DETAILED ACTION This office action is in response to the application filed on 9/26/2023. Claim(s) 1-17 is/are pending and are examined. Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority/Benefit Applicant’s priority claim is hereby acknowledged of CON of PCT/CN2021/083203 03/26/2021 , which papers have been placed of record in the file. Information Disclosure Statement PTO-1449 The Information Disclosure Statement(s) submitted by applicant on 9/26/2023, 3/21/2025, and 2/5/2026 has/have been considered. The submission is in compliance with the provisions of 37 CFR § 1.97. Form PTO-1449 signed and attached hereto. Examiner’s Note – Allowable Subject Matter Claims 4 -7 , 10-11, and 13-14 overcome the prior art and would otherwise be allowable if incorporated into the base claim along with any intervening claims. Further, the claims must overcome the 35 USC 101 rejection below. 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. Claim(s) 1-17 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The analysis is guided by the Supreme Court's two-step framework, described in Mayo and Alice ( Alice Corp. Pty Ltd. v. CLS Bank Int’l , 134 S. Ct. 2347, 2354 (2014) and Mayo Collaborative Servs. V. Prometheus Labs., Inc., 132 S. Ct. 1289, 1296-97 (2012)). Step 1: Is/Are the claim(s) directed to a process, machine, manufacture, or composition of matter? Answer: Yes. Step 2A Prong 1 : Is/Are the claim(s) directed to a law of nature, a natural phenomenon, or an abstract idea, i.e., judicially recognized exceptions ( both individually and as an ordered combination )? Answer: Yes, the claim s 1 and 16 is directed to the m ental process of “ constructing a literature evidence knowledge graph for variant interpretation; and performing evidence extraction on the literature evidence knowledge graph to obtain evidence corresponding to an entity, and constructing, based on the evidence and the database, the variant literature interpretation knowledge base ” beyond the scope of § 101. Dependent claims 2 -15 expand on the identified abstract idea. Step 2A Prong 2 : Is/Are the claim(s) implemented into a practical application ? Answer: No, the limitations of the claim as drafted, is a process that, under its broadest reasonable interpretation, covers implementation of the mental concepts which can be performed by the mind using pencil and paper but for the recitation of generic computer components (i.e., “process or ” and “memory ” ). The claims recite extra solution activity including the ingestion of data by a generic computer . This judicial exception is not integrated into a practical application. Extra solution activity of ingesting data on a generic computer amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, th ese additional element s do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea. Step 2B: Does/Do the claim(s) recite additional elements that when analyzed individually and in ordered combinations amount to significantly more than the judicial exception(s)? Answer: No, the claim(s) ( both individually and as an ordered combinations ) does/do not transform the nature of the claim(s) into a patent-eligible application of the abstract idea (i.e., significantly more than the abstract idea implemented using generic computer components). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element s to perform the processing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, claims are not patent eligible. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. (a)(2) the claimed invention was described in a patent issued under section 151 , or in an application for patent published or deemed published under section 122(b) , in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 1 and 15 , is/are rejected under AIA 35 U.S.C. 102(a)( 1 ) as being anticipated by Vishuo Medical Data Technology Beijing Co Ltd. ( CN 107357924 B ) , translation attached, hereinafter Vishuo . Regarding claim 1 , Vishuo teaches: “ A Natural Language Processing (NLP)-based method for constructing a variant literature interpretation knowledge base, the method comprising: obtaining disease-related literature ( Vishuo, Pg. 5 Ln. 24 – Pg. 6 Ln. 33 teaches collecting cancer related information from national databases and published literature ) ; constructing, based on the disease-related literature, a database of entities associated with genes and variants ( Vishuo, Pg. 5 Ln. 24 – Pg. 6 Ln. 33 and Pg. 6 Ln. 58-59 teaches collecting cancer related information including cancer DNA and genetic variations from national databases and published literature and organizing that information into data frames of information which are stored in a table structure ) ; constructing a literature evidence knowledge graph for variant interpretation ( Vishuo, Pg. 6 Ln. 33 – Pg. 7 Ln. teaches constructing a knowledge graph about the cancer information including the data frames with genetic variant information ) ; and performing evidence extraction on the literature evidence knowledge graph to obtain evidence corresponding to an entity, and constructing, based on the evidence and the database, the variant literature interpretation knowledge base ( Vishuo, Pg. 6 Ln. 33 – Pg. 7 Ln. evidence based clinical annotation models are used to create a medical knowledge graph for the exemplary case of lung cancer which helps medical staff reduce mistakes ) ”. Regarding claim 15 , Vishuo teaches: “ The NLP-based method for constructing the variant literature interpretation knowledge base according to claim 1 ( Vishuo teaches the limitations of the parent claim as discussed above ) , wherein the entity comprises one or more of a gene, a variant, a drug, a disease, and a phenotype ( Pg. 5 Ln. 11-25 teaches entity as drugs, genes and cancer types ) ”. Claim Rejections - 35 USC § 103 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 of this title, 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. Claim(s) 2 -3 , 8, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vishuo , in view of Suarez Saiz (US 2020/0185072 A1) hereinafter Suarez . Regarding claim 2 , Vishuo teaches: “ The NLP-based method for constructing the variant literature interpretation knowledge base according to claim 1, wherein said constructing, based on the disease-related literature, the database of entities associated with genes and variants ( Vishuo teaches the limitations of the parent claims as discussed above )”. Vishuo , does not, but in related art, Suarez teaches: “ constructing an entity extraction model using some literature of the disease-related literature ( Suarez, ¶ 21-24 teaches NLP system analyzes documents for information about the sentiment of a treatment and builds new information for the model for unknown words ) ; performing, through the entity extraction model, entity extraction on the remaining literature in the disease-related literature to obtain an entity name ( Suarez, ¶ 21-24 teaches NLP system analyzes documents for information about the sentiment of a treatment ) ; constructing an entity alignment model ( Suarez, ¶ 23-27 teaches checking information in different locations such as headers ) ; performing, through the entity alignment model, entity alignment on the entity name to obtain an entity standard term corresponding to the entity name ( Suarez, ¶ 23-27 teaches looking for specific words in the document ) ; and constructing, based on the entity name and the entity standard term corresponding to the entity name, the database of entities associated with genes and variants ( Suarez, ¶ 25-35 teaches building the database of names associated with the treatments of the disease ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Vishuo and Suarez, to modify the natural language processing system of Vishuo to include the method to interpret input data as taught in Suarez. The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results . Regarding claim 3 , Vishuo and Suarez teaches: “ The NLP-based method for constructing the variant literature interpretation knowledge base according to claim 2 ( Vishuo and Suarez teach the limitations of the parent claim as discussed above ) , wherein said constructing the entity extraction model using some literature of the disease-related literature comprises: performing entity annotating on certain literature ( Suarez, ¶ 24-25, 30 and 32 teaches annotating the document information ) ; adding a position and an entity classification tag to each word in the entity-annotated literature to obtain an entity tag sequence ( Suarez, ¶ 24-25, 30 and 54-55 teaches annotating the document information and adding the location of the information ) ; constructing a pre-training model of the entity extraction model ( Suarez, ¶ 24-25, 30 and 54-55 teaches annotating the document information and adding the location of the information to build the knowledge graph ) ; and adjusting, by using the entity tag sequence, the pre-training model to obtain the entity extraction model ( Suarez, ¶ 24-25, 30 and 54-55 teaches annotating the document information and adding the location of the information as well as metadata tags ) ”. Regarding claim 8 , Vishuo in view of Suarez teaches: “ The NLP-based method for constructing the variant literature interpretation knowledge base according to claim 2 ( Vishuo and Suarez teach the limitations of the parent claim as discussed above ) , further comprising, subsequent to said performing, through the entity extraction model, the entity extraction on the remaining literature in the disease-related literature to obtain the entity name ( Suarez, ¶ 24-27 teaches searching the information related to the given disease ) : matching the remaining literature with a pre-set entity dictionary and/or a pre-set entity writing pattern to supplement an entity name unrecognized by the entity extraction model ( Suarez, ¶ 21-27 teaches figuring out unknown words and how to determine their intent ) ”. Regarding claim 16 , Vishuo teaches: “ An NLP-based variant literature interpretation method, comprising: the method for constructing the variant literature interpretation knowledge base comprising: obtaining disease-related literature ( Vishuo, Pg. 5 Ln. 24 – Pg. 6 Ln. 33 teaches collecting cancer related information from national databases and published literature ) ; constructing, based on the disease-related literature, a database of entities associated with genes and variants ( Vishuo, Pg. 5 Ln. 24 – Pg. 6 Ln. 33 and Pg. 6 Ln. 58-59 teaches collecting cancer related information including cancer DNA and genetic variations from national databases and published literature and organizing that information into data frames of information which are stored in a table structure ) ; constructing a literature evidence knowledge graph for variant interpretation ( Vishuo, Pg. 6 Ln. 33 – Pg. 7 Ln. teaches constructing a knowledge graph about the cancer information including the data frames with genetic variant information ) ; and performing evidence extraction on the literature evidence knowledge graph to obtain evidence corresponding to an entity, and constructing, based on the evidence and the database, the variant literature interpretation knowledge base ( Vishuo, Pg. 6 Ln. 33 – Pg. 7 Ln. evidence based clinical annotation models are used to create a medical knowledge graph for the exemplary case of lung cancer which helps medical staff reduce mistakes ) ”. Vishuo does not, but in related art, Suarez teaches: “ obtaining an entity name to be interpreted ( Suarez, ¶ 21 user selects domain of interest including a disorder or therapy, or sets of them ) ; and inputting the entity name into a variant literature interpretation knowledge base to obtain an evidence criterion or evidence type ( Suarez, ¶ 22-24 NLP system analyzes documents for information about the sentiment of a treatment ) , an evidence sentence, and an evidence word corresponding to the entity name ( Suarez, Fig. 3A ¶ 41-45 teaches analyzing a sentence to identify words which correspond to the sentiment of the treatment being provided ) , wherein the variant literature interpretation knowledge base is constructed with an NLP -based method for constructing a variant literature interpretation knowledge base ( Suarez, ¶ 25-27 teaches creating a data structure and knowledge graph for the treatment using the information gleaned from the documents ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Vishuo and Suarez, to modify the natural language processing system of Vishuo to include the method to interpret input data as taught in Suarez. The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results . Regarding claim 17 , Vishuo in view of Suarez teaches: “ An electronic device, comprising: a memory ( Vishuo, Pg. 7 Ln. 54 – Pg. 8 Ln. 3 teaches memory ) ; a processor ( Vishuo, Pg. 7 Ln. 54 – Pg. 8 Ln. 3 teaches processor ) ; and a variant literature interpretation program stored in the memory and executable on the processor, wherein the processor, when ex ecuting the variant literature interpretation program ( Vishuo, Pg. 7 Ln. 54 – Pg. 8 Ln. 3 teaches processor, memory and medium to execute method steps ), implements the NLP-based variant literature interpretation method according to claim 16 ( Vishuo in view of Suarez teaches the limitations of the parent claim as discussed above ) ”. Claim(s) 9 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vishuo , in view of Suarez in view of Chen (US 2023/0007965 A1) . Regarding claim 9 , Vishuo in view of Suarez teaches : “ The NLP-based method for constructing the variant literature interpretation knowledge base according to claim 2, wherein said constructing the entity alignment model ( Vishuo and Suarez teach the limitations of the parent claim as discussed above )”. Vishuo and Suarez do not, but in related art, Chen teaches: “ obtaining an entity standard term and its other entity names, and constructing an entity alignment dictionary based on the entity standard term and the other entity names ( Chen, ¶ 27, 31, and 58-59 teaches building dictionary of standard terms with gene information and aligning that information ) ; and/or obtaining the entity standard term, and constructing an entity-aligned regular expression based on the entity standard term ( Chen, ¶ 27, 31, and 58-59 teaches building dictionary of standard terms with gene information and aligning that information ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Vishuo , Chen and Suarez, to modify the natural language processing system of Vishuo and Suarez to include the process genetic input data as taught in Chen . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results . Regarding claim 12 , Vishuo teaches : “ The NLP-based method for constructing the variant literature interpretation knowledge base according to claim 1, wherein the database of entities associated with genes and variants ( Vishuo teaches the limitations of the parent claim as discussed above )”. Vishuo and Suarez do not, but in related art, Chen teaches: “ {entity names: entity standard term} dictionary, (literature identification information, entity standard term) data list, and (literature identification information, entity name) data list ( Chen, ¶ 27, 31, and 58-59 teaches building dictionary of standard terms with gene information and aligning that information ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Vishuo , Chen and Suarez, to modify the natural language processing system of Vishuo and Suarez to include the process genetic input data as taught in Chen . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results . Conclusion In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: See PTO-892. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen T Gundry whose telephone number is (571) 270-0507. The examiner can normally be reached Monday-Friday 9AM-5PM (EST). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /STEPHEN T GUNDRY/ Primary Examiner, Art Unit 2435