DETAILED ACTION
Remarks
This Office Action is in response to the application 18/321538 filed on 22 May 2023.
Claims 1-18 have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-18 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.
As to claims 1, 17, and 18, the following is recited (emphasis added): “identifying a plurality of high-quality textual entities from the plurality of textual entities based on the respective quality levels of the plurality of extracted textual entities.” The claimed “high-quality textual entities” is relative terminology. The claims do not define “high-quality,” the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, these claim limitations render the claims vague and ambiguous. See MPEP 2173.05(b).
As to claims 2-16, they depend from claim 1 and therefore inherit its deficiencies.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 17, and 18, these claims recite “extracting a plurality of textual entities from the one or more documents.” The claims do not specify nor place any limits upon the claimed “entities” or “documents.” Under the broadest reasonable interpretation (BRI), these claims encompass a simple case of extracting just two entities from just one document. With the aid of pencil and paper, a human can mentally extract a couple of textual entities from a document. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with a pencil and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
These claims also recite “determining a quality level of each of the plurality of extracted textual entities based on the one or more exemplary entities received from the user, wherein a quality level of a textual entity indicates a degree of similarity between the textual entity and each exemplary entity of the one or more exemplary entities.” The claimed determining of quality levels amounts to no more than a series of judgements/evaluations. A human could, with the aid of pencil and paper, mentally judge/evaluate textual entities based on their degree of similarity to examplary entities to produce a series of quality levels, as claimed. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping.
These claims also recite “identifying a plurality of high-quality textual entities from the plurality of textual entities based on the respective quality levels of the plurality of extracted textual entities.” The claimed identification of high-quality textual entities amounts to no more than a series of judgements/evaluations. A human could, with the aid of pencil and paper, mentally judge/evaluate textual entities based on their respective quality levels to identify high-quality text entities, as claimed. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping.
These claims also recite “categorizing each the plurality of high-quality entities according to one or more sub-topics associated with the topic.” Given that the BRI of the claims encompasses a simple case, as set forth above, a human could, with the aid of pencil and paper, mentally categorize high-quality entities, as claimed. For example, a human could mentally judge/evaluate a couple of high-quality entities and assign sub-topics to them. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping.
These claims also recite “determining connection information indicating relationships between the one or more sub-topics based on documents of the one or more documents from which each of the plurality of high-quality entities originated.” Given that the BRI of the claims encompasses a simple case, as set forth above, a human could, with the aid of pencil and paper, mentally determine connection information as claimed. For example, a human could read a document and mentally determine from it certain connections/relationships between sub-topics. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping.
These claims also recite “generating, based on the one or more sub-topics and the connection information, a first knowledge graph for the topic that represents the one or more sub-topics in the one or more documents and the relationships between said sub-topics.” Given that the BRI of the claims encompasses a simple case, as set forth above, a human could, with the aid of pencil and paper, mentally generate a knowledge graph as claimed. For example, a human could draw out on a piece of paper a knowledge graph in the manner claimed. Hence, this limitation is also an abstract idea under the “Mental Processes” grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. Other than the abstract idea, the claims recite the following:
a) “receiving one or more documents from one or more information sources;”
b) “receiving an indication of one or more exemplary entities associated with the topic from a user;”
c) “automatically, using one or more artificial intelligence models”;
d) “one or more memories and one or more processors”; and
e) “A non-transitory computer readable storage medium storing instructions”.
Limitations (a) and (b) amount to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). Limitation (c) is recited at a high level of generality and amounts to mere instructions to apply the abstract idea on a computer, which cannot provide a practical application. See MPEP 2106.05(f). Limitations (d) and (e) are recited at a high level of generality, i.e. as generic computer components performing generic computing functions. 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. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Limitations (a) and (b) amount to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). In addition, the courts have deemed receiving data to be well-understood, routine, and conventional activity, as in the following cases: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory). See MPEP 2106.05(d)(II). Limitation (c) is recited at a high level of generality and amounts to mere instructions to apply the abstract idea on a computer, which cannot be deemed an inventive concept. See MPEP 2106.05(f). As discussed above with respect to integration of the abstract idea into a practical application, additional elements (d) and (e) amount to no more than mere field of use limitations and instructions to apply the exception using generic computer components. Mere instructions to apply an exception using conventional computer components and functions cannot provide an inventive concept. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not amount to significantly more than the abstract idea. These claims are not patent eligible.
As to dependent claim 2, this claim recites updating the plurality of high-quality textual entities based on the feedback received from the user. Given that the BRI of the claims encompasses a simple case, as set forth above in the parent claim, a human could, with the aid of pencil and paper, mentally perform the claimed updating in the manner claimed. Hence, this limitation is an abstract idea under the “Mental Processes” grouping. This claim also recites “providing an indication of the plurality of high-quality textual entities to the user” and “receiving feedback from the user indicating an accuracy of one or more of the plurality of high-quality textual entities.” These limitations are insignificant extra solution activity in the form of mere data ouputting (“providing an indication” limitation) and mere data gathering (“receiving feedback” limitation). The courts have ruled mere data gathering to be insignificant extra solution activity, as set forth above in the parent claims. In addition, the courts have ruled mere data outputting to also be insignificant extra solution activity. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). See MPEP 2106.05(g). Furthermore, Applicant’s specification provides few details about the claimed “providing an indication,” “receiving feedback,” or their functions (see para. 0032 of Applicant’s published specification). This indicates that these features are well known in the art. Cf Hybritech Inc. v. Monoclonal Antibodies, Inc., 802 F.2d 1367, 1384 (Fed. Cir. 1986) (explaining that "a patent need not teach, and preferably omits, what is well known in the art"). As a result, the written description adequately supports that the “providing an indication” and “receiving feedback” limitations are conventional and perform well-understood, routine, and conventional activities. See MPEP § 2106.07(a)(III)(A)1. Hence, these limitations cannot be deemed a practical application nor an inventive concept. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not amount to a practical application nor an inventive concept. This claim is not patent eligible.
As to dependent claim 3, this claim recites details of “determining the quality level of a textual entity of the plurality of textual entities.” This claim recites generating first and second vectors and computing a similarity score between the two vectors. These limitations amount to no more than a series of mathematical operations. Hence, this claim is an abstract idea under the “Mathematical Concepts” grouping. Alternatively, this claim may be deemed an abstract idea under the “Mental Processes” grouping, since a human could, with the aid of pencil and paper, mentally perform these limitations for the simple case encompassed by the BRI of the claims.
As to dependent claim 4, this claim recites “identifying textual entities of the plurality of textual entities with quality levels that exceed a threshold quality level.” The claimed identifying amounts to no more than mathematical operation(s). Hence, this claim is an abstract idea under the “Mathematical Concepts” grouping. Alternatively, this claim may be deemed an abstract idea under the “Mental Processes” grouping, since a human could, with the aid of pencil and paper, mentally perform this limitation for the simple case encompassed by the BRI of the claims.
As to dependent claim 5, this claim recites “generating a matrix that indicates which documents of the one or more documents contain which high-quality entities of the plurality of high-quality entities.” The claimed generating of a matrix amounts to no more than mathematical operation(s). Hence, this claim is an abstract idea under the “Mathematical Concepts” grouping. Alternatively, this claim may be deemed an abstract idea under the “Mental Processes” grouping, since a human could, with the aid of pencil and paper, mentally perform this limitation for the simple case encompassed by the BRI of the claims.
As to dependent claim 6, this claim repeats the same limitations recited in claim 1 except that the limtiations of claim 6 are performed upon “a second set of one or more documents.” The limitaitons of claim 6 are directed to an abstract idea without significantly more for the reasons set forth above with regards to claim 1.
As to dependent claim 7, this claim recites “combining the first knowledge graph with the second knowledge graph.” A human could, with the aid of pencil and paper, mentally perform this limitation for the simple case encompassed by the BRI of the claims. Hence, this claim is an abstract idea under the “Mental Processes” grouping.
As to dependent claims 8-9, these claims recite details about how to combine he first knowledge graph with the second knowledge graph. Given that the BRI of the claims encompass a simple case, as set forth above, nothing in these claims goes beyond what a human could mentally perform with the aid of pencil and paper. Hence, these claims are also directed to an abstract idea under the “Mental Processes” grouping, without significantly more.
As to dependent claim 10, this claim recites “wherein the steps for generating the first knowledge graph are executed automatically upon receipt of a threshold number of documents from the one or more information sources.” Receipt of a threshold number of documents is mere data gathering which is insignificant extra solution activity, as set forth above in the parent claim. Furthermore, a human can mentally perform certain specified steps automatically based on a certain condition being met (e.g. receipt of a threshold number of documents). Hence, this claim is directed to an abstract idea under the “Mental Processes” grouping, without significantly more.
As to dependent claim 11, this claim recites “receiving a request for the first knowledge graph for the topic from the user,” which is insignificant extra solution activity in the form of mere data gathering, for the same reasons set forth above in the parent claim. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not amount to a practical application nor an inventive concept. This claim is not patent eligible.
As to dependent claim 12, this claim recites “wherein the plurality of textual entities extracted from the one or more documents belong to the same part of speech class.” This claim merely recites a particular type of data upon which to apply the invention. This amounts to a mere description of field of use and/or technological environment, which cannot provide a practical application nor an inventive concept. See MPEP 2106.05(h).
As to dependent claim 13, this claim recites “wherein the one or more artificial intelligence models comprise one or more natural language processing algorithms.” This claim is recited at a high level of generality and amounts to a mere description of field of use and/or technological environment, which cannot provide a practical application nor an inventive concept. See MPEP 2106.05(h).
As to dependent claim 14, this claim recites “wherein, in the first knowledge graph, the one or more sub-topics are represented as one or more nodes and the relationships between the one or more sub-topics are represented as one or more edges connecting said nodes.” Given that the BRI of the claims encompasses a simple case, as set forth above in the parent claim, a human could, with the aid of pencil and paper, mentally generate a knowledge graph that is as described in this claim. Hence, this claim is an abstract idea under the “Mental Processes” grouping.
As to dependent claims 15 and 16, these claims recite featuers for use of a graphical user interface. These limitations amount to mere instructions to apply the abstract idea on a computer and/or merely a description of technological environment, neither of which can provide a practical application or inventive concept. See MPEP 2106.05 subsections (f) and (h).
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 of this title, 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.
Claims 1, 3-6, and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Tacchi et al. (U.S. Patent Application Publication No. 20170228435 A1, hereinafter referred to as Tacchi) in view of Pochernina et al. (U.S. Patent Application Publication No. 20230067688 A1, hereinafter referred to as Pochernina).
As to claim 1, Tacchi teaches a method for generating a knowledge graph for a topic, the method comprising:
receiving one or more documents from one or more information sources (Tacchi para. 0024: ingesting plain text documents);
receiving an indication of one or more exemplary entities associated with the topic from a user (Tacchi para. 0027: user queries for entity names; and Tacchi para. 0006: entities have associated topics);
automatically, using one or more artificial intelligence models (Tacchi para. 0018: the system utilizes machine learning models):
extracting a plurality of textual entities from the one or more documents (Tacchi para. 0030: extracting entities from unstructured text),
determining a quality level of each of the plurality of extracted textual entities based on the one or more exemplary entities received from the user, wherein a quality level of a textual entity indicates a degree of similarity between the textual entity and each exemplary entity of the one or more exemplary entities (Tacchi para. 0047: similarity scores based on selected features),
identifying a plurality of high-quality textual entities from the plurality of textual entities based on the respective quality levels of the plurality of extracted textual entities (Tacchi para. 0048: quality threshold),
categorizing each the plurality of high-quality entities according to one or more sub-topics associated with the topic (Tacchi para. 0067: sub-topics associated with correspoding topics; and see Tacchi para. 0005-0006: organizing documents by topic), and
determining connection information indicating relationships between the one or more sub-topics based on documents of the one or more documents from which each of the plurality of high-quality entities originated (Tacchi para. 0067: determing relations between topics and sub-topics; and Tacchi para. 0006: determining relationships between entities, topics, and terms); and
generating, based and the connection information, a first knowledge graph for the topic that represents the one or more documents (Tacchi para. 0018 and 0060: the system generates a graph; and Tacchi para. 0006: determining relationships between entities, topics, and terms).
Tacchi does not appear to explicitly disclose generating, based on the one or more sub-topics and the connection information, a first knowledge graph for the topic that represents the one or more sub-topics in the one or more documents and the relationships between said sub-topics.
However, Pochernina teaches:
receiving one or more documents from one or more information sources (Pochernina para. 0025-0026 and Fig. 1: documents received from data sources 116, 118, and 120);
receiving an indication of one or more exemplary entities associated with the topic from a user (Pochernina para. 0028: user queries for topics);
automatically, using one or more artificial intelligence models (Pochernina para. 0060: neural network deep language model):
extracting a plurality of textual entities from the one or more documents (Pochernina para. 0022: extracting topics from the documents; and Pochernina 0021: topics correspond to entities),
determining a quality level of each of the plurality of extracted textual entities based on the one or more exemplary entities received from the user, wherein a quality level of a textual entity indicates a degree of similarity between the textual entity and each exemplary entity of the one or more exemplary entities (Pochernina para. 0024: the system determines the degree of similarity between topics; and Pochernina para. 0021: topics correspond to entities),
categorizing each the plurality of high-quality entities according to one or more sub-topics associated with the topic (Pochernina para. 0058-0060: topic hierarchy comprising topics and subtopics), and
determining connection information indicating relationships between the one or more sub-topics based on documents of the one or more documents from which each of the plurality of high-quality entities originated (Pochernina Fig. 6. relationships between topics, sub-topics, and documents); and
generating, based on the one or more sub-topics and the connection information, a first knowledge graph for the topic that represents the one or more sub-topics in the one or more documents and the relationships between said sub-topics (Pochernina para. 0022 and Figs. 6-7: construction of knowledge graph representing relationships between topics, sub-topics, and documents).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Tacchi to include the teachings of Pochernina because it makes the knowledge graph more complete and improves the quality of inferences made from the knowledge graph (Pochernina para. 0022).
As to claim 3, Tacchi as modified by Pochernina teaches wherein determining the quality level of a textual entity of the plurality of textual entities comprises:
generating a first vector representing the textual entity (Tacchi para. 0036: analyzing similarity between vectors representing entities);
generating a second vector representing an exemplary entity of the one or more exemplary entities (Tacchi para. 0036: analyzing similarity between vectors representing entities); and
computing a similarity score between the first vector and the second vector, wherein the similarity score indicates a degree of similarity between the textual entity and the exemplary entity (Tacchi para. 0036: analyzing similarity between vectors representing entities).
As to claim 4, Tacchi as modified by Pochernina teaches wherein identifying the plurality of high-quality textual entities comprises identifying textual entities of the plurality of textual entities with quality levels that exceed a threshold quality level (Tacchi para. 0048: quality threshold).
As to claim 5, Tacchi as modified by Pochernina teaches wherein determining the connection information comprises generating a matrix that indicates which documents of the one or more documents contain which high-quality entities of the plurality of high-quality entities (Tacchi para. 0035: term document matrix).
As to claim 6, Tacchi as modified by Pochernina teaches comprising:
receiving a second set of one or more documents from the one or more information sources (Tacchi para. 0024: ingesting plain text documents; and Pochernina para. 0025-0026 and Fig. 1: documents received from data sources 116, 118, and 120);
automatically, using the one or more artificial intelligence models (Tacchi para. 0018: the system utilizes machine learning models; and Pochernina para. 0060: neural network deep language model):
extracting a second plurality of textual entities from the second set of one or more documents (Tacchi para. 0030: extracting entities from unstructured text; and Pochernina para. 0022: extracting topics from the documents; and Pochernina 0021: topics correspond to entities),
determining a quality level of each textual entity of the second plurality of textual entities based on the one or more exemplary entities received from the user, wherein a quality level of a textual entity indicates a degree of similarity between the textual entity and each exemplary entity of the one or more exemplary entities (Tacchi para. 0047: similarity scores based on selected features; and Pochernina para. 0024: the system determines the degree of similarity between topics; and Pochernina para. 0021: topics correspond to entities),
identifying a second plurality of high-quality textual entities from the second plurality of textual entities based on the quality levels of each textual entity of the second plurality of textual entities (Tacchi para. 0048: quality threshold),
categorizing the second plurality of high-quality entities according to a second set of one or more sub-topics associated with the topic (Tacchi para. 0067: sub-topics associated with correspoding topics; and see Tacchi para. 0005-0006: organizing documents by topic; and Pochernina para. 0058-0060: topic hierarchy comprising topics and subtopics), and
determining second connection information indicating relationships between the second set of one or more sub-topics (Tacchi para. 0067: determing relations between topics and sub-topics; and Tacchi para. 0006: determining relationships between entities, topics, and terms; and Pochernina Fig. 6. relationships between topics, sub-topics, and documents); and
generating, based on the second set of one or more sub-topics and the second connection information, a second knowledge graph for the topic that provides a visual representation of the second set of one or more sub-topics in the second set of one or more documents and the relationships between said sub-topics (Tacchi para. 0018 and 0060: the system generates a graph; and Tacchi para. 0006: determining relationships between entities, topics, and terms; and Pochernina para. 0022 and Figs. 6-7: construction of knowledge graph representing relationships between topics, sub-topics, and documents).
As to claim 11, Tacchi as modified by Pochernina teaches comprising receiving a request for the first knowledge graph for the topic from the user (Pochernina para. 0033: user queries knowledge graph for a particular topic).
As to claim 12, Tacchi as modified by Pochernina teaches wherein the plurality of textual entities extracted from the one or more documents belong to the same part of speech class (Tacchi para. 0030: extracted entities are nouns).
As to claim 13, Tacchi as modified by Pochernina teaches wherein the one or more artificial intelligence models comprise one or more natural language processing algorithms (Tacchi para. 0017-0018: machine learning models with natural language processing; and see Tacchi para. 0058: natural-language processing module 820).
As to claim 14, Tacchi as modified by Pochernina teaches wherein, in the first knowledge graph, the one or more sub-topics are represented as one or more nodes and the relationships between the one or more sub-topics are represented as one or more edges connecting said nodes (Pochernina Figs. 6-7: topics and sub-topics represented as nodes connected by edges).
As to claim 15, Tacchi as modified by Pochernina teaches comprising providing a graphical representation of the first knowledge graph to the user using a graphical user interface (Tacchi para. 0086: data visualization module 824 displays a visualization of the graphs on user devices).
As to claim 16, Tacchi as modified by Pochernina teaches comprising:
receiving, via the graphical user interface, user input comprising a selection of a subtopic of the one or more sub-topics represented in the first knowledge graph (Pochernina para. 0037: user selects a certain topic/sub-topic; in an illustrative example, the user selects “Breeze water bottle”); and
in response to receiving the user input comprising the selection, displaying, on the graphical user interface, information about the selected sub-topic, wherein the information comprises an indication of documents of the one or more documents that contain text related to the selected sub-topic (Pochernina Fig. 7: information is displayed about “Breeze water bottle” and its related documents).
As to claim 17, Tacchi teaches a system for generating a knowledge graph for a topic, the system comprising one or more memories and one or more processors (Tacchi para. 0102: one or more processors of one or more computers executing code stored in a machine readable medium) configured to:
receiving one or more documents from one or more information sources (Tacchi para. 0024: ingesting plain text documents);
receiving an indication of one or more exemplary entities associated with the topic from a user (Tacchi para. 0027: user queries for entity names; and Tacchi para. 0006: entities have associated topics);
automatically, using one or more artificial intelligence models (Tacchi para. 0018: the system utilizes machine learning models):
extracting a plurality of textual entities from the one or more documents (Tacchi para. 0030: extracting entities from unstructured text),
determining a quality level of each of the plurality of extracted textual entities based on the one or more exemplary entities received from the user, wherein a quality level of a textual entity indicates a degree of similarity between the textual entity and each exemplary entity of the one or more exemplary entities (Tacchi para. 0047: similarity scores based on selected features),
identifying a plurality of high-quality textual entities from the plurality of textual entities based on the respective quality levels of the plurality of extracted textual entities (Tacchi para. 0048: quality threshold),
categorizing each the plurality of high-quality entities according to one or more sub-topics associated with the topic (Tacchi para. 0067: sub-topics associated with correspoding topics; and see Tacchi para. 0005-0006: organizing documents by topic), and
determining connection information indicating relationships between the one or more sub-topics based on documents of the one or more documents from which each of the plurality of high-quality entities originated (Tacchi para. 0067: determing relations between topics and sub-topics; and Tacchi para. 0006: determining relationships between entities, topics, and terms); and
generating, based and the connection information, a first knowledge graph for the topic that represents the one or more documents (Tacchi para. 0018 and 0060: the system generates a graph; and Tacchi para. 0006: determining relationships between entities, topics, and terms).
Tacchi does not appear to explicitly disclose generating, based on the one or more sub-topics and the connection information, a first knowledge graph for the topic that represents the one or more sub-topics in the one or more documents and the relationships between said sub-topics.
However, Pochernina teaches:
receiving one or more documents from one or more information sources (Pochernina para. 0025-0026 and Fig. 1: documents received from data sources 116, 118, and 120);
receiving an indication of one or more exemplary entities associated with the topic from a user (Pochernina para. 0028: user queries for topics);
automatically, using one or more artificial intelligence models (Pochernina para. 0060: neural network deep language model):
extracting a plurality of textual entities from the one or more documents (Pochernina para. 0022: extracting topics from the documents; and Pochernina 0021: topics correspond to entities),
determining a quality level of each of the plurality of extracted textual entities based on the one or more exemplary entities received from the user, wherein a quality level of a textual entity indicates a degree of similarity between the textual entity and each exemplary entity of the one or more exemplary entities (Pochernina para. 0024: the system determines the degree of similarity between topics; and Pochernina para. 0021: topics correspond to entities),
categorizing each the plurality of high-quality entities according to one or more sub-topics associated with the topic (Pochernina para. 0058-0060: topic hierarchy comprising topics and subtopics), and
determining connection information indicating relationships between the one or more sub-topics based on documents of the one or more documents from which each of the plurality of high-quality entities originated (Pochernina Fig. 6. relationships between topics, sub-topics, and documents); and
generating, based on the one or more sub-topics and the connection information, a first knowledge graph for the topic that represents the one or more sub-topics in the one or more documents and the relationships between said sub-topics (Pochernina para. 0022 and Figs. 6-7: construction of knowledge graph representing relationships between topics, sub-topics, and documents).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Tacchi to include the teachings of Pochernina because it makes the knowledge graph more complete and improves the quality of inferences made from the knowledge graph (Pochernina para. 0022).
As to claim 18, Tacchi teaches a non-transitory computer readable storage medium storing instructions that, when executed by one or more processors of an electronic device (Tacchi para. 0102: one or more processors of one or more computers executing code stored in a non-transitory machine readable medium) cause the device to:
receiving one or more documents from one or more information sources (Tacchi para. 0024: ingesting plain text documents);
receiving an indication of one or more exemplary entities associated with the topic from a user (Tacchi para. 0027: user queries for entity names; and Tacchi para. 0006: entities have associated topics);
automatically, using one or more artificial intelligence models (Tacchi para. 0018: the system utilizes machine learning models):
extracting a plurality of textual entities from the one or more documents (Tacchi para. 0030: extracting entities from unstructured text),
determining a quality level of each of the plurality of extracted textual entities based on the one or more exemplary entities received from the user, wherein a quality level of a textual entity indicates a degree of similarity between the textual entity and each exemplary entity of the one or more exemplary entities (Tacchi para. 0047: similarity scores based on selected features),
identifying a plurality of high-quality textual entities from the plurality of textual entities based on the respective quality levels of the plurality of extracted textual entities (Tacchi para. 0048: quality threshold),
categorizing each the plurality of high-quality entities according to one or more sub-topics associated with the topic (Tacchi para. 0067: sub-topics associated with correspoding topics; and see Tacchi para. 0005-0006: organizing documents by topic), and
determining connection information indicating relationships between the one or more sub-topics based on documents of the one or more documents from which each of the plurality of high-quality entities originated (Tacchi para. 0067: determing relations between topics and sub-topics; and Tacchi para. 0006: determining relationships between entities, topics, and terms); and
generating, based and the connection information, a first knowledge graph for the topic that represents the one or more documents (Tacchi para. 0018 and 0060: the system generates a graph; and Tacchi para. 0006: determining relationships between entities, topics, and terms).
Tacchi does not appear to explicitly disclose generating, based on the one or more sub-topics and the connection information, a first knowledge graph for the topic that represents the one or more sub-topics in the one or more documents and the relationships between said sub-topics.
However, Pochernina teaches:
receiving one or more documents from one or more information sources (Pochernina para. 0025-0026 and Fig. 1: documents received from data sources 116, 118, and 120);
receiving an indication of one or more exemplary entities associated with the topic from a user (Pochernina para. 0028: user queries for topics);
automatically, using one or more artificial intelligence models (Pochernina para. 0060: neural network deep language model):
extracting a plurality of textual entities from the one or more documents (Pochernina para. 0022: extracting topics from the documents; and Pochernina 0021: topics correspond to entities),
determining a quality level of each of the plurality of extracted textual entities based on the one or more exemplary entities received from the user, wherein a quality level of a textual entity indicates a degree of similarity between the textual entity and each exemplary entity of the one or more exemplary entities (Pochernina para. 0024: the system determines the degree of similarity between topics; and Pochernina para. 0021: topics correspond to entities),
categorizing each the plurality of high-quality entities according to one or more sub-topics associated with the topic (Pochernina para. 0058-0060: topic hierarchy comprising topics and subtopics), and
determining connection information indicating relationships between the one or more sub-topics based on documents of the one or more documents from which each of the plurality of high-quality entities originated (Pochernina Fig. 6. relationships between topics, sub-topics, and documents); and
generating, based on the one or more sub-topics and the connection information, a first knowledge graph for the topic that represents the one or more sub-topics in the one or more documents and the relationships between said sub-topics (Pochernina para. 0022 and Figs. 6-7: construction of knowledge graph representing relationships between topics, sub-topics, and documents).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Tacchi to include the teachings of Pochernina because it makes the knowledge graph more complete and improves the quality of inferences made from the knowledge graph (Pochernina para. 0022).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Tacchi and Derzsy as applied to claim 6 above, and further in view of Zhai et al. (U.S. Patent Application Publication No. 20230022673 A1, hereinafter referred to as Derzsy).
As to claim 2, Tacchi as modified by Pochernina does not appear to explicitly disclose providing an indication of the plurality of high-quality textual entities to the user; receiving feedback from the user indicating an accuracy of one or more of the plurality of high-quality textual entities; and automatically updating the plurality of high-quality textual entities based on the feedback received from the user.
However, Derzsy teaches comprising:
providing an indication of the plurality of high-quality textual entities to the user (Derzsy para. 0048: refinement of entities based on feedback information; and Derzsy para. 0024: feedback information is received from users);
receiving feedback from the user indicating an accuracy of one or more of the plurality of high-quality textual entities (Derzsy para. 0048: refinement of entities based on feedback information; and Derzsy para. 0024: feedback information is received from users); and
automatically updating the plurality of high-quality textual entities based on the feedback received from the user (Derzsy para. 0048: refinement of entities based on feedback information; and Derzsy para. 0024: feedback information is received from users).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Tacchi as modified by Pochernina to include the teachings of Derzsy because it allows for improving a scoring model over time (Derzsy para. 0048).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Tacchi and Pochernina as applied to claim 6 above, and further in view of Zhai et al. (U.S. Patent Application Publication No. 20180039696 A1, hereinafter referred to as Zhai).
As to claim 7, Tacchi as modified by Pochernina does not appear to explicitly disclose comprising combining the first knowledge graph with the second knowledge graph.
However, Zhai teaches comprising combining the first knowledge graph with the second knowledge graph (Zhai abstract and para. 0014 and 0026: merging first and second knowledge graphs).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Tacchi as modified by Pochernina to include the teachings of Zhai because it provides for enhancing and reinforcing knowledge graphs (Zhai para. 0003 and 0014).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Tacchi and Pochernina as applied to claim 1 above, and further in view of Bly et al. (U.S. Patent Application Publication No. 20230060252 A1, hereinafter referred to as Bly).
As to claim 10, Tacchi as modified by Pochernina does not appear to explicitly disclose wherein the steps for generating the first knowledge graph are executed automatically upon receipt of a threshold number of documents from the one or more information sources.
However, Bly teaches wherein the steps for generating the first knowledge graph are executed automatically upon receipt of a threshold number of documents from the one or more information sources (Bly para. 0227-0233: automatically generating a graph based on sources and thresholds specified by the user).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Tacchi as modified by Pochernina to include the teachings of Bly because it provides automated extraction of information from documents, improving search capabilities (Bly para. 0175).
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/Umar Mian/
Examiner, Art Unit 2163
1 MPEP § 2106.07(a)(III)(A) explains that a specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes the additional elements as well-understood or routine or conventional ( or an equivalent term) or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a).