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 .
Priority
Receipt is acknowledged that application is a National Stage application of PCT/SG2023/050570. Priority to PCT/SG2023/050570 with a priority date of 08/19/2022 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
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.
Claim(s) 1-3, 6-7, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elchik et al.. (US Patent No. 20170213469) in view of Colas et al.. (US Patent No. 20200380030) and Jayasiriwardene et al.
Regarding claim 1 and substantially similar limitations in claim 17, Elchik et al., henceforth Elchik, discloses “a method of creating an interactive class, comprising: processing content of a class to identify one or more topics covered by the content (see paragraph 7: “[…] a system analyzes streaming video and an associated audio or text channel and automatically generates a learning exercise based on data extracted from the channel […] The content analysis engine includes programming instructions that are configured to cause the processing device to extract text corresponding to words spoken or captioned in the channel and identify […] one or more topics”) […] processing each topic to produce a learning intervention corresponding to the topic (see paragraph 47: “[…] the system automatically generates a lesson 207 by automatically generating questions or other exercises in which the exercise is relevant to the topic”).”
However, although Elchik discloses that the questions generated by the system are “served […] to the user's media presentation device in a time frame that is temporally relevant to the user's consumption of the digital media asset,” Elchik is silent on specifically whether their system comprises “processing content of a class to identify […] a timestamp corresponding to each said topic,” “creating the interactive class by embedding each learning intervention at the corresponding timestamp in the class,” and “the interactive class being configured such that on a user progressing through the interactive class to a first said timestamp the interactive class is paused for display of the learning intervention corresponding to the first timestamp.”
Colas et al., henceforth Colas, teaches a system for segmenting video tutorials using sentence-level encoding to identify segments of a video containing information which answers a query provided by a user. Because Elchik and Colas are all directed to systems which use natural language processing to derive semantic meaning from video content and generate or provide informative and/or educational content based on this processing, they are considered to belong to the same field of art. Notably, Colas teaches segmenting a video by topic and generating a table of contents corresponding to the topics covered in the video (see paragraph 43, “Each video in data store 104 may include an associated table of contents that points to different topics covered at different section of the video […] any known method of automatically segmenting videos into individual topics may be used to generate table of contents”), which reads on the limitation of “processing content of a class to identify […] a timestamp corresponding to each said topic.”
Jayasiriwardene et al., henceforth Jayasiriwardene, teaches an adaptive algorithm for dynamically recommending interactive assessments to learners based on the learners’ competency. Because Elchik and Jayasiriwardene are both directed to systems for generating knowledge-assessment questions based on educational content, serving these questions to users, and evaluating their responses, they are considered to belong to the same field of art. Notably, Jayasiriwardene teaches embedding interactive exercises at different timestamps in a video lesson, where these interactive exercises assess students’ understanding of the topic discussed during the segment of the video corresponding to the timestamp (see Section III: “While the student watches a video lesson, the activity manager will monitor for the defined time points. Then it pop-ups interactive activities that the user is expected to answer for the video to continue. When such a timestamp is reached, it fetches the relevant activity from the database where the lessons are ontologically mapped and stored”), which reads on the recited limitations of “creating the interactive class by embedding each learning intervention at the corresponding timestamp in the class” and “the interactive class being configured such that on a user progressing through the interactive class to a first said timestamp the interactive class is paused for display of the learning intervention corresponding to the first timestamp.”
Therefore, 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 the system of Elchik to segment the educational video content processed by the system to segment the video by topic (thereby identifying a timestamp corresponding to each topic), as taught by Colas, and to insert the knowledge-assessment questions generated by the system into the video at timestamps corresponding to the topic addressed by the question, as taught by Jayasiriwardene, and finally to wait until the user provides a response to the question before resuming the video, as taught by Jayasiriwardene, and they would have been motivated to do so to ensure that the questions generated by the system are served to the user in a time frame that is temporally relevant to the user’s consumption of the educational video content, as taught by Elchik, with reasonable expectation of success.
Regarding claim 2 and substantially similar limitations in claim 18, Elchik (in view of Colas and Jayasiriwardene) discloses “the method of claim 1, wherein processing content of a class to identify one or more topics covered by the content and a timestamp corresponding to each said topic, comprises processing a transcript of the class using a natural language processing (NLP) algorithm to identify the one or more topics (see Elchik paragraph 45: “the system may process an encoded data component that contains closed captions by […] parsing the closed captions to identify […] the topic […] Suitable engines for assisting with these tasks include the Stanford Parser, the Stanford CoreNLP Natural Language Processing ToolKit”).”
Regarding claim 3, Elchik (in view of Colas and Jayasiriwardene) discloses “the method of claim 1, further comprising analysing the class using at least one machine learning model to produce the content of the class, the content comprising a transcript of the class (see Elchik paragraph 45: “The system may extract this information 203 from the content using any suitable content analysis method. For example, the system may process an audio track of the video with a speech-to-text conversion engine to yield text output”).”
Regarding claim 6, Elchik (in view of Colas and Jayasiriwardene) discloses “the method of claim 1.”
However, Elchik (in view of Jayasiriwardene) is silent on “processing content of a class to identify one or more topics covered by the content and a timestamp corresponding to each said topic, comprises determining a time point at which teaching of each topic concludes, the timestamp for each respective topic being the time point at which teaching of each topic concludes.”
Colas teaches a system for segmenting video tutorials using sentence-level encoding to identify segments of a video containing information which answers a query provided by a user. Because Elchik and Colas are both directed to systems which use natural language processing to derive semantic meaning from video content and generate or provide informative and/or educational content based on this processing, they are considered to belong to the same field of art. Notably, Colas teaches segmenting a video by topic and generating a table of contents corresponding to the topics covered in the video (see paragraph 43, “Each video in data store 104 may include an associated table of contents that points to different topics covered at different section of the video […] any known method of automatically segmenting videos into individual topics may be used to generate table of contents”). In particular, Colas’ system is capable of determining the exact time points in a video timeline at which an answer to a particular question begins and ends (see paragraph 36: “Video module 104 may display an answer span by itself or within a video with markings within a timeline of the video (e.g., highlighting, markers at start and end of the span, etc.) pointing to the span within the video that includes a potential answer to the query”). Thus, one of ordinary skill in the art would recognize that Colas’ technology would similarly be capable of “determining a time point at which teaching of each topic concludes.”
Therefore, it would have been obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the system of Elchik (in view of Colas and Jayasiriwardene) to insert the knowledge-assessment questions generated by the system at the timestamps in the educational video content at which teaching of each topic concludes, as taught by Colas, they would have been motivated to do so to ensure that the questions generated by the system are served to the user in a time frame that is temporally relevant to the user’s consumption of the educational video content, as taught by Elchik, with reasonable expectation of success.
Regarding claim 7, Elchik (in view of Colas and Jayasiriwardene) discloses “the method of claim 1, further comprising receiving class content and processing the class content to generate the interactive class (see Elchik abstract: “An automated lesson generation learning system extracts text-based content from a digital programming file […] The system then automatically generates and outputs a lesson containing content that is relevant to the content that was extracted from the digital programming file.”).”
Claim(s) 9-11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elchik et al. (US Patent No. 20170213469) in view of Colas et al. (US Patent No. 20200380030) and Jayasiriwardene et al. as applied to claims 7 and 17 above, and further in view of Ingel at al (US Patent No. 20200213680).
Regarding claim 9 and substantially similar limitations in claim 19, Elchik (in view of Colas and Jayasiriwardene) discloses “the method of claim 7.”
However, Elchik (in view of Colas and Jayasiriwardene) does not disclose “processing the class content comprises using a synthetic video generation model to generate a synthetic video corresponding to the class content.”
Ingel et al., henceforth Ingel, teaches a system for generating videos using a machine learning model based on the content of a script analyzed using natural language processing. Because Elchik and Ingel are both directed to the generation of educational and/or informative content based on natural language processing of digital video content (Ingel is directed to the generation of personalized weather forecasts in particular embodiments), they are considered to belong to the same field of art. Notably, Ingel teaches generating novel (synthetic) video content depicting an character (“avatar”) narrating a segment of a script while visually indicating an image discussed in the script (see paragraph 657: “In one example, a machine learning model may be trained using training examples to generate videos of images and characters presenting scripts and indicating particular regions of the images while presenting particular segments of the scripts from characteristics of characters, scripts, images, indications of the particular segments of the scripts, and indications of the particular regions of the images”). One of ordinary skill in the art would recognize that Ingel’s system would be perfectly suited for generating educational content, where the animated character could serve as a substitute for an instructor by directing viewers’ attention to different regions of an image containing educational content (such as an infographic) while verbally presenting the content of a script which expounds on the topic or concept represented by the image.
Therefore, 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 the system of Elchik (in view of Colas and Jayasiriwardene) to supplement the lessons generated by Elchik’s system based on textual content by additionally generating educational synthetic video content comprising an animated character or “avatar” reading said textual content, as taught by Ingel, and they would have been motivated to do so to provide users with the experience of receiving personal instruction on said content without requiring filming a human instructor presenting the content, with reasonable expectation of success.
Regarding claim 10, Elchik (in view of Colas, Jayasiriwardene, and Ingel) discloses “the method of claim 9, wherein processing the class content comprises using a NLP model to produce the content based on the class content (see Elchik paragraph 45: “the system may process an encoded data component that contains closed captions by […] parsing the closed captions to identify […] the topic […] Suitable engines for assisting with these tasks include the Stanford Parser, the Stanford CoreNLP Natural Language Processing ToolKit”).”
However, Elchik (in view of Colas and Jayasiriwardene) does not disclose “using the synthetic video generation model to generate the synthetic video based on the content.”
Ingel et al., henceforth Ingel, teaches a system for generating videos using a machine learning model based on the content of a script analyzed using natural language processing. Because Elchik and Ingel are both directed to the generation of educational and/or informative content based on natural language processing of digital video content (Ingel is directed to the generation of personalized weather forecasts in particular embodiments), they are considered to belong to the same field of art. Notably, Ingel teaches generating novel (synthetic) video content depicting an character (“avatar”) narrating a segment of a script while visually indicating an image discussed in the script (see paragraph 657: “In one example, a machine learning model may be trained using training examples to generate videos of images and characters presenting scripts and indicating particular regions of the images while presenting particular segments of the scripts from characteristics of characters, scripts, images, indications of the particular segments of the scripts, and indications of the particular regions of the images”). One of ordinary skill in the art would recognize that Ingel’s system would be perfectly suited for generating educational content, where the animated character could serve as a substitute for an instructor by directing viewers’ attention to different regions of an image containing educational content (such as an infographic) while verbally presenting the content of a script which expounds on the topic or concept represented by the image. It should be noted that Ingel, like Elichik in paragraph 45, also contemplates the application of natural language processing for analyzing textual information obtained from source audio data (see paragraphs 149, 161, and 163).
Therefore, 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 the system of Elchik (in view of Colas and Jayasiriwardene) to supplement the lessons generated by Elchik’s system based on textual content by additionally generating educational synthetic video content comprising an animated character or “avatar” reading said textual content, as taught by Ingel, and they would have been motivated to do so to provide users with the experience of receiving personal instruction on said content without requiring filming a human instructor presenting the content, with reasonable expectation of success.
Regarding claim 11, Elchik (in view of Colas, Jayasiriwardene, and Ingel) discloses “the method of claim 9.”
However, Elchik (in view of Colas and Jayasiriwardene) does not disclose “the synthetic video comprises an avatar reading the content.”
Ingel et al., henceforth Ingel, teaches a system for generating videos using a machine learning model based on the content of a script analyzed using natural language processing. Because Elchik and Ingel are both directed to the generation of educational and/or informative content based on natural language processing of digital video content (Ingel is directed to the generation of personalized weather forecasts in particular embodiments), they are considered to belong to the same field of art. Notably, Ingel teaches generating novel (synthetic) video content depicting an character (“avatar”) narrating a segment of a script while visually indicating an image discussed in the script (see paragraph 657: “In one example, a machine learning model may be trained using training examples to generate videos of images and characters presenting scripts and indicating particular regions of the images while presenting particular segments of the scripts from characteristics of characters, scripts, images, indications of the particular segments of the scripts, and indications of the particular regions of the images”). One of ordinary skill in the art would recognize that Ingel’s system would be perfectly suited for generating educational content, where the animated character could serve as a substitute for an instructor by directing viewers’ attention to different regions of an image containing educational content (such as an infographic) while verbally presenting the content of a script which expounds on the topic or concept represented by the image.
Therefore, 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 the system of Elchik (in view of Colas and Jayasiriwardene) to supplement the lessons generated by Elchik’s system based on textual content by additionally generating educational synthetic video content comprising an animated character or “avatar” reading said textual content, as taught by Ingel, and they would have been motivated to do so to provide users with the experience of receiving personal instruction on said content without requiring filming a human instructor presenting the content, with reasonable expectation of success.
Claim(s) 12-13, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elchik et al. (US Patent No. 20170213469) in view of Colas et al. (US Patent No. 20200380030) and Jayasiriwardene et al. as applied to claims 1 and 17 above, and further in view of Boguraev at al (US Patent No. 20170293680).
Regarding claim 12 and substantially similar limitations in claim 20, Elchik (in view of Colas and Jayasiriwardene) discloses “the method of claim 1.”
However, Elchik (in view of Colas and Jayasiriwardene) does not disclose “receiving a student input from the user; processing the student input to identify a query; and generating a response to the query using a response model.”
Boguraev et al., henceforth Boguraev, teaches a question-answering natural language processing system which receives input text representing a query from a user, processes the text to identify the query, and generates an appropriate response (see Fig. 2 and paragraph 59: “As shown in FIG. 2, QA processing pipeline 200 includes a set of stages 210-280 through which QA processing pipeline 200 operates to analyze an input question and generate a final response. In a question input stage 210, QA processing pipeline 200 receives an input question (for example, input text 110 in FIG. 1) that is presented in a natural language format […] the next stage of QA processing pipeline 200, i.e. the question and topic analysis stage 220, parses the input question using natural language processing (NLP) techniques […]”). The NLP question-answer systems discussed by Boguraev directly read on the limitations of “receiving a [input] from the user; processing the […] input to identify a query, and generating a response to the query using a response model.”
One of ordinary skill in the art would recognize that one of the most integral aspects of any pedagogical or educational program involves providing an opportunity for students to ask and receive answers to questions, preferably by an instructor or subject matter expert who can answer the students’ questions in a satisfying and informative manner. Consequently, it would be apparent to one of ordinary skill in the art that a system which seeks to provide educational content to a large virtual userbase (such as that of Elchik), which might otherwise struggle to find enough individual instructors or subject matter experts to service their expansive userbase, would benefit immensely from the capability provided by Boguraev’ system of automatically processing and generating an answer in response to student questions without human input or assistance.
Therefore, 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 the system of Elchik (in view of Colas and Jayasiriwardene) to incorporate a question-answering natural language processing system, as taught by Boguraev, and they would have been motivated to do so to allow the system to provide detailed and informative responses to user questions regarding the educational content generated and presented by the system without human input or assistance, with reasonable expectation of success.
Regarding claim 13, Elchik (in view of Colas, Jayasiriwardene, and Boguraev) discloses “the method of claim 12.”
However, Elchik (in view of Jayasiriwardene) does not disclose “processing the student input to identify a query comprises one of a speech-to-text model to a verbal input from the user, and applying a NLP model to a text input from the user, to identify a query corresponding to the verbal input or text input.”
Boguraev et al., henceforth Boguraev, teaches a question-answering natural language processing system which receives input text representing a query from a user, processes the text to identify the query, and generates an appropriate response (see Fig. 2 and paragraph 59: “As shown in FIG. 2, QA processing pipeline 200 includes a set of stages 210-280 through which QA processing pipeline 200 operates to analyze an input question and generate a final response. In a question input stage 210, QA processing pipeline 200 receives an input question (for example, input text 110 in FIG. 1) that is presented in a natural language format […] the next stage of QA processing pipeline 200, i.e. the question and topic analysis stage 220, parses the input question using natural language processing (NLP) techniques […]”). The NLP question-answer systems discussed by Boguraev directly read on the limitations of “receiving a [input] from the user; processing the […] input to identify a query, and generating a response to the query using a response model.”
One of ordinary skill in the art would recognize that one of the most integral aspects of any pedagogical or educational program involves providing an opportunity for students to ask and receive answers to questions, preferably by an instructor or subject matter expert who can answer the students’ questions in a satisfying and informative manner. Consequently, it would be apparent to one of ordinary skill in the art that a system which seeks to provide educational content to a large virtual userbase (such as that of Elchik), which might otherwise struggle to find enough individual instructors or subject matter experts to service their expansive userbase, would benefit immensely from the capability provided by Boguraev’ system of automatically processing and generating an answer in response to student questions without human input or assistance.
Therefore, 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 the system of Elchik (in view of Colas and Jayasiriwardene) to incorporate a question-answering natural language processing system, as taught by Boguraev, and they would have been motivated to do so to allow the system to provide detailed and informative responses to user questions regarding the educational content generated and presented by the system without human input or assistance, with reasonable expectation of success.
Regarding claim 15, Elchik (in view of Colas, Jayasiriwardene, and Boguraev) discloses “the method of claim 12.”
However, Elchik (in view of Jarasiriwardene and Boguraev) does not disclose “generating a response to the query using a response model comprises at least one of identifying a video corresponding to the query and displaying the video to the user, and generating a text or audio response to the query and outputting the text or audio response to the user.”
Colas teaches a system for segmenting video tutorials using sentence-level encoding to identify segments of a video containing information which answers a query provided by a user. Because Elchik and Colas are both directed to systems which use natural language processing to derive semantic meaning from video content and generate or provide informative and/or educational content based on this processing, they are considered to belong to the same field of art. Notably, Colas teaches identifying and displaying videos corresponding to a vocal or written query provided by a user (see paragraph 17, “In operation and at a high level, a neural network may be used to determine a video and an associate span within the video that answers a question asked by a user via a natural language text or voice query within an application. The neural network may identify a video tutorial and a span (i.e., a start and an end sentence) within the video tutorial that includes an answer to the question”), which reads on the limitation of “[…] identifying a video corresponding to the query and displaying the video to the user.”
One of ordinary skill in the art would recognize that one of the most integral aspects of any pedagogical or educational program involves providing an opportunity for students to ask and receive answers to questions, preferably by an instructor or subject matter expert who can answer the students’ questions in a satisfying and informative manner. Consequently, it would be apparent to one of ordinary skill in the art that a system which seeks to provide educational content to a large virtual userbase (such as that of Elchik), which might otherwise struggle to find enough individual instructors or subject matter experts to service their expansive userbase, would benefit immensely from the capability provided by Colas’ system of automatically processing and generating an answer in response to student questions without human input or assistance.
Therefore, 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 the system of Elchik (in view of Colas, Jayasiriwadene, and Boguraev) to identify and display videos corresponding to user queries in response to said queries, as taught by Colas, and they would have been motivated to do so to allow the system to provide detailed and informative responses to user questions regarding the educational content generated and presented by the system without human input or assistance, with reasonable expectation of success.
Claim(s) 16 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elchik et al. (US Patent No. 20170213469) in view of Colas et al. (US Patent No. 20200380030) and Jayasiriwardene et al. as applied to claims 1 and 17 above, and further in view of Levinson (US Patent No. 20190221131).
Regarding claim 16 and substantially similar limitations in claim 21, Elchik (in view of Colas and Jayasiriwardene) discloses “the method of claim 1, wherein the interactive class is a first video of a plurality of videos […] the method comprising: […] collecting a response from [a] viewer to [a] learning intervention; and […] selecting one or more respective videos from the plurality of videos to display to the viewer based on the response of the viewer (see paragraph 13: “[…] the system also may be programmed to analyze a set of responses from a user to determine a language proficiency score for the user. If so, the system may identify an additional video that is available at the remote video server and that has a language level that corresponds to the language proficiency score. The system may cause the video presentation engine to cause a display device to output the additional video […]”).”
However, Elchik (in view of Colas and Jayasiriwadene) is silent on “the user is one of a plurality of viewers” and “synchronizing display of the first video to the plurality of viewers; identifying each timestamp in the first video; synchronizing display of each learning intervention to the viewers at each timestamp; collecting a response from each viewer to each learning intervention; and for each viewer, selecting one or more respective videos from the plurality of videos to display to the viewer based on the response of the viewer.”
Colas teaches a system for segmenting video tutorials using sentence-level encoding to identify segments of a video containing information which answers a query provided by a user. Because Elchik and Colas are both directed to systems which use natural language processing to derive semantic meaning from video content and generate or provide informative and/or educational content based on this processing, they are considered to belong to the same field of art. Notably, Colas teaches segmenting a video by topic and generating a table of contents corresponding to the topics covered in the video (see paragraph 43, “Each video in data store 104 may include an associated table of contents that points to different topics covered at different section of the video […] any known method of automatically segmenting videos into individual topics may be used to generate table of contents”), which reads on the limitation of “identifying each timestamp in the first video.”
Jayasiriwardene, teaches an adaptive algorithm for dynamically recommending interactive assessments to learners based on the learners’ competency. Because Elchik and Jayasiriwardene are both directed to systems for generating knowledge-assessment questions based on educational content, serving these questions to users, and evaluating their responses, they are considered to belong to the same field of art. Notably, Jayasiriwardene teaches embedding interactive exercises at different timestamps in a video lesson, where these interactive exercises assess students’ understanding of the topic discussed during the segment of the video corresponding to the timestamp (see Section III: “While the student watches a video lesson, the activity manager will monitor for the defined time points. Then it pop-ups interactive activities that the user is expected to answer for the video to continue. When such a timestamp is reached, it fetches the relevant activity from the database where the lessons are ontologically mapped and stored”).
Therefore, 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 the system of Elchik to segment the educational video content processed by the system to segment the video by topic (thereby identifying a timestamp corresponding to each topic), as taught by Colas, and to insert the knowledge-assessment questions generated by the system into the video at timestamps corresponding to the topic addressed by the question, as taught by Jayasiriwardene, and finally to wait until the user provides a response to the question before resuming the video, as taught by Jayasiriwardene, and they would have been motivated to do so to ensure that the questions generated by the system are served to the user in a time frame that is temporally relevant to the user’s consumption of the educational video content, as taught by Elchik, with reasonable expectation of success.
Regarding the remaining limitations recited in the claim and not disclosed by Elchik (in view of Colas and Jayasiriwardene), Examiner notes that Elchik discloses the essential features of these limitations with respect to an individual user, with the only substantial difference being that the claimed invention contemplates accommodating a plurality of users simultaneously.
Levinson teaches a system and method for producing and providing customized user-specific educational digital media modules. Because Elchik and Levinson are both directed to the production and provision of educational digital media modules, they are considered to belong to the same field of art. Notably, Levinson teaches dividing a digital video file obtained from a first source into a plurality of segments defined by a plurality of timestamps and synchronizing supplemental content from a second source with the video so that they may be displayed simultaneously for a plurality of users (see paragraph 90).
One of ordinary skill in the art would certainly notice the marked similarities between the system of Levinson and that of Elchik (in view of Colas and Jayasiriwardene), which also discloses segmenting a digital video file according to a plurality of timestamps and inserting supplementary content. Owing to these similarities, it would not require extraordinary creativity to incorporate Levinson’s synchronization technology into the system of Elchik (in view of Colas and Jayasiriwardene), and this modification would be especially appealing considering it would expand the capabilities of the system to service a plurality of users simultaneously (see Levinson paragraph 90: “In some implementations, the system may include the ability to […] simultaneously display, for a plurality of users, the core media file 112 (e.g., video) from the first source and the supplemental content (e.g., text) from the second source).”
Therefore, it would have been obvious to one of ordinary skill in the art to have modified the system of Elchik (in view of Colas and Jayasiriwardene) to synchronize the educational digital video file displayed by the system with the supplementary content generated by the system (such as Elchik’s knowledge-assessment questions) so that the educational video and accompanying supplementary content are displayed simultaneously to a plurality of users, as taught by Levinson, and they would have been motivated to do so to increase the capacity of the system and improve its suitability for use in a classroom environment.
Claim(s) 36 and 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elchik et al. (US Patent No. 20170213469) in view of Colas et al. (US Patent No. 20200380030), Jayasiriwardene et al., and Levinson (US Patent No. 20190221131).
Regarding claim 36, Elchik discloses “a system of creating a virtual class based on a plurality of videos, comprising a memory and a plurality of processors configured to: […] collect a response from [a] viewer to the one or more learning interventions related to the first video to the viewers […] select one or more respective videos from the plurality of videos to display to the viewer based on the response of the viewer (see paragraph 13: “[…] the system also may be programmed to analyze a set of responses from a user to determine a language proficiency score for the user. If so, the system may identify an additional video that is available at the remote video server and that has a language level that corresponds to the language proficiency score. The system may cause the video presentation engine to cause a display device to output the additional video […]”).”
However, Elchik does not disclose “synchronise display of a first video to a plurality of viewers; identify one or more timestamps in the first video; synchronise display of one or more learning interventions related to the first video to the viewers at each of the one or more timestamps; collect a response from each viewer to the one or more learning interventions; and for each viewer, select one or more respective videos from the plurality of videos to display to the viewer based on the response of the viewer.”
Colas teaches a system for segmenting video tutorials using sentence-level encoding to identify segments of a video containing information which answers a query provided by a user. Because Elchik and Colas are both directed to systems which use natural language processing to derive semantic meaning from video content and generate or provide informative and/or educational content based on this processing, they are considered to belong to the same field of art. Notably, Colas teaches segmenting a video by topic and generating a table of contents corresponding to the topics covered in the video (see paragraph 43, “Each video in data store 104 may include an associated table of contents that points to different topics covered at different section of the video […] any known method of automatically segmenting videos into individual topics may be used to generate table of contents”), which reads on the limitation of “identifying each timestamp in the first video.”
Jayasiriwardene, teaches an adaptive algorithm for dynamically recommending interactive assessments to learners based on the learners’ competency. Because Elchik and Jayasiriwardene are both directed to systems for generating knowledge-assessment questions based on educational content, serving these questions to users, and evaluating their responses, they are considered to belong to the same field of art. Notably, Jayasiriwardene teaches embedding interactive exercises at different timestamps in a video lesson, where these interactive exercises assess students’ understanding of the topic discussed during the segment of the video corresponding to the timestamp (see Section III: “While the student watches a video lesson, the activity manager will monitor for the defined time points. Then it pop-ups interactive activities that the user is expected to answer for the video to continue. When such a timestamp is reached, it fetches the relevant activity from the database where the lessons are ontologically mapped and stored”).
Therefore, 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 the system of Elchik to segment the educational video content processed by the system to segment the video by topic (thereby identifying a timestamp corresponding to each topic), as taught by Colas, and to insert the knowledge-assessment questions generated by the system into the video at timestamps corresponding to the topic addressed by the question, as taught by Jayasiriwardene, and finally to wait until the user provides a response to the question before resuming the video, as taught by Jayasiriwardene, and they would have been motivated to do so to ensure that the questions generated by the system are served to the user in a time frame that is temporally relevant to the user’s consumption of the educational video content, as taught by Elchik, with reasonable expectation of success.
Regarding the remaining limitations recited in the claim and not disclosed by Elchik (in view of Colas and Jayasiriwardene), Examiner notes that Elchik discloses the essential features of these limitations with respect to an individual user, with the only substantial difference being that the claimed invention contemplates accommodating a plurality of users simultaneously.
Levinson teaches a system and method for producing and providing customized user-specific educational digital media modules. Because Elchik and Levinson are both directed to the production and provision of educational digital media modules, they are considered to belong to the same field of art. Notably, Levinson teaches dividing a digital video file obtained from a first source into a plurality of segments defined by a plurality of timestamps and synchronizing supplemental content from a second source with the video so that they may be displayed simultaneously for a plurality of users (see paragraph 90).
One of ordinary skill in the art would certainly notice the marked similarities between the system of Levinson and that of Elchik (in view of Colas and Jayasiriwardene), which also discloses segmenting a digital video file according to a plurality of timestamps and inserting supplementary content. Owing to these similarities, it would not require extraordinary creativity to incorporate Levinson’s synchronization technology into the system of Elchik (in view of Colas and Jayasiriwardene), and this modification would be especially appealing considering it would expand the capabilities of the system to service a plurality of users simultaneously (see Levinson paragraph 90: “In some implementations, the system may include the ability to […] simultaneously display, for a plurality of users, the core media file 112 (e.g., video) from the first source and the supplemental content (e.g., text) from the second source).”
Therefore, it would have been obvious to one of ordinary skill in the art to have modified the system of Elchik (in view of Colas and Jayasiriwardene) to synchronize the educational digital video file displayed by the system with the supplementary content generated by the system (such as Elchik’s knowledge-assessment questions) so that the educational video and accompanying supplementary content are displayed simultaneously to a plurality of users, as taught by Levinson, and they would have been motivated to do so to increase the capacity of the system and improve its suitability for use in a classroom environment.
Regarding claim 37, Elchik (in view of Colas, Jayasiriwardene, and Levinson) discloses “the system of claim 36.”
However, Elchik (in view of Colas and Jayasiriwardene) is silent on “the plurality of processors are configured to capture the first video from an original video according to the content of the learning interventions.”
Levinson teaches a system and method for producing and providing customized user-specific educational digital media modules. Because Elchik and Levinson are both directed to the production and provision of educational digital media modules, they are considered to belong to the same field of art. Notably, Levinson teaches selecting segments of a video for presentation to a user based on the user’s profile and a “customization rule set” (see paragraph 22), where in particular, the segments may be selected according to the user’s proficiency (see paragraph 28: In some implementations, customization tool 207 may be configured to select complementary digital media files 113, 114 from segments 115 according to a user's proficiency”). Because Elchik already discloses assessing the proficiency of a user based on their responses to a learning intervention and identifying videos to display to the user based on this proficiency (see paragraph 13: “[…] the system also may be programmed to analyze a set of responses from a user to determine a language proficiency score for the user. If so, the system may identify an additional video that is available at the remote video server and that has a language level that corresponds to the language proficiency score. The system may cause the video presentation engine to cause a display device to output the additional video […]), one of ordinary skill in the art would recognize that Levinson’s technology could readily be incorporated into the system of Elchik (in view of Colas and Jayasiriwardene) to identify individual segments of a video to display to the user based on their proficiency as determined by a learning intervention (the user’s response to Elchik’s knowledge-assessment questions) instead of an entire video, and this combination would read on the recited limitation of “the plurality of processors are configured to capture the first video from an original video according to the content of the learning interventions.”
Finally, Levinson also teaches a motivation for implementing this feature, that being addressing individual student needs more directly by customizing the educational content served to the user based on their proficiency (see paragraph 28: “A user of higher proficiency may receive media files that explain concepts in greater depth and/or at a higher level of abstraction. Thus, even though two users may be studying the same lesson, materials provided to the users may differ according to the users' demonstrated abilities”).
Therefore, 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 the system of Elchik (in view of Colas, Jayasiriwardene, and Levinson) to identify individual segments of a video to display to a user based on their responses to knowledge-assessment questions generated, as taught by Levinson, and they would have been motivated to do so to provide a more personalized learning experience tailored to each user’s individual proficiency, as taught by Levinson.
Claim(s) 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elchik et al. (US Patent No. 20170213469) in view of Colas et al. (US Patent No. 20200380030), Jayasiriwardene et al., and Levinson (US Patent No. 20190221131) as applied to claim 36 above, and further in view of Pandit et al. (US Patent No. 20210073664).
Regarding claim 44, Elchik (in view of Colas, Jayasiriwardene, and Levinson) discloses “the system of claim 36.”
However, although Elchik discloses matching lessons with students based on their individual user profiles (see paragraph 72: “Finally, lessons are coupled with extracted topics and matched with the profiles of users 606 (students) so that the appropriate lessons may be routed to the appropriate users”), Elchik (in view of Colas, Jayasiriwardene, and Levinson) is silent on “the plurality of processors are configured to stream the viewers to a plurality of groups based on the response and/or behavior of each viewer.”
Pandit et al., henceforth Pandit, teaches an adaptive learning platform which combines a plurality of users into clusters based on the users’ comprehension of a topic, wherein the users’ responses are analyzed using natural language processing (see paragraph 2: “Embodiments of the present invention may include combining a plurality of users of an adaptive learning platform into one or more clusters based on an initial questionnaire. Embodiments of the present invention may include monitoring key events of the plurality of users, wherein the key events are indicative of a level of comprehension of a topic for the plurality of users”). Because Elchik and Pandit are both directed to assessing the proficiency of a user in an educational context based on their responses to questions served to the user by the system, they are considered to belong to the same field of art. One of ordinary skill in the art would recognize that the knowledge-assessment questions generated by Elchik’s system, already being used for assessing student comprehension of a topic based (see paragraph 13: “Optionally, the system also may be programmed to analyze a set of responses from a user to determine a language proficiency score for the user”), would be perfectly suitable as a replacement for the questionnaires of Pandit’s system, and that the proficiency score calculated by Elchik’s system could be used to cluster students into groups based on this proficiency score, as taught by Pandit.
Therefore, 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 the system of Elchik (in view of Colas, Jayasiriwardene, and Levinson) to use the proficiency scores calculated by the system based on users’ responses to questions generated by the system to cluster users into groups based on these proficiency scores, as taught by Pandit, and they would have been motivated to do so to group the user with other users of a similar proficiency level, allowing for targeted instruction tailored based on the students’ proficiency level.
Additional References
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
US Patent No. 20210233570 to Talbot et al.; and
US Patent No. 20200302817 to Williams et al.
Conclusion
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/K.H.J./
Examiner, Art Unit 3715
/WILLIAM H MCCULLOCH JR/Primary Examiner, Art Unit 3715
