Detailed Action
This action is in response to the amendment filed on 04/30/2026 for application 17/563,411, in which:
Claims 1, 10, and 16 are independent claims.
Claims 1, 9, 10, and 16 are currently amended.
Claims 2, 11 and 17 are cancelled.
Claims 1, 3-10, 12-16 and 18-20 are currently pending.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN201910583845.0, filed on 06/29/2019.
Response to Arguments
Applicant's arguments filed 04/30/2026 have been fully considered but they are not persuasive.
Regarding the 35 USC § 101 Rejections:
Applicant's arguments regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant's asserts (Page 11), that the amended independent claims do not merely recite an abstract idea performed on a generic computer. Rather, the claims recite a specific computer-implemented technique for generating entity and relationship embedding representations from text-associated semantic information and then using those trained representations to complete a target knowledge graph by generating, scoring, and adding predicted fact triplets. Accordingly, the pending claims are directed to patent-eligible subject matter.
Examiner respectfully disagrees. The claims are directed to an abstract idea (Step 2A Prong 1: a-i) and do not integrate the abstract ideas into a practical application (Step 2A Prong 2: a-e). The abstract ideas are evaluations or judgements that can be performed in the human mind, or by a human using pen and paper. The additional elements recited within the independent claim only recites performance of an abstract idea within a computer or restricting the abstract idea to a particular technological environment; thus, as the additional elements fall within MPEP 2106.05 they are unable to integrate the judicial exception as they are unable to provide significantly more. The limitations are unable to provide improvement as they are currently being evaluated as either abstract idea(s) or additional elements that fall within MPEP 2106.05. The applicant alleges that the claims recite a specific computer-implemented technique for generating entity and relationship embedding representations from text-associated semantic information and then using those trained representations to complete a target knowledge graph by generating, scoring, and adding predicted fact triplets; however, the claim merely recites (within the broadest reasonable interpretation) obtaining knowledge graphs with specific values/data (comprising textual or semantic descriptors) to be able to determine semantic correlations between values which leads to modeling representations and training the model to be able to determine a score and associate the score. Therefore, for the reasons given above and in the rejections below, the rejection to all Claims (including Claim 1, similar independent claims, and all dependent Claims) are maintained. More specific details are discussed below within the 35 USC § 101 Rejections and responses below.
Applicant asserts (Pages 11-12), that the characterization of obtaining entities, obtaining related entities and concepts, determining semantic correlations, performing vectorization, performing average summation, and modeling an embedding representation is inconsistent with the actual claim language. The claims do not merely recite observation, evaluation, or judgment. Instead, as amended, the claims require that K concepts comprise textual or semantic descriptors automatically identified on a page linked to a related entity, that vectorization processing be performed on those concepts by using a word-vector generation model, that first entity embedding representations be generated from the resulting word vectors, that an embedding representation model be trained to obtain second entity embedding representations and relationship embedding representations, and that those trained embeddings then be used to obtain, score, and add predicted fact triplets to the target knowledge graph. These are specific processor-executed operations performed on text-derived and graph-structured data, not acts that can practically be performed in the human mind.
Examiner respectfully disagrees. As noted in the office action the obtaining … entities … limitations are evaluated as mental processes as a human being is able to obtain specific knowledge graph information and values, the determining … semantic correlations … limitation is evaluated as a mental process as humans are able to determine semantic correlations between specific values (based off observations/evaluations/judgements/comparisons), … performing vectorization … and average summations are considered mental processes as they are simple mathematical concepts that can be done mentally with the aid of pen and paper to determine embedding representations. The modeling … limitation is merely obtaining a model embedding representation which can be done mentally. The alleged specific processor-executed operations are operations that are able to be performed in the human mind and are merely performed on a computer. More specific details are discussed below within the 35 USC § 101 Rejections and responses below.
Applicant asserts (Page 12), that the "using a word-vector generation model" and "training the embedding representation model" being claimed as performing an abstract idea does not adequately account for the claimed subject matter. The claims do not recite a result and then merely instruct a computer to achieve it. Rather, the claims recite a particular sequence of operations by which linked textual data is transformed into machine-usable vector representations, those vector representations are aggregated into first entity embedding representations, semantic correlation information is used in modeling entity and relationship embeddings, and the resulting trained embeddings are then applied to complete the target knowledge graph. That ordered combination is a specific computational technique, not a generic instruction to apply an abstract idea on a computer.
Examiner respectfully disagrees. Both the use of the word-vector generation model and training the embedding representation model are used to apply the abstract ideas on a computer to be able to obtain word vectors/embedding representations, respectively. The automatically identifying K concepts is being evaluated as an additional element (more information below) where the limitation is restricting the abstract idea; the limitation merely mentions what K concepts comprise of for automatic identification and their representations of specific information. The performing vectorization using a word-vector generation model limitation is being evaluated as two different limitations; where the performing of vectorization is an abstract idea (a human being can mentally apply evaluation to determine a embedding representation by performing vectorization on specific concepts in specific entities with the aid of pen and paper) and using a word-vector generation model limitation is an additional elements as it is merely applying the abstract idea on a computer. The limitation of training an embedding representation model is merely performing a mental process on a computer; which is no more than instructions to “apply it” on a computer. The extensiveness of numerical computations or amount of computations do not dictate judicial exception from being a mental process as merely invoking computers or machinery as a tool to perform abstract idea are mere instructions to apply it.
Applicant asserts (Pages 12-14), that the claims expressly set forth the manner in which the improvement is obtained and recites the overall limitations. The specification supports the improvement by noting that the limitations are improving representation capability for complex relationships and improving knowledge graph completion effect. The pending claims therefore do not merely state the desired outcome. They recite the concrete computational steps by which that outcome is produced. the claims integrate any such exception into a practical application. The claimed computation is applied to a specific technological task, including completion and updating of the target knowledge graph itself This is not mere data analysis or presentation of information. It is processor-executed modification of a graph data structure used by a computer system. Applicant further supports these assets by noting the specification and the claim reciting the alleged improvement.
Examiner respectfully disagrees. The claims are directed towards the improvement of an abstract idea. Improvements to an abstract idea are still considered to an abstract idea. The independent claim fails to recite the steps that achieve the improvement. The pending Claims are directed to a judicial exception due to reciting limitations which fall within the “mental processes” group of abstract ideas; where the judicial exception is unable to be directed to significantly more than the judicial exception due to the pending Claims not including additional elements that contribute to an “inventive concept”. The amended claims do not integrate the judicial exception into a practical application nor amount to significantly more. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims.
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology … the claim must include the components or steps of the invention that provide the improvement described in the specification
…
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.
Applicant fails to show how any alleged technical improvement would be provided by anything more than the judicial exception on its own. Additionally, applicant fails to show how the claim includes components or steps that would provide the alleged improvement described in the specification or by the cited case law. By MPEP 2106.05(f)(1), "the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished". Moreover, the examiner maintains that the Claim does not impose any meaningful limits on the judicial exceptions. As noted in the rejection, due to the additional elements falling under MPEP 2106.05, the judicial exception is not integrated into a practical application.
Applicant asserts (Page 14), that the Office Action's analysis improperly dissects the claims into individual elements and evaluates those elements in isolation. Eligibility must be assessed based on the claim as a whole. When viewed as an integrated whole, the claims recite a specific computer-implemented architecture for improving how knowledge graphs are represented and completed. The rejection's piecemeal treatment does not adequately address that ordered combination. Accordingly, Applicants respectfully submit that the pending claims are not directed to a mental process under Step 2A, Prong One. At a minimum, the claims integrate any alleged exception into a practical application under Step 2A, Prong Two, because the claimed computations are used to generate, score, and add predicted fact triplets to the target knowledge graph. Moreover, even if Step 2B were reached, the ordered combination of automatic concept extraction from linked pages, vectorization using a word-vector generation model, embedding generation and training, and score-based insertion of predicted fact triplets into the graph provides significantly more than any alleged abstract idea. For at least these reasons, Applicants respectfully submit that independent claims 1, 10, and 16, and their dependent claims, are patent eligible under 35 U.S.C. § 101, and withdrawal of the rejection is respectfully requested.
Examiner respectfully disagrees. Examiner respectfully disagrees. 35 U.S.C. § 101 rejections for the amended claims are directed to an abstract idea (Step 2A Prong 1) and do not integrate the abstract idea into a practical application (Step 2A Prong 2). The office action establishes a proper and well-supported prima facie case as the claims are explained to be not patentable via the Patent Subject Matter Eligibility steps within MPEP 2106; thus, the additional elements noted within Step 2A Prong 2 are unable to amount to significantly more than the judicial exception (when evaluated individually and holistically). The limitations are unable to provide the alleged improvement as they are currently being evaluated as either abstract idea(s) or additional elements that fall within MPEP 2106.05. The claims are not a technical solution to a technical problem as the independent claim is merely performing abstract ideas with specific restrictions within a computer. The additional elements noted within Step 2A Prong 2 are unable to amount to significantly more than the judicial exception (when evaluated individually and holistically) as they fall within MPEP 2106.05. Thus, the additional elements are not able to integrate the abstract ideas in a practical application. The claims are directed towards the improvement of an abstract idea. Therefore, the claims do not integrate the judicial exception into a practical application. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, analogous independent Claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. More specific details are discussed below within the 35 USC § 101 Rejections.
Regarding the 35 USC § 103 Rejections:
Applicant's arguments regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses the 103 rejections (Pages 14-15) for claims 1, 3, 4, 9, 10, 12, 13, 16, 18 and 19, due to the amended claims where the Lin and Xie prior arts, alone or in combination, fail to teach the additional features. Applicant further supports the traversals by asserting (Pages 15), that the replace … , determine … , and add … limitations that the examiner notes as being taught by Lin are cited sections are for testing and evaluation protocol for link prediction and that they describe generating candidate replacement triples and ranking them for purposes of measuring model performance, such as mean rank and Hits@10, not for modifying the knowledge graph itself. Accordingly, the cited score function and replacement procedure are directed to evaluation of prediction quality, not to the claimed adding of a predicted fact triplet to the target knowledge graph based on the score.
Examiner respectfully disagrees. The additional features/limitations, that are noted by the applicant to be not taught by Lin/Xie, are from previously presented Claim 9. replace … , determine … , and add … limitations were previously presented to be taught by Lin and have been updated for clarity due to the amendments of Claim 1 and the noted remarks. The applicant notes that the replace … , determine … , and add … that were cited from Lin are for testing/evaluation for link prediction and not for modifying the knowledge graph itself; however, the replace … limitation is explicitly taught via the training methodology which replaces corrupted/perturbed triplets, the determine … limitation is taught via the score function to determine how plausible a triplet is considered by scoring the fact and corrupt triplets and ranking them, and the add … limitation is taught via the link prediction as mentioned by the applicant and replacing/adding links for sub-optimal or missing links which is modifying the knowledge graph itself.
Applicant further supports their traversal (Page 15-16), by noting that the cited passages do not teach updating a stored knowledge graph, inserting a newly predicted fact triplet, or otherwise committing a selected candidate triple back into the graph. Rather, the cited sections describe ranking candidate replacement triples during link-prediction evaluation. Prediction and ranking of candidate triples are not the same as score-based insertion of a selected predicted fact triplet into the target knowledge graph.
Examiner respectfully disagrees. As mentioned above, the add … limitation is taught via the link prediction as mentioned by the applicant and replacing/adding links for sub-optimal or missing links which is modifying the knowledge graph itself. The citations from Lin note replacing entities within the knowledge graph and scoring the replacement. This is interpreted by the examiner as removing a value and adding a different value; thus, adding to the target knowledge graph.
Applicant asserts (Page 16), that Xie does not cure this deficiency. Xie's DKRL model uses fact triples and entity descriptions to learn representations and evaluates those representations on completion tasks, but Xie likewise does not teach the claimed sequence of replacing within a known fact triplet, determining a recommended score based on trained embeddings, and then adding the predicted fact triplet to the target knowledge graph. Xie is directed to learning and evaluating representations, not to updating the target knowledge graph in the manner recited by claims 1, 10, and 16. In view of the above, Lin and Xie fail to teach claim 1 (and similarly claims 10 and 16). Thus, claims 1, 10 and 16 and their dependent claims are not unpatentable over the combination of Lin and Xie at least for this reason. Accordingly, Applicants respectfully request the rejection under 35 U.S.C. §103 be withdrawn.
Examiner respectfully disagrees. Xie does not need to teach the claimed sequence of replacing within a known fact triplet, determining a recommended score based on trained embeddings, and then adding the predicted fact triplet to the target knowledge graph as Lin already taught these features, as noted above. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, analogous independent Claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. More specific details are discussed below within the 35 USC § 103 Rejections.
Applicant traverses the 103 rejections (Pages 16-17) for claims 5-8, 14, 15 and 20, due to the amended claims where the Lin and Xie prior arts, alone or in combination, fail to teach the additional features. Neither Lin nor Xie teach the additional features recited in the independent claims, including obtaining a predicted fact triplet by replacing an entity relationship or entity in a known fact triplet, determining a recommended score for the predicted fact triplet based on the trained embeddings, and adding the predicted fact triplet to the target knowledge graph based on that score. Lin's cited disclosure is directed to ranking candidate triples for link-prediction evaluation, not adding a selected predicted fact triplet to the graph, and Xie does not cure that deficiency. Liu is directed to general deep learning and natural language processing techniques and likewise does not teach generating, scoring, and adding predicted fact triplets to a knowledge graph. Accordingly, the alleged combination fails to render obvious independent claims 1, 10, and 16, and their dependent claims 5-8, 14, 15, and 20. Withdrawal of the rejection is respectfully requested.
Examiner respectfully disagrees. As noted above, the additional features are taught by Lin and do not need to be taught by Xie. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, analogous independent Claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. More specific details are discussed below within the 35 USC § 103 Rejections.
Claim Objections
Claims 1, 10, and 16 are objected to because of the following informalities:
Claims 1, 10, and 16 recite “_” within the limitation “… training the embedding representation model to obtain a second entity embedding representation of each entity and a relationship embedding representation of the entity relationship_” which appears to be a typographical error.
Appropriate correction is required. For the purposes of examination, the claims are being interpreted as semicolons (“;”) instead of blanked underscores (“-_”).
Claim Rejections - 35 USC § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 1 further recites the process comprising of:
obtaining M entities in a target knowledge graph, wherein the M entities comprise an entity 1, an entity 2, through an entity M, and M is an integer greater than 1 (a human being can mentally apply evaluation to obtain entities within a knowledge graph)
obtaining, from a preset knowledge base, N related entities of an entity m in the M entities and K concepts corresponding to a related entity n in the N related entities, wherein the N related entities comprise a related entity 1, a related entity 2, through a related entity N, N and K are integers not less than 1, m = 1, 2, 3, through M, n = 1, 2, 3, through N, the entity m is semantically correlated with the N related entities, and the related entity n is semantically correlated with the K concepts … (a human being can mentally apply evaluation to obtain related entities and concepts of an entity wherein the entity is semantically correlated with the related entities and the concepts)
determining a semantic correlation between each of the M entities and each of the N related entities of the entity m, and determining a first entity embedding representation of each of the N related entities based on corresponding K concepts … (a human being can mentally apply evaluation to determine a semantic correlation between entities and related entities; and to determine an embedding representation of an entity based on corresponding concepts)
… wherein determining the first entity embedding representation comprises performing vectorization processing on each concept in the K concepts corresponding to the related entity n … (a human being can mentally apply evaluation to determine a embedding representation by performing vectorization on specific concepts in specific entities with the aid of pen and paper)
… performing average summation on the word vectors of the K concepts to obtain the first entity embedding representation of the related entity n (a human being can mentally apply evaluation to determine a embedding representation by performing average summation on specific vectors of specific concepts to obtain a specific embedding representation with the aid of pen and paper)
modeling, based on the first entity embedding representation and the semantic correlation, an embedding representation of the M entities and an embedding representation of an entity relationship between the M entities, to obtain an embedding representation model (a human being can mentally apply evaluation to model embedding representations to obtain an embedding representation model)
replacing the entity relationship comprised in the known fact triplet with another entity relationship between the M entities, or replacing one entity comprised in the known fact triplet with another entity in the M entities, to obtain a predicted fact triplet (a human being can mentally apply evaluation to replace an entity relationship within a knowledge graph with the aid of pen and paper to obtain a predicted fact)
determining a recommended score of the predicted fact triplet based on the second entity embedding representation of an entity in the predicted fact triplet and the relationship embedding representation of the entity relationship (a human being can mentally apply evaluation to determine a recommend score of a predicted fact triplet based on an embedding representation entities)
adding, based on the recommended score, the predicted fact triplet to the target knowledge graph (a human being can mentally apply evaluation to add a predicted triplet to a knowledge graph)
Claim 1 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
A knowledge graph embedding representation method, performed by an electronic device including at least one processor and a memory, comprising (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
wherein the K concepts comprise textual or semantic descriptors automatically identified on a page linked to the related entity n, the K concepts representing semantic information that characterizes attributes, categories, or contextual features of the related entity n (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
… by using a word-vector generation model to obtain a word vector of each concept … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
training the embedding representation model to obtain a second entity embedding representation of each entity and a relationship embedding representation of the entity relationship (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
wherein the target knowledge graph comprises a known fact triplet, and the operations further comprises (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a and c-d are merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Additional elements b and e are only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 3 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 3 further recites:
wherein the modeling, based on the first entity embedding representation and the semantic correlation, the embedding representation of the M entities and the embedding representation of the entity relationship between the M entities, to obtain the embedding representation model comprises: (a human being can mentally apply evaluation to model embedding representations to obtain an embedding representation model)
determining, based on the semantic correlation and a first entity embedding representation of the N related entities, a unary text embedding representation corresponding to each entity (a human being can mentally apply evaluation to determine a unary text embedding representation corresponding to each entity based on semantic correlations and an embedding representation of the related entities)
determining, based on the N related entities, a common related entity of every two entities in the M entities (a human being can mentally apply evaluation to determine a common related entity between two entities)
determining, based on the semantic correlation and a first entity embedding representation of the common related entity, a binary text embedding representation corresponding to the every two entities (a human being can mentally apply evaluation to determine a binary text embedding representation corresponding to the every two entities based on semantic correlations and an embedding representation of the related entities)
establishing, based on the unary text embedding representation and the binary text embedding representation, the embedding representation model (a human being can mentally apply evaluation to establish the embedding representation model based on unary and binary text embedding representations)
Claim 3 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 4 recites the method of Claim 3. Claim 3 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 4 further recites:
wherein the establishing, based on the unary text embedding representation and the binary text embedding representation, the embedding representation model comprises: (a human being can mentally apply evaluation to establish the embedding representation model based on unary and binary text embedding representations)
mapping the unary text embedding representation and the binary text embedding representation to a same vector space, to obtain a semantically enhanced unary text embedding representation and a semantically enhanced binary text embedding representation (a human being can mentally apply evaluation to map unary and binary text embeddings to obtain semantically enhanced text embedding representations)
establishing, based on the semantically enhanced unary text embedding representation and the semantically enhanced binary text embedding representation, the embedding representation model (a human being can mentally apply evaluation to establish the embedding representation model based off the semantically enhanced text embedding representations)
Claim 4 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 5 recites the method of Claim 3. Claim 3 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 5 further recites:
wherein the determining, based on the semantic correlation and the first entity embedding representation of the N related entities, the unary text embedding representation corresponding to each entity comprises: (a human being can mentally apply evaluation to determine a unary text embedding representation corresponding to each entity based on semantic correlations and an embedding representation of the related entities)
using the semantic correlation as a first weight coefficient of each of the N related entities; and performing, based on the first weight coefficient, weighted summation on the first entity embedding representation of the N related entities, to obtain the unary text embedding representation (a human being can mentally apply evaluation to perform a weighted summation on an embedding representation to obtain a unary text embedding representation using the semantic correlation as the weight coefficient).
Claim 5 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 6 recites the method of Claim 3. Claim 3 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 6 further recites:
wherein the determining, based on the semantic correlation and a first entity embedding representation of the common related entity, the binary text embedding representation corresponding to the every two entities comprises: (a human being can mentally apply evaluation to determine a binary text embedding representation corresponding to the every two entities based on semantic correlations and an embedding representation of the related entities)
using the common related entity and a minimum semantic correlation of semantic correlations of every two entities as a second weight coefficient of the common related entity; and performing, based on the second weight coefficient, weighted summation on the first entity embedding representation of the common related entity, to obtain the binary text embedding representation (a human being can mentally apply evaluation to perform a weighted summation on an embedding representation to obtain a unary text embedding representation using the semantic correlation as the weight coefficient).
Claim 6 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because there are no new additional elements recited.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no new additional elements recited. The judicial exception alone does not provide significantly more than the abstract idea itself. Thus, the claim is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 7 recites the method of Claim 5. Claim 5 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 7 further recites determining a loss function of the embedding representation model (a human being can mentally apply evaluation to determine a loss function of the embedding representation model). Claim 7 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
wherein the training the embedding representation model to obtain the second entity embedding representation of each entity and the relationship embedding representation of the entity relationship comprises: (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
training, according to a preset training method, the embedding representation model to minimize a function value of the loss function, to obtain the second entity embedding representation and the relationship embedding representation (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Additional element b is merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 8 recites the method of Claim 7. Claim 7 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 8 further recites initializing the embedding representation of each entity and the embedding representation of the entity relationship, to obtain an initial entity embedding representation and an initial relationship embedding representation (a human being can mentally apply evaluation to initialize embedding representations and relationship embedding representations to obtain an initial representation). Claim 8 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
wherein the function value is associated with an embedding representation of each entity, an embedding representation of the entity relationship, and a unary text embedding representation; the training, according to the preset training method, the embedding representation model to minimize the function value of the loss function, to obtain the second entity embedding representation and the relationship embedding comprises: (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
iteratively updating the first weight coefficient according to an attention mechanism to update the unary text embedding representation, and iteratively updating the initial entity embedding representation and the initial relationship embedding representation according to the preset training method (which is an insignificant extra-solution activity, by MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Additional element b falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of performing repetitive calculations (MPEP 2106.05(d)(II)(ii): “Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199”). Thus, the claim is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 9 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 9 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 9 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the sole additional elements recited consists of wherein the known fact triplet comprises two entities in the M entities and an entity relationship, and wherein adding the predicted fact triplet to the target knowledge graph comprises adding the predicted fact triplet based on the recommended score (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claims 10 and 12-15:
Claims 10 and 12-15 incorporate substantively all the limitations of Claims 1 and 3-6 in an apparatus (thus a machine) and further recites a new additional element at least one processor; and one or more memories coupled to the at least one processor and storing executable program instructions that, when executed by the at least one processor, cause the at least one processor to: (these claim limitations appear to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f)) and does not appear to integrate the abstract idea into a particular application; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claims 10 and 12-15 are rejected for reasons set forth in the rejections of Claims 1 and 3-6, respectively.
Regarding Claims 16 and 18-20:
Claims 16 and 18-20 incorporate substantively all the limitations of Claims 1 and 3-5 in a non-transitory computer-readable storage medium and further recites a new additional element at least one processor; and one or more memories coupled to the at least one processor and storing executable program instructions that, when executed by the at least one processor, cause the at least one processor to: (these claim limitations appear to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f)) and does not appear to integrate the abstract idea into a particular application; thus, the claim is subject-matter ineligible as it does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself); thus, Claims 16 and 18-20 are rejected for reasons set forth in the rejections of Claims 1 and 3-5, respectively.
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.
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.
Claims 1, 3-4, 9-10, 12-13, 16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al., “Learning Entity and Relation Embeddings for Knowledge Graph Completion”, in view of Xie et al., “Representation Learning of Knowledge Graphs with Entity Descriptions”.
Regarding Claim 1:
Lin teaches:
A knowledge graph embedding representation method, comprising, performed by an electronic device including at least one processor and a memory, comprising:
(Lin, Page 2183, Column 2, Paragraph 5, “In this paper, we evaluate our methods with two typical knowledge graphs”; Page 2183, Column 1, Paragraph 4, “… we propose a new method, which models entities and relations in … entity space and relation spaces, and performs translation in relation space, hence named as TransR … each triple, entities embeddings are … and relation embedding …”; Page 2181, Abstract, “…source code … can be obtained from https: //github.com/mrlyk423/relation extraction”. The method taught by Lin utilizes a TransR model to represent the knowledge graph embeddings; where the method contains source code to handle the complexity of the TransR model for training, testing/evaluation which is interpreted as an device with at least one processor, memory, and a storage to run the source code within the electronic device).
obtaining M entities in a target knowledge graph, wherein the M entities comprise an entity 1, an entity 2, through an entity M, and M is an integer greater than 1;
(Lin, Table 1. Table 1 shows the data sets used for the target knowledge graphs within the methods/experiments within Lin comprising M entities where M is greater than 1).
obtaining, from a preset knowledge base,
(Lin, Table 1; Page 2183, Column 2, Paragraph 5, “In this paper, we evaluate our methods with two typical knowledge graphs, built with WordNet … and Freebase …”. Table 1 shows the WordNet and Freebase data set versions used to build the knowledge graphs (WordNet and Freebase is interpreted by the examiner as a preset knowledge base for the methods/experiments of embedding knowledge graphs for link prediction)).
N related entities of an entity m in the M entities and K concepts corresponding to a related entity n in the N related entities, wherein the N related entities comprise a related entity 1, a related entity 2, through a related entity N, N and K are integers not less than 1, m = 1, 2, 3, through M, n = 1, 2, 3, through N, the entity m is semantically correlated with the N related entities, and the related entity n is semantically correlated with the K concepts …
(Lin, Table 1; (Lin, Figure 1; Page 2183, Column 1, Paragraph 5, “In TransR, for each triple, entities embeddings are (h, r, t) set as h, t ∈ ℝk and relation embedding is set as r ∈ ℝd …”. Table 1 comprises #Ent (interpreted as the M entities) and #Rel (interpreted as relations/links for the N related entities where N is greater than 1). Knowledge graphs consist of two primary concepts which are nodes (entities or concepts) and edges (relationships). Triples (h, r, t) are used within Lin to represent the knowledge graph data where h = head (entity or concept), r = relationship (edge), t = tail (entity or concept); where r is the relationship between h and t. Thus, the K concepts are interpreted as entity nodes within the knowledge graphs and are linked (related) from entity to entity (node to node) based off the semantic correlation between an entity and a concept (entity)).
determining a semantic correlation between each of the M entities and each of the N related entities of the entity m, and determining a first entity embedding representation of each of the N related entities based on corresponding K concepts …
(Lin, Figure 1; Page 2183, Column 1, Paragraph 3, “we propose TransR, which represent entities and relations in distinct semantic space bridged by relation-specific matrices … which models entities and relations in distinct spaces, i.e., entity space and relation spaces, and performs translation in relation space … In TransR, for each triple, entities embeddings are (h, r, t) set as h, t ∈ ℝk and relation embedding is set as r ∈ ℝd … For each relation r, we set a projection matrix Mr ∈ ℝkxd, which may projects entities from entity space to relation space. With the mapping matrix, we define the projected vectors of entities as
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The score function is correspondingly defined as
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”; Page 2184, Column 2, Paragraph 2, “… TransR achieves great improvement consistently on all mapping categories of relations … TransR provides more precise representation for both entities and relation and their complex correlations, as illustrated in Fig. 1; … which shows the ability of TransR to discriminate relevant from irrelevant entities via relation-specific projection”. The TransR model models entities as vectors in the entity space and models each relation as a vector in the relation space as a projection matrix. The score function is used to rank entities in descending order of similarity scores. As the modelling represents entities and relations in a semantic space, the model determines a semantic correlation between each entity and each related entity (which are based on the concepts (related nodes)) through the entity/relation spaces. Each triple (entity embedding vectors which are interpreted as entity embedding representations) will be projected with each relation vector; thus, a first entity embedding representation of each of the N related entities based on corresponding K concepts will be determined; where the scores with the highest ranking will be the most relevant semantically correlated entities for link prediction/triple classification between entity to related concepts (based on entities and relations). Fig. 1 shows a simple illustration of the mapping which indicates the precise representation for both entities and relations and their complex correlations used for prediction).
modeling, based on the first entity embedding representation and the semantic correlation, an embedding representation of the M entities and an embedding representation of an entity relationship between the M entities, to obtain an embedding representation model; and
(Lin, Figure 1; Page 2183, Column 1, Paragraph 3, “we propose TransR, which represent entities and relations in distinct semantic space bridged by relation-specific matrices … which models entities and relations in distinct spaces, i.e., entity space and relation spaces, and performs translation in relation space … In TransR, for each triple, entities embeddings are (h, r, t) set as h, t ∈ ℝk and relation embedding is set as r ∈ ℝd … For each relation r, we set a projection matrix Mr ∈ ℝkxd, which may projects entities from entity space to relation space. With the mapping matrix, we define the projected vectors of entities as
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The score function is correspondingly defined as
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”; Page 2184, Column 2, Paragraph 2, “… TransR achieves great improvement consistently on all mapping categories of relations … TransR provides more precise representation for both entities and relation and their complex correlations, as illustrated in Fig. 1; … which shows the ability of TransR to discriminate relevant from irrelevant entities via relation-specific projection”. The TransR model models entities as vectors in the entity space and models each relation as a vector in the relation space as a projection matrix. The score function is used to rank entities in descending order of similarity scores. As the modelling represents entities and relations in a semantic space, the model determines a semantic correlation between each entity and each related entity (which are based on the concepts (related nodes)) through the entity/relation spaces. Each triple (entity embedding vectors which are interpreted as entity embedding representations) will be projected with each relation vector; thus, a first entity embedding representation of each of the N related entities based on corresponding K concepts will be determined; where the scores with the highest ranking will be the most relevant semantically correlated entities for link prediction between entity to related concepts (based on entities and relations). Fig. 1 shows a simple illustration of the mapping which indicates the precise representation for both entities and relations and their complex correlations used for prediction).
training the embedding representation model to obtain a second entity embedding representation of each entity and a relationship embedding representation of the entity relationship
(Lin, Table 1; Page 2183, Column 2, Paragraph 4, “The learning process of TransR and CTransR is carried out using stochastic gradient descent (SGD) … we initialize entity and relation embeddings with results of TransE, and initialize relation matrices as identity matrices”; Page 2184, Column 1, Paragraph 1, “Link prediction aims to predict the missing h or t for a relation fact triple (h, r, t),”; Page 2184, Column 2, Paragraph 4, “Triple classification aims to judge whether a given triple (h, r, t) is correct or not” Table 1 shows the training data set partition within the preset knowledge bases for the target knowledge graphs used within the method. The training and learning of the model is used to predict missing links and make judgements on whether a triple is correct or not to obtain a new entity embedding representation. Thus, the model TransR with the initial values for the first entity/relation embedding representation being from the TransE model are trained to obtain the second entity embedding representations).
wherein the target knowledge graph comprises a known fact triplet, and the method further comprises: … or replacing one entity comprised in the known fact triplet with another entity in the M entities, to obtain a predicted fact triplet;
(Lin, Page 2184, Column 1, Paragraph 2, “… for each test triple (h, r, t), we replace the head/tail entity by all entities in the knowledge graph and rank these entities in descending order …”; Page 2183, Column 2, Paragraph 3, “Existing knowledge graphs only contain correct triples. It is routine to corrupt correct triples (h, r, t) … by replacing entities, and construct incorrect triples (h’, r, t’) … we follow (Wang et al. 2014) and assign different probabilities for head/tail entity replacement”. The entity relationships are replaced by the best results/scoring relationships for the triplets to obtain a predicted fact triplet as the corrupted and correct triplets are ranked to evaluate existing and corrupted triplets; thus, interpreted by the examiner as obtaining a predicted fact triplet for a corrupted/perturbed triplet(as the corrupted triplet is a predicted factual outcome as the corrupted triplet is scored)).
determining a recommended score of the predicted fact triplet based on the second entity embedding representation of an entity in the predicted fact triplet and the relationship embedding representation of the entity relationship; and
(Lin, Page 2184. Column 1, Paragraph 2, “… and rank these entities in descending order of similarity scores calculated by score function fr”; Page 2183, Column 1, Paragraph 6, “… The score function is correspondingly defined as
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”. The score function is utilized to determine a recommended score for the predicted fact triplet).
adding, based on the recommended score, the predicted fact triplet to the target knowledge graph.
(Lin, Page 2184, Column 1, Paragraph 2, “… for each test triple (h, r, t), we replace the head/tail entity by all entities in the knowledge graph, and rank these entities in descending order of similarity scores calculated by score function fr”; Page 2184, Column 1, Paragraph 1, “Link prediction aims to predict the missing h or t for a relation fact triple (h, r, t)”. For missing links/relations between entities or sub-optimal links, Lin teaches the replacing of the head/tail entities with the highest similarity scored triple based off the score function. Thus, adding the predicted fact triplet is taught by Lin by replacing or adding links for sub-optimal or missing links, respectively, to optimize the knowledge graph).
While Lin teaches the determining of entity embedding representations with performing vectorization to obtain word vectors… Lin does not explicitly disclose the K concepts comprising textual or semantic descriptors representing semantic information.
However, Xie explicitly discloses:
… wherein the K concepts comprise textual or semantic descriptors automatically identified on a page linked to the related entity n, the K concepts representing semantic information that characterizes attributes, categories, or contextual features of the related entity n;
(Xie, Page 2660, Column 1, Paragraph 1, “… DKRL model can build representations for those novel entities automatically from their descriptions”; Page 2660, Column 2, Paragraph 6, “… we propose two encoders to build description-based representations … a continuous bag-of-words encoder for entity construction, then we propose a deep convolutional neural network encoder for a better understanding of textual information”; Page 2661, Column 1, Paragraph 3, “In … (CBOW), we select top n keywords in the description for each entity as the input (some classical textual features ”; Figures 2-3. The K concepts in Xie are interpreted as the descriptions of the nodes (which are entities comprising contextual features that characterize the entities description). Xie’s DKRL model creates knowledge graph representations where nodes are automatically linked to related entities based on descriptions for a better understanding of textual information (interpreted by the examiner as textual descriptors); where textual descriptors are handled with the CBOW encoder (Fig. 2 ) and the semantic descriptors are handled via CNN encoder (Fig. 3)).
… wherein determining the first entity embedding representation comprises performing vectorization processing on each concept in the K concepts corresponding to the related entity n by using a word-vector generation model to obtain a word vector of each concept and performing average summation on the word vectors of the K concepts to obtain the first entity embedding representation of the related entity n;
(Xie, Page 2661, Figure 2, Column 1, Paragraph 2, “From each short description, we can generate a set of keywords … similar entities should have similar descriptions, and … similar keywords … CBOW … we select top n keywords in the description for each entity as the input … TF-IDF … Then we simply sum up the embeddings of keywords to get the entity embedding … Equation (4)”.; Page 2661, Column 2, Paragraph 2, “In our experiments, we use the word embeddings trained on Wikipedia by word2vec … as inputs for the CNN Encoder”. The DKRL model taught by Xie comprises two types of encoders (CBOW: continuous bag of words for entity construction; CNN: convolutional neural network for understanding textual information) for parsing textual inputs and outputting entity embeddings. The CNN encoder utilizes Word2Vec (word vector generation) which teaches performing vectorization on the textual inputs for each concept using the DKRL model. The CBOW encoder teaches the adding up the embeddings from the bag of words; thus, interpreted by the examiner as performing summation via Equation (4). The embeddings are keywords from a bag of words where the input is like TF-IDF which is weighted averaging; thus, interpreted by the examiner as teaching performing average summation on the word embedded vectors).
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention to utilize the TransR methodology of Lin for entity/relational generation in respective separate spaces, with the encoders taught within Xie’s methodology for Knowledge Graphs with entity descriptions to illustrate the performance and effectiveness of adding together different weighed components of the entity representations to consider all entities equally to obtain the representation as Xie notes the explicit extensions of the TransR and similar models (see Xie, Page 2660, Column 1, Paragraph 1, “…indicates the good generalization ability and robustness of
the DKRL model, which is … important for largescale KGs and their applications in Web domain”; Page 2665, Column 1, Paragraph 1, “… We verify the effectiveness of description-based representations only with TransE, and it is not difficult for further explorations with more sophisticated extension models of TransE …”).
Regarding Claim 3:
Lin/Xie teach the method of Claim 1 and Lin further teaches:
wherein the modeling, based on the first entity embedding representation and the semantic correlation, the embedding representation of the M entities and the embedding representation of the entity relationship between the M entities, to obtain the embedding representation model comprises:
determining, based on the semantic correlation and a first entity embedding representation of the N related entities,
(Lin, Figure 1; Page 2183, Column 1, Paragraph 3, “we propose TransR, which represent entities and relations in distinct semantic space bridged by relation-specific matrices … which models entities and relations in distinct spaces, i.e., entity space and relation spaces, and performs translation in relation space …”; Page 2184, Column 2, Paragraph 2, “… TransR achieves great improvement consistently on all mapping categories of relations … TransR provides more precise representation for both entities and relation and their complex correlations, as illustrated in Fig. 1; … which shows the ability of TransR to discriminate relevant from irrelevant entities via relation-specific projection”. TransR models entities as vectors in the entity space and models each relation as a vector in the relation space as a projection matrix. The score function is used to rank entities in descending order of similarity scores. As the modelling represents entities and relations in a semantic space, the model determines a semantic correlation between each entity and each related entity through translation (which are based on the concepts (related nodes)) through the entity/relation spaces. Each triple (entity embedding vectors which are interpreted as entity embedding representations) will be projected with each relation vector; thus, a first entity embedding representation of each of the N related entities based on corresponding K concepts will be determined; where the scores with the highest ranking will be the most relevant semantically correlated entities for link prediction between entity to related concepts (based on entities and relations). Fig. 1 shows a simple illustration of the mapping which indicates the precise representation for both entities and relations and their complex correlations used for prediction).
a unary text embedding representation corresponding to each entity;
(Lin, Figure 1; Page 2183, Column 1, Paragraph 3, “In TransR, for each triple, entities embeddings are (h, r, t) set as h, t ∈ ℝk …”. The head and tail entities within a triple are interpreted as unary text embedding representations as the respective embedded vector representations are involving a single component. The unary text embedding representations of the entities are based on semantic correlations due to being projected into distinct semantic space which is bridged by relation-specific matrix which allows the determination of scores to find the most relevant entities for link prediction/classification).
determining, based on the N related entities, a common related entity of every two entities in the M entities; determining, based on the semantic correlation and a first entity embedding representation of the common related entity, a binary text embedding representation corresponding to the every two entities; and
(Lin, Figure 1; Page 2183, Column 1, Paragraph 3, “In TransR … relation embedding is set as r ∈ ℝd …”; Page 2184, Column 1, Paragraph 1, “Link prediction aims to predict the missing h or t for a relation fact triple (h, r, t)”. The relation vectors are considered binary as it relates the head and tail vectors which is interpreted as a binary text embedding representation as the vector involves two components. The relation between the every two entities is based on the N related entities (which is based on the concepts (entities) that can be related to an entity). The relationship will be the edge between the two nodes (entity to entity (based on concept)); thus, a common related entity of every two entities in the M entities as the knowledge graph is built to predict/create links between all missing relationships for h or t entities to have a complete related triple).
establishing, based on the unary text embedding representation and the binary text embedding representation, the embedding representation model.
(Lin, Figure 1; Page 2183, Column 1, Paragraph 3, “With the mapping matrix, we define the projected vectors of entities as
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…”. The unary and binary text embedding representations are used for the TransR knowledge graph (embedding representation model) with the use of mapping the unary and binary embedding vectors in the same vector space to construct the knowledge graph).
Regarding Claim 4:
Lin/Xie teach the method of Claim 3 and Lin further teaches:
wherein the establishing, based on the unary text embedding representation and the binary text embedding representation, the embedding representation model comprises:
mapping the unary text embedding representation and the binary text embedding representation to a same vector space, to obtain a semantically enhanced unary text embedding representation and a semantically enhanced binary text embedding representation; and
(Lin, Figure 1; Page 2183, Column 1, Paragraph 3, “With the mapping matrix, we define the projected vectors of entities as
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… ”; Page 2184, Column 2, Paragraph 2, “… TransR achieves great improvement consistently on all mapping categories of relations … TransR provides more precise representation for both entities and relation and their complex correlations, as illustrated in Fig. 1; … which shows the ability of TransR to discriminate relevant from irrelevant entities via relation-specific projection”; Page 2181, Column 2, Paragraph 5, “The relation-specific projection can make the head/tail entities that actually hold the relation (denoted as colored circles) close with each other, and also get far away from those that do not hold the relation (denoted as colored triangles)”. The TransR model is mapping the unary text embedding representation and the binary text embedding representation in distinct spaces and then translates them to the same vector space (relation space) and can be shown in Figure 1. The mapping creates a semantically enhanced representation for both embedding representations as Figure 1 shows the related entities will get closer within the relation space if there is more of a relation and farther apart if there is less of a relation. As the modelling represents entities and relations in a semantic space, the model determines a semantic correlation between each entity and each related entity (which are based on the concepts (related nodes)) through the entity/relation spaces. The scores with the highest ranking will be the most relevant semantically correlated entities for link prediction between entity to related concepts (based on entities and relations). Fig. 1 shows a simple illustration of the mapping which indicates the precise representation for both entities and relations and their complex correlations used for prediction).
establishing, based on the semantically enhanced unary text embedding representation and the semantically enhanced binary text embedding representation, the embedding representation model.
(Lin, Figure 1; Page 2183, Column 1, Paragraph 3, “With the mapping matrix, we define the projected vectors of entities as
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…”. The semantically enhanced unary and binary text embedding representations are used for the TransR knowledge graph (embedding representation model) with the use of mapping the unary and binary embedding vectors in the same vector space to construct the knowledge graph).
Regarding Claim 9:
Lin/Xie teach the method of Claim 1 and Lin further teaches:
wherein the known fact triplet comprises two entities in the M entities and an entity relationship and wherein adding the predicted fact triplet to the target knowledge graph comprises adding the predicted fact triplet based on the recommended score;
(Lin, Page 2184, Column 1, Paragraph 2, “… for each test triple (h, r, t), we replace the head/tail entity by all entities in the knowledge graph, and rank these entities in descending order of similarity scores calculated by score function fr”; Page 2184, Column 1, Paragraph 1, “Link prediction aims to predict the missing h or t for a relation fact triple (h, r, t)”. The triple (h, r, t) comprises two entities, as it has a head and tail entity. For missing links/relations between entities or sub-optimal links, Lin teaches the replacing of the head/tail entities with the highest similarity scored triple based off the score function (interpreted as based on the recommended score). Thus, adding the predicted fact triplet is taught by Lin by replacing or adding links for sub-optimal or missing links, respectively, to optimize the knowledge graph).
Regarding Claims 10 and 12-13:
Claims 10 and 12-13 incorporate substantively all the limitations of Claims 1 and 3-4 in an apparatus and further recites a new additional element at least one processor; and one or more memories coupled to the at least one processor and storing executable program instructions that, when executed by the at least one processor, cause the at least one processor to: (Lin, Abstract, “The source code of this paper can be obtained from https: //github.com/mrlyk423/relation extraction”; Page 2184, Paragraph 1, “… the system is asked to rank…”; Page 2185, Paragraph 2, “The computation complexity of TransR is higher than both TransE and TransH, which takes about 3 hours for training”. The system of Lin utilizes a computer to handle the complexity of the TransR model for training, testing/evaluation which is interpreted as an apparatus with at least one processor, memory, and a storage to run the source code within the system); thus, Claims 10 and 12-13 are rejected for reasons set forth in the rejections of Claims 1 and 3-4, respectively.
Regarding Claims 16 and 18-19:
Claims 16 and 18-19 incorporate substantively all the limitations of Claims 1 and 3-4 in a non-transitory computer-readable storage medium and further recites a new additional element at least one processor; and one or more memories coupled to the at least one processor and storing executable program instructions that, when executed by the at least one processor, cause the at least one processor to: (Lin, Abstract, “The source code of this paper can be obtained from https: //github.com/mrlyk423/relation extraction”; Page 2184, Paragraph 1, “… the system is asked to rank…”; Page 2185, Paragraph 2, “The computation complexity of TransR is higher than both TransE and TransH, which takes about 3 hours for training”. The system of Lin utilizes a computer to handle the complexity of the TransR model for training, testing/evaluation which is interpreted as a manufacture with at least one processor, memory, and a storage to run the source code); thus, Claims 16 and 18-19 are rejected for reasons set forth in the rejections of Claims 1 and 3-4, respectively.
Conclusion
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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/I.R./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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