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. Claim Rejections - 35 USC § 101 Claims 1- 20 are rejected under 35 U.S.C. 101 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. Regarding to claims 1- 7 Claim 1 A computer-implemented method, comprising: a. obtaining a graph that comprises a collection of nodes connected by a plurality of edges; b. selecting one or more target nodes from the collection of nodes; c. forming a hierarchical sequence of node groups, each node group comprising one or more nodes in the graph, a first node group in the hierarchical sequence comprising the one or more target nodes, a subsequent node group comprising one or more nodes directly connected to at least one of one or more nodes in a node group that is immediately before the subsequent node group in the hierarchical sequence; d. generating a hierarchical adjacency matrix that comprises a plurality of elements encoding at least a subset of the plurality of edges, the hierarchical adjacency matrix comprising a plurality of rows, each row representing a respective node in the graph, the plurality of rows arranged in the hierarchical adjacency matrix in accordance with the hierarchical sequence; and e. inputting the hierarchical adjacency matrix into a neural network, the neural network outputting an update in one or more embeddings of the one or more target nodes, an embedding encoding a characteristic of a target node. Step 1 , This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including steps a) - e ). Thus, the claim is a process, which is one of the statutory categories of invention. (Step 1: YES). Step 2A – Prong One : This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. Step b. “ selecting one or more target nodes from the collection of nodes . ” This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process abstract idea ) with assistance of paper and pencil. Step c. “ forming a hierarchical sequence of node groups, each node group comprising one or more nodes in the graph, a first node group in the hierarchical sequence comprising the one or more target nodes, a subsequent node group comprising one or more nodes directly connected to at least one of one or more nodes in a node group that is immediately before the subsequent node group in the hierarchical sequence ” This step identif ies 1 st neighbor, 2 nd neighbor of the target node(s). This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process abstract idea ) with assistance of paper and pencil. Step d. “ generating a hierarchical adjacency matrix that comprises a plurality of elements encoding at least a subset of the plurality of edges, the hierarchical adjacency matrix comprising a plurality of rows, each row representing a respective node in the graph, the plurality of rows arranged in the hierarchical adjacency matrix in accordance with the hierarchical sequence ” The adjacency matrix is a table that encode a value of 1 if there is a connection between the nodes and this step can be perform in human mind with the assistance of paper and pencil. Further, the rows is arranged in accordance with the hierarchical sequence is recited at a high level of generality and given broadest reasonable interpretation, one row after another row is interpreted as hierarchical sequence. “Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos , 561 U.S. 593 (2010)). Here, steps b, c, and d fall within the mental process grouping of abstract ideas. Limitations (b) - ( d ) are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES). Step 2A, Prong Two : This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements/limitations : a. obtaining a graph that comprises a collection of nodes connected by a plurality of edges and e. inputting the hierarchical adjacency matrix into a neural network, the neural network outputting an update in one or more embeddings of the one or more target nodes, an embedding encoding a characteristic of a target node . a) MPEP § 2106.05(a) "Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field." There is no improvement to Functioning of a Computer or to Any Other Technology or Technical Field. The limitation a) is simply collecting data /graph and e ) updating embeddings of target nodes in accordance with the input of neural network as hierarchical adjacency matrix . These limitations do not make any improvements to the functionalities of a computer, neural network, database technology, or any other technologies. b) MPEP § 2106.05(b) Particular Machine. The judicial exception does not apply to any particular machine. The claim is silent regarding specific limitations directed to an improved computer system, processor, memory, network, database, or Internet, nor do applicant direct examiner’s attention to such specific limitations. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. at 223; see also Bascom Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1348 (Fed. Cir. 2016) ("An abstract idea on 'an Internet computer network' or on a generic computer is still an abstract idea."). Applying this reasoning here, the claim is not directed to a particular machine, but rather merely implement an abstract idea using generic computer components such as “graph”, “nodes”, “edge”, “node group”, “ hierarchical adjacency matrix ”, “ neural network ”, “embedding” . Thus, the claims fail to satisfy the "tied to a particular machine" prong of the Bilski machine-or-transformation test. c) MPEP § 2106.05(c) Particular Transformation. Step d. updating embeddings of target nodes is not a "transformation or reduction of an article into a different state or thing constituting patent-eligible subject matter[.] " See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) ( en bane), aff'd sub nom, Bilski v. Kappas , 561 U.S. 593 ( 2010); see also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) ("The mere manipulation or reorganization of data ... does not satisfy the transformation prong."). Applying this guidance here, the claims fail to satisfy the transformation prong of the Bilski machine-or-transformation test. d) MPEP § 2106.05(e) Other Meaningful Limitations. This section of the MPEP guides: Diamond v. Diehr provides an example of a claim that recited meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. 450 U.S. 175, ... (1981). In Diehr, the claim was directed to the use of the Arrhenius equation ( an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78 .... The Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products. 450 U.S. at 184... In contrast, the claims in Alice Corp. v. CLS Bank International did not meaningfully limit the abstract idea of mitigating settlement risk. 573 U.S._ .... In particular, the Court concluded that the additional elements such as the data processing system and communications controllers recited in the system claims did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers") or were well-understood, routine, conventional activity. MPEP § 2106.05(e). The limitations a) collecting /obtaining data /graph and e. updating embeddings of target nodes are not meaningful limitations because collecting and updating are pre and post-solution activities. The limitations are not meaningful limitations. e) MPEP § 2106.05(g) Insignificant Extra-Solution Activity. The limitations a) collecting /obtaining data /graph and e. updating embeddings of target nodes are not meaningful limitations because collecting and displaying are pre and post-solution activities f ) MPEP § 2106.05(h) Field of Use and Technological Environment. [T]he Supreme Court has stated that, even if a claim does not wholly pre-empt an abstract idea, it still will not be limited meaningfully if it contains only insignificant or token pre- or post-solution activity-such as identifying a relevant audience, a category of use, field of use, or technological environment. Ultramercial, Inc. v. Hulu, LLC, 722 F.3d 1335, 1346 (Fed. Cir. 2013). “database”, “transaction database”, “machine learning model”, “computer processor”, “artificial intelligence program”, “text embedded vector analysis” limitations are simply a field of use that attempts to limit the abstract idea to a particular technological environment. Accordingly, the additional limitations a) e ) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Step 2B , the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim do es not recite any non-convention or non-generic arrangement because a. collecting /obtaining data, e. updating embeddings of a target node are conventional activities. For instance, U.S. Pub 20020/0356858 – Hedge discloses [0124], “… Let V t,s be the set of M seed nodes selected in epoch t…” ( <examiner note: seed nodes target nodes > ), [0126], “… selecting all n-degree (in an example 2-hop neighbours) of the seed nodes …” ( <examiner note: 1 st node group seed nodes, 2 nd node group 1 st hop neighbors of seed nodes, 3 rd node group 2 nd hop neighbors of seed nodes > ), [ 0106], “… Consider an undirected graph, G =(V, ε) with nodes v i ϵ V and edges (v i , v j ) ϵ ε, with adjacency matrix A ϵ R n×n , n=|V|, where a j =1 denotes the presence of edge (v i , v j ) (0 indicating absence) …” ( <examiner note: generating adjacency matrix for hierarchical group of nodes by encoding presence and absence of edges between nodes>), [0109], “… GCN in model 120 uses the connectivity structure of the graph and the input node features to compute low dimensional embeddings for every node …”, [0129], “… aggregates and propagates starting at the leaf nodes (that are also 2-hop neighbours of the seed nodes) of the resulting sampled graph G t =(V t , ε t ). The embeddings of the seed nodes target nodes are updated. Taking these limitations as an ordered combination adds nothing that is not already present when the elements are taken individually. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible. Claim 2 depends on claim 1 and includes all the limitations of claim 1. Claim 2 recites “forming a second node group comprising one or more second nodes, each of which is directly connected to at least one of the one or more target nodes in the graph; and forming a third node group comprising one or more third nodes, each of which is directly connected to at least one of the one or more second nodes in the graph” This limitation identifies neighbor of target nodes, neighbor of neighbor of target nodes, and so on. The limitation can be performed in human with the assistance of paper and pencil. The claim does not have any addition limitation that amount to significantly more than the abstract idea. Claim 3 depends on claim 1 and includes all the limitations of claim 1. Claim 3 recites “wherein forming the hierarchical sequence of node groups comprises: determining a layer count of the neural network, the layer count indicating how many layers are present in the neural network; and forming the hierarchical sequence of node groups based on the layer count.” T his limitation can be performed in human mind with the assistance of paper and pencil. For instance, if the neural network has three layer, three nodes can be formed (e.g., target nodes, 1 st hop nodes, and 2 nd hop nodes). The claim does not have any addition limitation that amount to significantly more than the abstract idea. Claim 4 depends on claim 3 and includes all the limitations of claim 3 . Claim 4 recites “wherein a number of node groups in the hierarchical adjacency matrix is equal to the layer count” The claim does not have any addition limitation that amount to significantly more than the abstract idea. Claim 5 depends on claim 1 and includes all the limitations of claim 1. Claim 5 recites “ wherein generating the hierarchical adjacency matrix comprises: retrieving, from a memory, an adjacency matrix in a compressed format, the adjacency matrix in the compressed format comprising one or more values that represent one or more of the plurality of edges in the graph; and determining values of the plurality of elements in the hierarchical adjacency matrix based on the adjacency matrix in a compressed format” The adjacency matrix in compressed format is recited at a high level of generality and it is similar to conventional adjacency matrix. The claim does not have any addition limitation that amount to significantly more than the abstract idea. Claim 6 depends on claim 1 and includes all the limitations of claim 1. Claim 6 recites “wherein inputting the hierarchical adjacency matrix into the neural network comprises: inputting different portions of the hierarchical adjacency matrix into different layers of the neural network.” The claim does not have any addition limitation that amount to significantly more than the abstract idea. Claim 7 depends on claim 6 and includes all the limitations of claim 6 . Claim 7 recites “wherein the neural network comprises a sequence of layers that includes a first layer and a second layer arranged after the first layer, and inputting the different portions of the hierarchical adjacency matrix into the different layers of the neural network comprises: inputting the plurality of elements of the hierarchical adjacency matrix into the first layer; and inputting a subset of the plurality of elements into the second layer.” The claim does not have any addition limitation that amount to significantly more than the abstract idea. Claim 8-14, and 15-20 are similar to claim 1-7. The claims are rejected based on the same reasons. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 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. Claim(s) 1 -2, 8-9, and 15-16 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Hedge (U.S. Pub 2020/0356858 A1) Claim 1 Hedge discloses a computer-implemented method, comprising: obtaining a graph that comprises a collection of nodes connected by a plurality of edges ([0023], “… A graph data structure is first received…” [0080], line 7 -8 , “… The graphs may be directed or undirected … ” [0106, line 1-2, “… an undirected graph, G = (V, ε) with nodes v i ϵ V and edges (v i , v j ) ϵ ε ) … ”) ; selecting one or more target nodes from the collection of nodes ( [0118], line 3-4, “… Let V k be the set of K seed nodes…” <examiner note: seed nodes target nodes> ) ; forming a hierarchical sequence of node groups, each node group comprising one or more nodes in the graph, a first node group in the hierarchical sequence comprising the one or more target nodes, a subsequent node group comprising one or more nodes directly connected to at least one of one or more nodes in a node group that is immediately before the subsequent node group in the hierarchical sequence ([0120], “… all 2-hop neighbours (or other n-hops, 2 is used as an example) of the seed nodes may be selected. This results in the node sets : <examiner note: 1 st node group seed nodes; 2 nd node group 1 st neighbor of seed nodes; 3 rd node group 2 nd neighbor of seed nodes ” ; generating a hierarchical adjacency matrix that comprises a plurality of elements encoding at least a subset of the plurality of edges, the hierarchical adjacency matrix comprising a plurality of rows, each row representing a respective node in the graph, the plurality of rows arranged in the hierarchical adjacency matrix in accordance with the hierarchical sequence ( [0106], “… Consider an undirected graph, G =(V, ε) with nodes v i ϵ V and edges (v i , v j ) ϵ ε, with adjacency matrix A ϵ R n×n , n=|V|, where a j =1 denotes the presence of edge (v i , v j ) (0 indicating absence) …” <examiner note : generating adjacency matrix for hierarchical group of nodes by encoding presence and absence of edges between nodes . This adjacency matrix is generated for seed nodes, 1 st neighbors of seed nodes, and 2 nd neighbors of seed nodes >) ; and inputting the hierarchical adjacency matrix into a neural network, the neural network outputting an update in one or more embeddings of the one or more target nodes, an embedding encoding a characteristic of a target node ( [0109], “… GCN in model 120 uses the connectivity structure of the graph and the input node features to compute low dimensional embeddings for every node…”, [0129], “… aggregates and propagates starting at the leaf nodes (that are also 2-hop neighbours of the seed nodes) of the resulting sampled graph G t =(V t , ε t ) [0210], “… the model can used in relation to existing, new or updated nodes in the graph… for example, a social network may have nodes either missing information (e.g., individuals with no titles) and the model 120 can used to suggest job titles…” <examiner note: if seed node miss title, the seed node is updated with suggested title>) Claim 2 C laim 1 is included , Hedge discloses wherein forming the hierarchical sequence of node groups comprises: forming a second node group comprising one or more second nodes, each of which is directly connected to at least one of the one or more target nodes in the graph; and forming a third node group comprising one or more third nodes, each of which is directly connected to at least one of the one or more second nodes in the graph ([0120], “… all 2-hop neighbours (or other n-hops, 2 is used as an example) of the seed nodes may be selected. This results in the node sets : <examiner note: 1 st node group seed nodes; 2 nd node group 1 st neighbor of seed nodes; 3 rd node group 2 nd neighbor of seed nodes ” ; Claims 8-9 and 15-16 are similar to claim 1-2. The claims re rejected based on the same reason. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim (s) 3 -4 , 10-11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable Hedge (U.S. Pub 2020/0356858 A1), as applied to claim 1 , 8, and 15 respectively , and further in view of Accurate, Efficient and Scalable Training of Graph Neural Networks written by Hanqing Zeng, October 8, 2020 Claim 3 C laim 1 is included; however, Hedge does not explicitly disclose wherein forming the hierarchical sequence of node groups comprises: determining a layer count of the neural network, the layer count indicating how many layers are present in the neural network; and forming the hierarchical sequence of node groups based on the layer count. Zeng discloses wherein forming the hierarchical sequence of node groups comprises: determining a layer count of the neural network, the layer count indicating how many layers are present in the neural network; and forming the hierarchical sequence of node groups based on the layer count (pg. 9, section 3 3.1. Design of the Minibatch Training Algorithm, “… consider a GNN with one hidden layer. If a particular method selects 1000, 100 and 10 nodes in the input, hidden and output layers respectively, then we say the minibatch size is 10, the 1-hop neighborhood size is 100 and the 2-hop neighborhood size is 1000. In this case, the GNN only generates label predictions for the 10 minibatch nodes. The number of hops is with respect to minibatch nodes…” <examiner note : the GNN has input layer, one hidden layer, and output layer. There are 3 node groups 10, 100, and 1000 nodes> ) Claim 4 C laim 3 is included , Zeng further discloses wherein a number of node groups in the hierarchical adjacency matrix is equal to the layer count ( pg. 10, ˜A s ← adjacency matrix of G s <examiner note: there are three groups of nodes. As adjacency matrix equal to the number of layers ) Hedge disclose wherein forming the hierarchical sequence of node groups ; however, Hedge does not disclose determining a layer count of the neural network, the layer count indicating how many layers are present in the neural network; and forming the hierarchical sequence of node groups based on the layer count. Zeng discloses forming hierarchical sequence of node groups in accordance with the number of layers of the neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the graph sampling-based approach as disclosed by Zeng into Hedge because it requires much less computation In training due to reduced redundancy. Claim (s) 6, 7 , 13, 14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable Hedge (U.S. Pub 2020/0356858 A1), as applied to claim 1 , 8, and 15 respectively and further in view of Lee (U.S. Pub 2020/0285944 A1) Claim 6 C laim 1 is included , however, Hedge does not explicitly disclose wherein inputting the hierarchical adjacency matrix into the neural network comprises: inputting different portions of the hierarchical adjacency matrix into different layers of the neural network. Lee discloses wherein inputting the hierarchical adjacency matrix into the neural network comprises: inputting different portions of the hierarchical adjacency matrix into different layers of the neural network ([0024], line 11-14, “… The graph convolutional networks disclosed herein also have the flexibility to select different multi-hop motifs for a same target node in different graph convolutional layers in a graph convolutional network…” fig. 8, T motifs with K=2 hops are inputting into hidden layers>) Claim 7 Cl aim 6 is included , Lee further discloses wherein the neural network comprises a sequence of layers that includes a first layer and a second layer arranged after the first layer (fig. 8 , layer 820 and 840) , and inputting the different portions of the hierarchical adjacency matrix into the different layers of the neural network comprises: inputting the plurality of elements of the hierarchical adjacency matrix into the first layer; and inputting a subset of the plurality of elements into the second layer ( fig. 8, T motifs with K=2 hops are inputting into hidden layers>) Hedge discloses the adjacency matrix are fed into the neural network to update one or more embeddings of the one or more target nodes . However, Hedge does not explicitly disclose i nputting the different portions of the hierarchical adjacency matrix into the different layers of the neural network comprises: inputting the plurality of elements of the hierarchical adjacency matrix into the first layer; and inputting a subset of the plurality of elements into the second layer . Lee discloses limitations of claims 6 and 7 by inputting T motifs, i.e., parts of the adjacency matrix, of K=2 hops into different layers of the neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Lee into Hedge because the neural network generally only use edge-defined immediate neighbors (i.e., nodes connected to a target node through one edge, which is herein referred to as one-hop edge-based adjacency) for information integration at each target node. In many applications, such GCNs based on one-hop edge-based adjacency are not efficient or would not make correct predictions. Claim (s) 5 , 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable Hedge (U.S. Pub 2020/0356858 A1), as applied to claim 1, 8, and 15 respectively , and further in view of Kunitomi (U.S. Pub 2020/0388384 A1) Claim 5 C laim 1 is included , however, Hedge does not explicitly disclose wherein generating the hierarchical adjacency matrix comprises: retrieving, from a memory, an adjacency matrix in a compressed format, the adjacency matrix in the compressed format comprising one or more values that represent one or more of the plurality of edges in the graph; and determining values of the plurality of elements in the hierarchical adjacency matrix based on the adjacency matrix in a compressed format. Kunitomi discloses wherein generating the hierarchical adjacency matrix comprises: retrieving, from a memory, an adjacency matrix in a compressed format, the adjacency matrix in the compressed format comprising one or more values that represent one or more of the plurality of edges in the graph ([0059] , “… an adjacency matrix R (initial matrix) is created 18. The adjacency matrix R is an m×n matrix containing m rows of biosample IDs and n columns of metadata instances and/or categories, with entries being the assigned values … The adjacency matrix R is the matrix to be factored …” <examiner note: the compressed adjacency matrix factored adjacency matrix) ; and determining values of the plurality of elements in the hierarchical adjacency matrix based on the adjacency matrix in a compressed format ([0060] , “… ii) factorizing the adjacency matrix into an m×k ′ first factor matrix B and a k′×n second factor matrix A, where the product of A×B is Q, and Q is a reconstruction matrix that approximates R … The reconstruction matrix formed by the matrix factorization using k latent factors is then inspected for differences with adjacency matrix R. The non-zero differences reveal latent associations between biosamples of the reconstruction matrix Q, which can then used to make predictions based on the associations. An association is recognized when the reconstruction matrix Q contains a non-zero element and the corresponding element in R is zero …”) Hedge discloses a hierarchical adjacency is built based on associations between nodes in the graph. Kunitomi discloses the adjacency matrix is factored that is used to generate the reconstruction matrix Q that approximates R . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Kunitomi into Hedge because t he reconstruction matrix formed by the matrix factorization using k latent factors is then inspected for differences with adjacency matrix R. The non-zero differences reveal latent associations between data of the reconstruction matrix Q, which can then used to make predictions based on the associations . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT HAU HAI HOANG whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-5894 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT Boris Gorney can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-270-5626 . 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. FILLIN "Examiner Stamp" \* MERGEFORMAT HAU HAI. HOANG Primary Examiner Art Unit 2154 /HAU H HOANG/ Primary Examiner, Art Unit 2154