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 .
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,
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a method/process.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
determining one or more graph operators used to perform fusion processing on the target entity field and the target relationship description; (mental evaluation, a human can look at the data received and determine how to fuse them based on some set of rules)
processing the data instances by using the one or more graph operators to generate a fused knowledge graph (mental evaluation, a person can for example draw on a piece of paper the fused knowledge graph after determining some arbitrary rules)
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The limitations of:
obtaining a target entity field and a target relationship description from ontology definition data of two or more knowledge graphs, the ontology definition data of a knowledge graph including an entity field used to indicate an entity and a relationship description used to indicate a relationship between entities; (data gathering, insignificant extra-solution activity as well as applying the abstract idea to a particular field of use, MPEP 2106.05(g) and 2106.05(h) respectively)
obtaining data instances corresponding to the target entity field and the target relationship description from the two or more knowledge graphs; (data gathering, insignificant extra-solution activity)
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The limitations of:
obtaining a target entity field and a target relationship description from ontology definition data of two or more knowledge graphs, the ontology definition data of a knowledge graph including an entity field used to indicate an entity and a relationship description used to indicate a relationship between entities; (data gathering, insignificant extra-solution activity as well as applying the abstract idea to a particular field of use, MPEP 2106.05(g) and 2106.05(h) respectively, data gathering is well-known understood and routine, MPEP 2106.05(d)(II)(i))
obtaining data instances corresponding to the target entity field and the target relationship description from the two or more knowledge graphs; (data gathering, insignificant extra-solution activity, data gathering is well-known understood and routine, MPEP 2106.05(d)(II)(i))
Note that independent claims 11 and 18 recite the same substantial subject matter as independent claim 1, only differing in embodiments. The difference in embodiments, a system and non-transitory medium do not meaningfully change the above analysis as they amount to generic computing components to carry out the abstract idea MPEP 2106.05(f). Therefore the claims are subject to the same rejection,
Dependent claim 2 recites receiving user input, data gathering MPEP 2106.05(d)(II)(i).
Dependent claim 3 recites determining graph operators, mental evaluations.
Dependent claim 4 recites obtaining ontology definition data, data gathering MPEP 2016.05(d)(II)(i).
Dependent claim 5 recites entity fields, and processing the data instances, MPEP2106.05(h) and mental evaluation.
Dependent claim 6 recites obtaining the data instances, data gathering MPEP 2106.05(d)(II)(i).
Dependent claim 7 recites obtaining the data instances and processing them, MPEP2106.05(h) and mental evaluation.
Dependent claim 8 recites a trusted environment and processing the graph, MPEP 2106.05(h).
Dependent claim 9 recites the algorithm specified by a user, MPEP 2106.05(h).
Dependent claim 10 recites receiving from providers, MPEP 2106.05(h).
Dependent claims 12-17 correspond to their respective corresponding claims above.
Dependent claims 19 and 20 correspond to claims 4 and 6 respectively.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen, Hoang Long, Dang Thinh Vu, and Jason J. Jung. "Knowledge graph fusion for smart systems: A survey." in view of CN112906826A [herein CN826].
Regarding claims 1, 11, and 18, Nguyen teaches “a method, comprising: obtaining a target entity field and a target relationship description from ontology definition data of two or more knowledge graphs” (pg. 2 §2.2 “Knowledge graph fusion is an effective solution to deal with this problem by focusing on capturing knowledge from different sources to construct a knowledge graph, and extracting useful knowledge and insights [18,19] from these graphs to combine them into a unified knowledge graph.”), “the ontology definition data of a knowledge graph including an entity field used to indicate an entity and a relationship description used to indicate a relationship between entities” (pg. 1 ¶1 “ontology, which is an extension of taxonomy, can describe and capture all knowledge of our world as concepts and entities using multiple classes, relationships, and constraints [2] . Hence, ontology is capable of efficiently recording complex structures and schema in particular domains.”);
“determining one or more graph operators used to perform fusion processing on the target entity field and the target relationship description” (pg. 3 §3 “Table 1 summarizes different techniques for the knowledge graph fusion, some of which are adapted only for either approach A1 or approach A2, while others can support both approaches, as depicted in the previous section and Fig. 1 .” note table 1 shows various fusion techniques. These techniques are interpreted as operators and are functionally analogous);
“obtaining data instances corresponding to the target entity field and the target relationship description from the two or more knowledge graphs” (pg. 7 §3.4 “Leveraging web-based information as well as other knowledge graphs (e.g., Freebase and schema.org), authors in [51] captured the necessary knowledge from text and HTML DOM (using natural language processing tools including named entity recognition, part of speech tag- ging, dependency parsing, co-reference resolution, and entity linkage), HTML tables (using named entity linkage on columns), and human- annotated pages (using 14 people-driven predicates) to construct triple candidates in the form of ( subject, predicate, object ). Applying two proba- bilistic models, which are path ranking [76] and a neural network with a multi-layer perceptron (MLP), the authors aimed at verifying which candidate was more precise. Finally, they produced a fused, complete, and efficient knowledge graph that was approximately 38 times larger than the existing ones”); and
“processing the data instances by using the one or more graph operators to generate a fused knowledge graph” (previous citation, ¶2 “After fusing knowledge graphs, refining is an essential task for fill- ing missing relations, adding types for undefined entities, and correcting false axioms.” after fusing, i.e. fusing is done)
While Nguyen teaches the fusion of knowledge graphs above, CN826 as well teaches fusion using rules, (CN826 pg. 5 ¶2 “And S130, fusing the entities in each data source according to a preset entity fusion rule to obtain the fused entities in each data source.”) which additionally reads on the above claim elements as well.
It would have been obvious to one having ordinary skill in the art at the time that the invention was effectively filed to combine the teachings of Nguyen with that of CN826 since a combination of known methods would yield predictable results. Both references pertain to knowledge graph fusing and offer similar overlapping techniques that would translate well to either system.
Independent claims 11 and 18 recite the same substantial subject matter as independent claim 1, only differing in embodiment. The differences in embodiments, a system and non-transitory medium compared to a medium are obvious variations of another and are taught in the various embodiments of CN826, pg. 8 “Referring to fig. 15, the device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a storage medium 503 and an internal memory 504.”
Regarding claim 2, the Nguyen and CN826 references have been addressed above. CN826 further teaches “comprising receiving user input on the target entity field and the target relationship description” (pg. 4 “a plurality of pieces of specific website information are input into a preset web crawler program in advance, and data crawling is performed by the web crawler program, so that data of entities of a plurality of data sources can be obtained, and then data cleaning is performed on the data, so as to obtain cleaned data” which would inherently come from a user input at some point wherein the data would lead to the entity and relationship)
Regarding claims 3 and 12, the Nguyen and CN826 references have been addressed above. CN826 further teaches “wherein the determining the one or more graph operators includes: determining the one or more graph operators based on a user input, or determining the one or more graph operators that are automatically generated” (CN826 middle of pg. 7 “In the multidimensional-based knowledge graph fusion method provided by the embodiment of the invention, data from entities of a plurality of data sources are obtained, and the obtained data are subjected to data cleaning to obtain cleaned data; extracting entities in each data source, entity attributes in each data source and connection relations among the entities in each data source from the cleaned data; fusing the entities in each data source according to a preset entity fusion rule to obtain fused entities in each data source; fusing the entity attributes between the data sources according to a preset attribute similarity rule to obtain fused entity attributes; constructing a knowledge graph of each data source according to the fused entities in each data source, the fused entity attributes and the connection relation among the entities in each data source; and fusing the knowledge graph of each data source according to a preset graph matching rule to obtain a fused knowledge graph.” graph operators or rules)
Regarding claims 4, 13, and 19, the Nguyen and CN826 references have been addressed above. Nguyen further teaches “further comprising: obtaining ontology definition data of the fused knowledge graph based on the target entity field, the target relationship description, and the one or more graph operators” (pg. 1 ¶1 “ontology, which is an extension of taxonomy, can describe and capture all knowledge of our world as concepts and entities using multiple classes, relationships, and constraints [2] . Hence, ontology is capable of efficiently recording complex structures and schema in particular domains.”),
CN826 further teaches “and presenting the ontology definition data of the fused knowledge graph in an image form of a knowledge graph” (CN826 pg. 2 last ¶ “Knowledge map (Knowledge Graph) is a series of different graphs displaying Knowledge development process and structure relationship in the book intelligence field, describing Knowledge resources and carriers thereof by using visualization technology, mining, analyzing, constructing, drawing and displaying Knowledge and mutual relation between Knowledge resources and Knowledge carriers.”)
Regarding claims 5 and 14, the Nguyen and CN826 references have been addressed above. CN826 further teaches “wherein the entity field corresponds to one or more attribute fields” (CN826 bottom of pg. 4 “And S120, extracting the entity in each data source, the entity attribute in each data source and the connection relationship among the entities in each data source from the cleaned data.”),
“and wherein the processing the data instances by using the one or more graph operators to generate a fused knowledge graph includes one or more of: performing expression standardization processing on an instance value of an attribute field corresponding to the target entity field; fusing two or more target entity fields to obtain a fused entity field, an attribute field corresponding to the fused entity field being obtained based on an attribute field corresponding to at least one of the two or more target entity fields, and a relationship description related to the fused entity field including a target relationship description related to each of the two or more target entity fields;
establishing a relationship description for two target entities based on an attribute field corresponding to at least one of two target entity fields corresponding to the two target entities; or
invoking a natural language processing model to determine similar instances in the data instances, to fuse the similar instances in the data instances” (previous citation, above last ¶ “and S112, removing the special symbols in the entity after the complex and simple body conversion based on the regular expression to obtain the cleaned entity.” i.e. performing expression standardization processing)
Regarding claims 6, 15, and 20, the Nguyen and CN826 references have been addressed above. CN826 further teaches “wherein the obtaining the data instances corresponding to the target entity field and the target relationship description from the two or more knowledge graphs, includes: determining a target entity field and a target relationship description that are related to the graph operator, as an entity field and a relationship description of a minimal sub-graph” (CN826 bottom of pg. 4 “And S120, extracting the entity in each data source, the entity attribute in each data source and the connection relationship among the entities in each data source from the cleaned data.”); and
“obtaining, from each knowledge graph, data instances corresponding to the entity field and the relationship description of the minimal sub-graph” (previous citation, determining and obtaining are done simultaneously); and
“wherein the processing the data instances by using the graph operator to generate the fused knowledge graph includes: processing, by using the graph operator, the data instances corresponding to the entity field and the relationship description of the minimal sub- graph, to obtain the minimal sub-graph” (CN826 middle of pg. 7 “In the multidimensional-based knowledge graph fusion method provided by the embodiment of the invention, data from entities of a plurality of data sources are obtained, and the obtained data are subjected to data cleaning to obtain cleaned data; extracting entities in each data source, entity attributes in each data source and connection relations among the entities in each data source from the cleaned data; fusing the entities in each data source according to a preset entity fusion rule to obtain fused entities in each data source; fusing the entity attributes between the data sources according to a preset attribute similarity rule to obtain fused entity attributes; constructing a knowledge graph of each data source according to the fused entities in each data source, the fused entity attributes and the connection relation among the entities in each data source; and fusing the knowledge graph of each data source according to a preset graph matching rule to obtain a fused knowledge graph.”); and
“obtaining, from each knowledge graph, data instances corresponding to a target entity field and the target relationship description other than the entity field and the relationship description of the minimal sub-graph, to obtain a sub-graph other than the minimal sub-graph of the fused knowledge graph” (CN826 bottom of pg. 4 “And S120, extracting the entity in each data source, the entity attribute in each data source and the connection relationship among the entities in each data source from the cleaned data.” The obtaining is not limited to a single data set and can retrieve any data, i.e. each data source)
Regarding claims 7 and 16, the Nguyen and CN826 references have been addressed above. Nguyen further teaches “wherein the obtaining the data instances corresponding to the target entity field and the target relationship description from the two or more knowledge graphs, and processing the data instances by using the graph operator to generate the fused knowledge graph is executed in a trusted environment” (pg. 11 §4.5 “Furthermore, knowledge graph fusion contributes to the develop- ment of many other smart systems. In the task of fact-checking, Pan et al. [128] proposed using a knowledge graph as a truth information source to verify the veracity of the news. They constructed two knowl- edge graphs from actual news and fake news datasets. In each knowl- edge graph, they learn the low-dimensional embedding of each entity and relation. Finally, they extract a set of triples ( subject, predicate, ob- ject ) from the news, and subsequently verify the possibility of a triple using the learned entity’s embedding. In the study of Ciampaglia et al.” which is an example of a trusted environment)
Regarding claims 8 and 17, the Nguyen and CN826 references have been addressed above. Nguyen further teaches “further comprising: in the trusted environment, processing the fused knowledge graph by using a target task algorithm to obtain and output a target task result, the target task algorithm including one or more of a graph rule reasoning algorithm or a graph-based machine learning model prediction algorithm” (pg. 10 §4.2 “Question answering (QA) is a system that automatically answers questions posed by humans in a natural language form. A question- answering system finds an answer to a given question by query information on the web, collections of documents, or databases of knowledge [148] . Most of the question-answering systems focus on factoid questions, which can be answered with a simple fact expressed in short texts [89] . For example, the question “Where is the Louvre museum located? ” can be answered by looking for a simple fact about “the location of Louvre.”)
Regarding claim 9, the Nguyen and CN826 references have been addressed above. Nguyen further teaches “wherein the target task algorithm is specified by a user” (previous citation QA originates from a user query)
Regarding claim 10, the Nguyen and CN826 references have been addressed above. Nguyen further teaches “wherein the two or more knowledge graphs are received from one or more knowledge graph providers” (pg. 2 right col. ¶1 “In addition to Google’s Knowledge Graph, there are, still, many popular knowledge graphs, some of which are DBpedia, Freebase, Wikidata, Microsoft Satori, and Facebook’s entity graph.”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN W FIGUEROA whose telephone number is (571)272-4623. The examiner can normally be reached Monday-Friday, 10AM-6PM EST.
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KEVIN W FIGUEROA
Primary Examiner
Art Unit 2124
/Kevin W Figueroa/Primary Examiner, Art Unit 2124
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