DETAILED ACTION
The Applicant’s filing, received 24 April 2023, has been fully considered. The following rejections and/or objections 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 .
Status of the Claims
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
This application is a CON of PCT/GB2021/052813, filed 29 October 2021,
which claims priority of Foreign Application UNITED KINGDOM GB2017177.3, filed 29 October 2020.
Unless otherwise noted, the effective filing date of the claimed invention is 29 October 2020.
Information Disclosure Statement
The information disclosure statements (IDS) received 21 December 2024, 30 October 2025, and 18 December 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings received 24 April 2023 are not accepted, and are objected to as noted below.
The drawings are objected to because separate views are not labeled with a number followed with a capital letter as required by 37 C.F.R. 1.84(u)(1). For example:
Figures 2A and 2B on sheet 2/11 are labeled as FIG. 2(a) and FIG. 2(b), respectively, but should be labeled as FIG. 2A and FIG. 2B, respectively. All subsequent figures should be relabeled accordingly, if necessary.
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 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Claim Interpretations
Independent claims 1 and 20 recite the limitation ‘ingesting/ingest publication data’. One of skill in the art would understand the broadest reasonable interpretation of this limitation to comprise a process of gathering, organizing, and structuring data from multiple different sources into a single, cohesive format.
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-19 recite a method for computational drug target selection (i.e., a process); and claim 20 recites computer device for computational drug target selection (i.e., a machine or s manufacture).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
searching the publication data to provide an indication, for each of the publication documents, as to whether the respective publication document is associated with one or more drug targets (i.e., mental processes, e.g., keyword association & semantic mapping, information foraging & scanning, critical evaluation, synthesis & integration, and pattern recognition);
determining an expected publication parameter for each of the one or more drug targets based on the searched publication data from the historical publication documents, and determining an actual publication parameter for each of the one or more drug targets based on the searched publication data from the current publication documents (i.e., mental processes, e.g., keyword association & semantic mapping, information foraging & scanning, critical evaluation, synthesis & integration, and pattern recognition); and
evaluating each of the one or more drug targets for selection based on its actual publication parameter relative to its expected publication parameter (i.e., mental processes, e.g., keyword association & semantic mapping, information foraging & scanning, critical evaluation, synthesis & integration, and pattern recognition).
Independent claim 20 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
search the publication data to provide an indication, for each of the publication documents, as to whether the respective publication document is associated with one or more drug targets (i.e., mental processes, e.g., keyword association & semantic mapping, information foraging & scanning, critical evaluation, synthesis & integration, and pattern recognition);
determine an expected publication parameter for each of the one or more drug targets based on the searched publication data from the historical publication documents, and determine an actual publication parameter for each of the one or more drug targets based on the searched publication data from the current publication documents (i.e., mental processes, e.g., keyword association & semantic mapping, information foraging & scanning, critical evaluation, synthesis & integration, and pattern recognition); and
evaluate each of the one or more drug targets for selection based on its actual publication parameter relative to its expected publication parameter (i.e., mental processes, e.g., keyword association & semantic mapping, information foraging & scanning, critical evaluation, synthesis & integration, and pattern recognition).
Dependent claims 2-19 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
defining, for each of the one or more drug targets, one or more character expressions referring to the respective drug target, wherein searching the publication data comprises searching the publication data for the one or more character expressions for each of the one or more drug targets (i.e., mental processes, e.g., keyword association & semantic mapping, information foraging & scanning, critical evaluation, synthesis & integration, and pattern recognition).
Dependent claim 3 further recites:
for each of the one or more drug targets:
classifying each of the one or more character expressions corresponding to the respective drug target as a safe character expression or an unsafe character expression, wherein the classification is based on a likelihood that an instance of a respective character expression in the publication data refers to the respective drug target (i.e., mental processes, e.g., probabilistic reasoning, cognitive calibration, and pattern recognition; and mathematical concepts, e.g., probability distributions), and
wherein, if the searched publication data from one of the publication documents includes a safe character expression, then the publication document is determined to be associated with the drug target (i.e., mental processes, e.g., probabilistic reasoning, cognitive calibration, and pattern recognition).
Dependent claim 4 further recites:
wherein one or more character expression unsafe characteristics are user-defined to indicate that a corresponding character expression is unsafe, and wherein character expressions in the searched publication data that exhibit one or more of the character expression unsafe characteristics are classified as unsafe character expressions (i.e., mental processes, e.g., perception & encoding, selective attention, reasoning & decision-making).
Dependent claim 5 further recites:
wherein one or more character expression ambiguity characteristics are defined to ascribe an ambiguity score to one or more of the character expressions, and wherein each of the character expressions is classified as a safe character expression or an unsafe character expression based on the corresponding ascribed ambiguity score (i.e., mental processes, e.g., perception & encoding, selective attention, reasoning & decision-making).
Dependent claim 6 further recites:
applying a machine learning algorithm to ascribe the ambiguity score to each of the one or more character expressions based on one or more character expression ambiguity characteristics, wherein the machine learning algorithm uses the one or more character expression unsafe characteristics to ascribe the ambiguity score to each of the one or more of the character expressions, and wherein the machine learning algorithm comprises a positive-unlabeled learning technique (i.e., mental processes, e.g., problem formulation, data abstraction, inductive reasoning, probabilistic evaluation, and iterative tuning; and mathematical concepts, e.g., linear algebra for data structuring, calculus for optimizing accuracy, and probability & statistics for handling uncertainty).
Dependent claim 7 further recites:
the publication data for at least some of the publication documents includes citation data indicative of citations made by one publication document to one or more other publication documents from the plurality of publication documents (i.e., mental processes, e.g., critical reading & contextualization, and synthesizing and pattern recognition); and
searching the publication data comprises identifying, using the citation data, pairs of publication documents that have been cited by the same publication document (i.e., mental processes, e.g., critical reading & contextualization, and synthesizing and pattern recognition).
Dependent claim 8 further recites:
determining, for each identified pair of publication documents, a co-citation value representative of a number of publication documents that cite both of the publication documents of the respective identified pair of publication documents (i.e., mental processes, e.g., critical reading & contextualization, network & spatial thinking, and synthesizing and pattern recognition).
Dependent claim 9 further recites:
assigning pairs of publication documents to one of a plurality of communities of publication documents based on their determined co-citation value and on the publication documents that cite the pairs of publication documents (i.e., mental processes, e.g., abstraction & pattern recognition, semantic synthesis & sense-making, clustering, and validation & interpretation).
Dependent claim 10 further recites:
defining, for each of the one or more drug targets, one or more character expressions referring to the respective drug target, wherein searching the publication data comprises searching the publication data for the one or more character expressions for each of the one or more drug targets (i.e., mental processes, e.g., concept formulation, query mapping, relevance judgement, and pattern abstraction); and
determining, for each of the plurality of communities of publication documents, whether to associate the community with one of the drug targets, wherein the determination comprises determining which of the defined character expressions referring to the one drug target are present in the publication data of each of the publication documents in the community (i.e., mental processes, e.g., concept formulation, query mapping, relevance judgement, and pattern abstraction).
Dependent claim 11 further recites:
for each of the one or more drug targets:
classifying each of the one or more character expressions as a safe character expression or an unsafe character expression, wherein the classification is based on a likelihood that an instance of the character expression in the publication data refers to the drug target (i.e., mental processes, e.g., probabilistic reasoning, cognitive calibration, and pattern recognition; and mathematical concepts, e.g., probability distributions), and
wherein determining whether to associate the community with one of the drug targets comprises determining a proportion of the publication documents in the community that include at least one safe character expression in their publication data (i.e., mental processes, e.g., probabilistic reasoning, cognitive calibration, and pattern recognition; and mathematical concepts, e.g., probability distributions).
Dependent claim 12 further recites:
defining, for each of the one or more drug targets, one or more character expressions referring to the respective drug target, wherein searching the publication data comprises searching the publication data for the one or more character expressions for each of the one or more drug targets, and wherein searching for the pairs of publication documents includes searching for pairs of publication documents that each includes at least one of the character expressions defined as referring to one of the drug targets (i.e., mental processes, e.g., concept formulation, query mapping, relevance judgement, and pattern abstraction).
Dependent claim 13 further recites:
determining the expected publication parameter comprises using a machine learning algorithm trained using the searched publication data from the historical publication documents (i.e., mental processes, e.g., problem formulation, data abstraction, inductive reasoning, probabilistic evaluation, and iterative tuning; and mathematical concepts, e.g., linear algebra for data structuring, calculus for optimizing accuracy, and probability & statistics for handling uncertainty).
Dependent claim 14 further recites:
determining a target-target co-occurrence parameter between pairs of the drug targets, the target-target co-occurrence parameter being determined based on the indication from the searched publication data of which publication documents both drug targets in a pair are associated with, each target-target co-occurrence parameter being indicative of the number of publication documents in which both of the drug targets in a respective pair appear (i.e., mental processes, e.g., probabilistic reasoning, cognitive calibration, and pattern recognition); and
evaluating the one or more drug targets for selection based on the determined target-target co-occurrence parameters (i.e., mental processes, e.g., probabilistic reasoning, cognitive calibration, and pattern recognition).
Dependent claim 15 further recites:
searching the publication data to provide an indication, for each of the publication documents, as to whether the respective publication document is associated with one or more diseases (i.e., mental processes, e.g., critical reading & contextualization, and synthesizing & pattern recognition); and
determining a target-disease co-occurrence parameter between each of the drug targets and each of the diseases, the target-disease co-occurrence parameter being determined based on the indication from the searched publication data of which publication documents each drug target and each disease are associated with, each target-disease co-occurrence parameter being indicative of the number of publication documents in which one of the drug targets and one of the diseases appear (i.e., mental processes, e.g., probabilistic reasoning, cognitive calibration, and pattern recognition); and
evaluating the one or more drug targets for selection based on the determined target-disease co-occurrence parameters.
Dependent claim 16 further recites:
applying a topic modeling algorithm to the publication data for the publication documents associated with each of the drug targets to obtain one or more topics associated with each drug target (i.e., mental processes, e.g., algorithm selection, iterative evaluation, and semantic interpretation; and mathematical concepts, e.g., linear algebra, probability and statistics, and information theory); and
evaluating the one or more drug targets for selection based on the obtained one or more topics (i.e., mental processes, e.g., probabilistic reasoning, cognitive calibration, and pattern recognition).
Dependent claim 17 further recites:
wherein the publication data includes a publication date for each of the plurality of publication documents, and wherein the publication date defines whether each of the publication documents is a historical publication document or a current publication document (i.e., mental processes, e.g., critical reading & contextualization, and synthesizing & pattern recognition).
Dependent claim 18 further recites:
using the evaluation of the one or more drug targets to inform selection of at least one of the drug targets for use in a drug discovery project (i.e., mental processes, e.g., objectivity, pattern recognition, comparative analysis, cost-benefit thinking, probabilistic reasoning, and systems thinking); and
designing the drug discovery project by selecting at least one of the drug targets for use in the drug discovery project based on the evaluation (i.e., mental processes, e.g., empathy & divergent discovery, evaluative framing, convergent synthesis, concept selection, and validation & iteration).
Dependent claim 19 further recites:
undertaking the drug discovery project using the at least one selected drug target, wherein undertaking the drug discovery project includes selecting (i.e., mental processes, e.g., criteria definition, weighted prioritization, information retrieval & processing, cost-benefit analysis, comparative judgement, risk assessment, emotional regulation, and commitment & foreclosure) and testing (e.g., mathematical concepts, i.e., in silico methods for thermodynamics and energy calculations) compounds against the at least one selected drug target.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., searching the publication data to provide an indication, for each of the publication documents, as to whether the respective publication document is associated with one or more drug targets), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., applying a machine learning algorithm to ascribe the ambiguity score to each of the one or more character expressions) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Dependent claims 2-19 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
ingesting publication data, from at least one publication data source, relating to a plurality of publication documents, including historical publication documents and current publication documents (i.e., gathering data).
The additional elements in independent claim 20 include:
a computer device; and
ingest publication data, from at least one publication data source, relating to a plurality of publication documents, including historical publication documents and current publication documents (i.e., gather data).
The additional element of a computer device (claim 20) invokes a computer and/or computer-related components merely as a tool for use in the claimed process, such that it amounts to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore is not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, does not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional element of ingest/ingesting publication data, from at least one publication data source, relating to a plurality of publication documents, including historical publication documents and current publication documents (i.e., gather data) (claims 1 and 20) is merely a pre-solution activity of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as a tool; and/or amount to insignificant extra-solution activity; and as such, when all limitations in claims 1-20 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-19 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1 and 20 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a computer device (claim 20); and gathering data (claims 1 and 20); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
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.
Claims 1 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hatz et al. (“Identification of pharmacodynamic biomarker hypothesis through literature analysis with IBM Watson.” PLoS ONE, 2019, vol. 14(4): e0214619, pp. 1-18).
Independent claims 1 and 20 encompass a method and system for ingesting publication data, from at least one publication data source, relating to a plurality of publication documents, including historical publication documents and current publication documents; searching the publication data to provide an indication, for each of the publication documents, as to whether the respective publication document is associated with one or more drug targets; determining an expected publication parameter for each of the one or more drug targets based on the searched publication data from the historical publication documents, and determining an actual publication parameter for each of the one or more drug targets based on the searched publication data from the current publication documents; and evaluating each of the one or more drug targets for selection based on its actual publication parameter relative to its expected publication parameter.
Hatz et al. teaches a method and system for the identification of pharmacodynamic biomarker hypotheses through literature analysis with IBM Watson.
Regarding independent claims 1 and 20, Hatz et al. shows that pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials, however identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms, and further shows that a biological pathway network typically includes entities (e.g., genes, proteins, and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrent data (Abstract: Background). Hatz et al. further shows a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship (Abstract: Background). Hatz et al. further shows that through natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases, shows further still, using Bruton’s tyrosine kinase (BTK) as a case study, Watson for Drug Discovery’s automatically extracted relationship network (i.e., ‘actual’) was compared with a prominent manually curated physical interaction network (i.e., ‘expected’), and additionally, potential biomarkers for Bruton’s tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers (Abstract: Methods). Finally, Hatz et al. shows that Watson’s natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of BTK in a prominent manually curated physical interaction network, and matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery’s 13,595 ranked genes, with 7 in the top 5% (Abstract: Results).
Thus, Hatz et al. anticipates instant independent claims 1 and 20.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Martin et al. (“Hybrid natural language processing for high-performance patent and literature mining in IBM Watson for Drug Discovery.” IBM Journal of Research and Development, 2018, vol. 62, no. 6, pp. 1-12) and Frijters et al. (“Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases.” PLoS Computational Biology, 2010, vol. 6(9): e1000943, pp. 1-11) and Stoeger et al. (“Large-scale investigation of the reasons why potentially important genes are ignored.” PLoS Biology, 2018, vol. 16(9): e2006643, pp. 1-25) and Choudhury et al. (“Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-Based Discovery Prediction.” arXiv:1907.09395v2 [cs.SI], 2019, pp. 1-22).
Independent claims 1 and 20 broadly encompass a method of ingesting publication data relating to a plurality of publication documents, searching the publication data for an indication of whether a publication document is associated with one or more drug targets, and determining whether there are more or fewer current publication documents associated with the one or more drug targets than expected based on the historical publication documents.
Dependent claims 2-19 broadly comprise steps that further define the method for searching the publication data for information, e.g., citation data, drug targets, and character expressions for drug targets (e.g., synonyms); classifying the character expressions; and using a machine learning algorithm to score the data.
Martin et al. is directed to a method for using hybrid natural language processing for high-performance patent and literature mining in IBM Watson for drug discovery.
Frijters et al. is directed to a method of literature mining for the discovery of hidden connection between drugs, genes, and diseases.
Stoeger et al. is directed to a large-scale investigation of the reasons why potentially important genes are ignored.
Choudhury et al. is directed to a method for mining the temporal evolution of knowledge graphs and genealogical features for literature-based discovery prediction.
Regarding independent claims 1 and 20, Martin et al. shows a framework for high-throughput natural language processing (NLP) that includes a computing infrastructure for ingestion (content loading, processing, annotation, indexing, and so on) of literature into Watson for Drug Discovery (WDD) (page 6, col. 2, para. 5) including medical and intellectual property literature (page 6, col. 1, paras. 3-4); before WDD reads a single document, it is loaded with foundational knowledge that allows it to makes sense of the information found in free text, and this foundational knowledge comprises dictionaries (or thesauri) of names and synonyms of key concepts in the domain, as well as ontology labels and other types of structured data, as well as synonyms for each name that allow for WDD to have a wide range and understanding of each concept and the multitude of ways that it can be expressed in the literature, and further shows that the key concepts that WDD is presently trained in are genes, drugs and other chemicals, and conditions (i.e., diseases, signs, and symptoms) (page 2, col. 2, para. 3).
Regarding independent claims 1 and 20, Martin et al. does not explicitly show searching publication data to determine whether it is associated with one or more drug targets; determining an expected publication parameter for each of the one or more drug targets based on the searched publication data from the historical publication documents, and determining an actual publication parameter for each of the one or more drug targets based on the searched publication data from the current publication documents; or evaluating each of the one or more drug targets for selection based on its actual publication parameter relative to its expected publication parameter.
Regarding independent claims 1 and 20, Frijters et al. shows a web-based tool that mines the Medline database for novel relationships between genes, diseases, drugs, and pathways, and demonstrates results that show using hidden relationships, the tool can successfully identify novel disease-related genes, generate hypotheses on drug mode of actin and predict novel lead compound applications (page 7, col. 1, Discussion); and further shows that the tool identified several novel targets for known drugs, based on an algorithm and text corpus, which indicates that mining of literature is an interesting and fruitful approach to identify new drug-target relations, which is a first step in developing drugs towards new applications (page 7, col. 2, para. 3).
Regarding independent claims 1 and 20, Stoeger et al. shows that a small set of identifiable chemical, physical, and biological properties of genes can allow for accurate prediction of the number of publications on individual genes, the year of their first report, and the development of drugs against disease-associated genes, and further shows that by explicitly identifying the reasons for gene-specific bias and performing a meta-analysis of existing computational and experimental knowledge bases, gene-specific strategies can be described for the identification of important but hitherto ignored genes that can open novel directions for future investigation (Abstract).
Regarding independent claims 1 and 20, Choudhury et al. shows that a literature-based discovery process identifies the important but implicit relations among information embedded in published literature, and that existing techniques attempt to identify the hidden or unpublished connections between information concepts within published literature, however, these techniques overlook the concept of predicting the future and emerging relations among scientific knowledge components encapsulated within the literature (Abstract); and further shows a method using novel features to successfully predict the future literature-based discoveries, i.e., the emerging connections, using temporal importance, communities defined by genealogical relations, and the relative importance of temporal citation counts, which were input into a recurrent neural network to forecast the feature values and predict the future relations between different scientific concepts represented by user selected keywords, with the method showing high performance rates suggesting that the features are supportive both in predicting the future literature-based discoveries and emerging trend analysis (Abstract).
Regarding dependent claim 2, Martin et al. further shows that standardizing characters in the text avoids incorrect annotations, minimizes dictionary sizes, and reduces annotator model complexity (page 6, col. 2, para. 3); and extracting semantic relationships, i.e., connections, between entities that are identified by WDD parsing the literature (page 5, col. 2, para. 2).
Regarding dependent claims 3, 10, and 12, Martin et al. further shows that a hierarchical classification of gene entities is enabled through incorporation of Gene Ontology data, comprising approximately 150,000 associations between a gene entity and an ontology label, which allow WDD to compare, identify, and suggest gene entities that are ontologically similar to those in a user’s query (page 3, col. 1, paras. 2-5).
Regarding dependent claim 4, Martin et al. further shows that there are approximately 91,000 condition entities and 600,000 synonyms for those entities in WDD’s dictionary, which were compiled by reviewing and merging data from sources found in the UMLS (Unified Medical Language System) (page 3, col. 1, para. 3).
Regarding dependent claims 5, 6, and 11, Martin et al. further shows that data extracted from each of the structured sources are converted into an internal format and then standardized to the WDD known entities, and that this standardization process takes the curated data source entity type (e.g., gene or condition) and its name or other representation (e.g., chemical structure) and finds the corresponding entity from the WDD runtime dictionary (page 3, col. 2, para. 4); and further shows that in order to normalize an ambiguous gene synonym, a predictive context model of the possible canonical genes is needed, and that this context model is built from selected curated documents and comprises a list of words that occur in those documents together with each gene, plus score values that denote the frequency of this co-occurrence, and in addition to context words, the gene normalization model incorporates metadata associated with each document and with the canonical gene entities, e.g., Medical Subject Headings (MeSH), which are manually curated for each document by the NLM, are used for disambiguating gene annotations made on the MEDLINE corpus, and like the score system for context words, each MeSH term is stored with a value that represents how often that term co-occurs with the gene (page 5, col. 1, para. 3, and col. 2, para. 1).
Regarding dependent claims 7 and 8, Choudhury et al. further shows that statistical bibliography or bibliometrics has been supporting researchers to address the challenges related to the rapid growth of scholarly publications and scientific knowledge, and by employing two network-based methods (i.e., co-citation and keyword co-occurrences network), bibliographic coupling method in bibliometrics enabled the authors to explore the structure of scientific and technical knowledge, and that while the co-citation focuses on the structure of scientific communication by analyzing citation links, keyword co-occurrence network (KCN) or co-word network, focuses on knowledge components and knowledge structure by examining co-appearances of keywords found in the literature, and is therefore also known as a knowledge graph. Choudhury et al. further shows that the co-appearance of two author selected keywords in an article defines a certain intrinsic relationship between them whereas multiple such instances denote the strength of their relationships, and representing such co-occurring relationships between the knowledge entities, KCN bears both theoretical and practical implications including literature-based discovery (page 1, bottom, and page 2, top).
Regarding dependent claim 9, Choudhury et al. further shows temporal variations of genealogical traits demonstrated by keywords, where the color codes represent the genealogical communities of keywords (Figure 4).
Regarding dependent claim 13, Stoeger et al. further shows using machine learning methods to predict the number of publications on individual genes, the year of the first publication, and the existence of related medical drugs (page 1, Author Summary).
Regarding dependent claim 14, Stoeger et al. further shows using data sources for the linkage of genes to publications (page 11, bottom) and further shows that drugs and their targets were obtained from DrugBank (page 12, para. 5).
Regarding dependent claim 15, Frijters et al. further shows that a commonly used method to establish relationships between biomedical concepts from literature is co-occurrence, and further shows a tool that mines the literature for new relationships between biomedical concepts, and that is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid, which makes the tool useful for unraveling the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs (Abstract).
Regarding dependent claim 16, Choudhury et al. further shows that keyword co-occurrence networks (KCNs) and network analysis methods are found to be supportive in identifying technological trends, analyze research topics and follow their evolution and track the development of innovation system research (page 19, top); and further shows the link prediction methodology of network science describes the associations between different concepts/keywords/topics where the links represent their semantic or co-occurrence relationships (page 3, bottom).
Regarding dependent claim 17, Choudhury et al. further shows using dynamic KCNs for different scenarios where new edges emerge each year when new keywords co-appear in articles, and that new keywords form an edge with old (existing) keywords (appeared in previous years), and finally, that edges are formed in a year between two old keywords from the previous year(s), where these old keywords appeared in different articles but not co-appeared in the same article (page 5, bottom).
Regarding dependent claims 18 and 19, Frijters et al. further shows that several of the newly found relationships were validated using independent literature sources, and in addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay (Abstract).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Martin et al. by incorporating methods for mining literature to discover novel drug targets, as shown by Frijters et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Martin et al. with the methods of Frijters et al., because Frijters et al. shows discovering drug targets by mining literature to find hidden connections between drugs, genes and diseases. This modification would have had a reasonable expectation of success given that both Martin et al. and Frijters et al. disclose methods for computing-based techniques for mining literature for associations between biomedical concepts such as genes, diseases, and cellular processes.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Martin et al. by incorporating methods for predicting the number of publications on individual genes, as shown by Stoeger et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Martin et al. with the methods of Stoeger et al., because Stoeger et al. shows that by explicitly identifying the reasons for gene-specific bias and performing a meta-analysis of existing computational and experimental knowledge bases, gene-specific strategies can be determined for the identification of important but hitherto ignored genes that can open novel directions for future investigation. This modification would have had a reasonable expectation of success given that both Martin et al. and Stoeger et al. disclose methods for mining publication data for information on individual genes.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Martin et al. by incorporating methods for predicting the future and emerging relations among scientific knowledge components encapsulated within the literature, as shown by Choudhury et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Martin et al. with the methods of Choudhury et al., because Choudhury et al. shows that existing techniques from Information Retrieval (IR) and Natural Language Processing (NLP) attempt to identify the hidden or unpublished connections between information concepts within published literature, but overlook the concept of predicting emerging trends. This modification would have had a reasonable expectation of success given that both Martin et al. and Choudhury et al. disclose methods for mining scientific publication data for information to aid informed decision making in life science research and/or other fields.
Thus, the instant claimed invention is prima facie obvious.
Conclusion
No claims are allowed.
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this application.
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/STEVEN W. BAILEY/Examiner, Art Unit 1687
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