DETAILED ACTION 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. Claims 1-15, filed on 08/28/2023 are presented for examination. /Application claims priority to US PCT/JP2021/009344, filed on 09/03/2021/. 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. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 1-2 and 8 -10 is/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being Anticipated by “ TIANXING ” et al. (NPL Document 1, “Efficiently Embedding Dynamic Knowledge Graphs”). Regarding Claims 1/8/9: TIANXING discloses A non-transitory computer-readable storage medium storing a machine learning program that causes at least one computer to execute a process; A machine learning device comprising: one or more memories; and one or more processors coupled to the one or more memories and the one or more processors; A machine learning method for a computer to execute a process, the process/configured comprising: acquiring information regarding a first triple that includes a first node, a second node, and a first edge that indicates a relationship between the first node and the second node [ TIANXING discloses, "In Figure 6, after adding the triples (e7, r7, e6) and (e6, r5, e3) into the KG G, we have an emerging entity e7, an emerging relation r7, one existing relation with changed context r5, and two existing entities with changed context e3 and e6." Section IV-A, page 6; Figure 6 ] ; and updating a vector of a third node of a plurality of vectors and a vector of a third edge of the plurality of vectors, based on the information and the plurality of vectors, each of the plurality of vectors representing each of a plurality of nodes and each of a plurality of edges that indicate relationships between the plurality of nodes, the plurality of vectors being generated by machine learning that uses the plurality of nodes and the plurality of edges, the third node and the third edge being coupled to the first triple under a certain condition, the certain condition including that a distance from the first triple is equal to or smaller than a certain value when the first triple is added to graph data that includes the plurality of nodes and the plurality of edges, the distance being represented by a number of edges between the first triple and each of the plurality of nodes [ TIANXING discloses, “Knowledge is not static, and always evolves over the time, so that KGs should be updated very frequently with addition and deletion of triples. To adapt to such changes, KG embedding should also be dynamically updated in a short time. It raises challenges to existing models as they have to be re-trained on the whole KG with a high time cost. Thus, it is important to build an online embedding learning algorithm which can efficiently generate new high-quality KG embedding based on the results of existing KG embedding. When the KG has an update, a good online learning algorithm should not only rapidly learn the embeddings of emerging entities and relations, but also consider the impacts on the embeddings of existing entities and relations. Such impacts should be limited in certain regions, not in the entire graph. Based on these principles, we apply the idea of inductive learning so that: parameters in two learnt AGCNs are kept unchanged; two learnt gate vectors are kept unchanged; contextual element embeddings of existing entities and relations are kept unchanged. After a KG update, in many triples, the contexts of all entities and relations are unchanged. With unchanged context element embeddings and unchanged parameters in the learnt AGCNs, the contextual subgraph embeddings of such entities and relations are unchanged. Based on this, with unchanged gated vectors and their existing knowledge embeddings, these triples already have h* + r* ≈ t*, so we also constrain that: knowledge embeddings of existing entities and relations are kept unchanged as long as their contexts are unchanged. Thus, we only need to learn knowledge embeddings and contextual element embeddings of emerging entities and relations, as well as knowledge embeddings of existing entities and relations with changed contexts. This greatly reduces the number of triples which need to be re-trained while preserving h* + r* ≈ t* on the whole KG." Section IV-A, pages 5-6; "Based on the above idea of online learning, we only need to re-train six triples containing e3, e6, e7, r5, and r7 (i.e., (e3, r1, e4), (e3, r4, e2), (e1, r5, e3), (e1, r6, e6), (e6, r5, e3), and (e7, r7, e6)), instead of all ten triples in G." Section IV-A, page 6; All triples for which re-training is necessary are a distance of 1 away) ] . TIANXING discloses claim 2/10 . The non-transitory computer-readable storage medium/method according to claim 1/9, wherein the certain value is a distance represented by the number of edges 1 [ TIANXING discloses emerging nodes at a distance of one, see Examples 3, Section IV-A, page 3 ] . Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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- 7 and 11-1 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over “ TIANXING ” et al. ( NPL Document 1, “Efficiently Embedding Dynamic Knowledge Graphs” ) in view of “ ZHANG ” et al. ( NPL Document 2, “Improve the translational distance model for knowledge graph embedding” ). TIANXING discloses The non-transitory computer-readable storage medium/method according to claim 2/10. TIANXING may not; but, ZHANG , discloses claim 3/11 ., wherein, when a node on a starting point side of an edge is a subject and a node on an end point side of an edge is an object, and a ratio of the number of nodes that serve as both a subject and an object among the plurality of nodes is equal to or smaller than a certain ratio, the certain condition includes that a node that serves as a subject in the first triple is coupled as an object and a node that serves as an object in the first triple is coupled as a subject [ see Figure 1 of ZHANG , where different structures of knowledge graphs; such that the features are related to mathematical steps ] . Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the system TIANXING by incorporating the teachings ZHANG for unifying translational distance models . TIANXING in view of ZHANG further disclose claim 4/12 . The non-transitory computer-readable storage medium/method according to claim 2/10, wherein, when a node on a starting point side of an edge is a subject and a node on an end point side of an edge is an object, and a ratio of the number of nodes that serve as both a subject and an object among the plurality of nodes exceeds a certain ratio, the certain condition includes that a node that serves as a subject in the first triple is coupled as a subject and a node that serves as an object in the first triple is coupled as an object [ see Figure 1 of ZHANG , where different structures of knowledge graphs; such that the features are related to mathematical steps ] . The motivation to combine is the same as that of claim 3 above. TIANXING in view of ZHANG further disclose claim 5/13 . The non-transitory computer-readable storage medium/method according to claim 1/9, wherein the certain condition includes to be a node and an edge included from one of the first node and the second node that has a shorter distance to a prediction node coupled to an edge to be predicted at a time of prediction that uses the graph data to the prediction node [ TIANXING discloses link prediction, see Section V-B, pages 8-9 ] . TIANXING in view of ZHANG further disclose claim 6/14 . The non-transitory computer-readable storage medium/method according to claim 5/13, wherein the certain condition includes to be a node and an edge included from another node of the first node and the second node to a node that has the same distance as the distance from one node to the prediction node [ TIANXING discloses link prediction, see Section V-B, pages 8-9 ] . TIANXING in view of ZHANG further disclose claim 7/15 . The non-transitory computer-readable storage medium/method according to claim 1/9, wherein the process further comprising: acquiring input data to be predicted; and predicting presence or absence of an edge to be predicted in the input data based on the information, by using graph data that includes the plurality of nodes and the plurality of edges [ TIANXING discloses Section V-B, page 8-9 ] . Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. ( See PTO—892 ). For example, US 8145588 B2 is directed to Determination Of Graph Connectivity Metrics Using Bit-vectors. Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT AMARE F TABOR whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571) 270-3155 . 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