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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/22/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Further, as noted on the website where the refence can be obtained at: https://discovery.dundee.ac.uk/en/studentTheses/machine-learning-for-novel-therapeutic-target-identification-and-/ , the Embargo End Date (the date where the reference would be published and available to the public) is 10/31/2023, not 01/01/2022. Correction has been indicated in the considered IDS.
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, 4-5, 7-8, 11, 14, 19, 25-28, and 33-40 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
Claims 1, 4-5, 7-8, 11, 14, 19, 25-28, and 33-40 are related to methods (i.e., a process). Accordingly, these claims are all within at least one of the four statutory categories.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Representative independent claim 1 includes limitations that recite at least one abstract idea as underlined in the following limitations. Specifically, independent claim 1 recites:
A method for drug target selection, the method comprising computer-implemented steps of:
ingesting biomedical publication data from at least one biomedical publication data source that includes text-based biomedical publication documents;
searching the biomedical publication data to identify occurrences of terms indicating a plurality of genes and occurrences of terms indicating at least one disease;
defining a vocabulary comprising a plurality of terms from the biomedical publication data, the plurality of terms comprising the terms indicating the plurality of genes and the terms indicating the at least one disease;
training a defined language model using the vocabulary to obtain vector representations of the plurality of terms; and
for the at least one disease:
determining likelihood scores for the plurality of genes based on the vector representations, the likelihood scores indicating likelihoods of the plurality of genes co-occurring with the at least one disease in the biomedical publication data;
generating a ranking of the plurality of genes according to the likelihood scores; and
selecting at least one gene from the plurality of genes as a drug target for the at least one disease based on the ranking.
The Examiner submits that the foregoing underlined limitations constitute a “mental process”, as the following abstract limitations are related to observations and analysis that can be practically performed in the human mind:
“searching” the biomedical publication data to “identify” occurrences of terms indicating a plurality of genes and occurrences of terms indicating at least one disease, which are abstract limitations of observation and analysis of the publication data to further observe the occurrence of terms,
“defining” a vocabulary comprising a plurality of terms from the biomedical publication data, the plurality of terms comprising the terms indicating the plurality of genes and the terms indicating the at least one disease, which are abstract limitations of analysis of the publication data to determine the vocabulary that includes the terms as claimed,
for the at least one disease, “determining” likelihood scores for the plurality of genes, the likelihood scores indicating likelihoods of the plurality of genes co-occurring with the at least one disease in the biomedical publication data, which are abstract limitations of analysis of the vocabulary to generate the likelihood scores for the genes, and further analysis to determine that the co-occurrence of the genes and the disease,
“generating” a ranking of the plurality of genes according to the likelihood scores, which recites further abstract limitation of analysis of the previously generated abstract scores to rank them,
“selecting” at least one gene from the plurality of genes as a drug target for the at least one disease based on the ranking, which recites an abstract limitation of further analysis of the previously generated ranking to select the gene for the drug target.
Accordingly, the claim recites the steps for drug target selection that can practically be performed in the human mind.
The abstract idea recited in claims 36 and 39 are similar to that of claim 1.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., processor) and will be discussed in further detail below.
Accordingly, the claim as a whole recites at least one abstract idea.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claim 4 recites further abstract limitations of “searching” the publication data to “identify” occurrences of terms indicating a gene and then “converting” the occurrences of terms into a standard gene term, further describing the abstract idea of the analysis that can practically be performed in the human mind. Claim 5 recites further abstract limitations of “searching” the publication data to “identify” occurrences of terms indicating a disease and then “converting” the occurrences of terms into a standard disease term, further describing the abstract idea of the analysis that can practically be performed in the human mind. Claim 7 recites further abstract limitations of “searching” the publication data by “identifying” terms occurring at least a threshold number of times in the publication data and then “defining” the vocabulary from the terms occurring at the threshold times, further describing the abstract idea of the analysis that can practically be performed in the human mind. Claim 8 recites further abstract limitations of “searching” the publication data by “identifying” terms comprising unigrams and “generating” phrases of n-grams, where n is greater than or equal two, based on the unigrams, further describing the abstract idea of the analysis that can practically be performed in the human mind. Claims 14 and 37 recite further abstract limitations of “generating” a prediction of the plurality of terms from the contextual data, further describing the abstract idea of the analysis that can practically be performed in the human mind. Claim 25 recites further abstract limitations of “designing” a drug discovery project for developing a drug for the disease based on the gene being the drug target for the project, further describing the abstract idea of the analysis that can practically be performed in the human mind. Claim 33 recites further abstract limitations of “defining” the vocabulary from the n-grams based on “determining” that the phrases of n-grams occur a threshold of times, further describing the abstract idea of the analysis that can practically be performed in the human mind. Claim 34 recites further abstract limitations of “defining” the vocabulary from the n-grams based on “determining” that the phrases of n-grams satisfy a threshold mutual information score, further describing the abstract idea of the analysis that can practically be performed in the human mind.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A method for drug target selection, the method comprising computer-implemented steps of (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
ingesting biomedical publication data from at least one biomedical publication data source that includes text-based biomedical publication documents (merely data gathering steps as noted below, see MPEP 2106.05(g) and Versata Dev. Group, Inc. v. SAP Am., Inc.);
searching the biomedical publication data to identify occurrences of terms indicating a plurality of genes and occurrences of terms indicating at least one disease;
defining a vocabulary comprising a plurality of terms from the biomedical publication data, the plurality of terms comprising the terms indicating the plurality of genes and the terms indicating the at least one disease;
training a defined language model using the vocabulary to obtain vector representations of the plurality of terms (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)); and
for the at least one disease:
determining likelihood scores for the plurality of genes based on the vector representations, the likelihood scores indicating likelihoods of the plurality of genes co-occurring with the at least one disease in the biomedical publication data;
generating a ranking of the plurality of genes according to the likelihood scores; and
selecting at least one gene from the plurality of genes as a drug target for the at least one disease based on the ranking.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the overall computer implemented method, and training a defined language model using the vocabulary to obtain vector representations of the plurality of terms, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [Page 20] of the Applicant’s Specification recites the overall generic computing components of processors and memory to perform the steps in a computing environment. [Page 5] recites further the defined language model generally using a Word2Vec algorithm that is trained in a generic matter using the vocabulary as described in [Page 17] using CBOW. [Page 5] recites the generation of the vectors related to neurons of a neural network model, however the configuration and generation of these vectors are generic and merely recites the use of these generic vectors as a tool for the abstract idea. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Claims 36 and 39 recite similar additional elements as claim 1 and are analyzed in a similar manner.
Regarding the additional limitation of ingesting biomedical publication data from at least one biomedical publication data source that includes text-based biomedical publication documents, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g)). [Page 3] of the Applicant’s Specification recites receiving the biomedical publication data from publication data sources The use of the data source to retrieve data form it is used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities.
Claim 36 recites similar additional elements as claim 1 and is analyzed in a similar manner.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to select drug targets, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claim 11 recites further additional elements of generating the vectors utilizing weights of neurons of a NN, however there is no specific implementation of the NN to generate the vector representations and thus merely recites the use of generic computing components as a tool to carry out the abstract idea. Claims 14 and 37 recite further additional elements of the training step using extracted contextual data, however there is no specific implementation of the training data to train the model and thus merely recites the use of generic computing components as a tool to carry out the abstract idea. Claim 19 recites further additional elements determining a distance metric between the vectors, however this process is generically recited and does not provide a technical improvement and thus merely recites the use of the distance between vectors as a tool to carry out the abstract idea. Claims 26-28 and 40 recite an additional element of performing the designed drug discovery project and describes performing the actions of selecting and testing compounds against the gene for therapeutic effects and synthesizing the compound and determining the properties of the compound; these actions would have been determined previously in the “designing” the drug discovery project and are merely being carried out without describing an improvement or specific implementation of the structure of the drug discovery project, thus reciting insignificant post solution activity. Claims 35 and 38 recites further additional elements describing the training of the model probability distribution of contextual data from the terms, however there is no specific implementation of this step to the training of the model, and does not recite a technical improvement using this process.
Thus, taken alone and in ordered combination, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding the additional limitations of the overall computer implemented method, and training a defined language model using the vocabulary to obtain vector representations of the plurality of terms, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [Page 20] of the Applicant’s Specification recites the overall generic computing components of processors and memory to perform the steps in a computing environment. [Page 5] recites further the defined language model generally using a Word2Vec algorithm that is trained in a generic matter using the vocabulary as described in [Page 17] using CBOW. [Page 5] recites the generation of the vectors related to neurons of a neural network model, however the configuration and generation of these vectors are generic and merely recites the use of these generic vectors as a tool for the abstract idea. The additional elements recite the use of generic computing components with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception.
Claims 36 and 39 recite similar additional elements as claim 1 and are analyzed in a similar manner.
Regarding the additional limitation of ingesting biomedical publication data from at least one biomedical publication data source that includes text-based biomedical publication documents, this is merely pre-solution activity. The Examiner submits that this additional limitation merely adds insignificant extra-solution activity of collecting data to the at least one abstract idea in a manner that does not meaningfully limit the at least one abstract idea (see MPEP § 2106.05(g) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”). [Page 3] of the Applicant’s Specification recites receiving the biomedical publication data from publication data sources The use of the data source to retrieve data form it is used to perform actions for the system including data gathering for the abstract idea, and thus recites insignificant pre-solution activities and does not recite significantly more than the judicial exception. The retrieval of the data from a data source recites well understood, routine, and conventional activity.
Claim 36 recites similar additional elements as claim 1 and is analyzed in a similar manner.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exceptions for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1, 4-5, 7-8, 11, 14, 19, 25-28, and 33-40 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
The following references have been considered as relevant, however have not been used in the above rejections and do not teach the invention individually nor in combination:
US-20210090694-A1 to Colley et al. teaches of a system optimized for research and including clinical record data of a database for determining services to be given to the patient.
WO-2022140642-A1 to Bontrager et al. teaches of obtaining genomic data of a cancerous cell to then use a hierarchical rule set with nomenclature matches to determine treatment options for the patient.
NPL “Discovering gene-disease associations with biomedical word embeddings” to Mitra et al. teaches of training a model with published medical articles to be used for determining gene and disease pairings.
These references do not teach aspects of the current invention including but not limited to: “defining a vocabulary comprising a plurality of terms from the biomedical publication data, the plurality of terms comprising the terms indicating the plurality of genes and the terms indicating the at least one disease; training a defined language model using the vocabulary; generating, utilizing the defined language model, vector representations of the plurality of terms; and for the at least one disease: determining likelihood scores for the plurality of genes based on the vector representations, the likelihood scores indicating likelihoods of the plurality of genes co- occurring with the at least one disease in the biomedical publication data”
Conclusion
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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686
Prosecution Insights