12, target protein information is provided as set forth above.
With respect to claim 14, “health economic data” is met by the drug information provided throughout.
With respect to claim 16, Ranking of the drugs is based on the scoring, throughout. [0005-0006, 0012, 0015, 0018, 0028, 0033, 0042-0043, 0046-0047, 0061-0062, et al.]
With respect to claim 17, the highest ranked drug can be selected for treatment, throughout.
With respect to claim 18, the display can display the graph and scores as set forth in the figures and tables, throughout.
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 s 1-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-20 of copending Application No. 18/617713 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the medical information processing apparati both comprise omics data, ontology data (gene related), graphed in graphs with nodes and edges. Drug target information, protein-protein information and other information are used in both.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim s 1-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-13 of copending Application No. 18/473,372 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the medical information processing apparati both comprise protein-protein information, drug data, Drug target information etc all graphed in graphs with nodes and edges. This application is generic to the ‘372 application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686
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