DETAILED ACTION
This action is responsive to claims filed on 9 June 2023.
Claims 1-20 are pending for examination.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites the limitation "the first selection in the ranked list of evidence snippets" in lines 4-5. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the first selection in the ranked list of evidence snippets" has been construed to be “a first selection in the ranked list of evidence snippets”.
Claim 13 recites the limitation "the second selection in the ranked list of evidence snippets" in lines 8-9. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the second selection in the ranked list of evidence snippets" has been construed to be “a second selection in the ranked list of evidence snippets”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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, 4-8, 11-12, 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Murray (U.S. Pre-Grant Publication No. 20110225159, hereinafter ‘Murray'), in view of Hatami-Hanza et al. (U.S. Pre-Grant Publication No. 20110218960, hereinafter 'Hatami-Hanza').
Regarding claim 1, Murray 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 ([0165] The steps of a method or algorithm described in connection with the examples disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. Furthermore the method and/or algorithm need not be performed in the exact order described, but instead may be varied. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). The ASIC may reside in a wireless modem. In the alternative, the processor and the storage medium may reside as discrete components in the wireless modem. In addition, the present invention may be implemented as a machine readable medium. The machine may be a computer. The present invention may be stored as instructions that when executed by a processor cause the present invention to be effected. The medium may be a tangible, non-transitory computer readable medium, or a computer readable storage medium, or combinations thereof.):
wherein the one or more prompts includes at least one of a domain corpora, a primary concept, a relationship, a related concept, and a ranking context ([0125] The fisheye view 33 presents a visual representation of kPOOL's retrieved results. The fisheye view 33 represents a graphical user interface upon which to display the results of the query applied to kPOOL. The fisheye view is composed of a database and query selection interface 199 and a retrieved documents output 205. The database and query selection interface 199 is comprised of a workspace drop menu 201, a database output 203, and a search box 207. The workspace drop menu 201 allows a user to wherein the one or more prompts includes at least one of a domain corpora select which domain corpus 13 to search, as shown in FIG. 1. The database output 203 displays which domain corpus 13 the user has selected. The search box 207 allows the user to a primary concept enter a key word or phrase comprising a natural language query 29. In the particular example shown in FIG. 24, the user has selected the domain corpus 13 of “NKJ_October —3” and has entered a natural language query 29 related to Abraham's wife. In the retrieved documents output 205, kPOOL displays the number of a related concept relevant documents retrieved and the number of topics 209 that correspond to those documents. In this particular example, kPOOL retrieved 165 documents and 68 topics.; [0126] The retrieved documents output 205 is comprised of a document rank 213 and an output of short phrases 215 representing each document. The output of short phrases 215 may comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.; [0127] The document rank 213 is based on a document score, which is a factor representing the similarity between the retrieved relevant document 31 and the natural language query.; [0163] Thus, the user may search the database 301 with a natural language query that semantically searches the original corpus, a natural language query that includes the attribute keywords, or a SQL phrase to filter the retrieved documents. A user may then input an a relationship attribute keyword such as “short,” in a natural language query and retrieve relevant documents. In addition, the user may search through the hierarchy of clustered documents related to the attribute keyword to determine other relevant information.; [0129] The topic ordering window 232 allows the user to select multiple methods of ordering topics (e.g., maximum, mean, RMS, RSS, RSC). A user may select a method of ordering the topics, to allow the and a ranking context topic rank to be calculated based on the selected method. For example, if a user selects “mean,” then the topic rank will be based on the mean document scores of the documents within that topic. Further, if a user selects “maximum,” then the topic rank will be based on the maximum document score of the documents within that topic.),
wherein the primary concept, the relationship, and the related concept are associated with semantic search terms, and wherein the ranking context provides context for semantic search results ([0061] FIG. 6 illustrates steps 51 and 53 of FIG. 5, of accessing and parsing a domain corpus 13. The process shown in wherein the primary concept, the relationship, and the related concept are associated with semantic search terms FIG. 6 comprises variations on standard Latent Semantic Analysis techniques that kPOOL utilizes to implement the methods contemplated by the present invention.; [0129] The topic ordering window 232 allows the user to select multiple methods of ordering topics (e.g., maximum, mean, RMS, RSS, RSC). A user may select a method of ordering the topics, to allow the topic rank to be calculated based on the selected method. For example, if a user selects “mean,” then the topic rank will be based on the mean document scores of the documents within that topic. Further, if a user selects “maximum,” then the topic rank will be wherein the ranking context provides context for semantic search results based on the maximum document score of the documents within that topic.);
receiving, via the GUI presented via a user device, a first query input of the query input including the domain corpora, wherein the domain corpora includes at least one data corpus or knowledge base ([0122] In FIG. 24, a user inputs a natural language query 29 into kPOOL's interface. An exemplary natural language query 29 includes a listing of terms or sentences a user seeks to match with the including the domain corpora documents 15 in the domain corpus 13, as shown in FIG. 1.; [0125] The fisheye view 33 presents a visual representation of kPOOL's retrieved results. The fisheye view 33 represents a graphical user interface upon which to display the results of the query applied to kPOOL. The fisheye view is composed of a database and query selection interface 199 and a retrieved documents output 205. The database and query selection interface 199 is comprised of a workspace drop menu 201, a database output 203, and a search box 207. The workspace drop menu 201 receiving, via the GUI presented via a user device, a first query input of the query input allows a user to select which domain corpus 13 to search, as shown in FIG. 1. The database output 203 displays which domain corpus 13 the user has selected.; [0061] FIG. 6 illustrates steps 51 and 53 of FIG. 5, of accessing and parsing a domain corpus 13. The process shown in FIG. 6 comprises variations on standard Latent Semantic Analysis techniques that kPOOL utilizes to implement the methods contemplated by the present invention. As shown in FIG. 6, kPOOL initially accesses a domain corpus 13. The domain corpus 13 may generally be or knowledge base any textual material, or material from which words are structured in a manner to convey information to a reader. Such textual material may include material on a website, a book, a collection of books, newspapers, legal case law, or any other material that is read by an individual. In addition, the domain corpus 13 may be material an individual does not normally read, including a series of symbols or text such as statistics or parameters. Such alternative embodiments of a domain corpus 13, are shown and described in relation to FIGS. 34-36 and wherein the domain corpora includes at least one data corpus may include multiple forms of machine-readable data. All embodiments of the domain corpus 13 discussed throughout this application, may be either classified as textual material, symbols, or machine-readable data.);
receiving, via the GUI presented via a user device, a second query input of the query input including the primary concept ([0122] FIG. 24 represents the process of applying a natural language query 29 to the reformed term-to-document matrix 23, as shown in FIG. 2. The natural language query 29 allows a user to search the hierarchy of clustered documents 11 formed by kPOOL. In FIG. 24, a user inputs a natural language query 29 into kPOOL's interface. An exemplary natural language query 29 includes receiving, via the GUI presented via a user device, a second query input of the query input including the primary concept a listing of terms or sentences a user seeks to match with the documents 15 in the domain corpus 13, as shown in FIG. 1.);
causing, via the GUI presented via the user device, display of research results associated with the query input, wherein the research results include a first ranked list of evidence snippets referencing the primary concept, wherein the first ranked list of evidence snippets includes highlighted textual expressions of the primary concept ([0125] The fisheye view 33 presents a visual representation of kPOOL's retrieved results. The fisheye view 33 represents a causing, via the GUI presented via the user device graphical user interface upon which to display the results of the query applied to kPOOL.; [0126] The display of research results associated with the query input retrieved documents output 205 is comprised of a wherein the research results include a first ranked list of evidence snippets referencing the primary concept document rank 213 and an output of short phrases 215 representing each document. The output of short phrases 215 may wherein the first ranked list of evidence snippets includes highlighted textual expressions of the primary concept comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.);
determining the highlighted textual expressions of the primary concept includes one or more first aspects of the primary concept, wherein the one or more first aspects includes one or more of a subcategory of the primary concept or an instance of the primary concept referenced in the first ranked list of evidence snippets ([0127] The document rank 213 is based on a document score, which is a factor representing the similarity between the retrieved relevant document 31 and the natural language query. A higher score represents a higher similarity between the document and the natural language query. kPOOL may takes the norm, RSS, or other similar norm of the document score, of the relevant documents within a single topic, to determining the highlighted textual expressions of the primary concept includes one or more first aspects of the primary concept form a topic rank. The topics are then presented in the order of the topic rank. Thus, in the retrieved documents output 205 shown in FIG. 24, the first topic 217 has a document with a document rank of 1. The first topic 217 has the highest topic rank, and is therefore listed first. The norm of the relevant documents within the first topic 217 is higher than any other topic. The wherein the one or more first aspects includes one or more of a subcategory of the primary concept or an instance of the primary concept referenced in the first ranked list of evidence snippets first topic 217 contains six other relevant documents within the same topic. The second topic 219 has a document with a document rank of 8. The second topic 219 has the second highest topic rank because it is listed second. The norm of the relevant documents within the second topic 219 is higher than any other topic except the first topic 217.);
presenting, for selection via the GUI presented via the user device, a first aspect filter associated with the primary concept, wherein the first aspect filter includes the one or more first aspects and is used to narrow the research results ([0136] The self-organizing map is formed, in part, in the manner shown in FIG. 27. FIG. 27 is a flowchart representing a method of formulating the exemplary topical concept map 35 shown in FIG. 26. In step 310, presenting, for selection via the GUI presented via the user device a document, or document node, is selected.; The filtering step 314 may occur for as many or as few terms as desired. Thus, in other words, the terms of other document nodes in the reformed term-to-document matrix 23, for the document nodes that were not selected, are a first aspect filter associated with the primary concept filtered based on the term that defines a document node in the term-to-document matrix 18, that corresponds to the document node that has been selected. The filtering process wherein the first aspect filter includes the one or more first aspects and is used to narrow the research results removes all terms from non-selected document nodes in the reformed term-to-document matrix 23, that do not correspond to terms that were present in the term-to-document matrix 18, for the selected document node. The terms from non-selected document nodes in the reformed term-to-document matrix 23, that do not correspond to terms that were present in the term-to-document matrix 18, are excluded.);
receiving, via the GUI presented via a user device, a third query input of the query input including the related concept ([0130] The retrieved documents output 205 additionally includes a “specs” button 305, an “append summary” button 307, and a topical map button 309. The “specs” button 305 opens a window that displays the text of the document, in addition to the text of other documents within that same topic. The user may quickly view the entire text of the topic, as it has been produced in various documents. The “append summary” button 307 allows a user to click on particular documents and allow the summary section of the document shown in the retrieved documents output 205 to be added into the search box 207. The receiving, via the GUI presented via a user device, a third query input of the query input including the related concept user may use the “append summary” button 307 to review search results, and further direct the user's original search, by appending those results into the text to be queried. The topical map button 309 will be further discussed in relation to FIG. 26.);
presenting, for selection via the GUI presented via the user device, a second ranked list of evidence snippets referencing one or more semantic links between the primary concept and the related concept, wherein the second ranked list of evidence snippets includes second highlighted textual expressions of the primary concept and the related concept ([0133] The fisheye view illustrated in FIGS. 24-25 allows a user to quickly refine a search and retrieve relevant documents. Upon an initial query in the search box 207, a presenting, for selection via the GUI presented via the user device, a second ranked list of evidence snippets referencing one or more semantic links between the primary concept and the related concept user is shown the topics that are relevant to the search. A user can quickly determine which topic most clearly corresponds to the search query. Thus, even if the search query does not directly address the user's true intent, the user can quickly assess other topics that may be more relevant. Upon expanding 223 the topic, the user can second ranked list of evidence snippets includes second highlighted textual expressions of the primary concept and the related concept see other documents within the same topic that may be more relevant. The user can then select 307 documents that clarify the user's idea before repeating the search. This process of search-then-clarify is how kPOOL enables cognitive acceleration on the part of the user. With each search, kPOOL's topics direct and develop the user's understanding until the query correctly expresses the user's idea.);
presenting, for selection via the GUI presented via the user device, a second aspect filter associated with the related concept, wherein the second aspect filter includes one or more second aspects of the related concept in the second ranked list of evidence snippets ([0123] presenting, for selection via the GUI presented via the user device kPOOL may return the relevant documents 31 to the user after the initial natural language search query 29. However, in the preferred embodiment, kPOOL matches the retrieved relevant documents 31 to the hierarchy of clustered documents 11 to determine which topics 175, as shown in FIG. 21, correlate to the relevant documents 31. Thus, kPOOL may not only retrieve relevant documents 31 that closely match the search query 29, but also retrieves relevant documents 31 that were clustered with the closely matching documents in the process of optimal agglomerative clustering 59 shown in FIG. 5. The processes of optimal agglomerative clustering 59, a second aspect filter associated with the related concept, wherein the second aspect filter includes one or more second aspects of the related concept in the second ranked list of evidence snippets including the filtering step 133 shown in FIG. 12, allow kPOOL to retrieve relevant documents 31 clustered with the closely matching relevant documents 31.);
presenting, for selection via the GUI presented via the user device, a ranked list of relation clusters associated with aggregating one or more relation clusters based at least in part on a degree of semantic similarity between the one or more semantic links ([0126] The presenting, for selection via the GUI presented via the user device, a ranked list of relation clusters retrieved documents output 205 is comprised of a document rank 213 and an output of short phrases 215 representing each document. The output of short phrases 215 may comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.; [0127] The document rank 213 is based on a document score, which is a associated with aggregating one or more relation clusters based at least in part on a degree of semantic similarity between the one or more semantic links factor representing the similarity between the retrieved relevant document 31 and the natural language query. A higher score represents a higher similarity between the document and the natural language query. kPOOL may takes the norm, RSS, or other similar norm of the document score, of the relevant documents within a single topic, to form a topic rank. The topics are then presented in the order of the topic rank. Thus, in the retrieved documents output 205 shown in FIG. 24, the first topic 217 has a document with a document rank of 1.);
receiving, via the GUI presented via the user device, fourth user input indicating a selection of a relation cluster from the ranked list of relation clusters ([0128] receiving, via the GUI presented via the user device, fourth user input indicating a selection of a relation cluster from the ranked list of relation clusters To view other documents within each topic, kPOOL includes a click button 223 that allows a user to expand the topic. A view of the first topic 217 after it has been expanded is shown in FIG. 25. In addition, a user may additionally click on a click button 225 to read the document referenced in the retrieved documents output 205. A window 224 that allows a user to read a document, after the click button has been clicked, is shown in FIG. 25.; [0129] Referring back to FIG. 24, additional features of the retrieved documents output 205 include arrows 222 that allow a user to scroll through the documents shown for each topic. The retrieved documents output 205 includes a topic ordering button 230 that opens a topic ordering window 232. The topic ordering window 232 allows the user to select multiple methods of ordering topics (e.g., maximum, mean, RMS, RSS, RSC). A user may select a method of ordering the topics, to allow the topic rank to be calculated based on the selected method. For example, if a user selects “mean,” then the topic rank will be based on the mean document scores of the documents within that topic. Further, if a user selects “maximum,” then the topic rank will be based on the maximum document score of the documents within that topic.); and
causing, via the GUI presented via the user device, display of a portion of the second ranked list of evidence snippets corresponding to the relation cluster, wherein the display includes third highlighted textual expressions of the semantic search terms ([0130] The retrieved documents output 205 additionally includes a “specs” button 305, an “append summary” button 307, and a topical map button 309. The “specs” button 305 opens a window that displays the text of the document, in addition to the text of other documents within that same topic. The user may quickly view the entire text of the topic, as it has been produced in various documents. The “append summary” button 307 allows a user to click on particular documents and allow the summary section of the document shown in the retrieved documents output 205 to be added into the search box 207. The user may use the “append summary” button 307 to review search results, and further direct the user's original search, by appending those results into the text to be queried. The topical map button 309 will be further discussed in relation to FIG. 26.; [0131] FIG. 25 shows the causing, via the GUI presented via the user device fisheye view output if the user selects to expand the topic with the click button 223, as shown in FIG. 24. In the example in FIG. 25, the user has selected to expand the topic corresponding to the document of rank 1. display of a portion of the second ranked list of evidence snippets corresponding to the relation cluster Expanding the topic displays the documents comprising the topic according to the document rank. Documents within the topic that do not strongly correlate with the natural language query are not included within the topic display. In this view, the user can review the short phrases 215 from each document and determine which phrase most strongly matches the user's natural language query 29, as shown in FIG. 24. Thus, kPOOL does not merely produce an indicator, comprising the short phrases 215, of the most relevant retrieved document that closely matches the search query. Rather, kPOOL additionally produces an indicator, wherein the display includes third highlighted textual expressions of the semantic search terms comprising a short phrase 215, of an additional document, that is clustered together with the most relevant retrieved document, that may not closely match the search query.).
Murray fails to teach causing display of a graphical user interface (GUI) to present one or more prompts to guide query input for a research session,
Hatami-Hanza teaches causing display of a graphical user interface (GUI) to present one or more prompts to guide query input for a research session ([0021] A causing display of a graphical user interface (GUI) graphical user interface GUI) is further devised that a user can use by pointing on a node/s and/or edge/s of the knowledge map in order to get the most credible content found in the body of knowledge related to that node or the nodes connected by the pointed edge.; [0151] In FIG. 11, another exemplary system of ISKDS in which the client provides the content or the BOK. Client could assemble a BOK and then use the system to start the interactive session services, or provide the databases for the system to build the BOK. For instance, to present one or more prompts to guide query input for a research session a researcher or an enterprise can put some or all of his/it's files or documents together and use the ISKDS system to find out the context of his documents, and/or gain knowledge of the whole corpus in a glance or by asking more specific questions from the system to find and become beware of important subject matters of his/it's data.),
Murray and Hatami-Hanza are considered to be analogous to the claimed invention because they are in the same field of semantic analysis. In view of the teachings of Murray, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Hatami-Hanza to Murray before the effective filing date of the claimed invention in order to provide user best exposure of valuable contents, while giving the service provider vendor the capability of soliciting more target advertiser if desired by the service provider (cf. Hatami-Hanza, [0023] Among the many advantages of the presented system and method of the knowledge discovery is that even a less known website that have one extremely valuable piece of information will be seen in the searching session. Therefore if a webpage has even one wining partitions it will make it to the top results and will have better chance of being seen and noticed. The system is therefore fairer giving the user the best exposure to valuable contents while it also give the service provider vendor the capability of soliciting more target advertiser if desired by the service provider.).
Regarding claim 4, Murray, as modified by Hatami-Hanza, teaches The system of claim 1.
Murray teaches wherein individual clusters of the ranked list of relation clusters are presented with previews of semantically similar terms identified in the portion of the second ranked list of evidence snippets ([0140] The topical concept map 35 is used to illustrate the presented with previews of semantically similar terms identified in the portion of the second ranked list of evidence snippets semantic relationships between the terms contained in documents. The documents utilized in the topical concept map 35 are not limited to those individual clusters of the ranked list of relation clusters contained within the same cluster or topic as the selected document. Rather, documents from an entire hierarchy of clustered documents 11, as shown in FIG. 21, may be used to form the topical concept map 35. Thus, a user may quickly view semantic connections between words contained within documents, within a hierarchy of clustered documents.), and
wherein a number of the ranked list of relation clusters presented via the GUI is limited to a threshold number ([0091] wherein a number of the ranked list of relation clusters presented via the GUI is limited to a threshold number The filtering process 133 serves to provide a threshold value that a similarity measure between two document nodes must exceed, for the two document nodes to be considered similar enough to initially combine, or cluster together. If the similarity measure between the two document nodes does not exceed the threshold value, then the two documents nodes will not be initially combined.).
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 5, Murray, as modified by Hatami-Hanza, teaches The system of claim 1.
Murray teaches wherein receiving the first query input causes display of the research results including a ranked list of topics to explore for the domain corpora ([0125] The workspace drop menu 201 allows a user to wherein receiving the first query input causes display of the research results including a ranked list of topics to explore for the domain corpora select which domain corpus 13 to search, as shown in FIG. 1. The database output 203 displays which domain corpus 13 the user has selected. The search box 207 allows the user to enter a key word or phrase comprising a natural language query 29. In the particular example shown in FIG. 24, the user has selected the domain corpus 13 of “NKJ_October —3” and has entered a natural language query 29 related to Abraham's wife. In the retrieved documents output 205, kPOOL displays the number of relevant documents retrieved and the number of topics 209 that correspond to those documents. In this particular example, kPOOL retrieved 165 documents and 68 topics.).
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 6, Murray teaches A computer-implemented method comprising ([0165] The steps of a method or algorithm described in connection with the examples disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. Furthermore the method and/or algorithm need not be performed in the exact order described, but instead may be varied. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). The ASIC may reside in a wireless modem. In the alternative, the processor and the storage medium may reside as discrete components in the wireless modem. In addition, the present invention may be implemented as a machine readable medium. The machine may be a computer. The present invention may be stored as instructions that when executed by a processor cause the present invention to be effected. The medium may be a tangible, non-transitory computer readable medium, or a computer readable storage medium, or combinations thereof.):
receiving, via the GUI presented via a user device, a first user input defining query parameters for a research session, the query parameters including one or more of a domain corpora, a primary concept, a relationship, a related concept, and a ranking context ([0125] The fisheye view 33 presents a visual representation of kPOOL's retrieved results. The causing display of a graphical user interface (GUI) to present one or more prompts to guide query input for a research topic fisheye view 33 represents a graphical user interface upon which to display the results of the query applied to kPOOL. The fisheye view is receiving, via the GUI presented via a user device, a first user input defining query parameters for a research session composed of a database and query selection interface 199 and a retrieved documents output 205. The database and query selection interface 199 is comprised of a workspace drop menu 201, a database output 203, and a search box 207. The workspace drop menu 201 allows a user to the query parameters including one or more of a domain corpora select which domain corpus 13 to search, as shown in FIG. 1. The database output 203 displays which domain corpus 13 the user has selected. The search box 207 allows the user to a primary concept enter a key word or phrase comprising a natural language query 29. In the particular example shown in FIG. 24, the user has selected the domain corpus 13 of “NKJ_October —3” and has entered a natural language query 29 related to Abraham's wife. In the retrieved documents output 205, kPOOL displays the number of a related concept relevant documents retrieved and the number of topics 209 that correspond to those documents. In this particular example, kPOOL retrieved 165 documents and 68 topics.; [0126] The retrieved documents output 205 is comprised of a document rank 213 and an output of short phrases 215 representing each document. The output of short phrases 215 may comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.; [0127] The document rank 213 is based on a document score, which is a factor representing the similarity between the retrieved relevant document 31 and the natural language query.; [0163] Thus, the user may search the database 301 with a natural language query that semantically searches the original corpus, a natural language query that includes the attribute keywords, or a SQL phrase to filter the retrieved documents. A user may then input an a relationship attribute keyword such as “short,” in a natural language query and retrieve relevant documents. In addition, the user may search through the hierarchy of clustered documents related to the attribute keyword to determine other relevant information.; [0129] The topic ordering window 232 allows the user to select multiple methods of ordering topics (e.g., maximum, mean, RMS, RSS, RSC). A user may select a method of ordering the topics, to allow the and a ranking context topic rank to be calculated based on the selected method. For example, if a user selects “mean,” then the topic rank will be based on the mean document scores of the documents within that topic. Further, if a user selects “maximum,” then the topic rank will be based on the maximum document score of the documents within that topic.),
wherein the primary concept, the relationship, and the related concept are associated with semantic search terms ([0061] FIG. 6 illustrates steps 51 and 53 of FIG. 5, of accessing and parsing a domain corpus 13. The process shown in wherein the primary concept, the relationship, and the related concept are associated with semantic search terms FIG. 6 comprises variations on standard Latent Semantic Analysis techniques that kPOOL utilizes to implement the methods contemplated by the present invention.), and
wherein the ranking context provides context for semantic search results, wherein the query parameters are used by a research assistant tool to determine evidence snippets associated with the research topic ([0129] The topic ordering window 232 allows the user to select multiple methods of ordering topics (e.g., maximum, mean, RMS, RSS, RSC). A user may select a method of ordering the topics, to allow the topic rank to be calculated based on the selected method. For example, if a user selects “mean,” then the topic rank will be based on the mean document scores of the documents within that topic. Further, if a user selects “maximum,” then the topic rank will be wherein the ranking context provides context for semantic search results based on the maximum document score of the documents within that topic.; [0126] The retrieved documents output 205 is comprised of a document rank 213 and an output of short phrases 215 representing each document. The wherein the query parameters are used by a research assistant tool to determine evidence snippets associated with the research topic output of short phrases 215 may comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.);
causing, via the GUI presented via the user device, display of query results that includes a visual representation of the query results, the query results including a ranked list of evidence snippets ([0126] The causing, via the GUI presented via the user device, display of query results that includes a visual representation of the query results, the query results including a ranked list of evidence snippets retrieved documents output 205 is comprised of a document rank 213 and an output of short phrases 215 representing each document. The output of short phrases 215 may comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.),
wherein an individual snippet of the ranked list of evidence snippets references a semantic link between the primary concept to the related concept by the relationship, wherein the ranked list of evidence snippets includes highlighted textual expressions of the primary concept, the relationship, and the related concept ([0131] FIG. 25 shows the fisheye view output if the user selects to expand the topic with the click button 223, as shown in FIG. 24. In the example in FIG. 25, the user has selected to wherein an individual snippet of the ranked list of evidence snippets references a semantic link between the primary concept to the related concept by the relationship expand the topic corresponding to the document of rank 1. Expanding the topic displays the documents comprising the topic according to the document rank. Documents within the topic that do not strongly correlate with the natural language query are not included within the topic display. In this view, the user can review the short phrases 215 from each document and determine which phrase most strongly matches the user's natural language query 29, as shown in FIG. 24.; Thus, kPOOL does not merely produce an indicator, comprising the short phrases 215, of the most relevant retrieved document that closely matches the search query. Rather, kPOOL additionally produces an wherein the ranked list of evidence snippets includes highlighted textual expressions of the primary concept, the relationship, and the related concept indicator, comprising a short phrase 215, of an additional document, that is clustered together with the most relevant retrieved document, that may not closely match the search query. The document nodes of the most relevant retrieved document, and the other documents, were clustered together during a process of optimal agglomerative clustering 58, as discussed in relation to FIG. 5. The documents shown in FIG. 25, correspond to the document nodes that were combined, in the process of optimal agglomerative clustering 58. Thus, kPOOL not only retrieves relevant documents that closely match the search query, but also displays other documents that have been combined with the relevant documents.);
presenting, for selection via the GUI presented via the user device, a first aspect filter and a second aspect filter, wherein the first aspect filter includes one or more first aspects of the primary concept including at least one instance of the primary concept referenced in the ranked list of evidence snippets, and wherein the second aspect filter includes one or more second aspects of the related concept including at least one instance of the related concept referenced in the ranked list of evidence snippets ([0136] The self-organizing map is formed, in part, in the manner shown in FIG. 27. FIG. 27 is a flowchart representing a method of formulating the exemplary topical concept map 35 shown in FIG. 26. In step 310, presenting, for selection via the GUI presented via the user device a document, or document node, is selected.; The filtering step 314 may occur for as many or as few terms as desired. Thus, in other words, the terms of other document nodes in the reformed term-to-document matrix 23, for the document nodes that were not selected, are a first aspect filter filtered based on the term that defines a document node in the term-to-document matrix 18, that corresponds to the document node that has been selected. The filtering process wherein the first aspect filter includes one or more first aspects of the primary concept including at least one instance of the primary concept referenced in the ranked list of evidence snippets removes all terms from non-selected document nodes in the reformed term-to-document matrix 23, that do not correspond to terms that were present in the term-to-document matrix 18, for the selected document node. The terms from non-selected document nodes in the reformed term-to-document matrix 23, that do not correspond to terms that were present in the term-to-document matrix 18, are excluded.; [0123] kPOOL may return the relevant documents 31 to the user after the initial natural language search query 29. However, in the preferred embodiment, kPOOL matches the retrieved relevant documents 31 to the hierarchy of clustered documents 11 to determine which topics 175, as shown in FIG. 21, correlate to the relevant documents 31. Thus, kPOOL may not only retrieve relevant documents 31 that closely match the search query 29, but also retrieves relevant documents 31 that were clustered with the closely matching documents in the process of optimal agglomerative clustering 59 shown in FIG. 5. The processes of optimal agglomerative clustering 59, and a second aspect filter, wherein the second aspect filter includes one or more second aspects of the related concept including at least one instance of the related concept referenced in the ranked list of evidence snippets including the filtering step 133 shown in FIG. 12, allow kPOOL to retrieve relevant documents 31 clustered with the closely matching relevant documents 31.); and
presenting, for selection via the GUI presented via the user device, one or more semantic qualifier filters including at least one of a how filter, when filter, a where filter, and a why filter, wherein a selection of the one or more semantic qualifier filters highlights a corresponding portion of texts in the ranked list of evidence snippets ([0072] In one embodiment, kPOOL may use a technique of shallow parsing to retain the proper names or geographical indicators in the document 15. In traditional Latent Semantic Analysis techniques, terms can become confused, effectively obfuscating the semantics. For example, New York and York are indistinguishable because New is treated like the adjective “new.” Using shallow parsing, New York and York are both treated as separate terms. Similarly, god and God are separate terms. Also, the presenting, for selection via the GUI presented via the user device, one or more semantic qualifier filters including at least one of a when filter, a where filter who-where-when terms comprising the terms related to names, locations, and dates, are tagged for wherein a selection of the one or more semantic qualifier filters highlights a corresponding portion of texts in the ranked list of evidence snippets additional filtering of retrieved information. Thus, in this embodiment, kPOOL may identify and retain proper names, locations and dates, as terms parsed from each document 15.).
Murray fails to teach causing display of a graphical user interface (GUI) to present one or more prompts to guide query input for a research topic;
Hatami-Hanza teaches causing display of a graphical user interface (GUI) to present one or more prompts to guide query input for a research topic ([0021] A causing display of a graphical user interface (GUI) graphical user interface GUI) is further devised that a user can use by pointing on a node/s and/or edge/s of the knowledge map in order to get the most credible content found in the body of knowledge related to that node or the nodes connected by the pointed edge.; [0151] In FIG. 11, another exemplary system of ISKDS in which the client provides the content or the BOK. Client could assemble a BOK and then use the system to start the interactive session services, or provide the databases for the system to build the BOK. For instance, to present one or more prompts to guide query input for a research topic a researcher or an enterprise can put some or all of his/it's files or documents together and use the ISKDS system to find out the context of his documents, and/or gain knowledge of the whole corpus in a glance or by asking more specific questions from the system to find and become beware of important subject matters of his/it's data.);
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 7, Murray, as modified by Hatami-Hanza, teaches The computer-implemented method of claim 6.
Murray teaches further comprising: receiving selection on a first aspect of the first aspect filter; determining an updated ranked list of evidence snippets include one or more evidence snippets referencing semantic links between the first aspect of the primary concept to the related concept by the relationship; and causing, via the GUI presented via the user device, display of the query results to include the updated ranked list of evidence snippets ([0057] FIG. 2 displays a natural language query 29 applied to the reformed term-to-document matrix 23. The natural language query 29 receiving selection on a first aspect of the first aspect filter maps into the reduced dimensional space of the reformed term-to-document matrix 23 and determining an updated ranked list of evidence snippets include one or more evidence snippets referencing semantic links between the first aspect of the primary concept to the related concept by the relationship retrieves relevant documents 31. kPOOL may correlate the relevant documents 31 to the hierarchy of clustered documents 11, to retrieve information regarding the topics the relevant documents 31 fall into. A causing, via the GUI presented via the user device, display of the query results to include the updated ranked list of evidence snippets fisheye view 33 displays the topics that correlate to the relevant documents 31. The fisheye view 33 displays short phrases from the relevant documents 31 and contains hyperlinks linking the short phrases directly to the domain corpus 13.).
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 8, Murray, as modified by Hatami-Hanza, teaches The computer-implemented method of claim 6.
Murray teaches further comprising: receiving, via the GUI presented via the user device, a request to generate a finding for the query results, the finding including one or more evidence snippets of the ranked list of evidence snippets; and causing, via the GUI presented via the user device, display of the finding with an evidence summary summarizing the one or more evidence snippets ([0125] The fisheye view 33 presents a visual representation of kPOOL's retrieved results. The fisheye view 33 represents a receiving, via the GUI presented via the user device, a request to generate a finding for the query results graphical user interface upon which to display the results of the query applied to kPOOL.; [0126] The retrieved documents output 205 is comprised of a the finding including one or more evidence snippets of the ranked list of evidence snippets document rank 213 and an output of short phrases 215 representing each document. The causing, via the GUI presented via the user device, display of the finding with an evidence summary summarizing the one or more evidence snippets output of short phrases 215 may comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.).
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 11, Murray, as modified by Hatami-Hanza, teaches The computer-implemented method of claim 8.
Hatami-Hanza teaches further comprising: determining, based at least in part on the finding, to generate one or more research question associated with the research session; and causing, via the GUI presented via the user device, display of the one or more research question ([0101] More importantly as shown in the FIG. 1, the client can ask about other subject matters and the content of the assembled BOK of the main subject matter of exploration. As will be explained in FIG. 3 a,b and 4 a-c, the system provides causing, via the GUI presented via the user device, display of the one or more research question user interfaces that a client can navigate and identifies the most important or the strongest associates of the main subject matter in the context of the BOK and request about the information or the knowledge expressing the relationships between two or more of the subject matters from the BOK. The answer in this form again would be the partitions of the BOK that contain the desired subject matter/s and have the predetermined range of value significance measures (VSMs). The system has the option to use one or more measures of the VSMs. The client and users are provided with visually pleasing graphic user interfaces and button and icons so that they can select their desired mode of service e.g.:; [0112] 11. determining, based at least in part on the finding, to generate one or more research question associated with the research session query suggestion, idea and question proposition, and research guidance; and).
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 12, Murray, as modified by Hatami-Hanza, teaches The computer-implemented method of claim 11.
Hatami-Hanza teaches further comprising: receiving a selection of the one or more research question; and causing, via the GUI presented via the user device, display of a new query, populating the query parameters based on the selection of the one or more research question ([0112] 11. receiving a selection of the one or more research question query suggestion, idea and question proposition, and research guidance; and; [0116] Referring to FIG. 2 a now, one of the above exemplary services is further illustrated. FIG. 2 a shows one exemplary way of causing, via the GUI presented via the user device, display of a new query, populating the query parameters based on the selection of the one or more research question displaying and presenting the most significant pieces of knowledge related to queried subject matter in the form of bulleted or a short list concise statements (sentences, paragraphs) found in the BOK which scored the desired ranges of Value significance Measures (VSMs) of the desired aspect of the value significance. The desired range simply can mean the partitions that scored the highest VSM1.).
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 15, Murray 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 ([0165] The steps of a method or algorithm described in connection with the examples disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. Furthermore the method and/or algorithm need not be performed in the exact order described, but instead may be varied. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). The ASIC may reside in a wireless modem. In the alternative, the processor and the storage medium may reside as discrete components in the wireless modem. In addition, the present invention may be implemented as a machine readable medium. The machine may be a computer. The present invention may be stored as instructions that when executed by a processor cause the present invention to be effected. The medium may be a tangible, non-transitory computer readable medium, or a computer readable storage medium, or combinations thereof.):
receiving, via the GUI presented via a user device, a first user input defining query parameters for a research session, the query parameters including one or more of a domain corpora, a primary concept, a relationship, a related concept, and a ranking context ([0125] The fisheye view 33 presents a visual representation of kPOOL's retrieved results. The causing display of a graphical user interface (GUI) to present one or more prompts to guide user input for a research topic fisheye view 33 represents a graphical user interface upon which to display the results of the query applied to kPOOL. The fisheye view is receiving, via the GUI presented via a user device, a first user input defining query parameters for a research session composed of a database and query selection interface 199 and a retrieved documents output 205. The database and query selection interface 199 is comprised of a workspace drop menu 201, a database output 203, and a search box 207. The workspace drop menu 201 allows a user to the query parameters including one or more of a domain corpora select which domain corpus 13 to search, as shown in FIG. 1. The database output 203 displays which domain corpus 13 the user has selected. The search box 207 allows the user to a primary concept enter a key word or phrase comprising a natural language query 29. In the particular example shown in FIG. 24, the user has selected the domain corpus 13 of “NKJ_October —3” and has entered a natural language query 29 related to Abraham's wife. In the retrieved documents output 205, kPOOL displays the number of a related concept relevant documents retrieved and the number of topics 209 that correspond to those documents. In this particular example, kPOOL retrieved 165 documents and 68 topics.; [0126] The retrieved documents output 205 is comprised of a document rank 213 and an output of short phrases 215 representing each document. The output of short phrases 215 may comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.; [0127] The document rank 213 is based on a document score, which is a factor representing the similarity between the retrieved relevant document 31 and the natural language query.; [0163] Thus, the user may search the database 301 with a natural language query that semantically searches the original corpus, a natural language query that includes the attribute keywords, or a SQL phrase to filter the retrieved documents. A user may then input an a relationship attribute keyword such as “short,” in a natural language query and retrieve relevant documents. In addition, the user may search through the hierarchy of clustered documents related to the attribute keyword to determine other relevant information.; [0129] The topic ordering window 232 allows the user to select multiple methods of ordering topics (e.g., maximum, mean, RMS, RSS, RSC). A user may select a method of ordering the topics, to allow the and a ranking context topic rank to be calculated based on the selected method. For example, if a user selects “mean,” then the topic rank will be based on the mean document scores of the documents within that topic. Further, if a user selects “maximum,” then the topic rank will be based on the maximum document score of the documents within that topic.),
wherein the primary concept, the relationship, and the related concept are associated with semantic search terms ([0061] FIG. 6 illustrates steps 51 and 53 of FIG. 5, of accessing and parsing a domain corpus 13. The process shown in wherein the primary concept, the relationship, and the related concept are associated with semantic search terms FIG. 6 comprises variations on standard Latent Semantic Analysis techniques that kPOOL utilizes to implement the methods contemplated by the present invention.), and
wherein the ranking context provides context for semantic search results, wherein the query parameters are used by a research assistant tool to determine evidence snippets associated with the research topic ([0129] The topic ordering window 232 allows the user to select multiple methods of ordering topics (e.g., maximum, mean, RMS, RSS, RSC). A user may select a method of ordering the topics, to allow the topic rank to be calculated based on the selected method. For example, if a user selects “mean,” then the topic rank will be based on the mean document scores of the documents within that topic. Further, if a user selects “maximum,” then the topic rank will be wherein the ranking context provides context for semantic search results based on the maximum document score of the documents within that topic.; [0126] The retrieved documents output 205 is comprised of a document rank 213 and an output of short phrases 215 representing each document. The wherein the query parameters are used by a research assistant tool to determine evidence snippets associated with the research topic output of short phrases 215 may comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.);
receiving, via the GUI presented via a user device, one or more search rules; performing a semantic search on the domain corpora using the query parameters and the one or more search rules; and causing, via the GUI presented via the user device, display of query results that includes a visual representation of the query results, the semantic search results including a ranked list of evidence snippets, wherein an individual snippet of the ranked list of evidence snippets references a semantic link between the primary concept to the related concept by the relationship ([0136] The self-organizing map is formed, in part, in the manner shown in FIG. 27. FIG. 27 is a flowchart representing a method of formulating the exemplary topical concept map 35 shown in FIG. 26. In step 310, a document, or document node, is selected.; The receiving, via the GUI presented via a user device, one or more search rules filtering step 314 may occur for as many or as few terms as desired. Thus, in other words, the terms of other document nodes in the reformed term-to-document matrix 23, for the document nodes that were not selected, are performing a semantic search on the domain corpora using the query parameters and the one or more search rules filtered based on the term that defines a document node in the term-to-document matrix 18, that corresponds to the document node that has been selected. The filtering process removes all terms from non-selected document nodes in the reformed term-to-document matrix 23, that do not correspond to terms that were present in the term-to-document matrix 18, for the selected document node. The terms from non-selected document nodes in the reformed term-to-document matrix 23, that do not correspond to terms that were present in the term-to-document matrix 18, are excluded.; [0123] kPOOL may causing, via the GUI presented via the user device, display of query results that includes a visual representation of the query results, the semantic search results including a ranked list of evidence snippets return the relevant documents 31 to the user after the initial natural language search query 29. However, in the preferred embodiment, kPOOL matches the retrieved relevant documents 31 to the hierarchy of clustered documents 11 to determine which topics 175, as shown in FIG. 21, correlate to the relevant documents 31. Thus, kPOOL may not only retrieve relevant documents 31 that closely match the search query 29, but also retrieves relevant documents 31 that were clustered with the closely matching documents in the process of optimal agglomerative clustering 59 shown in FIG. 5. The processes of optimal agglomerative clustering 59, including the filtering step 133 shown in FIG. 12, allow kPOOL to retrieve relevant documents 31 clustered with the closely matching relevant documents 31.; [0126] The retrieved documents output 205 is comprised of a document rank 213 and an output of short phrases 215 representing each document. The output of short phrases 215 may wherein an individual snippet of the ranked list of evidence snippets references a semantic link between the primary concept to the related concept by the relationship comprise an indicator representing a retrieved document, or relevant document 31, retrieved by kPOOL, and based on the query. The indicator is displayed on the fisheye view 33 graphical user interface.).
Murray fails to teach causing display of a graphical user interface (GUI) to present one or more prompts to guide user input for a research topic;
Hatami-Hanza teaches causing display of a graphical user interface (GUI) to present one or more prompts to guide user input for a research topic ([0021] A causing display of a graphical user interface (GUI) graphical user interface GUI) is further devised that a user can use by pointing on a node/s and/or edge/s of the knowledge map in order to get the most credible content found in the body of knowledge related to that node or the nodes connected by the pointed edge.; [0151] In FIG. 11, another exemplary system of ISKDS in which the client provides the content or the BOK. Client could assemble a BOK and then use the system to start the interactive session services, or provide the databases for the system to build the BOK. For instance, to present one or more prompts to guide user input for a research topic a researcher or an enterprise can put some or all of his/it's files or documents together and use the ISKDS system to find out the context of his documents, and/or gain knowledge of the whole corpus in a glance or by asking more specific questions from the system to find and become beware of important subject matters of his/it's data.);
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 16, Murray, as modified by Hatami-Hanza, teaches The one or more non-transitory computer-readable media of claim 15.
Murray teaches wherein the first user input includes natural language input and the operations further comprising: determining a structured representation for the natural language input; and determining to populate one or more of the query parameters based on the structured representation ([0122] FIG. 24 represents the process of determining a structured representation for the natural language input applying a natural language query 29 to the reformed term-to-document matrix 23, as shown in FIG. 2. The natural language query 29 allows a user to search the hierarchy of clustered documents 11 formed by kPOOL. In FIG. 24, a user inputs a natural language query 29 into kPOOL's interface. An exemplary natural language query 29 includes a determining to populate one or more of the query parameters based on the structured representation listing of terms or sentences a user seeks to match with the documents 15 in the domain corpus 13, as shown in FIG. 1. kPOOL recognizes the natural language query 29 as a pseudo-document and parses the query 29 into a series of key terms, similar to the process of parsing 67 the documents 15 in the original domain corpus, as shown in FIG. 1.).
Murray and Hatami-Hanza are combinable for the same rationale as set forth above with respect to claim 1.
Claims 2-3, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Murray, in view of Hatami-Hanza, and further in view of Tomkins et al. (U.S. Pre-Grant Publication No. 20210295822, hereinafter 'Tomkins').
Regarding claim 2, Murray, as modified by Hatami-Hanza, teaches The system of claim 1.
Murray, as modified by Hatami-Hanza, fails to teach wherein the operations further comprising: receiving, via the GUI presented via the user device, fifth user input to collect at least one of the portion of the second ranked list of evidence snippets as a new finding; and causing, via the GUI presented via the user device, display of interface elements for findings collection to add the new finding, the interface elements including tags for the new finding.
Tomkins teaches wherein the operations further comprising: receiving, via the GUI presented via the user device, fifth user input to collect at least one of the portion of the second ranked list of evidence snippets as a new finding ([0307] Some embodiments may provide a UI that includes one or more UI elements that may be interacted with to send a set of requests to a server based on an input or configuration of the UI. For example, the UI may cause the client computing device to send a second message to a computer system, where the second web message may include an n-gram indicated by a user and an update value corresponding with the n-gram, where the update value may indicate a change to a vertex or an addition to a vertex. Various types of updating operations may be performed, where the n-gram may be receiving, via the GUI presented via the user device updated to be a different n-gram, may be associated with a new ontology graph, or the like. For example, the UI may include a UI element in the form of a button with the rendered text “submit changes.”; [0347] FIG. 22 is a diagram of an example set of user interface elements indicating comparisons between different versions of a document, in accordance with some embodiments of the present techniques. The set of UI elements 2200 includes a change summary window 2210 and a text comparison window 2250. The change summary window 2210 includes a first summary window 2212 and a second summary window 2213. Each respective summary window of the first and second summary windows 2212-2213 fifth user input to collect at least one of the portion of the second ranked list of evidence snippets as a new finding summarizes both a total number of text sections and a count of text sections corresponding to ontology graph categories.); and
causing, via the GUI presented via the user device, display of interface elements for findings collection to add the new finding, the interface elements including tags for the new finding ([0348] The causing, via the GUI presented via the user device, display of interface elements for findings collection to add the new finding change summary window 2210 also includes a selection menu 2220, which provides a list of domain identifiers corresponding with different ontology graphs. Each domain identifier in the list of domain identifiers may be presented as an interactive UI element. For example, after selecting the UI element 2226, which includes the identifier “Medical Test,” a window 2228 may present text from a first document associated with the domain identified by the identifier “Medical Test.” While not shown in FIG. 22, some embodiments may provide a UI that includes other types of domain category values, such as expertise class values, concepts or other subdomains, or the like. The change summary window 2210 also includes a the interface elements including tags for the new finding tag selection window 2224 presents three UI elements such as the UI element 2225, where each UI element shows an identifier of a domain category associated with one or more updated text sections when comparing the first document with a second document. The three UI elements shown in the tag selection window 2224 may correspond with expertise class values, concepts, or other subdomains of the domain selected with the selection menu 2220 and may be used to further filter the display text in the window 2228.).
Murray, Hatami-Hanza, and Tomkins are considered to be analogous to the claimed invention because they are in the same field of semantic analysis. In view of the teachings of Murray and Hatami-Hanza, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Tomkins to Murray before the effective filing date of the claimed invention in order to generate ontology graphs arranged in a hierarchical ontology data model based on ingested documents, improving data ingestion accuracy (cf. Tomkins, [0037] Some embodiments may address this issue by generating ontology graphs arranged in a hierarchical ontology data model based on ingested documents. Ontology graphs may be associated with their own domain categories or may be arranged into subgraphs having vertices associated with specific domain categories. Some embodiments may obtain a plurality of documents and a corresponding set of domain vectors, where the set of domain vectors may be sent directly via an Application Program Interface (API), provided via a user interface (UI) element, determined from other information, or the like. Such other information can include the document's origin, metadata associated with the document, a data format, or the like. A domain vector for a document can indicate various types of information relevant to the usefulness of the document, such as an associated expertise level for each of a plurality of domains, a count of words, a count of words having more than a specified number of syllables, or the like. Some embodiments may then use one or more machine learning models to determine learned representations, such as categories, scalar values, or embedding vectors, for the documents. The machine learning models may include a transformer neural network model such as Elmo, BERT, or the like. In some embodiments, the machine learning model may improve data ingestion accuracy by generating attention vectors for n-grams of an ingested document when performing document analysis or text summarization operations.).
Regarding claim 3, Murray, as modified by Hatami-Hanza and Tomkins, teaches The system of claim 2.
Tomkins teaches wherein the operations further comprising: generating evidence summary for the at least one of the portion of the second ranked list of evidence snippets; presenting via the GUI presented via the user device, the evidence summary and links associated with the at least one of the portion of the second ranked list of evidence snippets for editing; receiving sixth user input to save the new finding; and storing data associated with the new finding ([0346] Some embodiments may provide a UI that permits a user to update one or more ontology graphs with a UI element. For example, some embodiments may provide a UI that permits a user to highlight the word “Serological” being displayed in the UI element 2110 and indicate that the word should be added to the second ontology graph via interactions with a set of UI elements. After updating the UI to indicate that the word “Serological” should be added to an ontology, a user may interact with the UI element 2132 by receiving sixth user input to save the new finding clicking on or tapping on the UI element 2132 to send a message that storing data associated with the new finding indicates an update to an ontology graph.; [0347] FIG. 22 is a diagram of an example set of presenting via the GUI presented via the user device, the evidence summary and links associated with the at least one of the portion of the second ranked list of evidence snippets for editing user interface elements indicating comparisons between different versions of a document, in accordance with some embodiments of the present techniques. The set of UI elements 2200 includes a change summary window 2210 and a text comparison window 2250. The change summary window 2210 includes a first summary window 2212 and a second summary window 2213. generating evidence summary for the at least one of the portion of the second ranked list of evidence snippets Each respective summary window of the first and second summary windows 2212-2213 summarizes both a total number of text sections and a count of text sections corresponding to ontology graph categories.).
Murray, Hatami-Hanza, and Tomkins are combinable for the same rationale as set forth above with respect to claim 2.
Regarding claim 20, Murray, as modified by Hatami-Hanza, teaches The one or more non-transitory computer-readable of claim 15.
Murray, as modified by Hatami-Hanza, fails to teach wherein the first user input includes a saved session file and the operations further comprising: reading a saved structure from the saved session file; and determining to populate one or more of the query parameters based on the saved structure.
Tomkins teaches wherein the first user input includes a saved session file and the operations further comprising: reading a saved structure from the saved session file; and
determining to populate one or more of the query parameters based on the saved structure ([0097] In some embodiments, one or more account parameters may be computed from a set of stored activities. For example, some embodiments may determine a set of previously-accessed documents and determine a set of domain vectors based on the set of previously-accessed documents.; [0100] Alternatively, or in addition, some embodiments may determine the set of query scores based on a set of account parameters, where the set of account parameters may include a login identifier, a hash value based on the login identifier, reading a saved structure from the saved session file data stored in an account of a user identified by the login identifier, or the like. For example, some embodiments may determine a query score vector comprising a weighted sum of a first domain vector and a second domain vector, where the first domain vector may include a set of domain indicators stored in a user account, and where the second domain vector may include a computed domain vector determined from the embedding vectors of the query.; [0101] The process 400 may include determining to populate one or more of the query parameters based on the saved structure retrieving a set of stored documents based on the query score and a set of ontology graphs, as indicated by block 420.).
Murray, Hatami-Hanza, and Tomkins are combinable for the same rationale as set forth above with respect to claim 2.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Murray, in view of Hatami-Hanza, and further in view of Dockhorn et al. (U.S. Patent No. 10198436, hereinafter 'Dockhorn').
Regarding claim 9, Murray, as modified by Hatami-Hanza, teaches The computer-implemented method of claim 8.
Murray, as modified by Hatami-Hanza, fails to teach further comprising: receiving user feedback for the evidence summary, wherein the user feedback indicates a positive association for an accuracy of the evidence summary in summarizing the one or more evidence snippets; and storing the finding with the evidence summary and the one or more evidence snippets associated with the user feedback.
Dockhorn teaches further comprising: receiving user feedback for the evidence summary, wherein the user feedback indicates a positive association for an accuracy of the evidence summary in summarizing the one or more evidence snippets; and storing the finding with the evidence summary and the one or more evidence snippets associated with the user feedback ([Col. 3, Lines 5-8] In one or more implementations, the accuracy and relevance of the determined key portions is further improved by biasing the output of the summarization techniques with captured reader feedback data.; [Col. 11, Line 29-Col. 12, Line 6] In one or more implementations, the reader feedback data 124 is used by the document highlighting system 102 to improve and refine summarization model 116. The document highlighting system 102 receiving user feedback for the evidence summary receives reader feedback data 124 associated with multiple different documents 112 from multiple instances of the reader application 108 implemented at various client devices 106. The document highlighting system 102 can then storing the finding with the evidence summary and the one or more evidence snippets associated with the user feedback use the reader feedback data 124, received for all documents in the system, as training data in order to improve the summarization model 116. For example, wherein the user feedback indicates a positive association for an accuracy of the evidence summary in summarizing the one or more evidence snippets positive reader feedback regarding key portions determined by the summarization model 116 indicates that the summarization model 116 correctly determined the key portion, whereas negative reader feedback may indicate that the summarization model 116 incorrectly determined the key portion. Thus, machine learning and/or deep learning techniques can be applied to the reader feedback data 124 to improve and refine the various rules of the summarization model 116. Notably, the feedback platform described herein makes it very easy for readers to provide feedback, which ensures that a large amount of feedback data will be received. For example, as discussed throughout, the platform makes it very easy for readers to reinforce a highlight, suggest removal of a highlight, or suggest a new highlight be added. This means that the document highlighting system will receive a large amount of reading feedback data which can be used to create a large training data set.).
Murray, Hatami-Hanza, and Dockhorn are considered to be analogous to the claimed invention because they are in the same field of semantic analysis. In view of the teachings of Murray and Hatami-Hanza, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Dockhorn to Murray before the effective filing date of the claimed invention in order to improve accuracy and relevance of the determined key portions, by biasing the output of the summarization techniques with captured reader feedback data (cf. Dockhorn, [Col. 3, Lines 5-28] In one or more implementations, the accuracy and relevance of the determined key portions is further improved by biasing the output of the summarization techniques with captured reader feedback data. The described techniques provide a platform for readers to provide feedback or suggestions regarding the key portions identified within the highlighted document. If the summarization techniques erroneously highlighted a key portion, the platform enables readers to simply click to remove the highlight. Similarly, the reader can provide positive feedback regarding a selected highlight in order to reinforce the highlighted key portion, and if the summarization techniques omitted a key portion, the reader can highlight the portion as important. Reader feedback data is generated based on the reader interactions, and the reader feedback data is communicated back to the summarization model which uses the reader feedback data to adjust the identified key portions, such as be adding or removing key portions identified within the highlighted document. In this way, the highlighted key portions change and improve dynamically as readers interact with the highlighted document. Additionally, the reading feedback data enables the summarization model to learn and improve from each document with human feedback, which is missing from conventional summarization models.).
Regarding claim 10, Murray, as modified by Hatami-Hanza, teaches The computer-implemented method of claim 8.
Murray, as modified by Hatami-Hanza, fails to teach further comprising: receiving user feedback for the evidence summary indicating a negative association for an accuracy of the evidence summary in summarizing the one or more evidence snippets; receiving user input for a corrected evidence summary; and storing the corrected evidence summary and the one or more evidence snippets associated with the user feedback as training data.
Dockhorn teaches further comprising: receiving user feedback for the evidence summary indicating a negative association for an accuracy of the evidence summary in summarizing the one or more evidence snippets; receiving user input for a corrected evidence summary; and
storing the corrected evidence summary and the one or more evidence snippets associated with the user feedback as training data ([Col. 3, Lines 5-8] In one or more implementations, the accuracy and relevance of the determined key portions is further improved by biasing the output of the summarization techniques with captured reader feedback data.; [Col. 11, Line 29-Col. 12, Line 6] In one or more implementations, the reader feedback data 124 is used by the document highlighting system 102 to improve and refine summarization model 116. The document highlighting system 102 receiving user feedback for the evidence summary receives reader feedback data 124 associated with multiple different documents 112 from multiple instances of the reader application 108 implemented at various client devices 106. The document highlighting system 102 can then use the reader feedback data 124, received for all documents in the system, as training data in order to improve the summarization model 116. For example, positive reader feedback regarding key portions determined by the summarization model 116 indicates that the summarization model 116 correctly determined the key portion, whereas indicating a negative association for an accuracy of the evidence summary in summarizing the one or more evidence snippets negative reader feedback may indicate that the summarization model 116 incorrectly determined the key portion. Thus, machine learning and/or deep learning techniques can be applied to the reader feedback data 124 to improve and refine the various rules of the summarization model 116. Notably, the feedback platform described herein makes it very easy for readers to provide feedback, which ensures that a large amount of feedback data will be received. For example, as discussed throughout, the platform makes it very easy for readers to receiving user input for a corrected evidence summary reinforce a highlight, suggest removal of a highlight, or suggest a new highlight be added. This means that the document highlighting system will receive a large amount of storing the corrected evidence summary and the one or more evidence snippets associated with the user feedback as training data reading feedback data which can be used to create a large training data set.).
Murray, Hatami-Hanza, and Dockhorn are combinable for the same rationale as set forth above with respect to claim 9.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Murray, in view of Hatami-Hanza, and further in view of Sathish et al. (U.S. Pre-Grant Publication No. 20170046523, hereinafter 'Sathish').
Regarding claim 13, Murray, as modified by Hatami-Hanza, teaches The computer-implemented method of claim 6.
Murray teaches further comprising: receiving a first selection of the one or more semantic qualifier filters ([0072] Also, the receiving a first selection of the one or more semantic qualifier filters who-where-when terms comprising the terms related to names, locations, and dates, are tagged for additional filtering of retrieved information. Thus, in this embodiment, kPOOL may identify and retain proper names, locations and dates, as terms parsed from each document 15.);
Murray, as modified by Hatami-Hanza, fails to teach causing, via the GUI presented via the user device, display of the query results to include highlighting, using a first color, a first portion of texts corresponding to the first selection in the ranked list of evidence snippets; receiving a second selection of the one or more semantic qualifier filters; and causing, via the GUI presented via the user device, display of the query results to include highlighting, using a second color, a second portion of texts corresponding to the second selection in the ranked list of evidence snippets.
Sathish teaches causing, via the GUI presented via the user device, display of the query results to include highlighting, using a first color, a first portion of texts corresponding to the first selection in the ranked list of evidence snippets; receiving a second selection of the one or more semantic qualifier filters; and causing, via the GUI presented via the user device, display of the query results to include highlighting, using a second color, a second portion of texts corresponding to the second selection in the ranked list of evidence snippets ([0061] The filter heuristics 210 provides details on how the receiving a second selection of the one or more semantic qualifier filters masking or highlighting of the at least one portion of the content needs to be performed. In an example, the at least one portion of the content is masked by changing the font style. In another example, the at least one portion of the content is completely masked. In another example, the at least one portion of the content is partially masked. In another example, the at least one portion of the content is masked with a different color. In another example, the causing, via the GUI presented via the user device, display of the query results to include highlighting, using a first color, a first portion of texts corresponding to the first selection in the ranked list of evidence snippets, causing, via the GUI presented via the user device, display of the query results to include highlighting, using a second color, a second portion of texts corresponding to the second selection in the ranked list of evidence snippets at least one portion of the content is highlighted with a color.).
Murray, Hatami-Hanza, and Sathish are considered to be analogous to the claimed invention because they are in the same field of semantic analysis. In view of the teachings of Murray and Hatami-Hanza, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Sathish to Murray before the effective filing date of the claimed invention in order to build content masks for the users, creation and application of the theme filters, where a cross-language application can be specified within such filters such that the filters for one language can be applicable for the content in other languages as well (cf. Sathish, [0040] Unlike the systems and methods of the related art, the proposed mechanism allows for building content masks for the users, creation and application of the theme filters, where a cross-language application can be specified within such filters such that the filters for one language can be applicable for the content in other languages as well.).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Murray, in view of Hatami-Hanza, and further in view of Miller et al. (U.S. Pre-Grant Publication No. 20190155804, hereinafter 'Miller').
Regarding claim 14, Murray, as modified by Hatami-Hanza, teaches The computer-implemented method of claim 6.
Murray, as modified by Hatami-Hanza, fails to teach further comprising: receiving, via the GUI presented via the user device, a request to save the research session; and storing query input and query parameter associated with the research session.
Miller teaches further comprising: receiving, via the GUI presented via the user device, a request to save the research session; and storing query input and query parameter associated with the research session ([0501] In some cases, the method further comprises constructing, by the first user, the first query using a query interface configured to display query results of the first query to the first user, receiving, via the GUI presented via the user device, a request to save the research session receiving, by the query interface, a request from the first user to save the first query, and storing query input and query parameter associated with the research session saving the first query in association with the query object based on the request from the first user, the saving comprising the assigning of the access permission of the first user to the query object.; [0502] In some cases, the method further comprises constructing, by a third user, a third query using a query interface configured to display query results of the third query to the third user, receiving, by the query interface, a request from the third user to save the third query, and saving the third query in association with another query object based on the request from the third user, the saving comprising assigning an access permission of the third user to the another query object as a runtime permission of the third query.).
Murray, Hatami-Hanza, and Miller are considered to be analogous to the claimed invention because they are in the same field of semantic analysis. In view of the teachings of Murray and Hatami-Hanza, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Miller to Murray before the effective filing date of the claimed invention in order to optimize searching by looking only in buckets for time ranges that are relevant to a query (cf. Miller, [0079] Each indexer 102 is responsible for storing and searching a subset of the events contained in a corresponding data store 103. By distributing events among the indexers and data stores, the indexers can analyze events for a query in parallel, for example using map-reduce techniques, wherein each indexer returns partial responses for a subset of events to a search head that combines the results to produce an answer for the query. By storing events in buckets for specific time ranges, an indexer may further optimize searching by looking only in buckets for time ranges that are relevant to a query.).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Murray, in view of Hatami-Hanza, and further in view of Khante et al. (U.S. Patent No. 10592525, hereinafter 'Khante').
Regarding claim 17, Murray, as modified by Hatami-Hanza, teaches The one or more non-transitory computer-readable media of claim 15.
Murray, as modified by Hatami-Hanza, fails to teach the operations further comprising:
receiving, via the GUI presented via a user device, a second user input defining a filter based on a period of time including a start time and an end time for the query results.
Khante teaches the operations further comprising: receiving, via the GUI presented via a user device, a second user input defining a filter based on a period of time including a start time and an end time for the query results ([Col. 69, Lines 50-63] The example process 2000 may begin at 2002 when a query for information related to machine data is sent. For example, a query for information related to machine data generated by one or more machine data sources 1812 of a cloud computing platform (CCP) 1802 is sent by the client computing device 1805 and to a cloud computing monitoring component 1804 of the CCP 1802. In some cases, the query may be formed using the native query language (e.g., structured query language (SQL, Datalog, and so forth), of the CCP 1802. The query that is sent may be generated based on a query string (e.g., a query template) that was obtained previously from the CCP 1802. In particular, the receiving, via the GUI presented via a user device, a second user input defining a filter based on a period of time including a start time and an end time for the query results query may include the query string and a time range (e.g., a start time and an end time) that the query is seeking information for.).
Murray, Hatami-Hanza, and Khante are considered to be analogous to the claimed invention because they are in the same field of semantic analysis. In view of the teachings of Murray and Hatami-Hanza, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Khante to Murray before the effective filing date of the claimed invention in order to enable a user to continue investigating and learn valuable insights about the machine data (cf. Khante, [Col. 7, Lines 18-38] In the data intake and query system, a field extractor may be configured to automatically generate extraction rules for certain fields in the events when the events are being created, indexed, or stored, or possibly at a later time. Alternatively, a user may manually define extraction rules for fields using a variety of techniques. In contrast to a conventional schema for a database system, a late-binding schema is not defined at data ingestion time. Instead, the late-binding schema can be developed on an ongoing basis until the time a query is actually executed. This means that extraction rules for the fields specified in a query may be provided in the query itself, or may be located during execution of the query. Hence, as a user learns more about the data in the events, the user can continue to refine the late-binding schema by adding new fields, deleting fields, or modifying the field extraction rules for use the next time the schema is used by the system. Because the data intake and query system maintains the underlying machine data and uses a late-binding schema for searching the machine data, it enables a user to continue investigating and learn valuable insights about the machine data.).
Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Murray, in view of Hatami-Hanza, Khante, and further in view of Tomkins.
Regarding claim 18, Murray, as modified by Hatami-Hanza and Khante, teaches The one or more non-transitory computer-readable media of claim 17.
Murray, as modified by Hatami-Hanza and Khante, fails to teach the operations further comprising: receiving, via the GUI presented via the user device, a request to display the query results as a graph as a function of evidence count over the period of time; and causing, via the GUI presented via the user device, display of the graph associated with the query results.
Tomkins teaches the operations further comprising: receiving, via the GUI presented via the user device, a request to display the query results as a graph as a function of evidence count over the period of time; and causing, via the GUI presented via the user device, display of the graph associated with the query results ([0037] Some embodiments may address this issue by causing, via the GUI presented via the user device, display of the graph associated with the query results generating ontology graphs arranged in a hierarchical ontology data model based on ingested documents. Ontology graphs may be associated with their own domain categories or may be arranged into subgraphs having vertices associated with specific domain categories.; [0295] For example, as further described below, some embodiments may update a machine learning operation based on an receiving, via the GUI presented via the user device update to a text document in a user interface. By augmenting a user interface with an updated ontology graph, some embodiments may reduce the computation time required to perform dynamic, user-specific content display in a user interface.; [0351] Some embodiments may then update the set of ontology graphs, such as by appending a subarray associating the pair of vertices to an array of subarrays, where each subarray may represent a graph edge of an ontology graph. Some embodiments may then update additional operations based on a discovered association between a first concept and a second concept, such as by updating text-displaying operations to display the second concept after a user highlights the first concept. In addition, some embodiments may store a time corresponding to when the association between the first and second concept was first detected. Some embodiments may then a request to display the query results as a graph as a function of evidence count over the period of time provide a visualized representation of a time-based map of the change in associations between different concepts or other n-grams.).
Murray, Hatami-Hanza, Khante, and Tomkins are considered to be analogous to the claimed invention because they are in the same field of semantic analysis. In view of the teachings of Murray, Hatami-Hanza, and Khante, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Tomkins to Murray before the effective filing date of the claimed invention in order to generate ontology graphs arranged in a hierarchical ontology data model based on ingested documents, improving data ingestion accuracy (cf. Tomkins, [0037] Some embodiments may address this issue by generating ontology graphs arranged in a hierarchical ontology data model based on ingested documents. Ontology graphs may be associated with their own domain categories or may be arranged into subgraphs having vertices associated with specific domain categories. Some embodiments may obtain a plurality of documents and a corresponding set of domain vectors, where the set of domain vectors may be sent directly via an Application Program Interface (API), provided via a user interface (UI) element, determined from other information, or the like. Such other information can include the document's origin, metadata associated with the document, a data format, or the like. A domain vector for a document can indicate various types of information relevant to the usefulness of the document, such as an associated expertise level for each of a plurality of domains, a count of words, a count of words having more than a specified number of syllables, or the like. Some embodiments may then use one or more machine learning models to determine learned representations, such as categories, scalar values, or embedding vectors, for the documents. The machine learning models may include a transformer neural network model such as Elmo, BERT, or the like. In some embodiments, the machine learning model may improve data ingestion accuracy by generating attention vectors for n-grams of an ingested document when performing document analysis or text summarization operations.).
Regarding claim 19, Murray, as modified by Hatami-Hanza, Khante, and Tomkins, teaches The one or more non-transitory computer-readable media of claim 18.
Tomkins teaches the operations further comprising: receiving, via the GUI presented via the user device, a second request to change a type of the graph; presenting, for selection via the GUI presented via the user device, a list of type of the graph; receiving, via the GUI presented via the user device, a selection of the type of the graph; and causing, via the GUI presented via the user device, to render the graph as the selection of the type of the graph ([0299] In some embodiments, the process 1900 may include presenting, for selection via the GUI presented via the user device, a list of type of the graph retrieving a set of ontology graphs based on the set of context parameters, as indicated by block 1904.; [0298] FIG. 19 is a flowchart of operations to for updating a user interface for displaying text of a document, in accordance with some embodiments of the present techniques. Operations of the process 1900 may begin at block 1902. In some embodiments, the process 1900 may include receiving, via the GUI presented via the user device, a selection of the type of the graph obtaining a set of context parameters, as indicated by block 1902.; [0301] In some embodiments, the process 1900 may include receiving, via the GUI presented via the user device, a second request to change a type of the graph sending a UI to modify a data ingestion and processing workflow based on the set of context parameters, as indicated by block 1910.; [0333] In some embodiments, the process 1900 may include updating the UI based on the causing, via the GUI presented via the user device, to render the graph as the selection of the type of the graph updates to the set of ontology graphs, as indicated by block 1942. Operations to update the UI may include one or more operations described above for block 1914.).
Murray, Hatami-Hanza, Khante, and Tomkins are combinable for the same rationale as set forth above with respect to claim 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vanderwende et al. (U.S. Pre-Grant Publication No. 20050220351) teaches a method and system for identifying words, text fragments, or concepts of interest in a corpus of text.
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/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129