DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant’s Amendment, filed December 11th, 2025, has been fully considered and entered. Accordingly, claims 1-20 are pending in this application. Claims 1, 5, and 14 were amended. Claims 1, 5, and 14 are independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are being rejected under 35 U.S.C. 103 as being unpatentable over Pell et al. (US 2009/0063472 A1) in view of Agarwal (US 2020/0104403 A1) in view of Eveland et al. (US 9,785,638 B1) in view of Vogel et al. (US 2010/0077001 A1), further in view of Giovannini et al. (US 2019/0130289 A1).
Regarding claim 1, Pell teaches a system comprising:
one or more processors; and memory storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising (see Pell, Paragraph [0026], “computing device 100 includes a bus 110 that directly or indirectly couples the following devices: memory 112, one or more processors 114”):
receiving, via a graphical user interface (GUI) presented via a user device, an input query that is associated with a research topic, the input query including a first input of a first concept that, by itself, is utilized as a starting point for the research topic and that includes one or more words that are utilized by a research assistant tool to initiate a search relating to the research topic, and a second input, separate from the first input, of a second concept that is an ending point for the research topic and that represents a final result for, and a termination of, the research topic (see Pell, Figures 4, 5, 6, Paragraphs [0078], [0079], “the search-entry area 410 allows for navigating to a particular document 620 within a database (e.g., Wikipedia®), while a second search-entry box 610 is provided for exploring within the particular document 620. A query entered to the second search-entry box 610 is “who did he marry.” … a query that includes one or more search terms therein is received from a client device at a natural language engine, as depicted at block 705. As depicted at block 710, a proposition may be derived from the search terms. As discussed above, the proposition is generally a logical representation of a conceptual meaning of the query.” [Figures 4 and 5 display search results for a first query (i.e., an input query including a first input of a first concept). Figure 6 displays a second query (an input query including a second input of a second concept).]),
However, Pell doesn’t explicitly teach:
a termination of, the research topic, wherein the search including the first concept and the second concept is initiated at a same time and is used by the research assistant tool to determine multiple relation links that each serve as different connective intermediate concepts and represent different potential casual pathways between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, wherein the different connective intermediate concepts are discovered by the research assistant tool and are different than, and are not included in, the input query;
Agarwal teaches:
a termination of, the research topic, wherein the search including the first concept and the second concept is initiated at a same time and is used by the research assistant tool to determine multiple relation links that each serve as different connective intermediate concepts and represent different potential casual pathways between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, wherein the different connective intermediate concepts are discovered by the research assistant tool and are different than, and are not included in, the input query (see Agarwal, Paragraph [0074], “The analyser module analyses each of the plurality of search queries for determining semantic-relations between the plurality of search queries, based on the concepts associated therewith. Throughout the present disclosure, the term “semantic-relation” relates to a relationship or association between two or more search queries of the plurality of search queries. It will be appreciated that, the associations between two or more search queries includes attributes and a type or definition that provides a conceptual meaning to how the two or more between two or more search queries are related to each other. Furthermore, the semantic-relation between the plurality of search queries is determined by one or more computing algorithms that is stored and executed by the analyser module. Optionally, the analyser module determines the concept associated with any two or more search queries for determining semantic-relations. For example, a first search query may be formed of a keyword “X”, and a second search query may be formed of a keyword “Y”, in such instance the one or more computing algorithms of the analyser module determines the concept associated with X and Y. Furthermore, in such instance, the one or more computing algorithms compares the attributes of X and Y and the definition of X and Y included in the concept to determine a relation therein. Thereafter, the one or more computing algorithms co-relates the first search query and the second search query and determined the semantic-relation” [Multiple semantic-relations (i.e., multiple relation links that each serve as different connective intermediate concepts and represent different paths between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic) may be determined between two different inputs.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner), and arrived at a system that incorporates semantic relations. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of optimizing the search process (see Agarwal, Paragraph [0074]). In addition, both the references (Pell and Agarwal) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between both of the references highly suggests an expectation of success.
However, the combination of Pell, and Agarwal do not explicitly teach:
a termination of, the research topic, wherein the search including the first concept and the second concept is initiated at a same time and is used by the research assistant tool to determine multiple relation links that each serve as different connective intermediate concepts and represent different potential casual pathways between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, wherein the different connective intermediate concepts are discovered by the research assistant tool and are different than, and are not included in, the input query;
Eveland teaches:
a termination of, the research topic, wherein the search including the first concept and the second concept is initiated at a same time and is used by the research assistant tool to determine multiple relation links that each serve as different connective intermediate concepts and represent different potential casual pathways between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, wherein the different connective intermediate concepts are discovered by the research assistant tool and are different than, and are not included in, the input query (see Eveland, [Columns 13-14, Lines 58-67 and 1-18], “Referring to FIG. 15, an advanced search interface 1500 is shown according to an example embodiment. In some arrangements, the advance search interface 1500 is integrated into screen display 200 and/or screen display 700 in place of the search query field 210. The advanced search interface 1500 allows for multiple search terms to be entered into an advance search query field 1502. The advance search query field 1502 allows users to search multiple terms and phrases grouped together with Boolean operators (e.g., AND, OR, NOT, NEAR, etc.) for terms within a single query. For example, in FIG. 15, the user has searched for the first search term “BASF” that appears near a second search term “Sonolastic”. The advanced search interface 1500 also provides for multiple separate compare query fields 1504, 1506, and 1508 that allow the user to compare the search query (entered into the advance search query field 1502).” [An advanced search may be executed that allows for separate inputs, which is initiated at the same time, and is a termination of a search topic.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner), further in view of Eveland (teaching document display system and method), and arrived at a system that incorporates multiple search inputs for a research topic. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of enhancing processing and analyzing information (see Eveland, Background). In addition, the references (Pell, Agarwal, and Eveland) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
The combination of Pell, Agarwal, and Eveland further teaches:
identifying, by a query component associated with the research assistant tool, one or more evidence passages that include one or more semantic links between the first concept and the second concept, wherein at least one of the one or more semantic links is a structured relational representation that connects the first concept and the second concept, and wherein the one or more evidence passages include one or more portions of a knowledge data source (see Pell, Paragraphs [0079], [0080], “As depicted at block 715, semantic representations are generated from passages/content within documents accessible to the natural language engine. As discussed above, the semantic representations are generally linguistic representations derived from content of passages within one or more documents. As depicted at block 720, the semantic representations, and a mapping to the passages from which they are derived, are maintained within a semantic index … As depicted at block 725, the proposition is compared against the semantic representations retained in the semantic index to determine a matching set.” [A semantic representation (i.e., semantic link) is compared with the proposition.]);
determining, by a natural language understanding engine associated with the research assistant tool, that the one or more semantic links include one or more relational representations connecting the first concept and the second concept (see Pell, Paragraphs [0051], [0079]-[0080], “The passages that are mapped to the matching set of semantic representations are identified, as depicted at block 730.” [The passages are mapped to the concepts of the query.]);
determining, by a knowledge aggregation engine associated with the research assistant tool, one or more relation clusters by aggregating the one or more relational representations based at least in part on a degree of semantic similarity between the one or more relational representations (see Pell, Paragraph [0054], “These documents 230, targeted by the associated locations, are collected and sorted by the ranking component 270. Sorting may be performed in any known method within the relevant field, and may include without limitation, ranking according to closeness of match, listing based on popularity of the returned documents 230, or sorting based on attributes of the user submitting the query 225.” [The related documents (i.e., relation clusters) can be sorted using a clustering technique.]);
However, the combination of Pell, Agarwal, and Eveland do not explicitly teach:
determining, by the knowledge aggregation engine, an aggregation confidence associated with a relation cluster of the one or more relation clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source;
Vogel teaches:
determining, by the knowledge aggregation engine, an aggregation confidence associated with a relation cluster of the one or more relation clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source (see Vogel, Paragraphs [0140]-[0143], “The elements which obtained the highest scores and ranks and/or the elements that occur the most frequently in the documents are included in the search result set, which will be presented to the user.” [The related documents (i.e., relation clusters) may be scored.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner) in view of Eveland (teaching document display systema and method), further in view of Vogel (teaching search system and method for serendipitous discoveries with faceted full-text classification), and arrived at a system that incorporates a reliability score. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of improved results (see Vogel, Paragraph [0143]). In addition, the references (Pell, Agarwal, Eveland, and Vogel) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
However, the combination of Pell, Agarwal, Eveland, and Vogel do not explicitly teach:
determining, by the knowledge aggregation engine, an aggregation confidence associated with a relation cluster of the one or more relation clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source;
Giovannini teaches:
determining, by the knowledge aggregation engine, an aggregation confidence associated with a relation cluster of the one or more relation clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source (see Giovannini, Paragraph [0064] “One way of measuring the relevance of a given concept within a thought unit is to count how often the concept was mentioned in the expression of the thought unit. This may include counting references to “it” when it is clear what concept “it” refers to. The relevance score can be integrated into the determination of whether or not concepts are original. For instance, originality of a thought may be dictated in part based on some concept of the thought unit having a minimum relevance score.” [The relevance of the concepts is based on a count, which indicates originality.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner) in view of Eveland (teaching document display systema and method) in view of Vogel (teaching search system and method for serendipitous discoveries with faceted full-text classification), further in view of Giovannini (teaching original idea extraction from written text data), and arrived at a system that incorporates originality. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of identifying original ideas (see Giovannini, Paragraph [0003]). In addition, the references (Pell, Agarwal, Eveland, Vogel and Giovannini) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
The combination of Pell, Agarwal, Eveland, Vogel, and Giovannini further teaches:
determining that a query result includes the relation cluster based at least in part on ranking of the one or more relation clusters, the relation cluster including a relation expression between the first concept and the second concept (see Pell, Paragraphs [0054], [0080], “These documents 230, targeted by the associated locations, are collected and sorted by the ranking component 270. Sorting may be performed in any known method within the relevant field, and may include without limitation, ranking according to closeness of match, listing based on popularity of the returned documents 230, or sorting based on attributes of the user submitting the query 225.” [The results can include the relation cluster based on the ranking.]);
and presenting, via the GUI presented via the user device, the query result with evidentiary support, the evidentiary support including the portion of the one or more evidence passages associated with the relation cluster (see Pell, Paragraph [0080], “Emphasis may be applied to the regions of the identified passages according to a highlighting scheme (see block 735), and the emphasized regions of the identified passages may be presented to the user as the search results relevant to the query (see block 740). Accordingly, the present invention offers relevant search results that include an emphasized region that corresponds with the true objective of the query.” [The results may be presented to the user.]).
Regarding claim 2, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 1. Vogel further teaches:
wherein ranking the one or more relation clusters is based at least in part on one or more reliability scores associated with the one or more evidence passages (see Vogel, Paragraphs [0140]-[0143], “The elements which obtained the highest scores and ranks and/or the elements that occur the most frequently in the documents are included in the search result set, which will be presented to the user.” [The relation clusters may be scored.]).
Regarding claim 3, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 1. Pell further teaches:
wherein knowledge data source includes natural language text, journals, literature, documents, knowledge base, market research documents, or structured databases (see Pell, Paragraph [0036], “the search engine includes one or more web crawlers that mine available data (e.g., newsgroups, databases, open directories, the data store 220, and the like) accessible via the Internet and build a semantic index 260 containing web addresses along with the subject matter of web pages or other documents stored in a meaningful format.”).
Regarding claim 4, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 1. Pell, and Vogel further teaches:
ranking the portion of the one or more evidence passages associated with the relation cluster based at least in part on a level of relevance of the one or more evidence passages, wherein the level of relevance is based at least in part on one or more of reliability scores and redundancy scores associated with the one or more evidence passages (see Vogel, Paragraphs [0054], [0140]-[0143], “The elements which obtained the highest scores and ranks and/or the elements that occur the most frequently in the documents are included in the search result set, which will be presented to the user.” [The relation clusters may be scored.]);
and annotating the portion of the one or more evidence passages with corresponding semantic interpretations of the portion of the one or more evidence passages, wherein the corresponding semantic interpretations translate natural language text into machine-readable knowledge representations (see Pell, Paragraph [0046], “recognizing words includes identifying words as names and annotating the word with a tag to facilitate retrieval when interrogating the semantic index 260.”).
Regarding claim 5, Pell teaches a computer-implemented method comprising:
receiving an input query including a first input of a first concept that is a starting point for a research topic and that includes one or more words that are utilized to initiate a search relating to the research topic, and a second input, separate from the first input, of a second concept that is an ending point for the research topic and that represents a final result for, and a termination of, the research topic, wherein a relation associated with the input query is a semantic link between the first concept and one or more variable concepts that serve as one or more connective intermediate concepts between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, and wherein the first concept and the relation are used to derive one or more propositions, wherein the one or more propositions include one or more statements indicating the semantic link (see Pell, Figures 4, 5, 6, Paragraphs [0051], [0078], [0079], “the search-entry area 410 allows for navigating to a particular document 620 within a database (e.g., Wikipedia®), while a second search-entry box 610 is provided for exploring within the particular document 620. A query entered to the second search-entry box 610 is “who did he marry.” … a query that includes one or more search terms therein is received from a client device at a natural language engine, as depicted at block 705. As depicted at block 710, a proposition may be derived from the search terms. As discussed above, the proposition is generally a logical representation of a conceptual meaning of the query. As depicted at block 715, semantic representations are generated from passages/content within documents accessible to the natural language engine.” [Figures 4 and 5 display search results for a first query (i.e., an input query including a first input of a first concept). Figure 6 displays a second query (an input query including a second input of a second concept). The semantic representation (i.e., semantic link) is compared with the proposition.]);
However, Pell doesn’t explicitly teach:
receiving an input query including a first input of a first concept that is a starting point for a research topic and that includes one or more words that are utilized to initiate a search relating to the research topic, and a second input, separate from the first input, of a second concept that is an ending point for the research topic and that represents a final result for, and a termination of, the research topic, wherein a relation associated with the input query is a semantic link between the first concept and one or more variable concepts that serve as one or more connective intermediate concepts between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, and wherein the first concept and the relation are used to derive one or more propositions, wherein the one or more propositions include one or more statements indicating the semantic link;
Agarwal teaches:
receiving an input query including a first input of a first concept that is a starting point for a research topic and that includes one or more words that are utilized to initiate a search relating to the research topic, and a second input, separate from the first input, of a second concept that is an ending point for the research topic and that represents a final result for, and a termination of, the research topic, wherein a relation associated with the input query is a semantic link between the first concept and one or more variable concepts that serve as one or more connective intermediate concepts between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, and wherein the first concept and the relation are used to derive one or more propositions, wherein the one or more propositions include one or more statements indicating the semantic link (see Agarwal, Paragraph [0074], “The analyser module analyses each of the plurality of search queries for determining semantic-relations between the plurality of search queries, based on the concepts associated therewith. Throughout the present disclosure, the term “semantic-relation” relates to a relationship or association between two or more search queries of the plurality of search queries. It will be appreciated that, the associations between two or more search queries includes attributes and a type or definition that provides a conceptual meaning to how the two or more between two or more search queries are related to each other. Furthermore, the semantic-relation between the plurality of search queries is determined by one or more computing algorithms that is stored and executed by the analyser module. Optionally, the analyser module determines the concept associated with any two or more search queries for determining semantic-relations. For example, a first search query may be formed of a keyword “X”, and a second search query may be formed of a keyword “Y”, in such instance the one or more computing algorithms of the analyser module determines the concept associated with X and Y. Furthermore, in such instance, the one or more computing algorithms compares the attributes of X and Y and the definition of X and Y included in the concept to determine a relation therein. Thereafter, the one or more computing algorithms co-relates the first search query and the second search query and determined the semantic-relation” [Multiple semantic-relations (i.e., a relation associated with the input query is a semantic link between the first concept and one or more variable concepts that serve as one or more connective intermediate concepts between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic) may be determined between two different inputs.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner), and arrived at a method that incorporates semantic relations. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of optimizing the search process (see Agarwal, Paragraph [0074]). In addition, both the references (Pell and Agarwal) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between both of the references highly suggests an expectation of success.
However, the combination of Pell, and Agarwal do not explicitly teach:
receiving an input query including a first input of a first concept that is a starting point for a research topic and that includes one or more words that are utilized to initiate a search relating to the research topic, and a second input, separate from the first input, of a second concept that is an ending point for the research topic and that represents a final result for, and a termination of, the research topic, wherein a relation associated with the input query is a semantic link between the first concept and one or more variable concepts that serve as one or more connective intermediate concepts between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, and wherein the first concept and the relation are used to derive one or more propositions, wherein the one or more propositions include one or more statements indicating the semantic link;
Eveland teaches:
receiving an input query including a first input of a first concept that is a starting point for a research topic and that includes one or more words that are utilized to initiate a search relating to the research topic, and a second input, separate from the first input, of a second concept that is an ending point for the research topic and that represents a final result for, and a termination of, the research topic, wherein a relation associated with the input query is a semantic link between the first concept and one or more variable concepts that serve as one or more connective intermediate concepts between (1) the first concept and the starting point for the research topic and (2) the second concept and the ending point for the research topic, and wherein the first concept and the relation are used to derive one or more propositions, wherein the one or more propositions include one or more statements indicating the semantic link (see Eveland, [Columns 13-14, Lines 58-67 and 1-18], “Referring to FIG. 15, an advanced search interface 1500 is shown according to an example embodiment. In some arrangements, the advance search interface 1500 is integrated into screen display 200 and/or screen display 700 in place of the search query field 210. The advanced search interface 1500 allows for multiple search terms to be entered into an advance search query field 1502. The advance search query field 1502 allows users to search multiple terms and phrases grouped together with Boolean operators (e.g., AND, OR, NOT, NEAR, etc.) for terms within a single query. For example, in FIG. 15, the user has searched for the first search term “BASF” that appears near a second search term “Sonolastic”. The advanced search interface 1500 also provides for multiple separate compare query fields 1504, 1506, and 1508 that allow the user to compare the search query (entered into the advance search query field 1502).” [An advanced search may be executed that allows for separate inputs, which is initiated at the same time, and is a termination of a search topic.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner), further in view of Eveland (teaching document display system and method), and arrived at a method that incorporates multiple search inputs for a research topic. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of enhancing processing and analyzing information (see Eveland, Background). In addition, the references (Pell, Agarwal, and Eveland) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
The combination of Pell, Agarwal, and Eveland further teaches:
retrieving one or more evidence passages that include the first concept and the relation; determining, from the one or more evidence passages, one or more relation links between the first concept and one or more second concepts (see Pell, Paragraphs [0079], [0080], “As depicted at block 725, the proposition is compared against the semantic representations retained in the semantic index to determine a matching set. The passages that are mapped to the matching set of semantic representations are identified, as depicted at block 730.” [The passages are mapped to the concepts of the query.]);
determining one or more concept clusters by aggregating one or more concept occurrences based at least in part on a degree of semantic relations between the one or more concept occurrences, wherein a concept occurrence of the one or more concept occurrences includes an expression of a concept in the one or more evidence passages (see Pell, Paragraph [0054], “These documents 230, targeted by the associated locations, are collected and sorted by the ranking component 270. Sorting may be performed in any known method within the relevant field, and may include without limitation, ranking according to closeness of match, listing based on popularity of the returned documents 230, or sorting based on attributes of the user submitting the query 225.” [The related documents (i.e., relation clusters) can be sorted using a clustering technique.]);
However, the combination of Pell, Agarwal, and Eveland do not explicitly teach:
determining an aggregation confidence associated with a concept cluster of the one or more concept clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source;
Vogel teaches:
determining an aggregation confidence associated with a concept cluster of the one or more concept clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source (see Vogel, Paragraphs [0140]-[0143], “The elements which obtained the highest scores and ranks and/or the elements that occur the most frequently in the documents are included in the search result set, which will be presented to the user.” [The related documents (i.e., relation clusters) may be scored.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner) in view of Eveland (teaching document display systema and method), further in view of Vogel (teaching search system and method for serendipitous discoveries with faceted full-text classification), and arrived at a method that incorporates a reliability score. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of improved results (see Vogel, Paragraph [0143]). In addition, the references (Pell, Agarwal, Eveland, and Vogel) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
However, the combination of Pell, Agarwal, Eveland, and Vogel do not explicitly teach:
determining an aggregation confidence associated with a concept cluster of the one or more concept clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source;
Giovannini teaches:
determining an aggregation confidence associated with a concept cluster of the one or more concept clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source (see Giovannini, Paragraph [0064] “One way of measuring the relevance of a given concept within a thought unit is to count how often the concept was mentioned in the expression of the thought unit. This may include counting references to “it” when it is clear what concept “it” refers to. The relevance score can be integrated into the determination of whether or not concepts are original. For instance, originality of a thought may be dictated in part based on some concept of the thought unit having a minimum relevance score.” [The relevance of the concepts is based on a count, which indicates originality.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner) in view of Eveland (teaching document display systema and method) in view of Vogel (teaching search system and method for serendipitous discoveries with faceted full-text classification), further in view of Giovannini (teaching original idea extraction from written text data), and arrived at a method that incorporates originality. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of identifying original ideas (see Giovannini, Paragraph [0003]). In addition, the references (Pell, Agarwal, Vogel and Giovannini) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
The combination of Pell, Agarwal, Vogel, and Giovannini further teaches:
and presenting, via a user interface presented via a user device, the concept cluster with the aggregation confidence (see Pell, Paragraphs [0038], [0080], “Emphasis may be applied to the regions of the identified passages according to a highlighting scheme (see block 735), and the emphasized regions of the identified passages may be presented to the user as the search results relevant to the query (see block 740). Accordingly, the present invention offers relevant search results that include an emphasized region that corresponds with the true objective of the query.” [The results may be presented to the user.]).
Regarding claim 6, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 5. Pell further teaches:
receiving, via the user interface presented via the user device, a selection of the concept cluster of the one or more concept clusters, the concept cluster associated with a third concept of the one or more second concepts; and presenting, via the user interface presented via the user device, query results for the selection with a portion of the one or more evidence passages associated with the concept cluster (see Pell, Paragraph [0080], “Emphasis may be applied to the regions of the identified passages according to a highlighting scheme (see block 735), and the emphasized regions of the identified passages may be presented to the user as the search results relevant to the query (see block 740). Accordingly, the present invention offers relevant search results that include an emphasized region that corresponds with the true objective of the query.” [The results may be presented to the user via the navigable user interface.]).
Regarding claim 7, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 6. Giovannini further teaches:
receiving user feedback for the query results; and storing the portion of the one or more evidence passages associated with the concept cluster in association with the user feedback (see Giovannini, Paragraph [0063], “sentiment analysis can be applied to a thought unit. Known algorithms and techniques can provide positive and negative sentiment values for a phrase, sentence, paragraph, etc., the sentiment values corresponding to, for instance, positive and negative wordings used in the expression. In some embodiments, the sentiment output is either neutral, positive, negative, or mixed. Additionally or alternatively, a sentiment scale may be generated, for instance one ranging from −10 (most negative sentiment) to +10 (most positive sentiment). In addition to possibly using the sentiment in the networking aspects described above, another enhancement uses the sentiment information to dictate which ideas (e.g. only positive or only very positive) are to be extracted and stored as original ideas.” [The feedback can be considered.]).
Regarding claim 8, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 6. Pell further teaches:
receiving, via the user interface presented via the user device, a second selection of a second concept cluster of the one or more concept clusters, the second concept cluster associated with a third concept of the one or more second concepts; and presenting, via the user interface presented via the user device, second query results for the second selection with a second portion of the one or more evidence passages associated with the second concept cluster (see Pell, Paragraphs [0078], [0080], “Emphasis may be applied to the regions of the identified passages according to a highlighting scheme (see block 735), and the emphasized regions of the identified passages may be presented to the user as the search results relevant to the query (see block 740). Accordingly, the present invention offers relevant search results that include an emphasized region that corresponds with the true objective of the query.” [The results may be presented to the user via the navigable user interface.]).
Regarding claim 9, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 6. Pell further teaches:
receiving, via the user interface presented via the user device, a request to perform a second query with the third concept; presenting, via the user interface presented via the user device, a prompt for the second query with the third concept, the prompt including an input request for a fourth concept or a second relation; and receiving, via the user interface presented via the user device, a user input for the prompt (see Pell, Paragraph [0057], [0078], “the UI display 295 is configured to present any of the exemplary user interfaces depicted in FIGS. 4, 5, or 6, which are described below.” [The user interface allows the user to submit queries as needed.]).
Regarding claim 10, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 9. Pell further teaches:
wherein the user input is the second relation: retrieving one or more second evidence passages that include the third concept and the second relation; and determining, from the one or more second evidence passages, one or more second concept clusters based at least in part on the third concept and the second relation (see Pell, Paragraph [0078], “Further, the passages that satisfy queries submitted to the search-entry box 610 are highlighted according to the procedure, implemented by the natural language engine, as discussed above.” [The user interface will handle the different queries similarly to the first query.]).
Regarding claim 11, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 9. Pell further teaches:
wherein the user input is the third concept: retrieving one or more second evidence passages that include the third concept and the fourth concept; and determining, from the one or more second evidence passages, one or more proposition clusters based at least in part on one or more semantic links between the third concept and the fourth concept (see Pell, Paragraph [0078], “Further, the passages that satisfy queries submitted to the search-entry box 610 are highlighted according to the procedure, implemented by the natural language engine, as discussed above.” [The user interface will handle the different queries similarly to the first query.]).
Regarding claim 12, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 11. Pell further teaches:
receiving, via the user interface presented via the user device, a second selection of a proposition cluster of the one or more proposition clusters; and presenting, via the user interface presented via the user device, second query results including causal links between the first concept, the third concept, and the fourth concept (see Pell, Paragraphs [0078], [0080], “Emphasis may be applied to the regions of the identified passages according to a highlighting scheme (see block 735), and the emphasized regions of the identified passages may be presented to the user as the search results relevant to the query (see block 740). Accordingly, the present invention offers relevant search results that include an emphasized region that corresponds with the true objective of the query.” [The results may be presented to the user via the navigable user interface.]).
Regarding claim 13, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 12. Pell further teaches:
receiving, via the user interface presented via the user device, a second request for a research results report; and presenting, via the user interface presented via the user device, the research results report including the causal links associated the portion of the one or more evidence passages and second portions of the one or more second evidence passages (see Pell, Paragraphs [0078], [0080], “Emphasis may be applied to the regions of the identified passages according to a highlighting scheme (see block 735), and the emphasized regions of the identified passages may be presented to the user as the search results relevant to the query (see block 740). Accordingly, the present invention offers relevant search results that include an emphasized region that corresponds with the true objective of the query.” [The results may be presented to the user via the navigable user interface.]).
Regarding claim 14, Pell teaches one or more non-transitory computer-readable media storing computer executable instructions that, when executed, cause one or more processors to perform operations comprising:
receiving an input query in natural language, wherein the input query includes a first input of one or more first words that are a starting point for a research topic and a second input, separate from the first input, of one or more second words that are an ending point for the research topic, wherein the one or more first words are utilized to initiate a search relating to the research topic and the one or more second words represent a final result or a final outcome for, and a termination of the research topic (see Pell, Figures 4, 5, 6, Paragraphs [0078], [0079], “the search-entry area 410 allows for navigating to a particular document 620 within a database (e.g., Wikipedia®), while a second search-entry box 610 is provided for exploring within the particular document 620. A query entered to the second search-entry box 610 is “who did he marry.” … a query that includes one or more search terms therein is received from a client device at a natural language engine, as depicted at block 705. As depicted at block 710, a proposition may be derived from the search terms. As discussed above, the proposition is generally a logical representation of a conceptual meaning of the query.” [Figures 4 and 5 display search results for a first query (i.e., an input query including a first input). Figure 6 displays a second query (an input query including a second input).]);
performing semantic parsing on the input query to determine at least a first concept, a second concept, and a relation, wherein the relation is a semantic link between the first concept and the second concept, wherein the first concept and the second concept serve as connective intermediate concepts between the starting point for the research topic and the ending point for the research topic, wherein the first concept, the second concept, and the relation are used to derive one or more propositions, and wherein the one or more propositions include one or more statements indicating the semantic link; determining one or more structured representations for the input query including one or more semantic indicators based at least in part on the relation (see Pell, Paragraphs [0051], [0079], “the query parsing component 235 receives the query 225 and performs various procedures to prepare it for semantic analysis. These procedures may be similar to the procedures employed by the document parsing component 240 such as text extraction, entity recognition, and parsing … As depicted at block 710, a proposition may be derived from the search terms. As discussed above, the proposition is generally a logical representation of a conceptual meaning of the query.” [The proposition (i.e., relation link that serves as a connective intermediate concept) is a link between the query search terms (i.e., concepts), and the semantic representation (i.e., semantic link) is compared with the proposition.]);
However, Pell doesn’t explicitly teach:
performing semantic parsing on the input query to determine at least a first concept, a second concept, and a relation, wherein the relation is a semantic link between the first concept and the second concept, wherein the first concept and the second concept serve as connective intermediate concepts between the starting point for the research topic and the ending point for the research topic, wherein the first concept, the second concept, and the relation are used to derive one or more propositions, and wherein the one or more propositions include one or more statements indicating the semantic link;
Agarwal teaches:
performing semantic parsing on the input query to determine at least a first concept, a second concept, and a relation, wherein the relation is a semantic link between the first concept and the second concept, wherein the first concept and the second concept serve as connective intermediate concepts between the starting point for the research topic and the ending point for the research topic, wherein the first concept, the second concept, and the relation are used to derive one or more propositions, and wherein the one or more propositions include one or more statements indicating the semantic link (see Agarwal, Paragraph [0074], “The analyser module analyses each of the plurality of search queries for determining semantic-relations between the plurality of search queries, based on the concepts associated therewith. Throughout the present disclosure, the term “semantic-relation” relates to a relationship or association between two or more search queries of the plurality of search queries. It will be appreciated that, the associations between two or more search queries includes attributes and a type or definition that provides a conceptual meaning to how the two or more between two or more search queries are related to each other. Furthermore, the semantic-relation between the plurality of search queries is determined by one or more computing algorithms that is stored and executed by the analyser module. Optionally, the analyser module determines the concept associated with any two or more search queries for determining semantic-relations. For example, a first search query may be formed of a keyword “X”, and a second search query may be formed of a keyword “Y”, in such instance the one or more computing algorithms of the analyser module determines the concept associated with X and Y. Furthermore, in such instance, the one or more computing algorithms compares the attributes of X and Y and the definition of X and Y included in the concept to determine a relation therein. Thereafter, the one or more computing algorithms co-relates the first search query and the second search query and determined the semantic-relation” [Multiple semantic-relations (i.e., performing semantic parsing on the input query to determine at least a first concept, a second concept, and a relation, wherein the relation is a semantic link between the first concept and the second concept) may be determined between two different inputs.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner), and arrived at a machine that incorporates semantic relations. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of optimizing the search process (see Agarwal, Paragraph [0074]). In addition, both the references (Pell and Agarwal) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between both of the references highly suggests an expectation of success.
However, the combination of Pell, and Agarwal do not explicitly teach:
and a termination of the research topic;
Eveland teaches:
and a termination of the research topic (see Eveland, [Columns 13-14, Lines 58-67 and 1-18], “Referring to FIG. 15, an advanced search interface 1500 is shown according to an example embodiment. In some arrangements, the advance search interface 1500 is integrated into screen display 200 and/or screen display 700 in place of the search query field 210. The advanced search interface 1500 allows for multiple search terms to be entered into an advance search query field 1502. The advance search query field 1502 allows users to search multiple terms and phrases grouped together with Boolean operators (e.g., AND, OR, NOT, NEAR, etc.) for terms within a single query. For example, in FIG. 15, the user has searched for the first search term “BASF” that appears near a second search term “Sonolastic”. The advanced search interface 1500 also provides for multiple separate compare query fields 1504, 1506, and 1508 that allow the user to compare the search query (entered into the advance search query field 1502).” [An advanced search may be executed that allows for separate inputs, which is initiated at the same time, and is a termination of a search topic.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner), further in view of Eveland (teaching document display systema and method), and arrived at a machine that incorporates multiple search inputs for a research topic. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of enhancing processing and analyzing information (see Eveland, Background). In addition, the references (Pell, Agarwal, and Eveland) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
The combination of Pell, Agarwal, and Eveland further teaches:
retrieving one or more evidence passages that include the first concept, the second concept, and the relation (see Pell, Paragraphs [0079], [0080], “As depicted at block 715, semantic representations are generated from passages/content within documents accessible to the natural language engine. As discussed above, the semantic representations are generally linguistic representations derived from content of passages within one or more documents. As depicted at block 720, the semantic representations, and a mapping to the passages from which they are derived, are maintained within a semantic index … As depicted at block 725, the proposition is compared against the semantic representations retained in the semantic index to determine a matching set. The passages that are mapped to the matching set of semantic representations are identified, as depicted at block 730.” [The passages are mapped to the concepts of the query.]);
determining one or more propositional clusters by aggregating the one or more propositions based at least in part on a degree of semantic similarity between the one or more propositions (see Pell, Paragraph [0054], “These documents 230, targeted by the associated locations, are collected and sorted by the ranking component 270. Sorting may be performed in any known method within the relevant field, and may include without limitation, ranking according to closeness of match, listing based on popularity of the returned documents 230, or sorting based on attributes of the user submitting the query 225.” [The related documents (i.e., relation clusters) can be sorted using a clustering technique.]);
However, the combination of Pell, Agarwal, and Eveland do not explicitly teach:
determining an aggregation confidence associated with a propositional cluster of the one or more propositional clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source;
Vogel teaches:
determining an aggregation confidence associated with a propositional cluster of the one or more propositional clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source (see Vogel, Paragraph [0143], “The elements which obtained the highest scores and ranks and/or the elements that occur the most frequently in the documents are included in the search result set, which will be presented to the user.” [The related documents (i.e., relation clusters) may be scored based on similarity.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner) in view of Eveland (teaching document display systema and method), further in view of Vogel (teaching search system and method for serendipitous discoveries with faceted full-text classification), and arrived at a machine that incorporates a reliability score. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of improved results (see Vogel, Paragraph [0143]). In addition, the references (Pell, Agarwal, Eveland, and Vogel) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
However, the combination of Pell, Agarwal, Eveland, and Vogel do not explicitly teach:
determining an aggregation confidence associated with a propositional cluster of the one or more propositional clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source;
Giovannini teaches:
determining an aggregation confidence associated with a propositional cluster of the one or more propositional clusters, wherein the aggregation confidence is based at least in part on a reliability score of a portion of the one or more evidence passages, wherein the relation cluster is associated with a count of the portion of the one or more evidence passages and the count is based on at least in part on originality of knowledge source (see Giovannini, Paragraph [0064] “One way of measuring the relevance of a given concept within a thought unit is to count how often the concept was mentioned in the expression of the thought unit. This may include counting references to “it” when it is clear what concept “it” refers to. The relevance score can be integrated into the determination of whether or not concepts are original. For instance, originality of a thought may be dictated in part based on some concept of the thought unit having a minimum relevance score.” [The relevance of the concepts is based on a count, which indicates originality.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Pell (teaching emphasizing search results according to conceptual meaning) in view of Agarwal (teaching system and method for visually representing user’s browsing history in structured manner) in view of Eveland (teaching document display systema and method) in view of Vogel (teaching search system and method for serendipitous discoveries with faceted full-text classification), further in view of Giovannini (teaching original idea extraction from written text data), and arrived at a system that incorporates originality. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of identifying original ideas (see Giovannini, Paragraph [0003]). In addition, the references (Pell, Agarwal, Eveland, Vogel and Giovannini) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as information retrieval. The close relation between the references highly suggests an expectation of success.
The combination of Pell, Agarwal, Eveland, Vogel, and Giovannini further teaches:
and generating a hypothesis based at least in part on the propositional cluster, the hypothesis including a second query based at least in part on the input query (see Pell, Paragraphs [0038], [0080], “The process of inspection may be carried out continuously, in predefined intervals, or upon an indication that a change has occurred to one or more documents aggregated at the data store 220 … Emphasis may be applied to the regions of the identified passages according to a highlighting scheme (see block 735), and the emphasized regions of the identified passages may be presented to the user as the search results relevant to the query (see block 740). Accordingly, the present invention offers relevant search results that include an emphasized region that corresponds with the true objective of the query.” [The user can submit multiple queries via the user interface.]).
Regarding claim 15, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 14. Pell further teaches:
wherein determining the propositional cluster includes ranking the one or more propositional clusters to generate a ranked list for the one or more propositional clusters (see Pell, Paragraph [0054], “These matching semantic representations may be mapped back to the documents 230 from which they were extracted by associating the documents 230, and the locations therein, from which the semantic representations were derived. These documents 230, targeted by the associated locations, are collected and sorted by the ranking component 270. Sorting may be performed in any known method within the relevant field, and may include without limitation, ranking according to closeness of match, listing based on popularity of the returned documents 230, or sorting based on attributes of the user submitting the query 225.” [The clusters may be ranked.]).
Regarding claim 16, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 14. Giovannini further teaches:
presenting, via a user interface presented via a user device, the propositional cluster and the hypothesis for user feedback (see Giovannini, Paragraph [0063], “sentiment analysis can be applied to a thought unit. Known algorithms and techniques can provide positive and negative sentiment values for a phrase, sentence, paragraph, etc., the sentiment values corresponding to, for instance, positive and negative wordings used in the expression. In some embodiments, the sentiment output is either neutral, positive, negative, or mixed. Additionally or alternatively, a sentiment scale may be generated, for instance one ranging from −10 (most negative sentiment) to +10 (most positive sentiment). In addition to possibly using the sentiment in the networking aspects described above, another enhancement uses the sentiment information to dictate which ideas (e.g. only positive or only very positive) are to be extracted and stored as original ideas.” [The feedback can be considered.]).
Regarding claim 17, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 16. Giovannini further teaches:
receiving, via the user interface presented via the user device, the user feedback for the hypothesis; determining structured representations for the second query; and retrieving one or more second evidence passages based at least in part on the second query (see Giovannini, Paragraph [0063], “sentiment analysis can be applied to a thought unit. Known algorithms and techniques can provide positive and negative sentiment values for a phrase, sentence, paragraph, etc., the sentiment values corresponding to, for instance, positive and negative wordings used in the expression. In some embodiments, the sentiment output is either neutral, positive, negative, or mixed. Additionally or alternatively, a sentiment scale may be generated, for instance one ranging from −10 (most negative sentiment) to +10 (most positive sentiment). In addition to possibly using the sentiment in the networking aspects described above, another enhancement uses the sentiment information to dictate which ideas (e.g. only positive or only very positive) are to be extracted and stored as original ideas.” [The feedback can be considered.]).
Regarding claim 18, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 14. Pell further teaches:
wherein the one or more semantic indicators define one or more conditions for occurrence of the relation, the one or more conditions including one or more of a temporal indicator of a time at which the relation is to occur, a spatial indicator of a location at which the relation is to occur, an instrument indicator of tool used to induce the relation to occur, a cause indicator of an identity of a concept that causes relation to occur, a purpose indicator of a purpose for the relation to occur, an extent indicator for a time period for the relation to occur, or a modal indicator of a certainty for the relation to occur (see Pell, Paragraph [0052], “By way of example, identifying the grammatical relationship includes identifying whether a keyword functions as the subject (agent of an action), object, predicate, indirect object, or temporal location of the proposition of the query 255. In another instance, the proposition is processed to identify a role (e.g., agent or target of action) associated with each of the keywords of the query 225.” [The temporal location of the proposition of the query can used.]).
Regarding claim 19, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 14. Giovannini further teaches:
wherein determining the one or more structured representations for the input query includes presenting the one or more structured representations, including the one or more semantic indicators, the relation, the first concept, and the second concept, for user feedback (see Giovannini, Paragraph [0063], “sentiment analysis can be applied to a thought unit. Known algorithms and techniques can provide positive and negative sentiment values for a phrase, sentence, paragraph, etc., the sentiment values corresponding to, for instance, positive and negative wordings used in the expression. In some embodiments, the sentiment output is either neutral, positive, negative, or mixed. Additionally or alternatively, a sentiment scale may be generated, for instance one ranging from −10 (most negative sentiment) to +10 (most positive sentiment). In addition to possibly using the sentiment in the networking aspects described above, another enhancement uses the sentiment information to dictate which ideas (e.g. only positive or only very positive) are to be extracted and stored as original ideas.” [The feedback can be considered.]).
Regarding claim 20, Pell in view of Agarwal in view of Eveland in view of Vogel, further in view of Giovannini teaches all the limitations of claim 19. Giovannini further teaches:
receiving the user feedback for the one or more structured representations; and storing the input query for the one or more structured representations in association with the user feedback (see Giovannini, Paragraph [0063], “sentiment analysis can be applied to a thought unit. Known algorithms and techniques can provide positive and negative sentiment values for a phrase, sentence, paragraph, etc., the sentiment values corresponding to, for instance, positive and negative wordings used in the expression. In some embodiments, the sentiment output is either neutral, positive, negative, or mixed. Additionally or alternatively, a sentiment scale may be generated, for instance one ranging from −10 (most negative sentiment) to +10 (most positive sentiment). In addition to possibly using the sentiment in the networking aspects described above, another enhancement uses the sentiment information to dictate which ideas (e.g. only positive or only very positive) are to be extracted and stored as original ideas.” [The feedback can be considered.]).
Response to Arguments
Applicant’s Arguments, filed December 11th, 2025, have been fully considered, but are moot in light of the new grounds of rejection.
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 extension fee 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 date of this final action.
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/HUSAM TURKI SAMARA/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161