CTNF 18/149,768 CTNF 101339 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Status Claims 1-8 are currently pending and under exam herein. Claims 1-8 are rejected. Claims 1-2, 4, and 6-8 are objected to. Priority The instant application also claims benefit to International Application PCT/JP2020/026730 filed on July 8, 2020. Domestic priority benefit is acknowledged. Thus, the effective filing date of Claims 1-8 is July 8, 2020 . Information Disclosure Statement The information disclosure statements (IDS) were filed on 01/04/2023, 08/01/2023, 12/21/2023, 04/25/2024, 11/12/2024, and 04/14/2025. All references in the IDS have been considered by the examiner and attached in this office action. Drawings The Drawings filed on 01/04/2023 are accepted. Specification The Specification filed on 01/04/2023 are accepted. Claim Objections 07-29-01 AIA Claim s 1, 2, 4, and 6-8 are objected to because of the following informalities: Claim 1: “a plurality of pieces of segmented codon data , (comma) obtained by encoding each of the plurality of pieces of segmented genome data into a codon unit on the basis of a codon conversion table , (comma) in which” instead of “in a codon unit”; “based on a plurality of reference codon data , (comma) obtained by encoding a plurality of reference genome data into codon units , (comma) and…” instead of “on the basis of reference codon data obtained by encoding reference genome data to be a reference in the codon unit and…” Claim 2: “wherein the identifying process is configured to identify a position” instead of “wherein the identifying identifies a position”; “a reference inverted index , (comma) in which the code … and an appearance position… are associated with each other,” Claim 4: “the obtaining process obtains a plurality… from a cancer patient” instead of “the obtaining obtains the plurality … of a cancer patient”; “ the generating process for generating the plurality of pieces of segmented codon data generates a plurality” instead of “the generating generates the plurality”; “ the identifying process identifies a type and position of an appearance of a gene mutation“ instead of “the identifying identifies the type and position of the appearance of the gene mutation”; “the generating process for generating the gene mutation inverted index generates a gene mutation inverted index“ instead of “the generating generates the gene mutation inverted index” Claim 6: “diagnosing which cancer type the new patient corresponds to , (comma) on the basis” instead of “corresponds to on the basis” Claim 7: “a plurality of pieces of segmented codon data , (comma) obtained by encoding each of the plurality of pieces of segmented genome data into a codon unit on the basis of a codon conversion table , (comma) in which” instead of “in a codon unit”; “based on a plurality of reference codon data , (comma) obtained by encoding a plurality of reference genome data into codon units , (comma) and…” instead of “on the basis of reference codon data obtained by encoding reference genome data to be a reference in the codon unit and…” Claim 8: “a plurality of pieces of segmented codon data , (comma) obtained by encoding each of the plurality of pieces of segmented genome data into a codon unit on the basis of a codon conversion table , (comma) in which” instead of “in a codon unit”; “based on a plurality of reference codon data , (comma) obtained by encoding a plurality of reference genome data into codon units , (comma) and…” instead of “on the basis of reference codon data obtained by encoding reference genome data to be a reference in the codon unit and…” Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claim 5 is 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. 07-34-05 Claim 5 recites the limitation “each bit”. There are insufficient antecedent basis for this limitations in the claim. 07-34-07 AIA The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. Please see above regarding claim objections. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES ) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon ( Step 2A, Prong 1 ). In the instant application, the claims recite the following limitations that equate to an abstract idea/law of nature/natural phenomenon: Claim 1 recites generating a plurality of pieces of segmented codon data by encoding each of the plurality of pieces of segmented genome data into a codon unit on the basis of a codon conversion table (Abstract Idea: Mental Process). The process of generating codon/amino acid sequences based on a known conversion table and genomic data is something that can be practically done in the human mind and/or with the help of a pen and paper, making this claim limitation a mental process. Claim 1 goes on to recite, identifying, based on a reference codon data obtained by encoding reference genome data, genetic mutations (type and position) that can be seen as differences between the reference codon data and the segmented codon data (Abstract Idea: Mental Process). The act of comparing two codon/amino acid sequences to find the difference between the two sequences and classifying the difference as a genetic mutation (frame shift, deletion, insertion, etc.) is a process that is well known and can be done in the human mind as well. Therefore, this step of identifying the gene mutations would also constitute a mental process. Lastly, claim 1 recites generating a gene mutation inverted index, where the type and position of the gene mutation are associated with each other (Abstract Idea: Mental Process). The step of creating a variable (inverted index) to characterize the type and position of the mutation so that it is easier to find again in the vast genomic data is a simple and obvious act that can be practically performed in the human mind. Hence, this last step of generating a gene mutation inverted index would also constitute as a mental process under abstract ideas. Claim 2 recites that step of identifying the gene mutation consists of identifying the correct position in the reference codon sequence through a reference inverted index, and then comparing the reference codon sequence to the segmented codon sequence in the same position, in order to identify the type and position of the gene mutation (Abstract Idea: Mental Process). Again, similar to above, this process of comparing two codon/amino acid sequences to identify differences and classifying the difference as a genetic mutation is a process that can be practically done in the human mind. Hence, this claim limitation would constitute a mental process. Claim 3 recites generating a gene mutation inverted index (Abstract Idea: Mental Process). The process creating a variable (inverted index) to identify the position and type of gene mutation is a process that can be done in the human mind, making this limitation a mental process. Claim 3 then goes on to generate data, by combining the identifier that identities a patient, the gene mutation inverted index, and the codon conversion table (Abstract Idea: Mental Process). The process of combining various known data is a process that can be practically done in the human mind or with the assistance of a pen and paper, making this limitation a mental process as well. Claim 4 recites generating a plurality of pieces of segmented codon data associated with a cancer patient (Abstract Idea: Mental Process). The process of generating codon/amino acid data based on known genomic data is something that can be practically done in the human mind and/or with the help of a pen and paper, making this claim limitation a mental process. Claim 4 goes on to recite, identifying, based on a reference codon data from a healthy person and the plurality of segmented codon data associated with the cancer patient, genetic mutations (type and position) (Abstract Idea: Mental Process). The act of comparing two codon/amino acid sequences to find the difference between the two sequences and classifying the difference as a genetic mutation (frame shift, deletion, insertion, etc.) is a process that is well known and can be done in the human mind as well. Therefore, this step of identifying the gene mutations would also constitute a mental process. Lastly, claim 4 recites generating a gene mutation inverted index corresponding to the cancer patient, using the type and position of the gene mutation (Abstract Idea: Mental Process). The step of creating a variable (inverted index) to characterize the type and position of the mutation so that it is easier to find again in the vast genomic data is a simple and obvious act that can be practically performed in the human mind. Hence, this last step of generating a gene mutation inverted index would also constitute as a mental process under abstract ideas. Claim 5 recites calculating a logical product of each bit in the gene mutation inverted index of each of a plurality of cancer patients (Abstract Idea: Mental Process and/or Mathematical Concept). The process of looking at each cancer patient’s gene mutation inverted indexes to see if they share a gene mutation (AND logic operation) to identify mutations unique to cancer patients is a process that can be practically done in the human mind with the assistance of a pen and paper or a calculator. Hence this claim limitation would constitute a mental process and/or mathematical concept. Claim 5 then goes on to recite generating a statistical inverted index that expresses a characteristic of the cancer patient, using the logical product calculated before (Abstract Idea: Mental Process and/or Mathematical Concept). The process of using the logical product to calculate a statistic for how often a gene mutation is seen in cancer patients utilizes mathematical operations to calculate a variable, making this claim limitation a mathematical concept. In addition, in the simplest sense, these calculations could also be practically carried out in the human mind, and hence this limitation would also constitute a mental process. Claim 6 recites calculating a logical product and a statistical inverted index in the gene mutation inverted index of a new patient (Abstract Idea: Mental Process and/or Mathematical Concept). As stated above, the calculation of a logical product and a statistic inverted index based on known number would constitute a mental process and/or mathematical concept. Then the logical product is used to diagnose what type of cancer the new patient is associated with (Abstract Idea: Mental Process). The process of looking at a new patient’s gene mutation and comparing it to known mutations that are prevalent in patients with diagnosed cancer before diagnosing the new patient with a cancer type, is something that can be practically done in the human mind. Hence, this claim limitation of diagnosing based on comparison of known information, would constitute a mental process. Claim 7 recites generating a plurality of pieces of segmented codon data by encoding each of the plurality of pieces of segmented genome data into a codon unit on the basis of a codon conversion table (Abstract Idea: Mental Process). The process of generating codon/amino acid sequences based on a known conversion table and genomic data is something that can be practically done in the human mind and/or with the help of a pen and paper, making this claim limitation a mental process. Claim 1 goes on to recite, identifying, based on a reference codon data obtained by encoding reference genome data, genetic mutations (type and position) that can be seen as differences between the reference codon data and the segmented codon data (Abstract Idea: Mental Process). The act of comparing two codon/amino acid sequences to find the difference between the two sequences and classifying the difference as a genetic mutation (frame shift, deletion, insertion, etc.) is a process that is well known and can be done in the human mind as well. Therefore, this step of identifying the gene mutations would also constitute a mental process. Lastly, claim 1 recites generating a gene mutation inverted index, where the type and position of the gene mutation are associated with each other (Abstract Idea: Mental Process). The step of creating a variable (inverted index) to characterize the type and position of the mutation so that it is easier to find again in the vast genomic data is a simple and obvious act that can be practically performed in the human mind. Hence, this last step of generating a gene mutation inverted index would also constitute as a mental process under abstract ideas. Claim 8 recites generating a plurality of pieces of segmented codon data by encoding each of the plurality of pieces of segmented genome data into a codon unit on the basis of a codon conversion table (Abstract Idea: Mental Process). The process of generating codon/amino acid sequences based on a known conversion table and genomic data is something that can be practically done in the human mind and/or with the help of a pen and paper, making this claim limitation a mental process. Claim 1 goes on to recite, identifying, based on a reference codon data obtained by encoding reference genome data, genetic mutations (type and position) that can be seen as differences between the reference codon data and the segmented codon data (Abstract Idea: Mental Process). The act of comparing two codon/amino acid sequences to find the difference between the two sequences and classifying the difference as a genetic mutation (frame shift, deletion, insertion, etc.) is a process that is well known and can be done in the human mind as well. Therefore, this step of identifying the gene mutations would also constitute a mental process. Lastly, claim 1 recites generating a gene mutation inverted index, where the type and position of the gene mutation are associated with each other (Abstract Idea: Mental Process). The step of creating a variable (inverted index) to characterize the type and position of the mutation so that it is easier to find again in the vast genomic data is a simple and obvious act that can be practically performed in the human mind. Hence, this last step of generating a gene mutation inverted index would also constitute as a mental process under abstract ideas. These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. While claims 1-6 and 8 recite performing some aspects of the analysis with an “apparatus” or “computer”, there are no additional limitations that indicate that this apparatus requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-8 recite an abstract idea ( Step 2A, Prong 1 : YES ). Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements: Claim 1-6 recites a non-transitory computer-readable storage medium that stores an information processing program that causes a computer to perform processes, which equates to a generic computer system. Claim 1, 4, and 6-8 recite obtaining a plurality of pieces of segmented genome data, which falls under data gathering in insignificant extra-solution activity. Please see MPEP 2106.05(g) for more information. Claim 8 recites an information processing apparatus comprising of a memory and a processor coupled to the memory, which equates to a generic computer system. There are no limitations that indicate that the claimed apparatus or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1-8 are directed to an abstract idea ( Step 2A, Prong 2 : NO ). Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself ( Step 2B ). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment. As discussed above, there are no additional limitations to indicate that the claimed apparatus requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No ). As such, claims 1-8 are not patent eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA 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. 07-21-aia AIA Claim s 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Przytycki et al. (Genome Medicine Vol 9 Article Num 79 Published Aug 25, 2017) in view of Layer et al. (Nature Methods Vol 13 pgs. 63-65, Published Nov 9, 2015) and Bailey (ThoughtCo, Updated Nov 5, 2019). The claim limitations of the instant application are italicized below . With respect to claim 1, Przytycki et al. teaches a computer program called DiffMut that compares mutational profiles of genes across cancer genomes with their natural germline variations across healthy individuals (pg. 1 Abstract, A non-transitory computer-readable storage medium storing an information processing program for causing a computer to perform processing ). Przytycki et al. discloses that the program starts by obtaining genomic mutation data from The Cancer Genome Atlas that corresponded to individual patients that were diagnosed with a cancer type (pg. 3 left col para 1 and pg. 3 Fig. 1, obtaining a plurality of pieces of segmented genome data, which is genome information of a specific individual ). Next, Przytycki et al. also obtains genomic data from the 1000 Genome Project that corresponds to 2504 healthy individuals (pg. 3 right col para 2 and pg. 3 Fig. 1, reference genome data to be a reference ). Although not explicitly stated, Przytycki et al. implies that the genomic data/sequences were converted into amino acid sequences to help identify the type of genetic mutation, for example if mutations changed the encoded amino acid, it would be classified as a missense mutation (pg. 3 left col para 1, generating a plurality of pieces of segmented codon data obtained by encoding each of the plurality of pieces of segmented genome data in a codon unit on the basis of a codon conversion table in which a codon and a code are associated with each other ; identifying, on the basis of reference codon data obtained by encoding reference genome data to be a reference in the codon unit and each of the plurality of pieces of segmented codon data, a type and a position of an appearance of gene mutation different from the code that appears in the reference codon data among a plurality of the codes that appears in the plurality of pieces of segmented codon data ). Przytycki et al. also specifies that they define a variant as any nucleotide that differs from the most common one across the healthy cohort (pg. 2 right col para 2, a type and a position of an appearance of gene mutation different from the code that appears in the reference codon data among a plurality of the codes that appears in the plurality of pieces of segmented codon data ). Regarding claim 2, Przytycki et al. teaches that point mutations from the cancer patient genomes were mapped based on their provided location in the human reference genome to identify mutation positions and mutation types (missense, nonsense, silent) (pg. 3 left col para 1, wherein identifying identifies a position of a reference codon sequence to be searched for from a reference inverted index in which the code of the codon in the reference codon data and an appearance position of the code of the codon are associated with each other , and compares the code in the reference codon data that corresponds to the identified position with the codes in the plurality of pieces of segmented codon data that corresponds to the position to identify the type and position of the appearance of the gene mutation ). Concerning claim 3, Przytycki et al. discloses that for each individual with cancer, they counted the number of mutations that were found in each gene in their cancer genome (pg. 4 left col para 2). Przytycki et al. also did this with the healthy individuals as well (pg. 4 left col para 2). Although not explicitly shown, it is implied that each individual has an identifier variable such that Przytycki et al. is able to identify the specific individuals within the cancer and healthy cohorts (Pg. 3 Fig. 1, generating data in which an identifier that identifies the patient ). Finally, after all the processing, Przytycki et al. states that the list of genes that are differently expressed between the cancer and healthy cohort are filtered and then outputted (pg. 10 left col para 3, outputting the data ). With respect to claim 4, Przytycki et al. teaches a computer program called DiffMut that compares mutational profiles of genes across cancer genomes with their natural germline variations across healthy individuals (pg. 1 Abstract). Przytycki et al. discloses that the program starts by obtaining genomic mutation data from The Cancer Genome Atlas that corresponded to individual patients that were diagnosed with a cancer type (pg. 3 left col para 1 and pg. 3 Fig. 1, the obtaining obtains the plurality of pieces of segmented genome data, which is genome information of a cancer patient ). Next, Przytycki et al. also obtains genomic data from the 1000 Genome Project that corresponds to 2504 healthy individuals (pg. 3 right col para 2 and pg. 3 Fig. 1, the reference codon data of a healthy person ). Although not explicitly stated, Przytycki et al. implies that the genomic data/sequences were converted into amino acid sequences to help identify the type of genetic mutation, for example if mutations changed the encoded amino acid, it would be classified as a missense mutation (pg. 3 left col para 1, the generating generates the plurality of pieces of segmented codon data that corresponds to the cancer patient ; the identifying identifies the type and position of the appearance of the gene mutation on the basis of the reference codon data of a healthy person and the plurality of pieces of segmented codon data that corresponds to the cancer patient ). Przytycki et al. also specifies that they define a variant as any nucleotide that differs from the most common one across the healthy cohort (pg. 2 right col para 2, the identifying identifies the type and position of the appearance of the gene mutation on the basis of the reference codon data of a healthy person and the plurality of pieces of segmented codon data that corresponds to the cancer patient ). Regarding claim 7, Przytycki et al. teaches a fast and simple approach for differential mutational analysis called DiffMut that compares mutational profiles of genes across cancer genomes with their natural germline variations across healthy individuals, that is inherently ran on a computer based on the code link (pg. 1 Abstract, an information processing method implemented by a computer ). Przytycki et al. discloses that the program starts by obtaining genomic mutation data from The Cancer Genome Atlas that corresponded to individual patients that were diagnosed with a cancer type (pg. 3 left col para 1 and pg. 3 Fig. 1, obtaining a plurality of pieces of segmented genome data, which is genome information of a specific individual ). Next, Przytycki et al. also obtains genomic data from the 1000 Genome Project that corresponds to 2504 healthy individuals (pg. 3 right col para 2 and pg. 3 Fig. 1, reference genome data to be a reference ). Although not explicitly stated, Przytycki et al. implies that the genomic data/sequences were converted into amino acid sequences to help identify the type of genetic mutation, for example if mutations changed the encoded amino acid, it would be classified as a missense mutation (pg. 3 left col para 1, generating a plurality of pieces of segmented codon data obtained by encoding each of the plurality of pieces of segmented genome data in a codon unit on the basis of a codon conversion table in which a codon and a code are associated with each other ; identifying, on the basis of reference codon data obtained by encoding reference genome data to be a reference in the codon unit and each of the plurality of pieces of segmented codon data, a type and a position of an appearance of gene mutation different from the code that appears in the reference codon data among a plurality of the codes that appears in the plurality of pieces of segmented codon data ). Przytycki et al. also specifies that they define a variant as any nucleotide that differs from the most common one across the healthy cohort (pg. 2 right col para 2, a type and a position of an appearance of gene mutation different from the code that appears in the reference codon data among a plurality of the codes that appears in the plurality of pieces of segmented codon data ). Concerning claim 8, Przytycki et al. teaches a computer program called DiffMut that compares mutational profiles of genes across cancer genomes with their natural germline variations across healthy individuals, which is inherently carried out on a computer with memory and processor as shown by its code link (pg. 1 Abstract, an information processing apparatus comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing ). Przytycki et al. discloses that the program starts by obtaining genomic mutation data from The Cancer Genome Atlas that corresponded to individual patients that were diagnosed with a cancer type (pg. 3 left col para 1 and pg. 3 Fig. 1, obtaining a plurality of pieces of segmented genome data, which is genome information of a specific individual ). Next, Przytycki et al. also obtains genomic data from the 1000 Genome Project that corresponds to 2504 healthy individuals (pg. 3 right col para 2 and pg. 3 Fig. 1, reference genome data to be a reference ). Although not explicitly stated, Przytycki et al. implies that the genomic data/sequences were converted into amino acid sequences to help identify the type of genetic mutation, for example if mutations changed the encoded amino acid, it would be classified as a missense mutation (pg. 3 left col para 1, generating a plurality of pieces of segmented codon data obtained by encoding each of the plurality of pieces of segmented genome data in a codon unit on the basis of a codon conversion table in which a codon and a code are associated with each other ; identifying, on the basis of reference codon data obtained by encoding reference genome data to be a reference in the codon unit and each of the plurality of pieces of segmented codon data, a type and a position of an appearance of gene mutation different from the code that appears in the reference codon data among a plurality of the codes that appears in the plurality of pieces of segmented codon data ). Przytycki et al. also specifies that they define a variant as any nucleotide that differs from the most common one across the healthy cohort (pg. 2 right col para 2, a type and a position of an appearance of gene mutation different from the code that appears in the reference codon data among a plurality of the codes that appears in the plurality of pieces of segmented codon data ). However, while Przytycki et al. does disclose identifying gene mutations (variants) that are unique to cancer patients, Przytycki et al. does not explicitly generate inverted indices for these point mutations (claim 1, 3-8). Hence, Przytycki et al. is not able to calculate the logical products based on the inverted indices (claim 5-6). Due to this, Przytycki et al. is not able to generate a statical inverted index that expresses a characteristic of the cancer patient using the logical product (claim 5-6). Finally, Przytycki et al. did not disclose diagnosing new patients with cancer types based on the logical product (claim 6). However, the representation of genetic mutations with bitmap inverted indices and the statistical analysis of such indices were well known in the art before the effective filing date of the instant application as demonstrated by Layer et al. With respect to claim 1, Layer et al. teaches a Genotype Query Tools (GQT) that is an “individual-centric” strategy for indexing and mining very large genetic-variation data sets, by transposing genomic data with indexes to facilitate queries based on the genotype, phenotype and relationships of one or more of the individuals in a study (pg. 63 left col para 3 – right col para 1). Specifically, Layer et al. teaches that each individual’s vector of genomic data, which stores information regarding the variants such as positions, variant ID, type of mutation, is encoded as a bitmap index, which uses a separate bit array for each possible attribute value (pg. 64 Fig. 1 and Online Methods (pg. 4 of PDF) right col para 1, generating a gene mutation inverted index in which the gene mutation and the type and position of the appearance of the gene mutation are associated with each other ). Regarding claim 2, Layer et al. teaches that each bit in the bitmap index represents the genotype of a particular individual at a particular genomic position, which means that the bitmap index encodes the variant position and type (Online Methods (pg. 5 of PDF) left col para 3, from a reference inverted index, in which the code of the codon in the reference codon data and an appearance position of the code of the codon are associated with each other ). Concerning claim 3, Layer et al. teaches an “individual-centric” strategy for indexing and mining very large genetic-variation data sets, by transposing genomic data with indexes to facilitate queries based on the genotype, phenotype and relationships of one or more of the individuals in a study (pg. 63 left col para 3 – right col para 1). Specifically, Layer et al. teaches that each individual’s vector of genomic data, which stores information regarding the variants, positions, variant ID, type of mutation, is encoded as a bitmap index, which uses a separate bit array for each possible attribute value (pg. 64 Fig. 1 and Online Methods (pg. 4 of PDF) right col para 1, generating the gene mutation inverted index that corresponds to the segmented genome data of a patient … generating data … the gene mutation inverted index ). With respect to claim 4, Layer et al. teaches an “individual-centric” strategy for indexing and mining very large genetic-variation data sets, by transposing genomic data with indexes to facilitate queries based on the genotype, phenotype and relationships of one or more of the individuals in a study (pg. 63 left col para 3 – right col para 1). Specifically, Layer et al. teaches that each individual’s vector of genomic data, which stores information regarding the variants, positions, variant ID, type of mutation, is encoded as a bitmap index, which uses a separate bit array for each possible attribute value (pg. 64 Fig. 1 and Online Methods (pg. 4 of PDF) right col para 1, the generating generates the gene mutation inverted index that corresponds to the cancer patient using the identified type and position of the appearance of the gene mutation ). Regarding claim 5, Layer et al. teaches the use of highly optimized bitwise logical operations to search for variants, and gives the example where they search for variants where all individuals in a population are heterozygous (Online Methods (pg. 4 of PDF), right col para 1, calculating a logical product of each bit in which the code of the codon and the appearance position of the code of the codon are associated with each other in the gene mutation inverted index of each of a plurality of the cancer patients ). In addition , Layer et al. emphasizes that this index format combined with the logical operations has the ability to identify variants that meet a complex set of conditions among millions of individuals and billions of genotypes in seconds (Online Methods (pg. 5 of PDF) right col para 4). Layer et al. also discloses searching for at the loci at which three individuals are heterozygous among eight variants with the logical operation in order to output a resulting bit array that shows the position of the variant along with if all the individuals are heterozygous (Online Methods (pg. 4 of PDF) right col para 2 and Supplementary Figure 3, generating a statistical inverted index that represents the type and position of the gene mutation, which expresses a characteristic of the cancer patient, using a result of the logical product .) Concerning claim 6, Layer et al. teaches an “individual-centric” strategy for indexing and mining very large genetic-variation data sets, by transposing genomic data with indexes to facilitate queries based on the genotype, phenotype and relationships of one or more of the individuals in a study (pg. 63 left col para 3 – right col para 1). Specifically, Layer et al. teaches that each individual’s vector of genomic data, which stores information regarding the variants, positions, variant ID, type of mutation, is encoded as a bitmap index, which uses a separate bit array for each possible attribute value (pg. 64 Fig. 1 and Online Methods (pg. 4 of PDF) right col para 1, when the segmented genome data of a new patient to be determined is obtained and the gene mutation inverted index is generated ). In addition, Layer et al. teaches the use of highly optimized bitwise logical operations to search for variants, and gives the example where they search for variants where all individuals in a population are heterozygous (Online Methods (pg. 4 of PDF), right col para 1, calculating a logical product of the gene mutation inverted index of the new patient ). In addition , Layer et al. emphasizes that this index format combined with the logical operations has the ability to identify variants that meet a complex set of conditions among millions of individuals and billions of genotypes in seconds (Online Methods (pg. 5 of PDF) right col para 4). Layer et al. also discloses searching for at the loci at which three individuals are heterozygous among eight variants with the logical operation in order to output a resulting bit array that shows the position of the variant along with if all the individuals are heterozygous for the variant (Online Methods (pg. 4 of PDF) right col para 2 and Supplementary Figure 3, the statistical inverted index generated for each cancer type ). Lastly, Layer et al. teaches using principal-component analysis to cluster and group individuals to a specific genetic phenotype (Super_Population) with the bit-wise logical operation (Online Methods (pg. 6 of PDF) right col para 4, diagnosing which cancer type the new patient corresponds to on the basis of a result of the logical product ). With respect to claim 7, Layer et al. teaches an “individual-centric” strategy for indexing and mining very large genetic-variation data sets, by transposing genomic data with indexes to facilitate queries based on the genotype, phenotype and relationships of one or more of the individuals in a study (pg. 63 left col para 3 – right col para 1). Specifically, Layer et al. teaches that each individual’s vector of genomic data, which stores information regarding the variants, positions, variant ID, type of mutation, is encoded as a bitmap index, which uses a separate bit array for each possible attribute value (pg. 64 Fig. 1 and Online Methods (pg. 4 of PDF) right col para 1, generating a gene mutation inverted index in which the gene mutation and the type and position of the appearance of the gene mutation are associated with each other ). Finally, Layer et al. discloses that the GQT is a tool written in C, that was ran on Ubuntu Linux v3.13.0-43, with gcc v4.9.2, 4 Intel Core i7-4790K 4.00 GHz CPUs with the Haswell microarchitecture, and a 550 MB/s read-write solid-state hard drive (pg. 7 right col para 3, implemented by a computer ). Regarding claim 8, Layer et al. teaches an “individual-centric” strategy for indexing and mining very large genetic-variation data sets, by transposing genomic data with indexes to facilitate queries based on the genotype, phenotype and relationships of one or more of the individuals in a study (pg. 63 left col para 3 – right col para 1). Specifically, Layer et al. teaches that each individual’s vector of genomic data, which stores information regarding the variants, positions, variant ID, type of mutation, is encoded as a bitmap index, which uses a separate bit array for each possible attribute value (pg. 64 Fig. 1 and Online Methods (pg. 4 of PDF) right col para 1, generating a gene mutation inverted index in which the gene mutation and the type and position of the appearance of the gene mutation are associated with each other ). Finally, Layer et al. discloses that the GQT is a tool written in C, that was ran on Ubuntu Linux v3.13.0-43, with gcc v4.9.2, 4 Intel Core i7-4790K 4.00 GHz CPUs with the Haswell microarchitecture, and a 550 MB/s read-write solid-state hard drive (pg. 7 right col para 3, an information processing apparatus comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing ). It would have prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to utilize the bitmap index mapping of Layer et al. within the differential mutation analysis framework of Przytycki et al. for a more efficient and faster identification of mutations and comparison of individual genomes. One of ordinary skill in the art would have been motivated to incorporate the bitmap index mapping into the differential mutation analysis framework in order to rapidly test cohort genomes of thousands to millions of individual, as stated in Layer et al. (pg. 64 left col para 2). In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at incorporating the bitmap index mapping of Layer et al. within the mutation analysis frame work of Przytycki et al. as both operate on the same type of data (genomic variant data) extracted from human populations (both explicitly utilize the 1000 Genomes Project data). Therefore, the application of a well-known database indexing structure to well-established databases of genomic data would have a predictable success rate. Lastly, Layer et al. has already demonstrated that the bitmap indexing structure works well with large datasets of genomic data and provides a 443-fold improvement in processing speed compared to other known structures (pg. 64 right col para 2). While Przytycki et al. does talk about converting the genomic information into codon unit information in the form of amino acids in order to determine the types of genetic mutation, Przytycki et al. did not explicitly list a conversion table. However, the process of converting nucleotide sequences into codon/amino acid sequences based on a conversion was well known in the art before the effective filing date of the instant application as taught by Bailey . With respect to claim 1, Bailey teaches that three continuous nucleotide bases (genomic data) code for an amino acid and the set of three bases is known as a codon (pg. 2 para 1). Bailey then gives an example of a codon conversion table (pg. 2 Codon Table, encoding each of the plurality of pieces of segmented genome data in a codon unit on the basis of a codon conversion table in which a codon and a code are associated with each other … reference codon data obtained by encoding reference genome data to be a reference in the codon unit ) Regarding claim 3, Bailey teaches that three continuous nucleotide bases (genomic data) code for an amino acid and the set of three bases is known as a codon (pg. 2 para 1). Bailey then gives an example of a codon conversion table (pg. 2 Codon Table, the codon conversion table ) Concerning claim 7, Bailey teaches that three continuous nucleotide bases (genomic data) code for an amino acid and the set of three bases is known as a codon (pg. 2 para 1). Bailey then gives an example of a codon conversion table (pg. 2 Codon Table, encoding each of the plurality of pieces of segmented genome data in a codon unit on the basis of a codon conversion table in which a codon and a code are associated with each other … reference codon data obtained by encoding reference genome data to be a reference in the codon unit ). With respect to claim 8, Bailey teaches that three continuous nucleotide bases (genomic data) code for an amino acid and the set of three bases is known as a codon (pg. 2 para 1). Bailey then gives an example of a codon conversion table (pg. 2 Codon Table, encoding each of the plurality of pieces of segmented genome data in a codon unit on the basis of a codon conversion table in which a codon and a code are associated with each other … reference codon data obtained by encoding reference genome data to be a reference in the codon unit ). It would have prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention to utilize codon conversion table of Bailey within the differential mutation analysis framework of Przytycki et al. and Layer et al. to determine the functional effect of the genetic mutations. One of ordinary skill in the art would have been motivated to incorporate codon conversion table to convert the genetic sequences into amino acid sequences in order to determine type of mutation and functional effect on the resulting protein, as stated in Bailey (pg. 5 para 1-2). In addition, one of skill in the art before the effective filing date of the claimed invention would have a reasonable expectation of success at converting the genetic sequences of Przytycki et al. into amino acid sequences through the conversion table of Bailey as translation between nucleotides sequences and amino acids sequences was a well-known technique. In addition, the analysis of the variants with the bitmap indexing structure of Layer et al. would also have a reasonable expectation of success as the data type (strings of letters) is similar between the two sequence types. The application of the codon conversion table to parse segment genome data into functional amino acid sequences is a well-established practice, and although not explicitly stated in Przytycki et al . and Layer et al. , its use is implied and a codon conversion table would have been obvious to utilize. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to WENYU YANG whose telephone number is (571)272-0035. The examiner can normally be reached 8:30am - 5:00 pm. 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. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /W.Y./Examiner, Art Unit 1685 /OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685 Application/Control Number: 18/149,768 Page 2 Art Unit: 1685 Application/Control Number: 18/149,768 Page 3 Art Unit: 1685 Application/Control Number: 18/149,768 Page 4 Art Unit: 1685 Application/Control Number: 18/149,768 Page 5 Art Unit: 1685 Application/Control Number: 18/149,768 Page 6 Art Unit: 1685 Application/Control Number: 18/149,768 Page 7 Art Unit: 1685 Application/Control Number: 18/149,768 Page 8 Art Unit: 1685 Application/Control Number: 18/149,768 Page 9 Art Unit: 1685 Application/Control Number: 18/149,768 Page 10 Art Unit: 1685 Application/Control Number: 18/149,768 Page 11 Art Unit: 1685 Application/Control Number: 18/149,768 Page 12 Art Unit: 1685 Application/Control Number: 18/149,768 Page 13 Art Unit: 1685 Application/Control Number: 18/149,768 Page 14 Art Unit: 1685 Application/Control Number: 18/149,768 Page 15 Art Unit: 1685 Application/Control Number: 18/149,768 Page 16 Art Unit: 1685 Application/Control Number: 18/149,768 Page 17 Art Unit: 1685 Application/Control Number: 18/149,768 Page 18 Art Unit: 1685 Application/Control Number: 18/149,768 Page 19 Art Unit: 1685 Application/Control Number: 18/149,768 Page 20 Art Unit: 1685 Application/Control Number: 18/149,768 Page 21 Art Unit: 1685 Application/Control Number: 18/149,768 Page 22 Art Unit: 1685 Application/Control Number: 18/149,768 Page 23 Art Unit: 1685 Application/Control Number: 18/149,768 Page 24 Art Unit: 1685 Application/Control Number: 18/149,768 Page 25 Art Unit: 1685
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