# US 11,122,343 B2 · Personalized video interjections based on learner model and learning objective > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,122,343 B2 - Document ID: US11122343B2 - Status: Granted - Kind: B2 - Application number: 16/674,725 - Filed: 2019-11-05 - Published: 2020-02-27 - Granted: 2021-09-14 - Earliest claimed: 2018-07-19 - Assignees: International Business Machines Corporation; Sesame Workshop - Applicants: International Business Machines Corporation; Sesame Workshop - Inventors: Ravindranath Kokku; Tamer E. Abuelsaad; Prasenjit Dey; Jodi M. Cutler; Allison C. Allain; Aditya Sinha; Satyanarayana V. Nitta; Miles Ludwig; Emily Reardon; Nick Bartzokas; James Gray; Michelle Newman-Kaplan - Web page: https://patentgrep.com/patent/US11122343B2 ## CPC - H04N 21/84: Generation or processing of descriptive data, e.g. content descriptors {(systems specially adapted for using meta-information in broadcast systems H04H60/73)} - G06F 16/447: {Temporal browsing, e.g. timeline} - G06F 16/78: Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually - G06V 10/7784: {based on feedback from supervisors} - G06V 10/7788: {the supervisor being a human, e.g. interactive learning with a human teacher} - G06V 20/41: {Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items (segmenting video sequences G06V20/49)} - G06V 20/46: {Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames} - G09B 5/00: Electrically-operated educational appliances (working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F) - H04N 21/44008: {involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream (arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59)} - H04N 21/458: Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming stream; Updating operations, e.g. for OS modules {; time-related management operations (arrangements for replacing or switching information during the broadcast or during the distribution H04H20/10)} - H04N 21/466: Learning process for intelligent management, e.g. learning user preferences for recommending movies {(services using the results of monitoring in broadcast systems H04H60/61)} - H04N 21/845: Structuring of content, e.g. decomposing content into time segments - H04N 21/8545: for generating interactive applications - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B) ## Abstract Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations. ## Claims ### Claim 1 A computer implemented method for adding interjections to a video, comprising: analyzing content of each of a plurality of videos based on a set of learning objectives; selecting one of the plurality of videos based on a learning objective; automatically determining a location for each of at least one type of video interjection using an analytics engine; generating a video interjection for each of the at least one video interjection type; and inserting the at least one video interjection into the selected video at the respective determined location. ### Claim 2 (depends on 1) The computer implemented method of claim 1, wherein selecting one of the plurality of videos includes selecting a video from a sub-set of videos consisting of one or more of videos previously watched for the learning objective. ### Claim 3 (depends on 1) The computer implemented method of claim 1, wherein generating a video interjection for each of the at least one video interjection type includes matching words of the learning objective with educational content metadata of the selected video. ### Claim 4 (depends on 3) The computer implemented method of claim 3, wherein matching words of the learning objective with educational content metadata of the selected video includes using a web of words. ### Claim 5 (depends on 1) The computer implemented method of claim 1, further including generating educational content metadata for each of the plurality of videos; storing the educational content metadata on a timeline for each of the plurality of videos; and automatically determining the at least one type of video interjection for the selected video by using the educational content metadata and wherein determining a location for each of the at least one determined type of video interjection includes using the timeline. ### Claim 6 (depends on 5) The computer implemented method of claim 5, wherein automatically determining at least one type of video interjection and automatically determining a location for each of the at least one type of video interjection further includes using a web of words. ### Claim 7 (depends on 1) The computer implemented method of claim 1, wherein automatically generating a video interjection includes generating one of an active listening mode interjection and a contextual assessment mode interjection. ### Claim 8 (depends on 1) The computer implemented method of claim 1, further including automatically determining the at least one type of video interaction using a learner model and dynamically adjusting the learner model based on video interactions of a user in response to a video playback of the selected video with the inserted at least one generated video interjection and dynamically changing the at least one type of video interjection based on the adjusted learner model. ### Claim 9 A computer system for adding interjections to a video, comprising: one or more computer processors; one or more non-transitory computer-readable storage media; program instructions, stored on the one or more non-transitory computer-readable storage media, which when implemented by the one or more processors, cause the computer system to comprise: an ingestion and tagging module configured to perform the steps of: analyzing content of each of a plurality of videos based on a set of learning objectives; and an analytics engine configured to perform the steps of: selecting one of the plurality of videos based on a learning objective; automatically determining a location for each of at least one type of video interjection; generating a video interjection for each of the at least one video interjection type; and inserting the at least one video interjection into the selected video at the respective determined location. ### Claim 10 (depends on 9) The computer system of claim 9, wherein selecting one of the plurality of videos includes selecting a video from a sub-set of videos consisting of one or more of videos previously watched for the learning objective. ### Claim 11 (depends on 9) The computer system of claim 9, wherein generating a video interjection for each of the at least one video interjection type includes matching words of the learning objective with educational content metadata of the selected video. ### Claim 12 (depends on 11) The computer system of claim 11, wherein matching words of the user specific learning objective with educational content metadata of the selected video includes using a web of words. ### Claim 13 (depends on 11) The computer system of claim 11, wherein the ingestion and tagging module is further configured to perform generating educational content metadata for each of the plurality of videos and storing the educational content metadata on a timeline for each of the plurality of videos; and the analytics engine is further configured to perform automatically determining the at least one type of video interjection for the selected video by using the educational content metadata and wherein determining a location for each of the at least one determined type of video interjection includes using the timeline. ### Claim 14 (depends on 13) The computer system of claim 13, wherein automatically determining at least one type of video interjection and automatically determining a location for each of the at least one type of video interjection further includes using a web of words. ### Claim 15 (depends on 9) The computer system of claim 9, wherein automatically generating a video interjection includes generating one of an active listening mode interjection and a contextual assessment mode interjection. ### Claim 16 (depends on 9) The computer system of claim 9, further including automatically determining the at least one type of video interactions using a learner model and dynamically adjusting the learner model based on video interactions of a user in response to a video playback of the selected video with the inserted at least one generated video interjection and dynamically changing the at least one type of video interjection based on the adjusted learner model. ### Claim 17 A non-transitory computer program product comprising: program instructions on a computer-readable storage medium, where execution of the program instructions using a computer causes the computer to perform a method for adding interjections to a video, comprising: analyzing the content of each of the plurality of videos based on a set of learning objectives; generating educational content metadata for each of the plurality of videos; selecting one of the plurality of videos based on a learning objective; automatically determining a location for each of at least one type of video interjection; generating a video interjection for each of the at least one video interjection type; and inserting the at least one video interjection into the selected video at the respective determined location. ### Claim 18 (depends on 17) The non-transitory computer program product of claim 17, wherein selecting one of the plurality of videos includes selecting a video from a sub-set of videos consisting of one or more of videos previously watched for the learning objective. ### Claim 19 (depends on 17) The non-transitory computer program product of claim 17, wherein generating a video interjection for each of the at least one video interjection type includes matching words of the learning objective with educational content metadata of the selected video using a web of words and the learner model and wherein automatically determining at least one type of video interjection and automatically determining a location for each of the at least one type of video interjection further includes using the web of words. ### Claim 20 (depends on 17) The non-transitory computer program product of claim 17, further including automatically determining the at least one type of video interactions using a learner model and dynamically adjusting the learner model based on video interactions of a user in response to a video playback of the selected video with the inserted at least one generated video interjection and dynamically changing the at least one type of video interjection based on the adjusted learner model.