DETAILED ACTION
This office action is in response to the above identified application filed on May 07, 2026. The application contains claims 1-20.
Claims 1, 2, 8, 9, 15, and 16 are amended
Claims 1-20 are pending
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 07, 2026 has been entered.
Response to Arguments
Applicant's arguments and amendments filed on May 07, 2026 have been fully considered and the objections and rejections are updated accordingly.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to the new limitations introduced with the amendments are addressed with new rationale.
Please refer to the updated 35 U.S.C. 103 rejections as set forth below for details.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ives et al. (US 20090006388 A1), in view of Short (US 20230214412 A1), and in further view of Sood et al. (US 20220337443 A1).
With respect to claim 1,
Ives teaches
a method (Fig. 17; [0129]) comprising:
executing, at a computer system via at least one processor (Fig. 1; [0109]: processor(s)), a search of training course content stored in a database ([0036]; [0133]: stored in a database), the search identifying at least one of new training course content or updated training course content, resulting in search result content (Fig. 17; [0129]: rescan the pages in a given web collection to determine their changes after a set period at step 670, wherein the rescanning the pages corresponds to “executing … a search”. Hence, it teaches: executing, at a computer system via at least one processor, a search of web pages stored in a database, the search identifying at least one of new web pages or updated web pages, resulting in search result content. It does not teach the content being searched is “training course content”. However, the limitation “training course content” is nonfunctional descriptive material that is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the above teaching to other types of data including “training course content” because such data does not functionally relate to the step claimed and the method would have been performed the same regardless of the data);
identifying, via the at least one processor for each piece of content in the search result content, a media type of the each piece of content, the media type comprising one of a video type, an article type, and a slide type (Fig. 17; [0129]: identify media types of files in the pages at step 610. [0135]-[0141]; [0040]: extracted web page content includes images, video files, audio files, text files, or parts of or combinations of any of these and so on, wherein video files indicate “a video type” and text files indicate “an article type”, i.e., the media type comprises at least one of … type);
executing, via the at least one processor on the each piece of content, at least one data extraction algorithm from a plurality of data extraction algorithms, … wherein the at least one data extraction algorithm is selected based on the media type, resulting in extracted data for each piece of content in the search result content (Fig. 17; [0129]: apply an analysis algorithm to each file according to the media type of the file to derive or extract content items at step 620, wherein the analysis algorithm corresponds to “at least one data extraction algorithm” and [0135]-[0141] and [0040] teach “a plurality of data extraction algorithms” as discussed above);
Ives does not teach
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm,
normalizing, via the at least one processor, the extracted data into normalized course data;
executing, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition;
adding, via the at least one processor, data produced by the one or more AI language service algorithms to a semantic search index.
Short teaches
normalizing, via the at least one processor, the extracted data into normalized course data (Fig. 4; [0070]-[0071]: A pre-processing step may perform any text processing or cleaning of data before a text data item from a corpus is indexed. The pre-processing step may involve collecting metadata and text data that will be passed to the tokenisation and filtering pipeline. The pre-processing step corresponds to the “normalizing”);
executing, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition (Fig. 7; [0091]: the server 110 may comprise an artificial intelligence, AI, module 116 that is used to analyse the received text data items to determine semantic information encoded by at least one portion of text within the received text data items. [0095]: the machine learning includes intelligent keyword searches and ontology-based search engines that utilize domain-specific taxonomies (especially of named entities));
adding, via the at least one processor (Fig. 7: processor(s) 102), data produced by the one or more AI language service algorithms to a semantic search index (Fig. 1; [0065]-[0067]: store the determined semantic information as an entry in the index file at S106. [0002]-[0004]: a semantic search index).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ives to incorporate the teachings of Short to normalize the extracted data into normalized course data, execute, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition, and add the extracted data to a semantic search index. Doing so would take advantage of a lexical-conceptual knowledge base to enable querying text by means of concepts and by relations between concepts - rather than only through literal or fuzzy string matching thus enable searches that retrieve information in novel and potentially highly efficient ways as taught by Short ([0096]; [0098]).
Ives and Short do not teach
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm,
Sood teaches
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm ([0069]-[0070]: use an extractive text summarization algorithm to an audio, video, text file, and the one or more presentation tools (e.g., Microsoft® PowerPoint slides, Microsoft® Excel files, spreadsheets, web pages, diagrams, flowcharts, etc.)),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ives and Short to incorporate the teachings of Sood to comprise a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm in the plurality of data extraction algorithms. Doing so would identify and extract the important sentences and information from a given audio, video, and/or text file and generate them verbatim to produce a subset of the sentences from the original text as a summary as taught by Sood ([0070]).
With respect to claim 2,
As discussed in claim 1, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the method of claim 1, further comprising:
receiving, at the computer system, a natural language query from a user (Fig. 5; [0072]: receive, via a user interface, a user text query (step S200). The user text query may be a single word or multiple words);
searching, via the at least one processor, the semantic search index for a response to the natural language query, resulting in query search results (Fig. 5; [0073]: search an index file of the concept-based search engine to identify at least one entry that matches the user text query (step S202)); and
displaying, via a display of the computer system, the query search results in response to the natural language query (Fig. 5; [0077]: output, via the user interface, information, from a text-based database of the concept-based search engine, identifying at least one text data item associated with an entry in the index file that matches the user text query (step S204)),
wherein the query search results comprise natural language responses providing answers to the natural language query and references to the training course content from which the answers were extracted (Fig. 8; [0119]; Fig. 9; [0120]: Fig. 9 shows a natural language response to a user text query entered in Fig. 8. It can be seen that the response includes multiple results with the origin of each result labeled).
With respect to claim 3,
As discussed in claim 2, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the method of claim 2, wherein the query search results further comprise at least one source for each query search result in the query search results (Fig. 5; [0077]: output, via the user interface, information, from a text-based database of the concept-based search engine, identifying at least one text data item associated with an entry in the index file that matches the user text query (step S204), wherein the information identifying at least one text data item identifies “at least one source”).
With respect to claim 4,
As discussed in claim 1, Ives and Short and Sood teach all the limitations therein.
Ives further teaches
the method of claim 1, wherein the training course content further comprises a plurality of courses, with each course in the plurality of courses comprising training content and exam questions (As discussed above, the limitation “training course content” is nonfunctional descriptive material that is not functionally involved in the steps recited. The method would have been performed the same regardless of the data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings already in the prior art to the particular type of data: “training course content”).
With respect to claim 5,
As discussed in claim 4, Ives and Short and Sood teach all the limitations therein.
Ives further teaches
the method of claim 4, wherein the training content comprises at least one of video course content, slide-based course content, and article course content (Fig. 17; [0129]; [0036]: web page content includes any collection of data files, audio, image or video files and so on).
With respect to claim 6,
As discussed in claim 1, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the method of claim 1, wherein the at least one data extraction algorithm comprises at least one Artificial Intelligence (Al) language service (Fig. 7; [0091]: the AI module 116 may be used to analyse the unstructured text data items received during the index file generation process, to determine semantic information encoded by at least one portion of text within the received text data items, wherein the AI module 116 corresponds to “at least one AI language service”).
With respect to claim 7,
As discussed in claim 6, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the method of claim 6, wherein the at least one Al language service comprises:
key phrase extraction;
recognition of named entities; and
domain extraction ([0095]: keyword, domain, and named entities. [0116]: keyword).
With respect to claim 8,
Ives teaches
a system (Fig. 1) comprising:
at least one processor (Fig. 1; [0109]: processor(s)); and
a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
executing a search of training course content stored in a database ([0036]; [0133]: stored in a database), the search identifying at least one of new training course content or updated training course content, resulting in search result content (Fig. 17; [0129]: rescan the pages in a given web collection to determine their changes after a set period at step 670, wherein the rescanning the pages corresponds to “executing … a search”. Hence, it teaches: executing a search of web pages stored in a database, the search identifying at least one of new web pages or updated web pages, resulting in search result content. It does not teach the content being searched is “training course content”. However, the limitation “training course content” is nonfunctional descriptive material that is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the above teaching to other types of data including “training course content” because such data does not functionally relate to the step claimed and the method would have been performed the same regardless of the data);
identifying, for each piece of content in the search result content, a media type of the each piece of content, the media type comprising one of a video type, an article type, and a slide type (Fig. 17; [0129]: identify media types of files in the pages at step 610. [0135]-[0141]; [0040]: extracted web page content includes images, video files, audio files, text files, or parts of or combinations of any of these and so on, wherein video files indicate “a video type” and text files indicate “an article type”, i.e., the media type comprises at least one of … type);
executing, on the each piece of content, at least one data extraction algorithm from a plurality of data extraction algorithms, … wherein the at least one data extraction algorithm is selected based on the media type, resulting in extracted data for each piece of content in the search result content (Fig. 17; [0129]: apply an analysis algorithm to each file according to the media type of the file to derive or extract content items at step 620, wherein the analysis algorithm corresponds to “at least one data extraction algorithm” and [0135]-[0141] and [0040] teach “a plurality of data extraction algorithms” as discussed above);
Ives does not teach
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm,
normalizing the extracted data into normalized course data;
executing, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition;
adding data produced by the one or more AI language service algorithms to a semantic search index.
Short teaches
normalizing the extracted data into normalized course data (Fig. 4; [0070]-[0071]: A pre-processing step may perform any text processing or cleaning of data before a text data item from a corpus is indexed. The pre-processing step may involve collecting metadata and text data that will be passed to the tokenisation and filtering pipeline. The pre-processing step corresponds to the “normalizing”);
executing, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition (Fig. 7; [0091]: the server 110 may comprise an artificial intelligence, AI, module 116 that is used to analyse the received text data items to determine semantic information encoded by at least one portion of text within the received text data items. [0095]: the machine learning includes intelligent keyword searches and ontology-based search engines that utilize domain-specific taxonomies (especially of named entities));
adding data produced by the one or more AI language service algorithms to a semantic search index (Fig. 1; [0065]-[0067]: store the determined semantic information as an entry in the index file at S106. [0002]-[0004]: a semantic search index).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ives to incorporate the teachings of Short to normalize the extracted data into normalized course data, execute, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition, and add the extracted data to a semantic search index. Doing so would take advantage of a lexical-conceptual knowledge base to enable querying text by means of concepts and by relations between concepts - rather than only through literal or fuzzy string matching thus enable searches that retrieve information in novel and potentially highly efficient ways as taught by Short ([0096]; [0098]).
Ives and Short do not teach
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm,
Sood teaches
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm ([0069]-[0070]: use an extractive text summarization algorithm to an audio, video, text file, and the one or more presentation tools (e.g., Microsoft® PowerPoint slides, Microsoft® Excel files, spreadsheets, web pages, diagrams, flowcharts, etc.)),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ives and Short to incorporate the teachings of Sood to comprise a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm in the plurality of data extraction algorithms. Doing so would identify and extract the important sentences and information from a given audio, video, and/or text file and generate them verbatim to produce a subset of the sentences from the original text as a summary as taught by Sood ([0070]).
With respect to claim 9,
As discussed in claim 8, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the system of claim 8, the non-transitory computer-readable storage medium having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving a natural language query from a user (Fig. 5; [0072]: receive, via a user interface, a user text query (step S200). The user text query may be a single word or multiple words);
searching the semantic search index for a response to the natural language query, resulting in query search results (Fig. 5; [0073]: search an index file of the concept-based search engine to identify at least one entry that matches the user text query (step S202)); and
displaying, via a display of the system, the query search results in response to the natural language query (Fig. 5; [0077]: output, via the user interface, information, from a text-based database of the concept-based search engine, identifying at least one text data item associated with an entry in the index file that matches the user text query (step S204)),
wherein the query search results comprise natural language responses providing answers to the natural language query and references to the training course content from which the answers were extracted (Fig. 8; [0119]; Fig. 9; [0120]: Fig. 9 shows a natural language response to a user text query entered in Fig. 8. It can be seen that the response includes multiple results with the origin of each result labeled).
With respect to claim 10,
As discussed in claim 9, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the system of claim 9, wherein the query search results further comprise at least one source for each query search result in the query search results (Fig. 5; [0077]: output, via the user interface, information, from a text-based database of the concept-based search engine, identifying at least one text data item associated with an entry in the index file that matches the user text query (step S204), wherein the information identifying at least one text data item identifies “at least one source”).
With respect to claim 11,
As discussed in claim 8, Ives and Short and Sood teach all the limitations therein.
Ives further teaches
the system of claim 8, wherein the training course content further comprises a plurality of courses, with each course in the plurality of courses comprising training content and exam questions (As discussed above, the limitation “training course content” is nonfunctional descriptive material that is not functionally involved in the steps recited. The method would have been performed the same regardless of the data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings already in the prior art to the particular type of data: “training course content”).
With respect to claim 12,
As discussed in claim 11, Ives and Short and Sood teach all the limitations therein.
Ives further teaches
the system of claim 11, wherein the training content comprises at least one of video course content, slide-based course content, and article course content (Fig. 17; [0129]; [0036]: web page content includes any collection of data files, audio, image or video files and so on).
With respect to claim 13,
As discussed in claim 8, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the system of claim 8, wherein the at least one data extraction algorithm comprises at least one Artificial Intelligence (Al) language service (Fig. 7; [0091]: the AI module 116 may be used to analyse the unstructured text data items received during the index file generation process, to determine semantic information encoded by at least one portion of text within the received text data items, wherein the AI module 116 corresponds to “at least one AI language service”).
With respect to claim 14,
As discussed in claim 13, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the system of claim 13, wherein the at least one Al language service comprises:
key phrase extraction;
recognition of named entities; and
domain extraction ([0095]: keyword, domain, and named entities. [0116]: keyword).
With respect to claim 15,
Ives teaches
a non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising:
executing a search of training course content stored in a database ([0036]; [0133]: stored in a database), the search identifying at least one of new training course content or updated training course content, resulting in search result content (Fig. 17; [0129]: rescan the pages in a given web collection to determine their changes after a set period at step 670, wherein the rescanning the pages corresponds to “executing … a search”. Hence, it teaches: executing a search of web pages stored in a database, the search identifying at least one of new web pages or updated web pages, resulting in search result content. It does not teach the content being searched is “training course content”. However, the limitation “training course content” is nonfunctional descriptive material that is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, see In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the above teaching to other types of data including “training course content” because such data does not functionally relate to the step claimed and the method would have been performed the same regardless of the data);
identifying, for each piece of content in the search result content, a media type of the each piece of content, the media type comprising one of a video type, an article type, and a slide type (Fig. 17; [0129]: identify media types of files in the pages at step 610. [0135]-[0141]; [0040]: extracted web page content includes images, video files, audio files, text files, or parts of or combinations of any of these and so on, wherein video files indicate “a video type” and text files indicate “an article type”, i.e., the media type comprises at least one of … type);
executing, on the each piece of content, at least one data extraction algorithm from a plurality of data extraction algorithms, … wherein the at least one data extraction algorithm is selected based on the media type, resulting in extracted data for each piece of content in the search result content (Fig. 17; [0129]: apply an analysis algorithm to each file according to the media type of the file to derive or extract content items at step 620, wherein the analysis algorithm corresponds to “at least one data extraction algorithm” and [0135]-[0141] and [0040] teach “a plurality of data extraction algorithms” as discussed above);
Ives does not teach
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm,
normalizing the extracted data into normalized course data;
executing, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition;
adding data produced by the one or more AI language service algorithms to a semantic search index.
Short teaches
normalizing the extracted data into normalized course data (Fig. 4; [0070]-[0071]: A pre-processing step may perform any text processing or cleaning of data before a text data item from a corpus is indexed. The pre-processing step may involve collecting metadata and text data that will be passed to the tokenisation and filtering pipeline. The pre-processing step corresponds to the “normalizing”);
executing, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition (Fig. 7; [0091]: the server 110 may comprise an artificial intelligence, AI, module 116 that is used to analyse the received text data items to determine semantic information encoded by at least one portion of text within the received text data items. [0095]: the machine learning includes intelligent keyword searches and ontology-based search engines that utilize domain-specific taxonomies (especially of named entities));
adding data produced by the one or more AI language service algorithms to a semantic search index (Fig. 1; [0065]-[0067]: store the determined semantic information as an entry in the index file at S106. [0002]-[0004]: a semantic search index).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ives to incorporate the teachings of Short to normalize the extracted data into normalized course data, execute, via a course data handler, one or more Artificial Intelligence (AI) language service algorithms on the normalized course data, the one or more AI language service algorithms comprising at least one of: key phrase extraction, industry-specific terms and domain extraction, timestamp and course content location identification, or named entity recognition, and add the extracted data to a semantic search index. Doing so would take advantage of a lexical-conceptual knowledge base to enable querying text by means of concepts and by relations between concepts - rather than only through literal or fuzzy string matching thus enable searches that retrieve information in novel and potentially highly efficient ways as taught by Short ([0096]; [0098]).
Ives and Short do not teach
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm,
Sood teaches
the plurality of data extraction algorithms comprising a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm ([0069]-[0070]: use an extractive text summarization algorithm to an audio, video, text file, and the one or more presentation tools (e.g., Microsoft® PowerPoint slides, Microsoft® Excel files, spreadsheets, web pages, diagrams, flowcharts, etc.)),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ives and Short to incorporate the teachings of Sood to comprise a video extraction algorithm, an article extraction algorithm, and a slide extraction algorithm in the plurality of data extraction algorithms. Doing so would identify and extract the important sentences and information from a given audio, video, and/or text file and generate them verbatim to produce a subset of the sentences from the original text as a summary as taught by Sood ([0070]).
With respect to claim 16,
As discussed in claim 15, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the non-transitory computer-readable storage medium of claim 15, having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving a natural language query from a user (Fig. 5; [0072]: receive, via a user interface, a user text query (step S200). The user text query may be a single word or multiple words);
searching the semantic search index for a response to the natural language query, resulting in query search results (Fig. 5; [0073]: search an index file of the concept-based search engine to identify at least one entry that matches the user text query (step S202)); and
displaying, via a display, the query search results in response to the natural language query (Fig. 5; [0077]: output, via the user interface, information, from a text-based database of the concept-based search engine, identifying at least one text data item associated with an entry in the index file that matches the user text query (step S204)),
wherein the query search results comprise natural language responses providing answers to the natural language query and references to the training course content from which the answers were extracted (Fig. 8; [0119]; Fig. 9; [0120]: Fig. 9 shows a natural language response to a user text query entered in Fig. 8. It can be seen that the response includes multiple results with the origin of each result labeled).
With respect to claim 17,
As discussed in claim 16, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the non-transitory computer-readable storage medium of claim 16, wherein the query search results further comprise at least one source for each query search result in the query search results (Fig. 5; [0077]: output, via the user interface, information, from a text-based database of the concept-based search engine, identifying at least one text data item associated with an entry in the index file that matches the user text query (step S204), wherein the information identifying at least one text data item identifies “at least one source”).
With respect to claim 18,
As discussed in claim 15, Ives and Short and Sood teach all the limitations therein.
Ives further teaches
the non-transitory computer-readable storage medium of claim 15, wherein the training course content further comprises a plurality of courses, with each course in the plurality of courses comprising training content and exam questions (As discussed above, the limitation “training course content” is nonfunctional descriptive material that is not functionally involved in the steps recited. The method would have been performed the same regardless of the data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings already in the prior art to the particular type of data: “training course content”).
With respect to claim 19,
As discussed in claim 18, Ives and Short and Sood teach all the limitations therein.
Ives further teaches
the non-transitory computer-readable storage medium of claim 18, wherein the training content comprises at least one of video course content, slide-based course content, and article course content (Fig. 17; [0129]; [0036]: web page content includes any collection of data files, audio, image or video files and so on).
With respect to claim 20,
As discussed in claim 15, Ives and Short and Sood teach all the limitations therein.
Short further teaches
the non-transitory computer-readable storage medium of claim 15, wherein the at least one data extraction algorithm comprises at least one Artificial Intelligence (Al) language service (Fig. 7; [0091]: the AI module 116 may be used to analyse the unstructured text data items received during the index file generation process, to determine semantic information encoded by at least one portion of text within the received text data items, wherein the AI module 116 corresponds to “at least one AI language service”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOQIN HU whose telephone number is (571)272-1792. The examiner can normally be reached on Monday-Friday 7:00am-3:30pm.
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/XIAOQIN HU/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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