DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/15/2025 has been entered. Claims 1, 3-9, 11-15, 17-22 and 24 are pending; claims 2, 10, 16 and 23 have been cancelled.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3-9, 11-15, 17-22 and 24 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 9, 15 recite the limitation: each of the plurality of entities being a noun or a noun phrase in the received document and referring to an object. The limitation: a noun, a noun phrase and an object are not supported in the applicant’s original specifications. The phrases noun, noun phrase and object were not included in the specifications.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3 – 4, 7 – 9, 11 – 12, 15 and 17 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Elchik et al. (US 2017/0213469 A1) in view of Sharma (US 2021/0314282 A1).
Re claims 1, 9, 15:
1. A method, performed by a device, for providing a virtual audience (Elchik, Abstract; [0032], “an audience member”; [0098]), the method comprising:
receiving, via a microphone, a voice signal indicating a speech of a user (Elchik, [0008]; [0045]; [0101]);
converting the speech of the user in the received voice signal into text (Elchik, [0008]; [0045]);
determining a topic of the speech based on the converted text (Elchik, fig. 6, “Topic(s) Extractor”; [0070]);
receiving a document related to the topic from a document database (DB) by using the topic as a search word (Elchik, [0047], “automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise”; [0064], “The automatic question generator 504 creates prompts for use in lessons based on content of the digital media asset … the digital media asset content was a news story with the text … a multiple choice or fill-in-the-blank automatically generated question might be “Russia bombed in Syria””; [0057], “to extract a topic (technology) … generate lesson learning exercises that also contain content that the system determines will be relevant to the user based on user attributes and/or a topic of the story”);
identifying a plurality of entities included in the received document (Elchik, [0038], “The content analysis engine will extract text corresponding to words spoken in the video or audio of a digital video or audio file, or words appearing in a digital document such as a web page”; [0064], “the digital media asset content was a news story with the text”; [0069], “content includes news stories”), each of the plurality of entities being a noun or a noun phrase in the received document and referring to an object (Elchik, Abstract, “named entities”; [0057]; [0062], “extracted text may be integrated into a Natural Language Processing analysis methodology 502 that may include NER, recognition of events, and key word extraction. NER is a method of information extraction that works by locating and classifying elements in text into pre-defined categories (each, an “entity”) that is used to identify a person, place or thing”; fig. 11, “People, Places, and Things”; [0077], “The system may tag each word of the content with a part of speech … Example senses that may be assigned to a word are noun.plant, noun.animal… ”; a named entities such as “plant, animal, or things” are object and nouns);
determining a logical relationship between the plurality of entities (Elchik, [0047], “After selecting the language learning template 206, the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise. The system may obtain a template for the exercise from a data storage facility containing candidate exercises such as (1) questions and associated answers, (2) missing word exercises, (3) sentence scramble exercises, and (4) multiple choice questions. The content of each exercise may include blanks in which named entities, parts of speech, or words relevant to the topic may be added”; [0057], “the system generates a multiple-choice question in which the part of speech (salary, a noun) is converted to a blank in the prompt”; [0058]; [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”; according to applicant’s specification, binary relationship template refers to semantic parts in a sentence);
generating one or more questions including an entity among the plurality of entities in the received document based on the logical relationship, corresponding to the entity (Elchik, [0047], “After selecting the language learning template 206, the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise. The system may obtain a template for the exercise from a data storage facility containing candidate exercises such as (1) questions and associated answers, (2) missing word exercises, (3) sentence scramble exercises, and (4) multiple choice questions. The content of each exercise may include blanks in which named entities, parts of speech, or words relevant to the topic may be added”; [0057], “the system generates a multiple-choice question in which the part of speech (salary, a noun) is converted to a blank in the prompt”; [0058]; [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”; according to applicant’s specification, binary relationship template refers to semantic parts in a sentence); and
based on the entity being detected as an invoke word in the speech after generating the one or more questions (Elchik, [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”), displaying, through a display, a question among the one or more questions (Elchik, [0047]; [0058]).
9. A device (Elchik, Abstract; [0032], “an audience member”; [0098]) comprising:
a microphone configured to receive a voice signal corresponding to a speech of a user (Elchik, [0008]; [0045]; [0101]);
memory storing one or more instructions; and
a processor operably coupled to the microphone and memory, the processor configured to execute the one or more instructions to:
convert the speech of the user in the received voice signal into text (Elchik, [0008]; [0045]);
determine a topic of the speech based on the converted text (Elchik, fig. 6, “Topic(s) Extractor”; [0070]);
receive a document related to the topic from a document database (DB) using the topic as a search word (Elchik, [0047], “automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise”; [0064], “The automatic question generator 504 creates prompts for use in lessons based on content of the digital media asset … the digital media asset content was a news story with the text … a multiple choice or fill-in-the-blank automatically generated question might be “Russia bombed in Syria””; [0057], “to extract a topic (technology) … generate lesson learning exercises that also contain content that the system determines will be relevant to the user based on user attributes and/or a topic of the story”);
identify a plurality of entities include in the received document (Elchik, [0038], “The content analysis engine will extract text corresponding to words spoken in the video or audio of a digital video or audio file, or words appearing in a digital document such as a web page”; [0064], “the digital media asset content was a news story with the text”; [0069], “content includes news stories”), each of the plurality of entities being a noun or a noun phrase in the received document and referring to an object (Elchik, Abstract, “named entities”; [0057]; [0062], “extracted text may be integrated into a Natural Language Processing analysis methodology 502 that may include NER, recognition of events, and key word extraction. NER is a method of information extraction that works by locating and classifying elements in text into pre-defined categories (each, an “entity”) that is used to identify a person, place or thing”; fig. 11, “People, Places, and Things”; [0077], “The system may tag each word of the content with a part of speech … Example senses that may be assigned to a word are noun.plant, noun.animal… ”; a named entities such as “plant, animal, or things” are object and nouns);
determine a logical relationship between the plurality of entities (Elchik, [0047], “After selecting the language learning template 206, the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise. The system may obtain a template for the exercise from a data storage facility containing candidate exercises such as (1) questions and associated answers, (2) missing word exercises, (3) sentence scramble exercises, and (4) multiple choice questions. The content of each exercise may include blanks in which named entities, parts of speech, or words relevant to the topic may be added”; [0057], “the system generates a multiple-choice question in which the part of speech (salary, a noun) is converted to a blank in the prompt”; [0058]; [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”; according to applicant’s specification, binary relationship template refers to semantic parts in a sentence);
generate one or more questions including an entity among the plurality of entities in the received document based on the logical relationship, corresponding to the entity (Elchik, [0047], “After selecting the language learning template 206, the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise. The system may obtain a template for the exercise from a data storage facility containing candidate exercises such as (1) questions and associated answers, (2) missing word exercises, (3) sentence scramble exercises, and (4) multiple choice questions. The content of each exercise may include blanks in which named entities, parts of speech, or words relevant to the topic may be added”; [0057], “the system generates a multiple-choice question in which the part of speech (salary, a noun) is converted to a blank in the prompt”; [0058]; [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”; according to applicant’s specification, binary relationship template refers to semantic parts in a sentence); and
display, through a display, a question among the one or more questions including the entity (Elchik, [0047]; [0058]).
15. A non-transitory computer-readable recording medium containing instructions that when executed cause a processor (Elchik, Abstract; [0032], “an audience member”; [0098]) to:
receive a voice signal corresponding to a speech of a user (Elchik, [0008]; [0045]; [0101]);
convert the speech in the received voice signal into text (Elchik, [0008]; [0045]);
determine a topic of the speech based on the converted text (Elchik, fig. 6, “Topic(s) Extractor”; [0070]);
receive a document related to the topic from a document database (DB) using the topic as a search word (Elchik, [0047], “automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise”; [0064], “The automatic question generator 504 creates prompts for use in lessons based on content of the digital media asset … the digital media asset content was a news story with the text … a multiple choice or fill-in-the-blank automatically generated question might be “Russia bombed in Syria””; [0057], “to extract a topic (technology) … generate lesson learning exercises that also contain content that the system determines will be relevant to the user based on user attributes and/or a topic of the story”);
identify a plurality of entities included in the received document (Elchik, [0038], “The content analysis engine will extract text corresponding to words spoken in the video or audio of a digital video or audio file, or words appearing in a digital document such as a web page”; [0064], “the digital media asset content was a news story with the text”; [0069], “content includes news stories”), each of the plurality of entities being a noun or a noun phrase in the received document and referring to an object (Elchik, Abstract, “named entities”; [0057]; [0062], “extracted text may be integrated into a Natural Language Processing analysis methodology 502 that may include NER, recognition of events, and key word extraction. NER is a method of information extraction that works by locating and classifying elements in text into pre-defined categories (each, an “entity”) that is used to identify a person, place or thing”; fig. 11, “People, Places, and Things”; [0077], “The system may tag each word of the content with a part of speech … Example senses that may be assigned to a word are noun.plant, noun.animal… ”; a named entities such as “plant, animal, or things” are object and nouns);
determine a logical relationship between the plurality of entities (Elchik, [0047], “After selecting the language learning template 206, the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise. The system may obtain a template for the exercise from a data storage facility containing candidate exercises such as (1) questions and associated answers, (2) missing word exercises, (3) sentence scramble exercises, and (4) multiple choice questions. The content of each exercise may include blanks in which named entities, parts of speech, or words relevant to the topic may be added”; [0057], “the system generates a multiple-choice question in which the part of speech (salary, a noun) is converted to a blank in the prompt”; [0058]; [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”; according to applicant’s specification, binary relationship template refers to semantic parts in a sentence);
generate one or more questions including an entity among the plurality of entities in the received document based on the logical relationship, corresponding to the entity (Elchik, [0047], “After selecting the language learning template 206, the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise. The system may obtain a template for the exercise from a data storage facility containing candidate exercises such as (1) questions and associated answers, (2) missing word exercises, (3) sentence scramble exercises, and (4) multiple choice questions. The content of each exercise may include blanks in which named entities, parts of speech, or words relevant to the topic may be added”; [0057], “the system generates a multiple-choice question in which the part of speech (salary, a noun) is converted to a blank in the prompt”; [0058]; [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”; according to applicant’s specification, binary relationship template refers to semantic parts in a sentence); and
display, through a display, a question among the one or more questions including the entity (Elchik, [0047]; [0058]).
Elchik does not explicitly disclose the following underlined features:
identifying a plurality of entities included in the received document using a machine learning model;
determining a logical relationship between the plurality of entities included in the received document based on one or more relation templates fetched from a binary relationship template database;
based on the entity being detected as an invoke word in the speech after generating the one or more questions, displaying, through a display, a video of the virtual audience comprising at least one character uttering a question among the one or more questions including the entity and outputting, through a speaker, voice data corresponding to the question.
Sharma (US 2021/0314282 A1) teaches methods, apparatuses, and computer program products for aggregating user sessions for conversational exchanges using a virtual assistant (Sharma, Abstract).
Sharma teaches a
identifying a plurality of entities included in the received document using a machine learning model (Sharma, [0007], “determining, for a first portion of said plurality of textual strings, to send a query”; [0056], “used to carry out a search based upon keywords or terms”; [0066], “chatbot may carry out a response module, such as a computer program, to query a database associated with the particular network entity, business, organization, or the like. In some embodiments, the response module can comprise an artificial intelligence program, such as a neural network or the like”);
determining a logical relationship between the plurality of entities included in the received document based on one or more relation templates fetched from a binary relationship template database (Sharma, [0094], “the user 601 can engage their virtual assistant 602, for instance, by asking a first question that can be answered by the managing chatbot 605 based upon information stored at the database 606”; [0097], “The first provider chatbot can then receive the user's question and provide the managing chatbot with an answer to the user's question, which the managing chatbot can either simply relay to the user device for presentation to the user or can aggregate with other information or answers to prepare a semantically suitable answer to the user's question”);
based on the entity being detected as an invoke word in the speech after generating the one or more questions (Sharma, [0056], “that a trigger word or a waking word can be used to awaken the smart speaker”; [0058], “when the processor determines that a trigger word has been spoken”), displaying, through a display, a video of the virtual audience comprising at least one character uttering a question among the one or more questions including the entity and outputting, through a speaker, voice data corresponding to the question (Sharma, [0067], “a chatbot may comprise at least one of a messaging chatbot, an audio chatbot, a visual chatbot, an audio-visual chatbot, a holographic chatbot, an augmented reality chatbot, a virtual reality chatbot, variations thereof, and/or the like”).
Therefore, in view of Sharma, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/device/computer program described in Elchik, by searching document using machine learning as taught by Sharma, since an artificial intelligence program used for natural language processing and generating chatbot responses can learn a user's preferences, can build a user-specific profile that can be considered in generating responses for that user, can learn the semantic and linguistic characteristics specific to the user and to extract information from the database (Sharma, [0066]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/device/computer program described in Elchik, by providing a visual and audio chatbot as taught by Sharma, in order to provide a relatable, life-like interactive and immersive chatbot to the end user.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/device/computer program described in Elchik, by providing invoke word as taught by Sharma, since a wake word or another suitable trigger word which can cause the sensor or microphone to transition from sleep mode to active listening mode or another suitable mode (Sharma, [0058]).
Re claims 3, 11:
3. The method of claim 1, further comprising:
receiving a document from a user (Elchik, [0006], “Each of the programming files corresponds to a digital media asset, such as a news report, article, video or other item of content”; [0030]; [0063]; [0066], “The authoring tool 506 …. with input capabilities that enable the administrator to modify, delete, add to, or replace any of the lesson content”; the authoring tool allows a user to create document for the lesson, template and question); and
determining a plurality of entities included in the received document, wherein generating the one or more questions applicable to the speech using the determined plurality of entities (Elchik, [0073]; figs. 6 – 10; [0078]) comprises:
determining a logical relationship between the plurality of entities included in the received document (Elchik, [0012]; [0073], “from Wikipedia containing information about The Beatles. Referring to FIGS. 8 and 9, in a vocabulary processing process the system may generate a list of most frequently appearing words 801 in the content, and it may attach a part of speech (POS) 802 and definition 803 to each word of the list, using part-of-speech tagging and by looking up definitions in a local or online database”; [0059]; [0063]; [0010], “the lesson generation engine may generate a correct definition and one or more foils that are false definitions”); and
generating the one or more questions applicable to the speech based on the logical relationship (Elchik, [0060]).
11. The device of claim 9, wherein the processor is further configured to: control a user interface to receive a document from a user, the user interface being coupled to the processor; determine a plurality of entities included in the received document; determine a logical relationship between the plurality of entities included in the received document; and generate the one or more questions applicable to the speech based on the logical relationship (see claim 3 above).
Re claims 4, 12:
4. The method of claim 1, further comprising:
generating a plurality of questions applicable to the speech using the identified plurality of entities (Elchik, Abstract, “the extracted content to identify one or more topics, parts of speech, named entities and/or other material in the content”; [0005]; [0047]; [0058]), receiving a user input for selecting one of the plurality of questions, and displaying the video of the virtual audience uttering the question comprises displaying the video of the virtual audience uttering the selected question (Elchik, [0060]; [0013]).
12. The device of claim 9, wherein the processor is further configured to: generate a plurality of questions applicable to the speech using the identified plurality of entities; control a user interface to receive a user input for selecting one of the plurality of questions, the user interface being coupled to the processor; and display the virtual audience uttering the selected question (See claim 4 above).
Re claims 7 – 8, 20;
7. The method of claim 1, further comprising: receiving an answer from the user in response to the question; and outputting response information for the virtual audience in response to the answer of the user. 20. The device of claim 9, wherein the processor is further configured to: receive an answer of the user to the question, and output response information of the virtual audience to the answer of the user (Elchik, [0047]; [0058]).
8. The method of claim 7, further comprising determining an answer to the question, and outputting the response information for the virtual audience based on a degree to which the answer of the user is similar to the determined answer (Elchik, [00013], “proficiency score for the user”; [0060], “system may analyze a set of responses from a user to determine a language (or other skill) proficiency score for the user, and it may generate and present the user with a new question that has a skill level that corresponds to the proficiency score”; [0012], “the system may output an indication of correctness and advance to a next question; otherwise, the system may output an indication of incorrectness”).
Re claim 18:
18. The device of claim 9, wherein the processor is further configured to: determine a logical relationship between a pair of entities among the identified plurality of entities; and generate the one or more questions applicable to the speech based on the logical relationship (Elchik, [0070], “identifying and extracting named entities, and also may include natural language parsing methods, such as phrase-structure parsers, dependency parsers and semantic parsers”; [0064], “(a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E”; the entity A, action B and slocation D are in logical relationship; [0047]; [0058]).
Re claims 17, 19:
17. The method of claim 1, further comprising determining a difficulty level of the generated questions, wherein displaying the providing the virtual audience uttering the question comprises: determining a profile corresponding to the determined difficulty level; and displaying the virtual audience so that the virtual audience corresponding to the determined profile utters the question (Elchik, [0060], “determine a language (or other skill) proficiency score for the user, and it may generate and present the user with a new question that has a skill level that corresponds to the proficiency score”; [0071], “Lessons are guided by lesson templates that specify the kind of information, the quantity, the format, and/or the sequencing and the presentation mode, depending on the input material and the level of difficulty”).
19. The device of claim 9, wherein the processor is further configured to: determine a difficulty level of the generated question, determine a profile corresponding to the determined difficulty level, and displaying the virtual audience so that the virtual audience corresponding to the determined profile utters the question (See claim 17 above).
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Elchik in view of Sharma as applied to claims 1 and 9 above, and further in view of Chen et al. (US 2020/0007380 A1).
Re claims 5, 13:
Elchik does not explicitly disclose further comprising: detecting a trigger text in the converted text; and providing the virtual audience uttering the question when the trigger text is detected. Chen teaches the deficiency; specifically, detecting a trigger text in the converted text; and displaying the virtual audience uttering the question when the trigger text is detected (Chen, fig. 1, 116 - “Yep”, 120 - “The Third One” – trigger words). Therefore, in view of Chen, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/device described in Elchik, by providing the trigger words as taught in Chen, since it was known in the art to provide a response (trigger text) from a user to initiate next prompt during a conversation.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Elchik in view of Sharma as applied to claims 1 and 9 above, and further in view of Herold et al. (US 7603413 B1).
Re claims 6, 14:
The combination of Elchik and Mizrahi does not explicitly disclose a wait period to initial a generated question. Herold teaches an automated agent, such as an instant message robot, is be used to facilitate introduction of a chat participant to a small group of other chat participants in a chat room. Herold further teaches further comprising: calculating a time during which the voice signal is not continuously received; and displaying the virtual audience uttering the question when the calculated time exceeds a previously determined threshold time (Herold, col. 3, lines 9 – 15, “a BOT may be used to monitor conversation among chat participants in a chat room. For example, a BOT may monitor conversation in an icebreaker group and, when a pause of a predetermined length is detected, the BOT may interject a question or comment to help stimulate additional conversation between the chat participants in the icebreaker group”). Therefore, in view of Herold, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method/device described in Elchik, by providing the wait period as taught by Herold, in order to interject a question or comment to help stimulate additional conversation between the chat participants in the icebreaker group (Herold, col. 3, lines 9 - 15).
Claims 21, 22 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Elchik in view of Sharma as applied to claims 1 and 9 above, and further in view of Doyle (US 10467122 B1) and Barborak et al. (US 2017/0371861 A1).
Re claims 21, 22, 24:
Elchik teaches the device of claim 9, wherein: the processor is further configured to determine the logical relationship based on patterns between the plurality of entities included in the received document using the one or more relation templates fetched from the binary relationship template database (Elchik, [0047], “After selecting the language learning template 206, the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise. The system may obtain a template for the exercise from a data storage facility containing candidate exercises such as (1) questions and associated answers, (2) missing word exercises, (3) sentence scramble exercises, and (4) multiple choice questions. The content of each exercise may include blanks in which named entities, parts of speech, or words relevant to the topic may be added”; [0057], “the system generates a multiple-choice question in which the part of speech (salary, a noun) is converted to a blank in the prompt”; [0058]; [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”; according to applicant’s specification, binary relationship template refers to semantic parts in a sentence).
24. The method of claim 1, further comprising: identifying the plurality of entities included in the received document using the machine-learning model that identifies a binary relation between entities in the plurality of entities and constructs chunkers segmenting and labeling multi-token sequences in the converted text to identify a type of the plurality of entities, the multi-token sequence being used to break the converted text to words (Elchik, [0047], “After selecting the language learning template 206, the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic, and/or in which the named entity or part of speech is part of the question, answer or other component of the exercise. The system may obtain a template for the exercise from a data storage facility containing candidate exercises such as (1) questions and associated answers, (2) missing word exercises, (3) sentence scramble exercises, and (4) multiple choice questions. The content of each exercise may include blanks in which named entities, parts of speech, or words relevant to the topic may be added”; [0057], “the system generates a multiple-choice question in which the part of speech (salary, a noun) is converted to a blank in the prompt”; [0058]; [0059], “the system may select one or more words from the content source that are based on the part of speech of a word in the definition such as plural noun, adjective (superlative), verb (tense) or other criteria, and include those words in the foil definition”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”; according to applicant’s specification, binary relationship template refers to semantic parts in a sentence).
Elchik further teaches identifies chunkers segmenting and labeling multi-token sequences to identify a type of the plurality of first and second entities (Elchik, [0008], “parse the text output to identify the language of the text output, the named entity, and/or the one or more parts of speech.”; [0064], “(2) cast the events into a standard template, via dependency parsing or a similar process, thus producing, for example: (a) Entity A did action B to entity C in location D, or (b) Entity A did action B which resulted in consequence E. The system may then (3) automatically create a fill-in-the-blank, multiple choice or other question based on the standard template”).
Elchik does not explicitly disclose the machine-learning model automatically learns the patterns from a training corpus and identifies chunkers, wherein a type of the plurality of entities is identified based on the chunkers that segment and label multi-token sequences in the converted text, and wherein the chunkers are constructed based on a rule-based operation using intervening words. Doyle teaches a capturing and classification of digital data and providing recommendations in real-time or nearly real-time.
The combination of Sharma and Doyle further teaches the machine-learning model automatically learns the patterns from a training corpus and identifies chunkers, wherein a type of the plurality of entities is identified based on the chunkers that segment and label multi-token sequences in the converted text, and wherein the chunkers are constructed based on a rule-based operation using intervening words. claim 24. identifying the plurality of entities included in the received document using the machine-learning model (Doyle, col. 22, line 45 – col. 23, line 57 and Barborak, fig. 7, “verb predicate”, “subject argument”, “object argument”; [0120], “A syntactic parse is similar to sentence diagramming that students learn in grammar school, where verbs, nouns, and such are structurally related”; [0164], “words of the sentence that are tagged or annotated with syntactic labels describing the words”). Therefore, in view of Doyle and Barborak, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system and method described in Elchik, by providing ML as taught by Doyle, since The machine learning module may be supplemented with active learning data to adjust or fine tune one or more data models that are subsequently used in processing the plurality of data sets for identification of terms of interest and in resolving or determining undetermined terms or terms associated with relatively lower accuracy measures ( e.g., confidence scores, confidence intervals, or any other appropriate measures or metrics) (Doyle, col. 23, lines 39 - 57).
Response to Arguments
Applicant's arguments filed 12/15/2025 have been fully considered but they are not persuasive.
Applicant argues: Claim 1, as amended, renders the rejection moot because the proposed combination of references as cited in the Office Action does not teach or suggest at least to "identifying a plurality of entities included in the received document using a machine-learning model, each of the plurality of entities being a noun or a noun phrase in the received document and referring to an object; based on the entity being detected as an invoke word in the speech after generating the one or more questions, displaying, through a display, a video of the virtual audience comprising at least one character uttering a question among the one or more questions including the entity" as recited in Claim 1.
The newly cited reference teaches an invoke word in the speech or a trigger word in Sharma (US 2021/0314282 A1).
Conclusion
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/JACK YIP/Primary Examiner, Art Unit 3715
