DETAILED ACTION
The Applicant’s filing, received 28 November 2022, 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 1-9 are pending.
Claims 1-9 are rejected.
Claims 1 and 9 are objected to.
Priority
This application claims benefit of 63/383,749, filed 15 November 2022.
Therefore, the effective filing date of the claimed invention is 15 November 2022.
Information Disclosure Statement
No Information Disclosure Statement (IDS) has been received.
The listing of references in the specification is not a proper information disclosure statement. See the specification throughout, for example at page 66, para. [0255]). 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Drawings
The replacement drawings received 01 May 2024 are not accepted.
The drawings received 28 November 2022 and the replacement drawings received 17 February 2025 are objected to as failing to comply with 37 CFR 1.84(t) because:
The sheets of drawings should be numbered in consecutive Arabic numerals, starting with 1, within the sight as defined in paragraph (g) of this section, and the number of each sheet should be shown by two Arabic numerals placed on either side of an oblique line, with the first being the sheet number and the second being the total number of sheets of drawings, with no other marking.
The drawings received 28 November 2022 and the replacement drawings received 17 February 2025 are further objected to as failing to comply with 37 CFR 1.84(p) because:
Reference characters not mentioned in the description shall not appear in the drawings. Reference characters mentioned in the description must appear in the drawings. For example:
Figure 9 in the drawings received 28 November 2022 shows reference #’s 900, 913, and 914, however the specification does not mention these reference #’s.
The drawings received 28 November 2022 and the replacement drawings received 17 February 2025 are further objected to as failing to comply with 37 CFR 1.84(p) because:
The reference characters (i.e., numerals) do not match the view numbers in multiple figures. For example:
For example, para. [0181] in the specification refers to Fig. 12 in the drawings and discusses various types of biomedical data are gathered from healthcare providers and hospitals (1201), laboratories and academic journals (1202), and public internet databases (1203) and stored in a central database 1204 that is connected to the cloud network. However, these reference numbers (and all other 1200 series reference numbers) appear in Fig. 11, while Fig. 12 contains the 1300 series reference numbers.
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. 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.
Specification
The abstract of the disclosure is objected to because:
In line five “…and disease data extracted extracted from one of…” the word “extracted” should appear only once.
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claim 1 is objected to because of the following informalities: The semicolon after the word “comprising” in line two should be replaced with a colon.
Claim 9 is objected to because of the following informalities: The semicolon after the word “comprising” in line two should be replaced with a colon.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is indefinite for reciting “extracting a first set of… from one of a database and documents” because it is not clear as to whether the limitation “from one of” means that the extracting is performed using either a database or alternatively, using documents; and it is further not clear as to whether the documents are data stored within the database, or alternatively, a data source separate from the database.
Claims 2-8 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1.
Claim 1 is further indefinite for reciting “extracting a second set of… extracted from one of a database and documents” because it is not clear as to whether the limitation “from one of” means that the extracting is performed using either a database or alternatively, using documents; and it is further not clear as to whether the documents are data stored within the database, or alternatively, a data source separate from the database; and it is still further not clear as to whether there is a second “extracting” step performed within this clause.
Claims 2-8 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1.
Claim 1 is further indefinite for reciting “creating nodes… extracted extracted from one of a database and documents” because it is not clear as to whether the limitation “from one of” means that the data has been extracted from either a database or alternatively, extracted from documents; and it is further not clear as to whether the documents are data stored within the database, or alternatively, a data source separate from the database; and it is still further not clear as to whether there is a second “extracted” step referenced within this clause.
Claims 2-8 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1.
Claim 1 is further indefinite for reciting training multiple machine learning (ML) algorithms on a set of extracted data with matching empirical results, because the claim previously recites extracting both a first and second set of data, and therefore it is not clear as to which set of extracted data is used for training the ML algorithms.
Claims 2-8 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1.
Claim 1 recites the limitation "the relative association of the nodes" in line seventeen. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites “determining relationships between the nodes,” and therefore it is not clear as to which association is being referred to.
Claims 2-8 are indefinite for depending from claim 1 and for failing to remedy the indefiniteness of claim 1.
Claim 2 recites the limitation "the software of claim 1" in line one. There is insufficient antecedent basis for this limitation in the claim, because the broadest reasonable interpretation of “software” comprises instructions that tell a computer what to do, and therefore it is not clear as to which set of instructions recited by claim 1 are being referred to by claim 2.
Claim 2 recites the limitation "the ML algorithm" in line one. There is insufficient antecedent basis for this limitation in the claim, because claim recites “training multiple machine learning (ML) algorithms” and therefore it is not clear as to which algorithm claim 2 is referring to.
Claim 3 recites the limitation "the software of claim 1" in line one. There is insufficient antecedent basis for this limitation in the claim, because the broadest reasonable interpretation of “software” comprises instructions that tell a computer what to do, and therefore it is not clear as to which set of instructions recited by claim 1 are being referred to by claim 3.
Claim 4 is indefinite for depending from claim 3 and for failing to remedy the indefiniteness of claim 3.
Claim 3 recites the limitation “the validated data” in line one. There is insufficient antecedent basis for this limitation in the claim, because claim 1 does not recite the limitation “validated data.”
Claim 4 is indefinite for depending from claim 3 and for failing to remedy the indefiniteness of claim 3.
Claim 5 recites the limitation "the software of claim 1" in line one. There is insufficient antecedent basis for this limitation in the claim, because the broadest reasonable interpretation of “software” comprises instructions that tell a computer what to do, and therefore it is not clear as to which set of instructions recited by claim 1 are being referred to by claim 5.
Claim 7 is indefinite for depending from claim 5 and for failing to remedy the indefiniteness of claim 5.
Claim 6 recites the limitation "the software of claim 1" in line one. There is insufficient antecedent basis for this limitation in the claim, because the broadest reasonable interpretation of “software” comprises instructions that tell a computer what to do, and therefore it is not clear as to which set of instructions recited by claim 1 are being referred to by claim 6.
Claim 8 recites the limitation "the software of claim 1" in line one. There is insufficient antecedent basis for this limitation in the claim, because the broadest reasonable interpretation of “software” comprises instructions that tell a computer what to do, and therefore it is not clear as to which set of instructions recited by claim 1 are being referred to by claim 8.
Claim 7 is further indefinite for reciting “wherein the orthologous animal is one of a zebrafish and mouse” because it is not clear as to whether the limitation “is one of a zebrafish and mouse” means that the claim requires that the orthologous animal is both a zebrafish and mouse, or whether the claim requires that the orthologous animal is either a zebrafish or a mouse.
Claim 9 is indefinite for reciting “one of a computer implemented database platform, application, and web application” because it is not clear as to whether the claim requires a user to choose only one from the group of a computer implemented database platform, application, and web application.
Claim 9 is further indefinite for reciting “a first set of… extracted from one of a database and documents” because it is not clear as to whether the limitation “from one of” means that the data is extracted from either a database or alternatively, extracted from documents; and it is further not clear as to whether the documents are data stored within the database, or alternatively, a data source separate from the database.
Claim 9 is further indefinite for reciting “nodes on a network… data extracted extracted from one of a database and documents” because it is not clear as to whether the limitation “from one of” means that the data has been extracted from either a database or alternatively, extracted from documents; and it is further not clear as to whether the documents are data stored within the database, or alternatively, a data source separate from the database; and it is still further not clear as to whether there is a second “extracted” step referenced within this clause.
Claim 9 is further indefinite for reciting multiple machine learning (ML) algorithms trained on a set of extracted data with matching empirical results, because the claim previously recites both a first set and a second set of extracted biological data, and therefore it is not clear as to which set of extracted data the ML algorithms are trained on.
Claim 9 recites the limitation "the relative association" in line seventeen. There is insufficient antecedent basis for this limitation in the claim, because the claim previously recites “relationship data between the nodes” and therefore it is not clear as to which association is being referred to.
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 1-9 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
Claim 9 recites the limitation “a first set of biological data containing at least gene data, phenotype data, and disease data extracted from one of a database and documents.” This limitation is interpreted to recite a product-by-process limitation with the product being the set of biological data, and further interpreted to not require the process of obtaining the data (i.e., extracting the data from a database).
Claim 4 recites the limitation “there is an ML algorithm trained for each phenotype in the phenotypic data.” This limitation is interpreted to recite a product-by-process limitation with the product being the trained algorithm, and further interpreted to not require the process of producing the product, i.e., active steps of training an algorithm.
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 1-8 recite a computer-implemented method (i.e., a process); and claim 9 recites a system comprising a computer implemented database platform (i.e., a machine and/or a 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:
creating nodes on a network pertaining to each individual gene, phenotype, and disease data extracted from one of a database and documents (i.e., mental processes and mathematical concepts);
training multiple machine learning (ML) algorithms on a set of extracted data with matching empirical results (i.e., mathematical concepts);
using at least one of the ML algorithms and the second set of biological data for determining relationships between the nodes (i.e., mental processes and mathematical concepts); and
creating at least one of a gene-disease association score, gene-phenotype association score, and disease-phenotype association score for the relationships based on the relative association of the nodes (i.e., mental processes and mathematical concepts).
Independent claim 9 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
a first set of biological data containing at least gene data, phenotype data, and disease data extracted from one of a database and documents (i.e., mental processes);
a second set of biological data containing at least one of gene ontology data, KEGG data, orthology data, protein domains, gene expression data, protein expression data, anatomy labels, protein sequences, and protein sequence embeddings (i.e., mental processes);
nodes on a network pertaining to each individual gene, phenotype, and disease data extracted from one of a database and documents (i.e., mental processes and mathematical concepts);
multiple machine learning (ML) algorithms trained on a set of extracted data with matching empirical results (i.e., mathematical concepts);
relationship data between the nodes created using at least one of the ML algorithms and the second set of biological data (i.e., mental processes and mathematical concepts); and
at least one of a gene-disease association score, gene-phenotype association score, and disease-phenotype association score created based on the relative association of the relationship between the nodes (i.e., mental processes and mathematical concepts).
Dependent claims 2-7 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:
the ML algorithm trains with multiple heterogeneous datasets simultaneously (i.e., mathematical concepts).
Dependent claim 3 further recites:
the validated data contains phenotypic data (i.e., mental processes).
Dependent claim 4 further recites:
there is an ML algorithm trained for each phenotype in the phenotypic data (i.e., mental processes and mathematical concepts).
Dependent claim 5 further recites:
the biological data also includes at least one of human gene name, human gene description, human phenotype name, human phenotype description, human disease name, human disease identifier, orthologous animal gene name, orthologous animal gene description, orthologous animal phenotype data, orthologous animal disease name, and orthologous animal disease identifier (i.e., mental processes).
Dependent claim 6 further recites:
the biological data also contains at least one of gene ontology data, KEGG data, orthology data, protein domains, gene expression data, protein expression data, anatomy labels, protein sequences, and sequence embeddings (i.e., mental processes).
Dependent claim 7 further recites:
the orthologous animal is one of a zebrafish and mouse (i.e., mental processes).
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., creating at least one of a gene-disease association score, gene-phenotype association score, and disease-phenotype association score for the relationships based on the relative association of the nodes), 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., using at least one of the ML algorithms and the second set of biological data for determining relationships between the nodes) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-9 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)).
Dependent claims 2-7 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:
a computer;
extracting a first set of biological data containing at least gene data, phenotype data, and disease data from one of a database and documents (i.e., gathering data);
extracting a second set of biological data containing at least one of gene ontology data, KEGG data, orthology data, protein domains, gene expression data, protein expression data, anatomy labels, protein sequences, and protein sequence embeddings extracted from one of a database and documents (i.e., gathering data); and
displaying, to a user, at least one of the gene-disease association score, gene-phenotype association score, and disease-phenotype association score (i.e., displaying data).
The additional elements in independent claim 9 include:
a computer implemented database platform; and
at least one of the gene-disease association score, gene-phenotype association score, and disease-phenotype association score displayed to a user (i.e., displayed data).
The additional element in dependent claim 8 includes:
the first set of biological data and the second set of biological data are extracted from the same database (i.e., gathering data).
The additional elements of a computer (claim 1); and a computer implemented database platform (claim 9); invoke a computer and/or computer-related components merely as tools for use in the claimed process, such that they amount to no more than mere instructions to apply the exceptions using a generic computer (MPEP 2106.05(f)), and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional elements of extracting a first set of biological data containing at least gene data, phenotype data, and disease data from one of a database and documents (i.e., gathering data) (claim 1); extracting a second set of biological data containing at least one of gene ontology data, KEGG data, orthology data, protein domains, gene expression data, protein expression data, anatomy labels, protein sequences, and protein sequence embeddings extracted from one of a database and documents (i.e., gathering data) (claim 1); and the first set of biological data and the second set of biological data are extracted from the same database (i.e., gathering data) (claim 8); are 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)).
The additional elements of displaying, to a user, at least one of the gene-disease association score, gene-phenotype association score, and disease-phenotype association score (i.e., displaying data) (claim 1); and at least one of the gene-disease association score, gene-phenotype association score, and disease-phenotype association score displayed to a user (i.e., displayed data) (claim 9); are merely a post-solution activity of outputting data for display – 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 tools; and/or amount to insignificant extra-solution activity; and as such, when all limitations in claims 1-9 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 1-9 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 2-7 do not recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1 and 9 and dependent claim 8 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 (claim 1); a computer implemented database platform (claim 9); gathering data (claims 1, 8, and 9); and displaying data (claims 1 and 9); 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 1-9 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 1-9 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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 8, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ata et al. (“Recent advances in network-based methods for disease gene prediction.” Briefings in Bioinformatics, 2021, vol. 22(4), pp. 1-15) and Sikandar et al. (“Analysis for Disease Gene Association Using Machine Learning.” IEEE Access, 2020, vol. 8, pp. 160616-160626).
Independent claims 1 and 9 are directed to a method and a system, respectively, for determining relationships between genes, phenotypes, and diseases, by mapping the data of genes, phenotypes, and diseases to nodes of a graph network, and training machine learning algorithms to determine relationships between the different nodes, and generating association scores based on the relative association of the nodes that describe the relative strength of the association, e.g., gene-disease association, gene-phenotype association, and/or disease-phenotype association.
Dependent claims 2-6 and 8 further define aspects of the machine-learning algorithm and the data used in the claimed method.
Ata et al. is directed to a comprehensive review of network-based methods for disease gene prediction, and an empirical analysis on 14 state-of-the-art methods.
Sikandar et al. is directed to computational methods for the identification of genes associated with diseases.
Regarding independent claims 1 and 9, Ata et al. shows multiple publicly available databases for gene-disease association data (page 8, Table 4) and further shows at least one method that integrates multiple genomic data sources including literature-based phenotypic and literature-based genomic information (page 7, col. 1, para. 1); network-based disease gene prediction that leverages graphs/network data as inputs to predict disease-causing genes (pages 3-4; and Figures 1, 2, and 3); using machine learning algorithms for determining relationships between nodes (pages 5-6; and Table 2); and at least one method for developing an association score (page 7, col. 2, para. 3).
Regarding independent claims 1 and 9, Ata et al. does not explicitly show training multiple machine learning (ML) algorithms on a set of extracted data with matching empirical results.
Regarding independent claims 1 and 9, Sikandar et al. shows that after computing the feature set for different genes, various computational models are trained to classify these genes based on different features into different disease classes (page 160619, col. 2, bottom).
Regarding dependent claim 2, Ata et al. further shows machine learning methods for disease gene prediction using features derived from walks in a heterogeneous phenotype-gene network (page 7, Table 2), which at least suggests the algorithm is trained on heterogeneous datasets; and further shows that there is a motivation to integrate different data sources into a heterogeneous network to improve prediction performance (page 10, col. 1, para. 2).
Regarding dependent claim 3, Ata et al. further shows multiple genomic data sources including literature-based phenotypic information (page 7, col. 1, para. 1).
Regarding dependent claims 5, 6, and 8, Ata et al. further shows that there are several publicly available databases for gene-disease associations as shown in Table 4, and further shows acquiring associations from the OMIM database, and given a specific disease/phenotype (e.g., Alzheimer disease), extracting MIM IDs from the OMIM Morbid Map and retrieving their corresponding protein IDs from the Uniprot conversion tool (page 8, col. 2, para. 2).
Regarding dependent claim 4, Ata et al. does not explicitly show that there is an ML algorithm trained for each phenotype in the phenotypic data.
Regarding dependent claim 4, Sikandar et al. further shows that after computing the feature set for different genes, various computational models are trained to classify these genes based on different features into different disease classes, i.e., Thalassemia, Diabetes, Malaria, and Asthma (page 160619, col. 2, bottom)
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 methods shown by Ata et al. by incorporating methods for training multiple algorithms using multiple heterogeneous datasets, as shown by Sikander et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Ata et al. with the methods of Sikander et al., because Sikander et al. shows methods for using datasets that are downloaded from different sources, extracting different biological and topological features, and training and testing different computational models using the feature set. This modification would have had a reasonable expectation of success given that both Ata et al. and Sikander et al. disclose methods for predicting disease-gene association.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ata et al. and Sikandar et al. as applied to claims 1-6, 8, and 9 above, and further in view of Sing-Blom et al. (“Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses.” PLoS ONE, 2013, vol. 8(5): e58977, pp. 1-17).
Dependent claim 7 further defines the type of orthologous animal data used in the claimed method.
Sing-Blom et al. is directed to the prediction and validation of gene-disease associations using methods inspired by social network analyses.
Regarding dependent claim 7, Ata et al. and Sikandar et al. as applied to claims 1-6, 8, and 9 above does not show the orthologous animal is one of a zebrafish and mouse.
Regarding dependent claim 7, Sing-Blom et al. shows collecting gene-phenotype associations from literature and public databases for eight different (non-human) species, including mouse and zebrafish, and determining orthology relationships between genes in model species and human (page 12, col. 1, para. 1).
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 Ata et al. and Sikandar et al. as applied to claims 1-6, 8, and 9 above by incorporating data of gene-phenotype associations from literature and public databases for eight different (non-human) species, including mouse and zebrafish, as shown by Sing-Blom et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Ata et al. and Sikandar et al. as applied to claims 1-6, 8, and 9 above with the methods of Sing-Blom et al., because Sing-Blom et al. shows integrating functional information from orthologous species into the algorithm that contributes to the algorithm’s predictive performance. This modification would have had a reasonable expectation of success given that both Ata et al. and Sikandar et al. as applied to claims 1-6, 8, and 9 above and Sing-Blom et al. disclose methods for predicting gene-disease associations.
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.
Claims 1-9 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of copending Application No. 18/150,168 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims broadly recite using multiple machine learning models with biological datasets to determine relationships between genes and phenotypes, and the reference claims recite using machine learning models with particular datasets and elements pertaining to zebrafish for determining relationship information between phenotypes and genetic data to predict zebrafish phenotypes. Therefore, reference claims 1-17 anticipate the broader instant claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN W. BAILEY whose telephone number is (571)272-8170. The examiner can normally be reached Mon - Fri. 1000 - 1800.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687
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