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. DETAILED ACTION Claims 1-10 are presented for examination. This is a Non- Final Action . Claim Rejections - 35 U.S.C. §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- 1 0 are rejected under 35 USC 101 as directed to an abstract idea without significantly more. With respect to independent claims, 1 and 10 , specifically claim 1 recites “ constructing an undirected graph as a gene map based on an inter-gene correlation strength ” ; “ performing clustering solution on the gene map ” which involves grouping genes based on similarity of relationships; “ fusing allele information and genome cluster number information ” which involves combining multiple attributes of genetic information; “ connecting the fused information in series to obtain a gene cluster code ” which involves organizing combined information into a structured representation; and “ screening a quality seed set based on the biological phenotype information ” which involves evaluating phenotype information and selecting items that satisfy quality criteria ” . These claim limitations evaluating relationships, grouping items, combining information, and selecting results, which are acts that can be performed in the human mind or using pen and paper. Accordingly the claim recites mental processes. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. At step 2A, prong two, claim(s) 1, and 10 recites the additional elements of “ memory, a processor and a computer program, wherein the computer program is stored in the memory and runes on the processor, wherein the processor, when executing the computer program…” acquiring genotype data and gene position information, inputting the gene cluster code and gene position information into a prediction model and obtaining phenotype information of the offspring are elements merely invoking a generic computer environment (processor, database, memory) and basic data-gathering , data manipulation outputting functions (MPEP 21.96.05(f)) used as inputs and outputs to perform the abstract mental process hence reciting insignificant extra solution activities. The claims do not recite any specific improvement to computer technology, a particular machine implementing the process in a non-generic manner, a transformation of an article to a different state or thing, or any other meaningful limitation that applies the abstract idea in a manger that imposes a meaningful limit on the claim. Instead, the additional element simply applies the abstract idea using generic data processing operations, which amounts to implementing the mental processes using a computer environment. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claims, 1, and 10 at step 2B do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained with respect to Step 2A Prong Two , the additional elements as recited in step 2A prong 2 recite acquiring genotype data, input data into a model and generating phenotype information . No elements individually or in combination adds “significantly more” than the abstract idea hence are no more than well-understood, routine and conventional computer functions that merely apply the abstract idea on a generic computer. When viewed as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and do not add significantly more than the abstract idea itself. According, claim 1 is ineligible under 101. Claims 2- 9 are dependent claims and do not recite any additional elements that would amount to significantly more than the abstract idea. Specifically, Claim 2. With respect to step 2A prong 1 “ obtaining by calculating a similarity of multiple SNP loci string of every two genes… comprising Pearson correlation coefficient, Jaccard correlation coefficient, Spearman correlation coefficient, Euclidean distance, cosine similarity of included angles, Manhattan distance, Hamming distance, editing distance, Chebyshev distance, Minkowski distance and information entropy . ” recites a bstract idea of recognized mathematical calculations including statistical correlation coefficients (Pearson, Spearman, Jaccard), vector distance metrics (Euclidean, Manhattan, Chebyshev, Minkowski), string distance metrics (Hamming distance, edit distance), information theory measures ( entropy ), similarity measures (cosine similarity). Such calculations represent mathematical relationships and formulas used to determine similarity between datasets, which fall within the “mathematical concepts” category of abstract idea. With respect to step 2A prong 2 the claim applies mathematical calculations to determine similarity values that are then used as edge weights in a graph representing gene relationships. Specifically, the claim further recites, calculating similarity between SNP loci strings, using the calculated similarity as an adjacent edge weight, constructing an undirected graph. These steps merely involve collecting biological data, performing mathematical calculations on the data and organizing the results into a data structure (a graph). With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. The claims do not recite any specific improvement to computer technology, provide a new computational technique for performing the calculations, improve genetic sequencing technology, or apply the mathematical calculations in a technological process beyond data analysis. Instead the claim simply uses mathematical formulas to analyze generic data and represent the results in a graph, which is an application of the abstract mathematical concept. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 3 . With respect to step 2A prong 1 “ estimating… calculating.. and clustering… ” recites a bstract idea of mental steps ( observation & evaluation ), a person can be based on evaluation, calculating, classification and labeling of information mentally and which can be done using pen or paper. Claim 4 . With respect to step 2A prong 1 “ gene clustering comprises spatial clustering, density clustering, hierarchical clustering or spectral clustering. ” These limitations involve grouping items based on relationships or similarity among the items, which constitute classification of information. Specifically the claim recites, evaluating relationships among genes within a gene map, grouping genes into clusters based on those relationships, assigning cluster membership to genes. These steps correspond to observing relationships among data, evaluating similarities and classifying items into group, which are acts that can be performed mentally or with pen and paper. Accordingly, this claim recites mental processes, which are category of abstract idea. Claim 5 . With respect to step 2A prong 1 “ estimating the number of co-regulated genomes comprises a statistical method, a random method, an exhaustive method or an iterative method, and wherein the iterative method comprises determining a clustering number method by bottom-up or top-down iterative clustering in hierarchical clustering. ” These limitations involve evaluating data relationships and determining the number of groups based on evaluations, which involves acts of, estimating quantities, evaluating relationships among terms , determining the number of groups, and iteratively refining group assignment. These steps correspond to observing relationships among data, evaluating similarities and classifying items into group, which are acts that can be performed mentally or with pen and paper. Accordingly, this claim recites mental processes, which are category of abstract idea. Claim 6 . With respect to step 2A prong 1 “ wherein the gene cluster number information is given by a clustering method itself, in a random way, or in a sequential way. ” These limitations involve assigning identifier to groups of items, which constitutes organizing and labeling information, specifically, determining cluster membership for genes, assigning identifiers (cluster numbers) to clusters, organizing cluster identifiers sequentially or randomly. These operations correspond to evaluation, classification and labeling of information, which are acts that can be performed mentally or with pen and paper. Accordingly, this claim recites mental processes, which are category of abstract idea. Claim 7 . With respect to step 2A prong 2 “ wherein the biological phenotype information comprises quantity, quality, percentage or classification related to a target phenotype. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 8 . With respect to step 2A prong 2 “ wherein the gene coding breeding prediction model comprises an input layer, an embedding layer, a convolution layer, a pooling layer, a fully-connected layer and an output layer of the gene cluster code. ” recites additional elements of insignificant extra solution activity. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 8. With respect to step 2A prong 2 “ wherein the gene coding breeding prediction model is obtained by two-phase training, and wherein a first phase based on a shared bridge network comprises a dual-channel gene cluster code input layer receiving gene cluster code inputs from two samples, respectively, and simultaneously learns difference tasks and addition tasks at an output layer; and a second phase based on fixed network parameters trained on the first phase only comprises a gene cluster code input layer accepting an input of the gene cluster code and the gene position information from one sample to participate in fine-tuning learning of a target task until the training is completed. ” recites additional elements of insignificant extra solution activity , by merely using a trained neural network model as a tool to analyze generic data an degenerate phenotype predictions, which constitutes applying the abstract idea using a generic machine learning model . With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught by the prior art of records. Claim 10 is similar to claim 1 hence rejected similarly . 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 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 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. Claims 1 - 8 and 10 rejected under 35 U.S.C. 103 as being unpatentable over Zamft et al. (US 2022/0301658 – IDS) in view of Booker et al. (US 2019/0073410) 1. Zamft teaches, A gene coding breeding prediction method (Abstract – teaches obtaining gene expression profiles… inputting the gene expression profiles into a prediction model constructed for a task of predicting a phenotype, Zamft)) comprising: acquiring genotype data and gene position information of an offspring to be predicted ( Paragraph 5 – teaches obtaining a set of gene expression profiles for a set of genes measured in a tissue samples of a plant, Zamft) ; fusing allele information and geno m e cluster number information corresponding to each gene in the genotype data, ( Paragraph 5, Fig 9 – teaches learn nonlinear relationships and correlations within the training sets of gene expression profiles – disclosing processing genomic feature vectors representing gene attributes for downstream predictive modeling. These genomic attributes correspond to allele-based genetic features , Zamft) ; inputting the gene cluster code and gene position information into a gene coding breeding prediction model to obtain biological phenotype information of the offspring to be predicted (Paragraph 5 – teaches inputting the set of gene expression profiles into a prediction mode… generating a prediction of the phenotype for the plant, Zamft) ; and screening a quality seed set based on the biological phenotype information of the predicted offspring (Paragraph 5 – teaches identifying candidate gene targets… for the phenotype as having the largest contribution to the prediction – determines biological characteristics based on genomic analysis, enabling selection of desired biological samples, Zamft) ; wherein the gene coding breeding prediction model is obtained by training based on a collected data set (Fig 8 (workflow) – teaches obtain training sets of gene expression profiles… input iteratively the training sets into a prediction model… train the prediction model – thus explicitly disclosing training predictive models using gene expression datasets, Zamft) , and each sample data of the data set comprises the gene cluster code, the gene position information and the biological phenotype information of the sample (Paragraph 5 – learning relationships or correlations between features of gene expression profiles and the phenotype – thus disclosing datasets containing gene features and corresponding phenotype labels used for model training, Zamft) . Zamft does not explicitly teach …based on graph clustering; constructing an undirected graph as a gene map based on an inter-gene correlation strength in the genotype data; performing clustering solution on the gene map to obtain a number of co-regulated genomes and a genome cluster number of each gene; … and connecting the fused information in series to obtain a gene cluster code of a sample . However, Booker teaches, …based on graph clustering (Abstract – cluster of users in the graph , Booker) ; constructing an undirected graph as a gene map based on an inter-gene correlation strength in the genotype data ( Paragraph 20 – teaches providing a graph that includes user nodes… and user edges each indicating a respective user correlation strength – thus disclosing graph whose edges represent correlation strengths between entities. Substituting genes for users would have been an obvious application of the same correlation-graph technique , Booker) ; performing clustering solution on the gene map to obtain a number of co-regulated genomes and a genome cluster number of each gene ( Paragraph 23 – teaches analyzing the graph to determine clustering…. Clustering algorithms such as k-means clustering or hierarchical clustering may be used , Booker) ; … and connecting the fused information in series to obtain a gene cluster code of a sample ( Paragraph 26 – teaches cluster records including cluster identifiers and associated nodes may be stored in data structures representing cluster memberships – thus disclosing storing cluster membership and identifiers in structured records. Serializing genomic attributes and cluster identifiers into a ordered representation constitutes the claimed series-connected gene cluster code , Booker) . It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify the genomic prediction method of Zamft to include the graph construction and clustering techniques taught by Booker because Booker teaches constructing graphs based on correlation strengths between entities and applying clustering algorithms to identify graphs of correlated entities, and such graph-clustering techniques are well-known data analysis tools applicable to correlated datasets such as genomic data. Applying Booker’s clustering techniques to the correlated gene datasets analyzed in Zamft would have predictably identified groups of related genes and thereby improved the analysis used for biological phenotype prediction. 2. The combination of Zamft and Booker teach, The method according to claim 1, wherein the inter-gene correlation strength is obtained by calculating a similarity of multiple SNP loci strings of every two genes in the genotype data (Paragraph 5 – obtaining gene expression profiles… learning relationships or correlations between features of gene expression profiles and the phenotype, Zamft) in a method comprising Pearson correlation coefficient, Jaccard correlation coefficient, Spearman correlation coefficient, Euclidean distance, cosine similarity of included angles, Manhattan distance, Hamming distance, editing distance, Chebyshev distance, Minkowski distance and information entropy (Paragraphs 20-23 – determining correlation strength between entities… correlation metrics may be used to determine similarity relationships between nodes – disclosing determining correlation strengths between entities to build the graph. The specific similarity metrics listed in the claim represent well-known mathematical similarity measures routinely used to compute such correlations, Brooker) ; and the calculated similarity is used as an adjacent edge weight to construct the undirected graph (Paragraph 20 – teaches providing a graph that includes user nodes… and user edges each indicating a respective user correlation strength, Brooker) . 3. The combination of Zamft and Booker teach, The method according to claim 1, wherein said performing clustering solution on the gene map to obtain the number of co-regulated genomes and the genome cluster number information of each gene comprises: estimating the number of co-regulated genomes based on a spatial distribution feature of the gene map to obtain a number of gene clustering cluster s (Paragraph 23 – teaches analyzing the graph to determine clusters… clustering algorithms such as k-means clustering or hierarchical clustering may be used – thus disclosing determining clusters based on relationships between nodes in the graph which inherently determines the number of clusters, Booker) ; calculating an intra-class distance and an inter-class distance for each gene according to the estimated number of the gene clustering clusters (Paragraph 23 – teaches clustering algorithms such as k-means which compute distance between nodes and cluster centroids to determine cluster assignments – K - means clustering inherently uses intra-cluster and inter-cluster distance to assign nodes to clusters, Booker ) to determine a cluster to which the gene belongs (Paragraph 26 – teaches cluster records may include cluster identifiers and member nodes associated with each cluster, Booker) ; and giving the each gene clustering cluster unique cluster number information as the genome cluster number of each gene in a corresponding gene clustering cluster after clustering (Paragraph 26 – teaches (Paragraph 26 – teaches cluster records may include cluster identifiers, Booker) . 4. The combination of Zamft and Booker teach, The method according to claim 3, wherein a method for gene clustering comprises spatial clustering, density clustering, hierarchical clustering or spectral clustering (Paragraph 23 – teaches clustering algorithms such as k-means clustering or hierarchical clustering may be used, Booker) . 5. The combination of Zamft and Booker teach, The method according to claim 3, wherein a method for estimating the number of co-regulated genomes comprises a statistical method, a random method, an exhaustive method or an iterative method, and wherein the iterative method comprises determining a clustering number method by bottom-up or top-down iterative clustering in hierarchical clustering (Paragraph 23 – teaches clustering algorithms that iteratively evaluate clusters based on node relationships – clustering algorithms such as k-means and hierarchical clustering inherently involve iterative determination of cluster structures, further hierarchical clusters inherently uses bottom up or bottom down, Booker) . 6. The combination of Zamft and Booker teach, The method according to claim 1, wherein the gene cluster number information is given by a clustering method itself, in a random way, or in a sequential way (Paragraph 26 – teaches clustering records may include cluster identifiers and membership nodes – cluster identifiers assigned to nodes correspond to cluster number information generated by the cluster process, Booker) . 7. The combination of Zamft and Booker teach, The method according to claim 1, wherein the biological phenotype information comprises quantity, quality, percentage or classification related to a target phenotype (Paragraph 5 – teaches prediction of the phenotype for the plan – therefore predict plant phenotypes, which may include quantitative or categorical phenotype outputs , Zamft) . 8. The combination of Zamft and Booker teach, The method according to claim 1, wherein the gene coding breeding prediction model comprises an input layer, an embedding layer, a convolution layer, a pooling layer, a fully-connected layer and an output layer of the gene cluster code ( Fig 2A / model description – teaches neural network architecture including input and hidden layers used to learn relationships between genomic gestures and phenotype – further disclosing neural network based prediction models with layered architectures, Zamft ) . Claim 10 is similar to claim 1 hence rejected similarly. Claims 1 -8 and 10 rejected under 35 U.S.C. 103 as being unpatentable over Zamft et al. (US 2022/0301658 – IDS) in view of Booker et al. ( Us 2019/0073410 ) further in view of Jain et al (US 2021/0398183) All the limitations of claim 1 are taught above. 9. The combination of Zamft and Booker teach, wherein the gene coding breeding prediction model ( Abstract, Zamft ) The combination of Zamft and Booker do not explicitly teach, is obtained by two-phase training, and wherein a first phase based on a shared bridge network comprises a dual-channel gene cluster code input layer receiving gene cluster code inputs from two samples, respectively, and simultaneously learns difference tasks and addition tasks at an output layer; and a second phase based on fixed network parameters trained on the first phase only comprises a gene cluster code input layer accepting an input of the gene cluster code and the gene position information from one sample to participate in fine-tuning learning of a target task until the training is completed. However, Jain teaches, is obtained by two-phase training ( Fig 17 – teaches a machine learning workflow including training data, model training, model evaluations and testing m and model deployment – discloses a multi-stage machine learning training workflow including training, evaluation, and deployment phases. A POSITA would recognize this as iterative training stage corresponding to phases of model training , Jain) , and wherein a first phase based on a shared bridge network ( Fig 7 – teaches Siamese NN… comprising two deep learning networks that process two input vectors – discloses a Siamese neural network architecture where two neural network branches share the same architecture and parameters, which corresponds to a shared network structure comparable to a shared bridge network , Jain) comprises a dual-channel gene cluster code input layer receiving gene cluster code inputs from two samples, respectively ( Fig 7 – teaches Text 1 input vector… Text 2 input vector.. provided to a Siamese NN – thus disclosing two input vectors provided simultaneously to Siamese neural network, corresponding to dual-channel inputs from two samples , Jain) , and simultaneously learns difference tasks and addition tasks at an output layer ( Fig 11 – the signatures are aggregated and processed through a feedforward network to generate a match probability – discloses combining encoded output and computing a similarity or match probability which corresponds to learning relationships (difference or similarity) between the two inputs , Jain) ; and a second phase based on fixed network parameters trained on the first phase ( Fig 17 – teaches model training… model evaluation and testing.. model deployment – discloses training a model and subsequently using trained parameters during later stages such as evaluation or deployment, corresponding to subsequent phases using trained parameters , Jain) only comprises a gene cluster code input layer accepting an input of the gene cluster code and the gene position information from one sample ( Claim 1 – teaches determining reference attribute data… matching the potential matching item… using the selected deep learning multimodal matching model – discloses using trained models to process input feature data from an item (single instance) to generate predictions, corresponding to inference using trained parameters , Jain) to participate in fine-tuning learning of a target task until the training is completed ( Fig 17 – teaches model training… model evaluation and testing… model selection – thus discloses iteratively training and evaluation of models, which corresponds to fine-tuning the model parameters during training cycles , Jain) . It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify the gene0based predictive modeling method of Zamft using the graph-based clustering techniques of Brooker and the Siamese neural network architectures of Jain in order to improve the capability of the predictive model to learn relationships between pairs of feature vectors, because Siamese neural networks are well-known architectures for determining similarity or relational pattern between paired inputs in machine-learning systems. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT AMRESH SINGH whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3560 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 8am-5pm . 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