DETAILED ACTION
Notice of 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 .
Response to Amendment
Applicant’s Amendment and remarks submitted on 1/26/2026 have been considered. Claim 3 has been cancelled and claim 21 has been added. Claims 1-2 and 4-21 are pending.
Response to Arguments
On page 9 of Applicant’s 1/26/2026 Amendment and remarks, Applicant asserts that at least paras. 0017, 0028, 0032, 0034, 0046, 0050, 0058, and 0062 provide sufficient written description support for the claim amendments.
The examiner agrees that the portions of the specification identified by Applicant provide sufficient written description support for the claim amendments.
On page 10 of Applicant’s 1/26/2026 Amendment and remarks, with respect to the rejections of claim 1 under 35 U.S.C. 101, with respect to Step 2A, Prong 1, Applicant argues:
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The examiner respectfully disagrees that the “obtaining text from a document of an initial data source using a document parser ...” cannot be performed in the human mind. Parsing a document is a mental activity, and so is obtaining text from a document.
The examiner respectfully disagrees that “generating an initial knowledge graph” cannot be performed in the human mind. A simple knowledge graph can be formed mentally, or written on a piece of paper.
The examiner agrees that the newly-added “providing the merged knowledge graph for use by an application in performing a task using the initial data source and the other data source” is not a mental process. Therefore, that limitation is addressed under Step 2A, Prong 2 and Step 2B.
On page 10 of Applicant’s 1/26/2026 Amendment and remarks, with respect to the rejections of claim 1 under 35 U.S.C. 101, with respect to Step 2A, Prong 2, Applicant argues:
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The examiner respectfully disagrees. Applying functions (such as mathematical functions) to nodes in a knowledge graph and expanding knowledge graphs are mental processes. These are not additional elements that integrate the judicial exception into a practical application.
On page 12 of Applicant’s 1/26/2026 Amendment and remarks, with respect to the rejections of claim 1 under 35 U.S.C. 103, Applicant argues with respect to the STETSON reference:
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The examiner agrees that STETSON does not teach the portion of the limitation that the function “identifies objects and items.” However, new grounds of rejection necessitated by Applicant’s claim amendments are now made herein in view of the GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU references are made herein, where CHEN discloses what is missing from STETSON.
On pages 12-13 of Applicant’s 1/26/2026 Amendment and remarks, Applicant argues that independent claims 11 and 21 and all dependent claims should be allowed for the same reasons argued with respect to claim 1.
The examiner respectfully disagrees for the same reasons explained with respect to claim 1.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-2 and 4-21 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1 recites the limitation “the other data source” in lines 15 and 22. There is insufficient antecedent basis for this limitation in the claim. It is unclear if “the other data source” is intended to refer to the “initial data source” (line 2), “another data source” (line 12) or some other data source. For purposes of compact prosecution, “the other data source” will be interpreted as referring to the “another data source” introduced in line 12.
Claims 2 and 4-10 depend from claim 1, do not remedy the deficiencies of claim 1, and are therefore rejected for the same reasons explained with respect to claim 1.
Claim 11 recites the limitation “the other data source” in lines 19 and 26. There is insufficient antecedent basis for this limitation in the claim. It is unclear if “the other data source” is intended to refer to the “initial data source” (line 6), “another data source” (line 16) or some other data source. For purposes of compact prosecution, “the other data source” will be interpreted as referring to the “another data source” introduced in line 16.
Claims 12-20 depend from claim 11, do not remedy the deficiencies of claim 11, and are therefore rejected for the same reasons explained with respect to claim 11.
Claim 21 recites the limitation “the other data source” in the last line. There is insufficient antecedent basis for this limitation in the claim. It is unclear if “the other data source” is intended to refer to the “initial data source” (line 3), “another data source” (line 10) or some other data source. For purposes of compact prosecution, “the other data source” will be interpreted as referring to the “another data source” introduced in line 10.
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 1-2 and 4-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-2, 4-10, and 21 are directed to a method (a process) and Claims 11-20 are directed to a system (a machine), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper.
obtaining text from a document of an initial data source using a document parser that extracts the text from the document in response to a request to generate a knowledge graph (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) review an initial data source (such as a newspaper article) and mentally parse the document to extract text in response to a request by a 3rd party to generate a knowledge graph)
generating an initial knowledge graph with a plurality of nodes and a plurality edges using the text from the document; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) review extracted text from a newspaper article and generate a simple knowledge graph such as a first node with the article’s title, a second node with the article’s author’s name, and a third node with the article’s topic spelled out, where edges between the first and second node (author of) and first and third node (topic of) are a plurality of edges between the nodes)
selecting at least one function to apply to a node of the plurality of nodes wherein the at least one function identifies objects and items and provides an output with a new node and a new edge; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) apply a function to the second node (the author’s name) that identifies a gender of the author and the creates a new node (the author’s title – Mr/Mrs/Ms) with a new corresponding edge)
generating an extended initial knowledge graph based on the output of the least one function, wherein the extended initial knowledge graph includes the initial knowledge graph with the new node connected to the node using the new edge; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) extend the initial knowledge graph by adding the new node and new edge, where such new node is connected to the second node)
generating a second knowledge graph using another data source, wherein the second knowledge graph includes the new node, a plurality of second nodes, the new edge, and a plurality of second edges, (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) generate a second knowledge graph, such as a second newspaper article written by the same author, having a knowledge graph with nodes for the article’s title, author’s name, topic, and the same “new node” and “new edge” from before relating to the author’s title)
wherein generating the second knowledge graph comprises searching the other data source based on the new node, and identifying and extracting second entities and second relationships to generate the plurality of second nodes and the plurality of second edges; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) generate a second knowledge graph, such as by searching a second newspaper article written by the same author, creating a second knowledge graph with nodes for the article’s title, author’s name, topic, and the same “new node” and “new edge” from before relating to the author’s title)
creating a merged knowledge graph with the initial knowledge graph and the second knowledge graph, wherein the node of the initial knowledge graph is connected to the new node of the second knowledge graph using the new edge. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) merge the initial knowledge graph and the second knowledge graph by linking them at the first node of the initial knowledge graph)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element (e.g., “application”) which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “providing the merged knowledge graph for use by an application in performing a task using the initial data source and the other data source,” limitation, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). Moreover, such additional element of a data transmitting step is recited at a high level of generality and amounts to extra-solution activity of transmitting data, i.e. post-solution activity of transmitting data from the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element (e.g., “application”) is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “providing the merged knowledge graph for use by an application in performing a task using the initial data source and the other data source,” limitation, this limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”). Moreover, as discussed above, the additional element of a data transmitting step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. post-solution activity of transmitting data from the claimed process. The courts have found limitations directed to transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 2
Step 2A, Prong 1
wherein the initial data source includes a plurality of documents including one or more of a portable document format (PDF), an article, a journal, or any source of text, (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally review a source of text, such as a paper folder including an article or a journal, where in each case the source of text is merely a physical aid for the human to review)
wherein the initial knowledge graph is generated by identifying and extracting a plurality of entities and a plurality of relationship among the plurality of entities from text of the plurality of documents of the initial data source, wherein each node of the plurality of nodes corresponds to an entity of the plurality of entities and each edge of the plurality of edges corresponds to a relationship among the plurality of relationships. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally review the initial data source and extract entities (such as article title, author, and topic) and the associated relationships between such entities)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 4
Step 2A, Prong 1
wherein the other data source includes a plurality of documents that are different from the plurality of documents in the initial data source, wherein the plurality of documents from the other data source include one or more of a portable document format (PDF), an article, a journal, or any source of text. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally review a different source of text, such as a different paper folder including a different article or a different journal, where in each case the source of text is merely a physical aid for the human to review)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 1
wherein the second knowledge graph is generated by: identifying and extracting a plurality of second entities and a plurality of second relationships among the plurality of second entities in text of the plurality of documents of the other data source, wherein each second node of the plurality of second nodes corresponds to a second entity of the plurality of second entities and each second edge the plurality of second edges corresponds to a second relationship among the plurality of second relationships. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally review the other data source and extract entities (such as article title, author, and topic) and the associated relationships between such entities)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 6
Step 2A, Prong 1
wherein the other data source includes one or more existing knowledge graphs, and the second knowledge graph is generated by using an existing knowledge graph of the one or more existing knowledge graphs of the other data source. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally review an other data source, such as a paper folder, where another individual has already drawn a knowledge graph on paper for the article in the paper folder)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 7
Step 2A, Prong 2
Regarding the “wherein the at least one function is a deep learning machine learning model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a deep learning machine learning model for performing a function. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a deep learning machine learning model for performing a function). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the at least one function is a deep learning machine learning model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 8
Step 2A, Prong 1
providing a plurality of functions selected based on a type of the node or a text of the node; and (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally review the type of node (e.g., author node) and identify potential functions to apply to such node (e.g., find other articles by the same author, delete the node, add previous co-authors, etc.).
receiving a selection of the at least one function from the plurality of functions. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally receive a selection of one of the functions, e.g., the human can decide to select the function for searching for other articles by the same author, or can receive that selection mentally by asking another person to choose from amongst the options)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 9
Step 2A, Prong 1
receiving input to edit one or more of the extended initial knowledge graph, the second knowledge graph, or the merged knowledge graph; and (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally receive input to edit one of the knowledge graphs, such as a request by a colleague to update the knowledge graphs that the human has written on paper)
providing modifications to one or more of the extended initial knowledge graph the second knowledge graph, or the merged knowledge graph based on the input. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally receive input to edit one of the knowledge graphs, such as a request by a colleague to update the knowledge graphs that the human has written on paper, and then actually updating the knowledge graphs, such as to add a new node/edge pair)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 10
Step 2A, Prong 1
selecting another function to apply to a selected node of the merged knowledge graph, wherein the other function outputs another new node and another new edge; and (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally review the type of node (e.g., author node) and identify potential functions to apply to such node (e.g., find other articles by the same author, delete the node, add previous co-authors, etc., and then section another function (such as add previous co-authors))
generating an extended merged knowledge graph with the merged knowledge graph, the other new node, and the other new edge, wherein the other new node is connected to the selected node using the other new edge. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally receive a selection of one of the functions, e.g., the human can decide to select the function for adding previous co-authors, or can receive that selection mentally by asking another person to choose from amongst the options)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 11
Step 2A, Prong 1
Claim 11 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 11. While claim 11 recites additional generic computing components (“one or more processors”, “memory in electronic communication with the one or more processors”, “instructions”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 11 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 11. While claim 11 recites additional generic computing components (“one or more processors”, “memory in electronic communication with the one or more processors”, “instructions”), such additional generic computing components do not change the analysis under Step 2A, Prong 2. Such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional elements (“one or more processors”, “memory in electronic communication with the one or more processors”, “instructions”). These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Claim 11 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 11. While claim 11 recites additional generic computing components (“one or more processors”, “memory in electronic communication with the one or more processors”, “instructions”), such additional generic computing components do not change the analysis under Step 2B. Such limitation are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, these additional elements do not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Claim 12 depends from claim 11 and claims a system that corresponds to the method of claim 2 (in part) and is therefore rejected for the same reasons explained above with respect to claims 2 and 11.
Regarding Claim 13
Step 2A, Prong 1
identifying and extracting a plurality of entities and a plurality of relationship among the plurality of entities from text of the plurality of documents of the initial data source, wherein each node of the plurality of nodes corresponds to an entity of the plurality of entities and each edge of the plurality of edges corresponds to a relationship among the plurality of relationships. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally review the initial data source and extract entities (such as article title, author, and topic) and the associated relationships between such entities)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Claim 14 depends from claim 11 and claims a system that corresponds to the method of claim 4 and is therefore rejected for the same reasons explained above with respect to claims 4 and 11.
Claim 15 depends from claim 14 and claims a system that corresponds to the method of claim 5 and is therefore rejected for the same reasons explained above with respect to claims 4 and 11.
Claim 16 depends from claim 11 and claims a system that corresponds to the method of claim 6 and is therefore rejected for the same reasons explained above with respect to claims 6 and 11.
Claim 17 depends from claim 11 and claims a system that corresponds to the method of claim 7 and is therefore rejected for the same reasons explained above with respect to claims 7 and 11.
Claim 18 depends from claim 11 and claims a system that corresponds to the method of claim 8 and is therefore rejected for the same reasons explained above with respect to claims 8 and 11.
Claim 19 depends from claim 11 and claims a system that corresponds to the method of claim 9 and is therefore rejected for the same reasons explained above with respect to claims 9 and 11.
Claim 20 depends from claim 11 and claims a system that corresponds to the method of claim 10 and is therefore rejected for the same reasons explained above with respect to claims 10 and 11.
Regarding Claim 21
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 21 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “processor”).
generating a merged knowledge graph by:
obtaining text from a document of an initial data source using a document parser that extracts the text from the document; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) review an initial data source (such as a newspaper article) and mentally parse the document to extract text)
generating an initial knowledge graph with a plurality of nodes and a plurality of edges using the text from the document on demand in response to receiving a request to generate the initial knowledge graph without requiring pre-computation; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) review extracted text from a newspaper article and in response to a request, generate a simple knowledge graph such as a first node with the article’s title, a second node with the article’s author’s name, and a third node with the article’s topic spelled out, where edges between the first and second node (author of) and first and third node (topic of) are a plurality of edges between the nodes, where no pre-computation is required and the knowledge graph is created in real-time)
generating an extended initial knowledge graph by using a function that identifies objects and items and provides an output with a new node and a new edge; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) apply a function to the second node (the author’s name) that identifies a gender of the author and the creates a new node (the author’s title – Mr/Mrs/Ms) with a new corresponding edge, and then extend the initial knowledge graph by adding the new node and new edge, where such new node is connected to the second node)
generating a second knowledge graph using another data source, wherein the second knowledge graph includes the new node, a plurality of second nodes, and the new edge; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) generate a second knowledge graph, such as a second newspaper article written by the same author, having a knowledge graph with nodes for the article’s title, author’s name, topic, and the same “new node” and “new edge” from before relating to the author’s title)
creating the merged knowledge graph by connecting the initial knowledge graph and the second knowledge graph using the new edge; and (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally (or with pencil and paper) merge the initial knowledge graph and the second knowledge graph by linking them at the first node of the initial knowledge graph)
using the merged knowledge graph in executing a graph-based query using the initial data source and the other data source. (under the broadest reasonable interpretation, a human can mentally perform a query of the merged knowledge graph searching, for example, for connections common to nodes corresponding to the initial data source and other data source)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element (e.g., “processor”) which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element (e.g., “processor”) is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
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 1-5, 8-15, and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220067590 A1, hereinafter referenced as GEORGOPOULOS, in view of US 20170221240 A1, hereinafter referenced as STETSON, and further in view of US 20180234303 A1, hereinafter referenced as CHEN, and further in view of US 20200118009 A1, hereinafter referenced as CONSTANTINESCU.
Regarding Claim 1
GEORGOPOULOS teaches:
A method, comprising: (GEORGOPOULOS, para. 0004: “According to one aspect of the present invention, a method for building a new knowledge graph may be provided.”)
obtaining text from a document of an initial data source using a document parser that extracts the text from the document in response to a request to generate a knowledge graph; (GEORGOPOULOS, para. 0015: “As a consequence, and according to another embodiment, the method may also comprise executing a parser for each predicted entity, thereby, determining at least one entity instance.”;
GEORGOPOULOS, para. 0059: “It has provided two different machine-learning models that may be used in the next phase, the deployment phase, in order to build or construct one or more new knowledge graphs from a new corpus of documents based on the auto-extracted core knowledge from the first document.”;
GEORGOPOULOS, para. 0066: “It may be noted that based on different received second corpuses, different knowledge graphs may be constructed and/or generated (more built knowledge graphs 220) based on the automatically extracted domain knowledge in the form of entities and edges from the first document(s) and the existing knowledge graph.”Examiner’s Note: in order to build a new knowledge graph (corresponding to recited “in response to a request to generate a knowledge graph), knowledge is extracted from documents in order to extract core knowledge for the knowledge graph)
generating an initial knowledge graph with a plurality of nodes and a plurality edges using the text from the document; (GEORGOPOULOS, para. 0056: “FIG. 1 shows a flowchart of an embodiment of method 100 for building a new knowledge graph that comprises vertices and edges, wherein the edges describe relationships between vertices and the vertices relate to entities, e.g., words. Method 100 comprises receiving 102 a first text document. The text document should relate to a defined knowledge domain. In general, the text document comprises a plurality of text documents or different kinds of documents building together a corpus of documents.”)
generating a second knowledge graph using another data source, wherein the second knowledge graph includes (GEORGOPOULOS, para. 0060: “Next, method 100 comprises receiving 108 a set of second text documents. This set of second text documents—which may be, in a minimalistic version, only one document—represents a new corpus from which the new knowledge graph shall be constructed.”
GEORGOPOULOS, para. 0063: “Consequently, method 100 comprises predicting 114 edges—i.e., relationship data—in the set of second text documents by using the predicted entities (predicted by the first machine-learning model) and associated embedding vectors of the predicted entities as input for the second trained machine-learning model and building 116 triplets of the predicted entities and the related predicted edges (or vice versa which combined build the new knowledge graph. It may be noted that building triplets may only be one form of storing a knowledge graph.”;
Examiner’s Note: GEORGOPOULOS discloses generating a second knowledge graph, using a second data corpus, where such second knowledge graph has different entities (corresponding to recited “nodes”) and edges)
wherein generating the second knowledge graph comprises searching the other data source (GEORGOPOULOS, para. 0030: “The term ‘first text document’—or a plurality thereof—may denote a text document used to define the domain specificity. From this document—which may, in particular, and in practice be also a plurality of documents of the selected knowledge domain—the core knowledge may be extracted from this document by learning (i.e., supervised learning) to identify entities and edges using two different machine-learning systems. The existing knowledge graph may contribute basic dependencies (i.e., relations between terms words and/or phrases), entities or vertices.”;
GEORGOPOULOS, para. 0057: “Method 100 comprises training 104 of a first machine-learning system to develop a first prediction model adapted to predict entities in the received text document, wherein the text document with labeled entities from the text document is used as training data. It may also be noted that the labeled entities should be suitable as nodes or cores or facts in a knowledge graph.”
GEORGOPOULOS, para. 0058: “Furthermore, method 100 comprises training 106 of a second machine-learning system to develop a second prediction model adapted to predict relationship data—in particular, to be usable as edges in a knowledge graph—between the entities. Thereby, entities and edges—i.e., the relationships—of an existing knowledge graph and determined first embedding vectors of the entities and the edges are used as training data.”;
Examiner’s Note: GEORGOPOULOS discloses using different machine learning models to identify entities, and corresponding relationships, from the text corpus and then using such entities and relationships to generate the nodes and edges of the second knowledge graph)
However, GEORGOPOULOS fails to explicitly teach:
selecting at least one function to apply to a node of the plurality of nodes wherein the at least one function identifies objects and items and provides an output with a new node and a new edge;
generating an extended initial knowledge graph based on the output of the least one function, wherein the extended initial knowledge graph includes the initial knowledge graph with the new node connected to the node using the new edge;
the new node ... the new edge ...
based on the new node
creating a merged knowledge graph with the initial knowledge graph and the second knowledge graph, wherein the node of the initial knowledge graph is connected to the new node of the second knowledge graph using the new edge
providing the merged knowledge graph for use by an application in performing a task using the initial data source and the other data source
However, in a related field of endeavor (visualization and manipulation of data in graph form, see paras. 0002-0004), STETSON teaches:
selecting at least one function to apply to a node of the plurality of nodes wherein the at least one function ... provides an output with a new node and a new edge; ; (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
Examiner’s Note: STETSON discloses an operation “NEW NODE” that a user selects to add a new node and a “LINK” operation to add a corresponding link to a graph (“NEW NODE” and “LINK” collectively corresponding to recited “function”); the GEORGOPOULOS-STETSON combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS so that a user can select a particular node and then select the “NEW NODE” and “LINK” functions to grow a new node stemming from the selected node with a corresponding edge)
generating an extended initial knowledge graph based on the output of the least one function, wherein the extended initial knowledge graph includes the initial knowledge graph with the new node connected to the node using the new edge; (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
STETSON, para. 0170: “FIG. 20B further illustrates the relational database operation of SELECTION in accordance with an embodiment of the invention, whereby the graph operation of NEW NODE and LINK create a selection.”;
Examiner’s Note: STETSON discloses an operation “NEW NODE” that a user selects to add a new node and a “LINK” operation to add a corresponding link to a graph (“NEW NODE” and “LINK” collectively corresponding to recited “function”); the GEORGOPOULOS-STETSON combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS so that a user can select a particular node and then select the “NEW NODE” and “LINK” functions to grow a new node stemming from the selected node with a corresponding edge, where now the modified graph with the new node and link is considered to be the recited “extended initial knowledge graph”)
generating a second knowledge graph using another data source, wherein the second knowledge graph includes the new node, a plurality of second nodes, the new edge, and a plurality of second edges, wherein generating the second knowledge graph comprises searching the other data source based on the new node, and identifying and extracting second entities and second relationships to generate the plurality of second nodes and the plurality of second edges; (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
STETSON, para. 0170: “FIG. 20B further illustrates the relational database operation of SELECTION in accordance with an embodiment of the invention, whereby the graph operation of NEW NODE and LINK create a selection.”;
Examiner’s Note: STETSON discloses an operation “NEW NODE” that a user selects to add a new node and a “LINK” operation to add a corresponding link to a graph (“NEW NODE” and “LINK” collectively corresponding to recited “function”); the GEORGOPOULOS-STETSON combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS so that a user can select a particular node and then select the “NEW NODE” and “LINK” functions to grow a new node stemming from the selected node with a corresponding edge, and now the second knowledge graph generated from a second corpus as in GEORGOPOULOS is similarly updated to add the new node and link added by STETSON, and information connected to such new node can be used to search the other data source as set forth by GEORGOPOULOS)
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS with the teachings of STETSON as explained above. As disclosed by STETSON, one of ordinary skill would have been motivated to do so in order to enable the “automatic application of operations to the graph database.” (para. 0166). One of ordinary skill would understand the benefit of having a user interface to enable a user, such as a subject matter expert, to manually edit a knowledge graph to add a node and corresponding edge as desired.
However, GEORGOPOULOS and STETSON fail to explicitly teach:
identifies objects and items and
creating a merged knowledge graph with the initial knowledge graph and the second knowledge graph, wherein the node of the initial knowledge graph is connected to the new node of the second knowledge graph using the new edge.
providing the merged knowledge graph for use by an application in performing a task using the initial data source and the other data source
However, in a related field of endeavor (using knowledge graphs to store context information, see para. 0002), CHEN teaches and makes obvious:
wherein the at least one function identifies objects and items and (CHEN, para. 0052: “In block 216, in an optional operation, a cognitive contextual-based procedural dialog can be enhanced by extracting or identifying missing IoT context node information and adding the node(s) (or additional information to existing node(s)) to the knowledge graph, e.g., at runtime—as described in more detail hereinafter with respect to FIG. 7.”;
Examiner’s Note: CHEN discloses an operation that identifies missing context node information (corresponding to recited “identifies objects and items”); the GEORGOPOULOS-STETSON-CHEN combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS, where such operations now identify missing information when growing the node as in CHEN)
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS with the teachings of STETSON and CHEN as explained above. As disclosed by CHEN, one of ordinary skill would have been motivated to do so in order to utilize knowledge graphs to support “cognitive and contextual problem diagnosis knowledge creation for enhanced problem diagnosis and maintenance. ... In some embodiments, a cognitive contextual-based dialog process is enhanced by extracting missing IoT context node information and adding the missing node(s) information to the knowledge graph, e.g., at runtime.” (para. 0045).
However, GEORGOPOULOS, STETSON, and CHEN fail to explicitly teach:
creating a merged knowledge graph with the initial knowledge graph and the second knowledge graph, wherein the node of the initial knowledge graph is connected to the new node of the second knowledge graph using the new edge.
providing the merged knowledge graph for use by an application in performing a task using the initial data source and the other data source
However, in a related field of endeavor (using knowledge graphs to store textual entity data, see paras. 0001-0004), CONSTANTINESCU teaches:
creating a merged knowledge graph with the initial knowledge graph and the second knowledge graph, wherein the node of the initial knowledge graph is connected to the new node of the second knowledge graph using the new edge. (CONSTANTINESCU, para. 0078: “At block 614, the system may onboard the plurality of entities with the existing knowledge graph. In some cases the entities described in the third party entity data may overlap with entities already represented in the existing knowledge graph. In such cases, nodes and/or relationships of the existing knowledge graph may be updated to include new information. However, to the extent the entities/relationships described in the third party entity data are not yet in the existing knowledge graph, they may be added to the existing knowledge graph as new nodes, or a separate, third party-specific knowledge “subgraph” may be created (e.g., using the schema of the existing knowledge graph) and linked to the existing knowledge graph.”;
Examiner’s Note: CONSTANTINESCU discloses linking a graph with another graph (a sub-graph of nodes/edges), effectively “merging” the graphs together; the GEORGOPOULOS-STETSON-CONSTANTINESCU combination now links the first and second knowledge graphs generated by GEORGOPOULOS, where such graphs are linked at the new node added by STETSON).
providing the merged knowledge graph for use by an application in performing a task using the initial data source and the other data source (CONSTANTINESCU, para. 0056: “As will be discussed in more detail below, knowledge graph engine 130 may be configured to receive, from one or more third party providers 132, third party entity data that originates, for example, from one or more third parties libraries 136. In particular, knowledge graph engine 130 may be configured to “onboard” third party entity data, e.g., so that tasks such as searches that rely on knowledge graph for entity resolution may be performed. ... Once third party entity data is onboarded with knowledge graph 134, a search or other operation that consults knowledge graph 134, e.g., via knowledge graph interface 128, may in effect have access to the third party entity data.”
CONSTANTINESCU, para. 0082: “At block 702, the system may receive, from one or more automated assistants (120), a request to perform a task related to a given entity of the plurality of entities described in the previously-onboarded third party entity data. At block 704, the system may identify, in the knowledge graph (134), a node representing the given entity. At block 706, the system may cause the task related to the given entity to be performed. For example, suppose a user requests that a particular song be played. The song may be matched to an entity in existing knowledge graph 134 that was successfully created/updated using techniques described herein to include a URL to the song associated with a third party streaming service that the user is subscribed to.”
Examiner’s Note: CONSTANTINESCU discloses linking a graph with another graph (a sub-graph of nodes/edges) for onboarded third party data, effectively “merging” the graphs together; the GEORGOPOULOS-STETSON-CHEN-CONSTANTINESCU combination now links the first and second knowledge graphs generated by GEORGOPOULOS, where such graphs are linked at the new node added by STETSON, and now a task is performed with reference to the newly-updated and merged knowledge graph as taught by CONSTANTINESCU, where such task can be an entity search in software (corresponding to recited “application”) that utilizes data from the new nodes (and the “other data source”) and existing nodes (from the “initial data source”)).
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU as explained above. As disclosed by CONSTANTINESCU, one of ordinary skill would have been motivated to do so in order to “onboard” third party entity data directly into an existing knowledge graph, so that once such information is obtained “a search or other operation that consults knowledge graph 134, e.g., via knowledge graph interface 128, may in effect have access to the third party entity data.” (para. 0056). One of ordinary skill would further understand the benefit of linking different knowledge graphs together by common nodes in order to link the knowledge graphs for traversal over both graphs.
Regarding Claim 2
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the method of claim 1. GEORGOPOULOS further teaches:
wherein the initial data source includes a plurality of documents including one or more of a portable document format (PDF), an article, journal, or any source of text. (GEORGOPOULOS, para. 0030: “The term ‘first text document’—or a plurality thereof—may denote a text document used to define the domain specificity. From this document—which may, in particular, and in practice be also a plurality of documents of the selected knowledge domain—the core knowledge may be extracted from this document by learning (i.e., supervised learning) to identify entities and edges using two different machine-learning systems. The existing knowledge graph may contribute basic dependencies (i.e., relations between terms words and/or phrases), entities or vertices.”;
GEORGOPOULOS, para. 0056: “FIG. 1 shows a flowchart of an embodiment of method 100 for building a new knowledge graph that comprises vertices and edges, wherein the edges describe relationships between vertices and the vertices relate to entities, e.g., words. Method 100 comprises receiving 102 a first text document. The text document should relate to a defined knowledge domain. In general, the text document comprises a plurality of text documents or different kinds of documents building together a corpus of documents.”)
wherein the initial knowledge graph is generated by identifying and extracting a plurality of entities and a plurality of relationship among the plurality of entities from text of the plurality of documents of the initial data source, wherein each node of the plurality of nodes corresponds to an entity of the plurality of entities and each edge of the plurality of edges corresponds to a relationship among the plurality of relationships. (GEORGOPOULOS, para. 0030: “The term ‘first text document’—or a plurality thereof—may denote a text document used to define the domain specificity. From this document—which may, in particular, and in practice be also a plurality of documents of the selected knowledge domain—the core knowledge may be extracted from this document by learning (i.e., supervised learning) to identify entities and edges using two different machine-learning systems. The existing knowledge graph may contribute basic dependencies (i.e., relations between terms words and/or phrases), entities or vertices.”;
GEORGOPOULOS, para. 0057: “Method 100 comprises training 104 of a first machine-learning system to develop a first prediction model adapted to predict entities in the received text document, wherein the text document with labeled entities from the text document is used as training data. It may also be noted that the labeled entities should be suitable as nodes or cores or facts in a knowledge graph.”
GEORGOPOULOS, para. 0058: “Furthermore, method 100 comprises training 106 of a second machine-learning system to develop a second prediction model adapted to predict relationship data—in particular, to be usable as edges in a knowledge graph—between the entities. Thereby, entities and edges—i.e., the relationships—of an existing knowledge graph and determined first embedding vectors of the entities and the edges are used as training data.”;
Examiner’s Note: GEORGOPOULOS discloses using different machine learning models to identify entities, and corresponding relationships, from the text corpus and then using such entities and relationships to generate the nodes and edges of the first knowledge graph)
Regarding Claim 4
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the method of claim 1. GEORGOPOULOS further teaches:
wherein the other data source includes a plurality of documents that are different from the plurality of documents in the initial data source, wherein the plurality of documents from the other data source include one or more of a portable document format (PDF), an article, a journal, or any source of text. (GEORGOPOULOS, para. 0065: “During the deployment phase 210, firstly a second corpus of documents—in particular, independent of the first document(s)—is received 212 from which entities are predicted 214 using the first machine-learning model. In a next step, edges—i.e., relationship data—are predicted 216 using the second machine-learning model. Once the entities and the related edges are known, triplets are built 218 comprising two entities and the related relationship edge, which can be stored as a record in a storage system. The combination of all triplets can then be managed as the newly built knowledge graph.”; Examiner’s Note: GEORGOPOULOS discloses that the second corpus of documents are also text documents from which entities and relationships can be extracted to build a new knowledge graph)
Regarding Claim 5
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the method of claim 4. GEORGOPOULOS further teaches:
wherein the second knowledge graph is generated by: identifying and extracting a plurality of second entities and a plurality of second relationships among the plurality of second entities in text of the plurality of documents of the other data source, wherein each second node of the plurality of second nodes corresponds to a second entity of the plurality of second entities and each second edge the plurality of second edges corresponds to a second relationship among the plurality of second relationships. (GEORGOPOULOS, para. 0065: “During the deployment phase 210, firstly a second corpus of documents—in particular, independent of the first document(s)—is received 212 from which entities are predicted 214 using the first machine-learning model. In a next step, edges—i.e., relationship data—are predicted 216 using the second machine-learning model. Once the entities and the related edges are known, triplets are built 218 comprising two entities and the related relationship edge, which can be stored as a record in a storage system. The combination of all triplets can then be managed as the newly built knowledge graph.”; Examiner’s Note: GEORGOPOULOS discloses that the second corpus of documents are also text documents from which entities and relationships can be extracted to build a new knowledge graph)
Regarding Claim 8
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the method of claim 1. However, GEORGOPOULOS fails to explicitly teach:
providing a plurality of functions selected based on a type of the node or a text of the node; and
receiving a selection of the at least one function from the plurality of functions.
However, in a related field of endeavor (visualization and manipulation of data in graph form, see paras. 0002-0004), STETSON teaches:
providing a plurality of functions selected based on a type of the node or a text of the node; and (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
Examiner’s Note: STETSON discloses a plurality of functions and GEORGOPOULOS (see para. 0015) discloses entity types; the GEORGOPOULOS-STETSON-CHEN-CONSTANTINESCU combination now provides a plurality of functions (e.g., link, un-link, new node, weight change, and thru) for selection for a node, where functions may be selectable based on the entity type (e.g., certain entities may not have sub-nodes, and therefore the “NEW NODE” and “NEW LINK” options are unavailable))
receiving a selection of the at least one function from the plurality of functions. (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
Examiner’s Note: STETSON discloses a plurality of functions and GEORGOPOULOS (see para. 0015) discloses entity types; the GEORGOPOULOS-STETSON-CHEN-CONSTANTINESCU combination now provides a plurality of functions (e.g., link, un-link, new node, weight change, and thru) for selection for a node, where functions may be selectable from a sub-window as disclosed by STETSON).
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU as explained above. As disclosed by STETSON, one of ordinary skill would have been motivated to do so in order to enable the “automatic application of operations to the graph database.” (para. 0166). One of ordinary skill would understand the benefit of having a user interface to enable a user, such as a subject matter expert, to manually edit a knowledge graph to add a node and corresponding edge as desired.
Regarding Claim 9
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the method of claim 1. However, GEORGOPOULOS fails to explicitly teach:
receiving input to edit one or more of the extended initial knowledge graph, the second knowledge graph, or the merged knowledge graph; and providing modifications to one or more of the extended initial knowledge graph the second knowledge graph, or the merged knowledge graph based on the input.
However, in a related field of endeavor (visualization and manipulation of data in graph form, see paras. 0002-0004), STETSON teaches:
receiving input to edit one or more of the extended initial knowledge graph, the second knowledge graph, or the merged knowledge graph; and providing modifications to one or more of the extended initial knowledge graph the second knowledge graph, or the merged knowledge graph based on the input. (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
Examiner’s Note: STETSON discloses a plurality of functions for a user to select to edit a knowledge graph, e.g., by adding a node and/or link and/or changing a weight, for example; the GEORGOPOULOS-STETSON-CHEN-CONSTANTINESCU combination now provides a plurality of functions (e.g., link, un-link, new node, weight change, and thru) for selection by a user to modify any of the extended initial knowledge graph, the second knowledge graph, or the merged knowledge graph)
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS with the teachings of STETSON, CHEN, and CONSTANTINESCU as explained above. As disclosed by STETSON, one of ordinary skill would have been motivated to do so in order to enable the “automatic application of operations to the graph database.” (para. 0166). One of ordinary skill would understand the benefit of having a user interface to enable a user, such as a subject matter expert, to manually edit a knowledge graph to add a node and corresponding edge as desired.
Regarding Claim 10
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the method of claim 1. However, GEORGOPOULOS fails to explicitly teach:
selecting another function to apply to a selected node of the merged knowledge graph, wherein the other function outputs another new node and another new edge; and (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
Examiner’s Note: STETSON discloses an operation “NEW NODE” that a user selects to add a new node and a “LINK” operation to add a corresponding link to a graph (“NEW NODE” and “LINK” collectively corresponding to recited “function”); the GEORGOPOULOS-STETSON-CHEN-CONSTANTINESCU combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS so that a user can select a particular node and then select the “NEW NODE” and “LINK” functions to grow a new node stemming from the selected node with a corresponding edge; pursuant to MPEP 2144.04 VI.B, duplication of parts (“another function”) “has no patentable significance unless a new and unexpected result is produced”, and in the present application, having a second function that adds a node/link has no patentable significance, moreover, the broadest reasonable interpretation of “another function” includes another instantiation of the “NEW NODE” and “LINK” operations to generate yet another new node)
generating an extended merged knowledge graph with the merged knowledge graph, the other new node, and the other new edge, wherein the other new node is connected to the selected node using the other new edge. (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
STETSON, para. 0170: “FIG. 20B further illustrates the relational database operation of SELECTION in accordance with an embodiment of the invention, whereby the graph operation of NEW NODE and LINK create a selection.”;
Examiner’s Note: STETSON discloses an operation “NEW NODE” that a user selects to add a new node and a “LINK” operation to add a corresponding link to a graph (“NEW NODE” and “LINK” collectively corresponding to recited “function”); the GEORGOPOULOS-STETSON-CHEN-CONSTANTINESCU combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS so that a user can select a particular node and then select the “NEW NODE” and “LINK” functions to grow a new node stemming from the selected node with a corresponding edge, where now the modified merged graph with the new node and link is considered to be the recited “extended merged knowledge graph”)
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS with the teachings of STETSON, CHEN, and CONSTANTINESCU as explained above. As disclosed by STETSON, one of ordinary skill would have been motivated to do so in order to enable the “automatic application of operations to the graph database.” (para. 0166). One of ordinary skill would understand the benefit of having a user interface to enable a user, such as a subject matter expert, to manually edit a knowledge graph to add a node and corresponding edge as desired.
Regarding Claim 11
GEORGOPOULOS discloses:
A system, comprising: one or more processors; memory in electronic communication with the one or more processors; and instructions stored in the memory, the instructions executable by the one or more processors to: (GEORGOPOULOS, para. 0007: “The knowledge graph construction system may comprise one or more computer processors, one or more computer readable storage media, and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors of the method as described above.”)
The remaining limitations of claim 11 correspond to the method of claim 1, and therefore this claim 11 is rejected under 35 U.S.C. 103 as obvious for the same reasons explained above with respect to claim 1 in view of the GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU references.
Claim 12 depends from claim 11 and claims a system that corresponds to the method of claim 2 and is therefore rejected for the same reasons explained above with respect to claims 2 and 11.
Regarding Claim 13
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the system of claim 12. GEORGOPOULOS further teaches:
identifying and extracting a plurality of entities and a plurality of relationship among the plurality of entities from text of the plurality of documents of the initial data source, wherein each node of the plurality of nodes corresponds to an entity of the plurality of entities and each edge of the plurality of edges corresponds to a relationship among the plurality of relationships. (GEORGOPOULOS, para. 0030: “The term ‘first text document’—or a plurality thereof—may denote a text document used to define the domain specificity. From this document—which may, in particular, and in practice be also a plurality of documents of the selected knowledge domain—the core knowledge may be extracted from this document by learning (i.e., supervised learning) to identify entities and edges using two different machine-learning systems. The existing knowledge graph may contribute basic dependencies (i.e., relations between terms words and/or phrases), entities or vertices.”;
GEORGOPOULOS, para. 0057: “Method 100 comprises training 104 of a first machine-learning system to develop a first prediction model adapted to predict entities in the received text document, wherein the text document with labeled entities from the text document is used as training data. It may also be noted that the labeled entities should be suitable as nodes or cores or facts in a knowledge graph.”
GEORGOPOULOS, para. 0058: “Furthermore, method 100 comprises training 106 of a second machine-learning system to develop a second prediction model adapted to predict relationship data—in particular, to be usable as edges in a knowledge graph—between the entities. Thereby, entities and edges—i.e., the relationships—of an existing knowledge graph and determined first embedding vectors of the entities and the edges are used as training data.”;
Examiner’s Note: GEORGOPOULOS discloses using different machine learning models to identify entities, and corresponding relationships, from the text corpus and then using such entities and relationships to generate the nodes and edges of the first knowledge graph)
Claim 14 depends from claim 11 and claims a system that corresponds to the method of claim 4 and is therefore rejected for the same reasons explained above with respect to claims 4 and 11.
Claim 15 depends from claim 14 and claims a system that corresponds to the method of claim 5 and is therefore rejected for the same reasons explained above with respect to claims 4 and 11.
Claim 18 depends from claim 11 and claims a system that corresponds to the method of claim 8 and is therefore rejected for the same reasons explained above with respect to claims 8 and 11.
Claim 19 depends from claim 11 and claims a system that corresponds to the method of claim 9 and is therefore rejected for the same reasons explained above with respect to claims 9 and 11.
Claim 20 depends from claim 11 and claims a system that corresponds to the method of claim 10 and is therefore rejected for the same reasons explained above with respect to claims 10 and 11.
Regarding Claim 21
A method implemented by a processor, comprising: (GEORGOPOULOS, para. 0004: “According to one aspect of the present invention, a method for building a new knowledge graph may be provided.”;
GEORGOPOULOS, para. 0070: “The knowledge graph construction system 500 comprises memory 502 and processor 504”)
generating a merged knowledge graph by: (GEORGOPOULOS, para. 0056: “FIG. 1 shows a flowchart of an embodiment of method 100 for building a new knowledge graph that comprises vertices and edges, wherein the edges describe relationships between vertices and the vertices relate to entities, e.g., words. Method 100 comprises receiving 102 a first text document. The text document should relate to a defined knowledge domain. In general, the text document comprises a plurality of text documents or different kinds of documents building together a corpus of documents.”)
obtaining text from a document of an initial data source using a document parser that extracts the text from the document; (GEORGOPOULOS, para. 0015: “As a consequence, and according to another embodiment, the method may also comprise executing a parser for each predicted entity, thereby, determining at least one entity instance.”;
GEORGOPOULOS, para. 0059: “It has provided two different machine-learning models that may be used in the next phase, the deployment phase, in order to build or construct one or more new knowledge graphs from a new corpus of documents based on the auto-extracted core knowledge from the first document.”;
GEORGOPOULOS, para. 0066: “It may be noted that based on different received second corpuses, different knowledge graphs may be constructed and/or generated (more built knowledge graphs 220) based on the automatically extracted domain knowledge in the form of entities and edges from the first document(s) and the existing knowledge graph.”Examiner’s Note: in order to build a new knowledge graph, knowledge is extracted from documents in order to extract core knowledge for the knowledge graph)
generating an initial knowledge graph with a plurality of nodes and a plurality of edges using the text from the document on demand in response to receiving a request to generate the initial knowledge graph without requiring pre-computation; (GEORGOPOULOS, para. 0054: “Hence, a plurality of new knowledge graphs may be generated automatically based on the core technology of an existing domain specific document(s) and a trained machine-learning system specific to the knowledge domain. No highly skilled personnel are required and the generation of the newly constructed knowledge graph can be executed fully automated and provided as a service.”;
GEORGOPOULOS, para. 0056: “FIG. 1 shows a flowchart of an embodiment of method 100 for building a new knowledge graph that comprises vertices and edges, wherein the edges describe relationships between vertices and the vertices relate to entities, e.g., words. Method 100 comprises receiving 102 a first text document. The text document should relate to a defined knowledge domain. In general, the text document comprises a plurality of text documents or different kinds of documents building together a corpus of documents.”)
GEORGOPOULOS, para. 0059: “It has provided two different machine-learning models that may be used in the next phase, the deployment phase, in order to build or construct one or more new knowledge graphs from a new corpus of documents based on the auto-extracted core knowledge from the first document.”;
Examiner’s Note: in order to build a new knowledge graph (corresponding to recited “in response to a request to generate the initial knowledge graph”), knowledge is extracted from documents in order to extract core knowledge for the knowledge graph, and GEORGOPOULOS is silent as to whether any “pre-computation is required” to generate graphs, and discloses that such graphs can be produced automatically using existing domain knowledge (e.g., without having to pre-compute additional domain knowledge))
generating an extended initial knowledge graph; (GEORGOPOULOS, para. 0056: “FIG. 1 shows a flowchart of an embodiment of method 100 for building a new knowledge graph that comprises vertices and edges, wherein the edges describe relationships between vertices and the vertices relate to entities, e.g., words. Method 100 comprises receiving 102 a first text document. The text document should relate to a defined knowledge domain. In general, the text document comprises a plurality of text documents or different kinds of documents building together a corpus of documents.”)
generating a second knowledge graph using another data source, wherein the second knowledge graph includes (GEORGOPOULOS, para. 0060: “Next, method 100 comprises receiving 108 a set of second text documents. This set of second text documents—which may be, in a minimalistic version, only one document—represents a new corpus from which the new knowledge graph shall be constructed.”
GEORGOPOULOS, para. 0063: “Consequently, method 100 comprises predicting 114 edges—i.e., relationship data—in the set of second text documents by using the predicted entities (predicted by the first machine-learning model) and associated embedding vectors of the predicted entities as input for the second trained machine-learning model and building 116 triplets of the predicted entities and the related predicted edges (or vice versa which combined build the new knowledge graph. It may be noted that building triplets may only be one form of storing a knowledge graph.”;
Examiner’s Note: GEORGOPOULOS discloses generating a second knowledge graph, using a second data corpus, where such second knowledge graph has different entities (corresponding to recited “nodes”) and edges)
However, GEORGOPOULOS fails to explicitly teach:
by using a function that ... provides an output with a new node and a new edge
the new node ... the new edge ...
However, in a related field of endeavor (visualization and manipulation of data in graph form, see paras. 0002-0004), STETSON teaches:
by using a function that ... provides an output with a new node and a new edge (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
Examiner’s Note: STETSON discloses an operation “NEW NODE” that a user selects to add a new node and a “LINK” operation to add a corresponding link to a graph (“NEW NODE” and “LINK” collectively corresponding to recited “function”); the GEORGOPOULOS-STETSON combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS so that a user can select a particular node and then select the “NEW NODE” and “LINK” functions to grow a new node stemming from the selected node with a corresponding edge)
generating a second knowledge graph using another data source, wherein the second knowledge graph includes the new node, a plurality of second nodes, and the new edge; (STETSON, para. 0167: “Turning now to FIGS. 20A-B, a set of graph manipulations equivalent to relational database queries is illustrated in accordance with an embodiment of the invention. Coordinates within windows are defined by the weights from the node corresponding to the window itself onto its successors, which are shown in the window. The window may therefore be considered to be either a representation of a single node with two-dimensional weights, or a representation of a pair of nodes each with 1-dimensional weights and projecting onto a common set of successors. Using the graph manipulation operations LINK (creating an edge, otherwise known as a link, from one node to one or more nodes selected by a sub-window), UN-LINK (deleting such a link or links), NEW NODE (creating a new node with a unique address), WEIGHT CHANGE (changing the weights described above by spatially translating) and THRU (e.g. assigning links from one of a node's predecessors to all of its successors), operations equivalent to the fundamental operations of the relational algebra may be performed.”;
STETSON, para. 0170: “FIG. 20B further illustrates the relational database operation of SELECTION in accordance with an embodiment of the invention, whereby the graph operation of NEW NODE and LINK create a selection.”;
Examiner’s Note: STETSON discloses an operation “NEW NODE” that a user selects to add a new node and a “LINK” operation to add a corresponding link to a graph (“NEW NODE” and “LINK” collectively corresponding to recited “function”); the GEORGOPOULOS-STETSON combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS so that a user can select a particular node and then select the “NEW NODE” and “LINK” functions to grow a new node stemming from the selected node with a corresponding edge, and now the second knowledge graph generated from a second corpus as in GEORGOPOULOS is similarly updated to add the new node and link added by STETSON, and information connected to such new node can be used to search the other data source as set forth by GEORGOPOULOS)
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS with the teachings of STETSON as explained above. As disclosed by STETSON, one of ordinary skill would have been motivated to do so in order to enable the “automatic application of operations to the graph database.” (para. 0166). One of ordinary skill would understand the benefit of having a user interface to enable a user, such as a subject matter expert, to manually edit a knowledge graph to add a node and corresponding edge as desired.
However, GEORGOPOULOS and STETSON fail to explicitly teach:
identifies objects and items and
creating the merged knowledge graph by connecting the initial knowledge graph and the second knowledge graph using the new edge; and
using the merged knowledge graph in executing a graph-based query using the initial data source and the other data source.
However, in a related field of endeavor (using knowledge graphs to store context information, see para. 0002), CHEN teaches and makes obvious:
identifies objects and items and (CHEN, para. 0052: “In block 216, in an optional operation, a cognitive contextual-based procedural dialog can be enhanced by extracting or identifying missing IoT context node information and adding the node(s) (or additional information to existing node(s)) to the knowledge graph, e.g., at runtime—as described in more detail hereinafter with respect to FIG. 7.”;
Examiner’s Note: CHEN discloses an operation that identifies missing context node information (corresponding to recited “identifies objects and items”); the GEORGOPOULOS-STETSON-CHEN combination now adds the user interface and “NEW NODE” and “LINK” operations of STETSON to the knowledge graph creation system of GEORGOPOULOS, where such operations now identify missing information when growing the node as in CHEN)
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS with the teachings of STETSON and CHEN as explained above. As disclosed by CHEN, one of ordinary skill would have been motivated to do so in order to utilize knowledge graphs to support “cognitive and contextual problem diagnosis knowledge creation for enhanced problem diagnosis and maintenance. ... In some embodiments, a cognitive contextual-based dialog process is enhanced by extracting missing IoT context node information and adding the missing node(s) information to the knowledge graph, e.g., at runtime.” (para. 0045).
However, GEORGOPOULOS, STETSON, and CHEN fail to explicitly teach:
creating the merged knowledge graph by connecting the initial knowledge graph and the second knowledge graph using the new edge; and
using the merged knowledge graph in executing a graph-based query using the initial data source and the other data source.
However, in a related field of endeavor (using knowledge graphs to store textual entity data, see paras. 0001-0004), CONSTANTINESCU teaches:
creating the merged knowledge graph by connecting the initial knowledge graph and the second knowledge graph using the new edge; and (CONSTANTINESCU, para. 0078: “At block 614, the system may onboard the plurality of entities with the existing knowledge graph. In some cases the entities described in the third party entity data may overlap with entities already represented in the existing knowledge graph. In such cases, nodes and/or relationships of the existing knowledge graph may be updated to include new information. However, to the extent the entities/relationships described in the third party entity data are not yet in the existing knowledge graph, they may be added to the existing knowledge graph as new nodes, or a separate, third party-specific knowledge “subgraph” may be created (e.g., using the schema of the existing knowledge graph) and linked to the existing knowledge graph.”;
Examiner’s Note: CONSTANTINESCU discloses linking a graph with another graph (a sub-graph of nodes/edges), effectively “merging” the graphs together; the GEORGOPOULOS-STETSON-CONSTANTINESCU combination now links the first and second knowledge graphs generated by GEORGOPOULOS, where such graphs are linked at the new node added by STETSON).
using the merged knowledge graph in executing a graph-based query using the initial data source and the other data source. (CONSTANTINESCU, para. 0056: “As will be discussed in more detail below, knowledge graph engine 130 may be configured to receive, from one or more third party providers 132, third party entity data that originates, for example, from one or more third parties libraries 136. In particular, knowledge graph engine 130 may be configured to “onboard” third party entity data, e.g., so that tasks such as searches that rely on knowledge graph for entity resolution may be performed. ... Once third party entity data is onboarded with knowledge graph 134, a search or other operation that consults knowledge graph 134, e.g., via knowledge graph interface 128, may in effect have access to the third party entity data.”
CONSTANTINESCU, para. 0082: “At block 702, the system may receive, from one or more automated assistants (120), a request to perform a task related to a given entity of the plurality of entities described in the previously-onboarded third party entity data. At block 704, the system may identify, in the knowledge graph (134), a node representing the given entity. At block 706, the system may cause the task related to the given entity to be performed. For example, suppose a user requests that a particular song be played. The song may be matched to an entity in existing knowledge graph 134 that was successfully created/updated using techniques described herein to include a URL to the song associated with a third party streaming service that the user is subscribed to.”
Examiner’s Note: CONSTANTINESCU discloses linking a graph with another graph (a sub-graph of nodes/edges) for onboarded third party data, effectively “merging” the graphs together; the GEORGOPOULOS-STETSON-CHEN-CONSTANTINESCU combination now links the first and second knowledge graphs generated by GEORGOPOULOS, where such graphs are linked at the new node added by STETSON, and now a task is performed with reference to the newly-updated and merged knowledge graph as taught by CONSTANTINESCU, where such task can be an entity search (or query) in software (corresponding to recited “application”) that utilizes data from the new nodes (and the “other data source”) and existing nodes (from the “initial data source”)).
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU as explained above. As disclosed by CONSTANTINESCU, one of ordinary skill would have been motivated to do so in order to “onboard” third party entity data directly into an existing knowledge graph, so that once such information is obtained “a search or other operation that consults knowledge graph 134, e.g., via knowledge graph interface 128, may in effect have access to the third party entity data.” (para. 0056). One of ordinary skill would further understand the benefit of linking different knowledge graphs together by common nodes in order to link the knowledge graphs for traversal over both graphs.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over GEORGOPOULOS in view of STETSON, CHEN, and CONSTANTINESCU and further in view of US 20190163835 A1, hereinafter referenced as SCHEIDELER.
Regarding Claim 6
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the method of claim 1. However, GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU fail to explicitly teach:
wherein the other data source includes one or more existing knowledge graphs, and the second knowledge graph is generated by using an existing knowledge graph of the one or more existing knowledge graphs of the other data source.
However, in a related field of endeavor (building knowledge graphs), SCHEIDELER teaches:
wherein the other data source includes one or more existing knowledge graphs, and the second knowledge graph is generated by using an existing knowledge graph of the one or more existing knowledge graphs of the other data source. (SCHEIDELER, para. 0044: “The term ‘base knowledge graph’ may denote an existing knowledge graph comprising existing nodes and existing edges having existing weights assigned to the existing edges. The base knowledge graph may in size, in particular, in the number of existing nodes and existing edges, be much larger than the newly to-be-created knowledge graph. The factor may be 100, 10,000 or even 1,000,000, or higher.”;
SCHEIDELER, para. 0049: “Step 104 receives a base knowledge graph comprising existing nodes selectively connected by existing edges, each one of the edges having an existing weight and superimposing. Step 106 superimposes the new nodes onto selected ones of the existing nodes from the base knowledge graph building pairs of new nodes and corresponding existing nodes.”;
Examiner’s Note: SCHEIDELER discloses having pre-existing base knowledge graphs and then generating new knowledge graphs by superimposing nodes onto the base knowledge graph)
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS, STETSON, CHEN, CONSTANTINESCU, and SCHEIDELER as explained above. One of ordinary skill would understand the benefit of having pre-existing knowledge graphs associated with document sources, for example, having the knowledge graphs pre-built saves computational resources so a brand new knowledge graph does not need to be generated for a known document source.
Claim 16 depends from claim 11 and claims a system that corresponds to the method of claim 6 and is therefore rejected for the same reasons explained above with respect to claims 6 and 11.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over GEORGOPOULOS in view of STETSON, CHEN, and CONSTANTINESCU and further in view of US 20180114111 A1, hereinafter referenced as GILL.
Regarding Claim 7
GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU disclose the method of claim 1. However, GEORGOPOULOS, STETSON, CHEN, and CONSTANTINESCU fail to explicitly teach:
wherein the at least one function is a deep learning machine learning model.
However, in a related field of endeavor (AI and knowledge graph models, see para. 0003), GILL teaches:
wherein the at least one function is a deep learning machine learning model. (GILL, para. 0087, “The user centric memory graph is updated at operation 408 by adding or updating nodes and links based on relationships created in space, time, and cognitive dimensions utilizing machine learning techniques and/or statistical modeling techniques on the already formed user centric memory graph to form an updated user centric memory graph.”;
Examiner’s Note: GILL teaches applying a machine learning technique to update a node; the GEORGOPOULOS-STETSON-CHEN-CONSTANTINESCU-GILL combination now updates the knowledge graph generator system of GEORGOPOLOUS to also permit machine learning operations to be applied directly to nodes as in GILL)
However, before the effective filing date of the present application, one of ordinary skill in the art would have been motivated to combine the automatic knowledge graph construction teachings of GEORGOPOULOS with the teachings of STETSON, CHEN, CONSTANTINESCU, and GILL as explained above. As disclosed by GILL, one of ordinary skill would have been motivated to do so in order to “enrich” the knowledge graph. (para. 0087).
Claim 17 depends from claim 11 and claims a system that corresponds to the method of claim 7 and is therefore rejected for the same reasons explained above with respect to claims 7 and 11.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190392330 A1 (Martineau). “[E]xecuting the graph representation function 208 to add a node to the knowledge graph for each user or item not already identified in the knowledge graph. This could also include the processor 120 of the electronic device executing the graph representation function 208 to add nodes representing properties of the items.” (para. 0114).
US 20200134757 A1 (Raphael). “FIG. 5 illustrates, in a flowchart, operations for generating an e-discovery query in accordance with certain embodiments. Control begins at block 500 with the query assistive engine 120 mapping a predicate clause of an initial query to a legal semantic type by: identifying a metadata field and operator combination; determining the legal semantic type from a glossary; adding a first semantic type node to the semantic knowledge graph; connecting the first semantic type node to a legal matter node with an edge weight; and adding an expression term node for the metadata field with another edge weight”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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/MICHAEL C. LEE/Examiner, Art Unit 2128
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