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 .
REMARKS
On page 10, Applicant asserts Applicant has amended claim 1 to include allowable features of previous claim 6 (now cancelled), and amended claim 14 to include allowable features of previous claim 19 (now cancelled). However, the amendment does not incorporate the allowable dependent claims and intervening claims in their entirety for the claims to be allowable.
New claim 21 recites allowable features of previous claim 7 is allowable.
The 35 USC 101 rejection as applied to claims 1-4, 9-11, and 14-16 is withdrawn as necessitated by claim amendments.
The 35 USC 102 rejection as applied to claims 1-5, 10, 11, and 14-18 in view of Liu et al. is withdrawn as necessitated by claim amendment.
The 35 USC 103 rejections as applied to claims 9, 12, and 13 are withdrawn as necessitated by claim amendment.
Claims 6, 12, 13, and 19 are cancelled.
Claims 21-24 are new.
Claims 1-5, 7-11, 14-18, 20, and 21-24, filed March 23, 2026, are examined on the merits.
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) 1-5, 10, 11, 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. Liu herafter, KompaRe: A Knowledge Graph Comparative Reasoning System, 2021) in view of Wang et al. (Wang hereafter, US 20230206084 A1).
Claim 1, Liu discloses a method, comprising:
obtaining a query entity and a query relationship (page 2, column 2, e.g. query input, where the user can input various queries on the web page (e.g,. node, edge and query graph));
selecting one or more nearest neighbor entities of the query entity from a knowledge graph (page 3, section 3.2, e.g. each triple in the knowledge graph and its adjacent neighboring triples as a document, and use a TF-IDF like weighting strategy to calculate the predicate similarity).
determining a first probability of a nearest neighbor entity of the one or more nearest neighbor entities (page 3, section 3.1, e.g. For a given node, assume it contains 𝑑 out links whose label is 𝑖, we have 0 ≤ 𝑑 ≤ 𝐷. Let V𝑑 𝑖 denote the node set which contains
𝑑 out links with label 𝑖, E denote the entropy, and P𝑑𝑖 denote the probability of a node having 𝑑 out links with label/predicate 𝑖);
selecting a nearest neighbor entity of the one or more nearest neighbor entities as a candidate entity based on the first probability (page 3, section 3.2, e.g. we find k-simple shortest paths [11] from the subject to the object of the given query edge as its knowledge segment); and
selecting a candidate entity matching the query entity and the query relationship as a result entity (page 3, section 3.3, e.g. For a given node, assume it contains 𝑑 out links whose label is 𝑖, we have 0 ≤ 𝑑 ≤ 𝐷. Let V𝑑 𝑖 denote the node set which contains 𝑑 out links with label 𝑖, E denote the entropy, and P𝑑𝑖 denote the probability of a node having 𝑑 out links with label/predicate 𝑖).
However, Liu does not disclose wherein the determining the first probability of the nearest neighbor entity includes: extracting a sub-knowledge graph of the nearest neighbor entity from the knowledge graph; inputting graph structure data of the sub-knowledge graph to a graph neural network model to determine type distribution information of the nearest neighbor entity, wherein the graph structure data includes an embedding representation of an entity and an embedding representation of an entity relationship: and determining the first probability of the nearest neighbor entity based on the type distribution information.
Wang discloses wherein the determining the first probability of the nearest neighbor entity includes: extracting a sub-knowledge graph of the nearest neighbor entity from the knowledge graph ([0024], e.g. historical sequence 310 may include a plurality of historical knowledge graphs, and subgraph sequence 320 may include a plurality of subgraphs respectively extracted from the plurality of historical knowledge graphs. With an example implementation of the present disclosure, features can be extracted based on a portion of the knowledge graphs (i.e., subgraphs)); inputting graph structure data of the sub-knowledge graph to a graph neural network model to determine type distribution information of the nearest neighbor entity ([0043], e.g. a TGNN (Temporal Graph Neural Network) can be used to determine the sequence feature of the entire knowledge graph sequence. Hereinafter, more details about TGNN will be described with reference to FIG. 7. FIG. 7 schematically illustrates block diagram 700 of a process for determining a sequence feature according to an example implementation of the present disclosure. As shown in FIG. 7, input 710 may include receiving an input from each knowledge graph in a sequence of knowledge graphs. For example, input X_T0 712 may represent a subgraph feature of a subgraph of the knowledge graph at time point T0, input X _T1 714 may represent a subgraph feature of a subgraph of the knowledge graph at time point T1, ..., and input X_TN 716 may represent a subgraph feature of a subgraph of the knowledge graph corresponding to time point TN), wherein the graph structure data includes an embedding representation of an entity and an embedding representation of an entity relationship ([0029], e.g. the attention score of edge Ek between node Vi and Vj in the knowledge graph can be determined based on the following equation:
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Equation I where Wh, W,, and Wr respectively represent weights obtained by training based on historical data, and vi, v1, and ek respectively represent embeddings of the correspond-ing nodes Vi and Vj and edge Ek. In other words, the atten-tion score for given edge Ek can be determined based on Equation 1 and using the given edge and nodes V; and V1 on both sides of the edge. The function LeakyReL U denotes a leaky rectified linear unit); and determining the first probability of the nearest neighbor entity based on the type distribution information ([0058], e.g. determining the node score of the node based on the changed node and the path includes: determining an edge score of an edge on the path; and determining the node score based on the edge score and a distance between the edge and the node).
Wang discloses it is possible to improve the accuracy of the sequence feature while reducing data processing resource requirements, thereby facilitating the improvement of the accuracy of the prediction model ([0045]). One of ordinary skill in the art at the time before the effective filing date of the claimed invention would have been motivated by Wang to improve the method of Liu. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Liu with the improvement described by Wang. The benefit would be to improve the accuracy.
Claim 2, Liu as modified discloses the selecting the one or more nearest neighbor entities of the query entity from the knowledge graph includes: selecting an entity whose proximity to the query entity is less than or equal to a K1 order from the knowledge graph as a nearest neighbor entity (page 5, column 2, e.g. Note that according to Lemma 1, if an element only belongs to 𝐾𝑆1 or 𝐾𝑆2, its influence function value will be 0. In order to avoid this, we introduce a fully connected background graph to 𝐾𝑆1 and 𝐾𝑆2, respectively. This background graph contains all the nodes in 𝐾𝑆1 and 𝐾𝑆2, and it is disconnected with 𝐾𝑆1 and 𝐾𝑆2. If we treat 𝐾𝑆1 and 𝐾𝑆2 as two documents, we can think of this background graph as the background word distribution in a language model).
Claim 3, Liu as modified discloses wherein the type distribution information indicates a possibility that the nearest neighbor entity is in communication with a plurality of known entity relationships: and wherein the determining the first probability of the nearest neighbor entity includes: determining the first probability of the nearest neighbor entity based on the type distribution information and the query relationship (page 5, column 1, e.g. We use random walk graph kernel with node attribute to measure the similarity between these two knowledge segments [20]. Sim(𝐾𝑆1,𝐾𝑆2) = 𝑞′× (𝐼 − 𝑐𝑁×𝐴×)−1𝑁×𝑃× (1) where 𝑞′ × and 𝑝× are the stopping probability distribution and the initial probability distribution of random walks on the product matrix, respectively).
Claim 4, Liu as modified discloses the graph neural network model is part a type distribution prediction model (page 1, column 2, e.g. in knowledge graph search [17], it returns the most relevant concepts for a given entity; in link prediction [10], given the ‘subject’ and the ‘object’ of a triple, it predicts the relation; in fact checking [13], given a claim (e.g., represented as a triple of the knowledge graph), it decides whether it is authentic or falsified; in subgraph matching [9], given a query graph, it finds exact or inexact matching subgraphs).
Claim 5, Liu as modified discloses the extracting the sub-knowledge graph of the nearest neighbor entity from the knowledge graph includes: selecting one or more entities each having proximity to the nearest neighbor entity less than or equal to a K2 order from the knowledge graph; and extracting a sub-knowledge graph that includes the one or more entities each having proximity to the nearest neighbor entity less than or equal to the K2 order (page 5, column 2, e.g. Note that according to Lemma 1, if an element only belongs to 𝐾𝑆1 or 𝐾𝑆2, its influence function value will be 0. In order to avoid this, we introduce a fully connected background graph to 𝐾𝑆1 and 𝐾𝑆2, respectively. This background graph contains all the nodes in 𝐾𝑆1 and 𝐾𝑆2, and it is disconnected with 𝐾𝑆1 and 𝐾𝑆2. If we treat 𝐾𝑆1 and 𝐾𝑆2 as two documents, we can think of this background graph as the background word distribution in a language model).
Claim 10, Liu as modified discloses the selecting a candidate entity matching the query entity and the query relationship as the result entity includes: constructing a candidate triplet based on a candidate entity, the query entity, and the query relationship; selecting a candidate triplet as a target triplet based on a confidence of the candidate triplet; and determining a candidate entity in the target triplet as the result entity (page 3, section 3.2, e.g. Edge-specific knowledge segment extraction aims at finding a knowledge segment to best characterize the semantic context of the given edge (i.e. a triple). Several connection subgraph extraction methods exist for a weighted graph, e.g. [16], [11], [7]. We propose to use a TF-IDF based method3 to measure the similarity between different predicates, and transfer the knowledge graph into a weighted graph whose edge weight represents the similarity between the edge predicate and query predicate. Then, we find k-simple shortest paths [11] from the subject to the object of the given query edge as its knowledge segment).
Claim 11, Liu as modified discloses the query entity is a head entity, and the result entity is a tail entity; or the query entity is the tail entity and the result entity is the head entity (page 3, 3.2, e.g. we find k-simple shortest paths [11] from the subject to the object of the given query edge as its knowledge segment. The key idea behind predicate similarity is to treat each triple in the knowledge graph and its adjacent neighboring triples as a document, and use a TF-IDF like weighting strategy to calculate the predicate similarity. Consider a triple 𝑒𝑡 = <s, receiveDegreeFrom, o> in the knowledge graph whose predicate is 𝑖 = receiveDegreeFrom).
Claims 14-18, Liu discloses a computer system (page 6, section 5, e.g. a moderate desktop with an Intel Core-i7 3.00GHz CPU and 64GB memory) for implementing the above cited method. Therefore, clams 14-18 are rejected with the same citations and rationale.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. Liu herafter, KompaRe: A Knowledge Graph Comparative Reasoning System, 2021) and Wang et al. (Wang hereafter, US 20230206084 A1), as applied to claims 1-5, 10, 11, and 14-18.
Claim 9, Liu as modified discloses the claimed invention except for calculating a second probability. It is noted that the calculating a second probability has been reasonably interpreted as a duplication of the calculation of the first probability. Although the reference did not disclose the calculating of the second probability, the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.
CONCLUSION
Claims 7, 8, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 21-24 are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
6/22/2026
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