# US 2019/0318252 A1 · Media object grouping and classification for predictive enhancement > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0318252 A1 - Document ID: US20190318252A1 - Status: Published application - Kind: A1 - Application number: 15/953,299 - Filed: 2018-04-13 - Published: 2019-10-17 - Earliest claimed: 2018-04-13 - Assignees: Fuji Xerox Co., Ltd. - Applicants: Fuji Xerox Co., Ltd. - Inventors: David Ayman SHAMMA; Lyndon KENNEDY; Francine CHEN; Yin-Ying CHEN - Web page: https://patentgrep.com/patent/US20190318252A1 ## CPC - G06N 5/04: Inference or reasoning models - G06F 16/55: Clustering; Classification - G06F 16/5866: {using information manually generated, e.g. tags, keywords, comments, manually generated location and time information} - 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 20/49: {Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes} - G06N 20/00: Machine learning - G06V 20/44: {Event detection} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A computer implemented method of grouping media objects is provided, as well as systems, interfaces and devices therefor. The method includes generating a group from the media objects based on a combination of a script of sequential events and an actor associated with one or more of the media objects in the script, segmenting the group into segments each including one or more of the media objects, based on clustering or classification, providing titling and captioning for the segments, and generating filter and annotation recommendations based on knowledge associations in the media objects, data, and the combination of the script and the actor, across the media objects of the group. ## Claims ### Claim 1 A computer implemented method of grouping media objects, comprising: generating a group from the media objects based on a combination of a script of sequential events and an actor associated with one or more of the media objects in the script; segmenting the group into segments each including one or more of the media objects, based on clustering or classification; providing titling and captioning for the segments; and generating recommendations based on knowledge associations in the media objects, data, and the combination of the script and the actor, across the media objects of the group. ### Claim 2 (depends on 1) The computer implemented method of claim 1, wherein the recommendations comprise at least one of filter effect recommendations and sticker recommendations, and the recommendations are predictive based on data associated with a knowledge base. ### Claim 3 (depends on 2) The computer implemented method of claim 2, wherein the filter effect recommendations are based on the data including online feedback received from one or more other users. ### Claim 4 (depends on 2) The computer implemented method of claim 2, wherein the sticker recommendations are based on the data including information associated with the images. ### Claim 5 (depends on 2) The computer implemented method of claim 2, wherein the recommendations comprise one or more of a type, a location and a density of at least one of a sticker, a text box and an emoji. ### Claim 6 (depends on 1) The computer implemented method of claim 1, wherein the generating the group further comprises basing the grouping on a layout of the actor and one or more other actors in the group of media objects. ### Claim 7 A non-transitory computer readable medium including a processor configured to execute instructions stored in a storage, the instructions comprising: generating a group from the media objects based on a combination of a script of sequential events and an actor associated with one or more of the media objects in the script; segmenting the group into segments each including one or more of the media objects, based on clustering or classification; providing titling and captioning for the segments; and generating recommendations based on knowledge associations in the media objects, data, and the combination of the script and the actor, across the media objects of the group. ### Claim 8 (depends on 7) The non-transitory computer readable medium of claim 7, wherein the recommendations comprise at least one of filter effect recommendations and annotation recommendations, and the recommendations are predictive based on data associated with a knowledge base. ### Claim 9 (depends on 8) The non-transitory computer readable medium of claim 8, wherein the filter effect recommendations are based on the data including online feedback received from one or more other users. ### Claim 10 (depends on 8) The non-transitory computer readable medium of claim 8, wherein the sticker recommendations are based on the data including information associated with the images. ### Claim 11 (depends on 8) The non-transitory computer readable medium of claim 8, wherein the recommendations comprise one or more of a type, a location and a density of at least one of a sticker, a text box and an emoji. ### Claim 12 (depends on 7) The non-transitory computer readable medium of claim 7, wherein the generating the group further comprises basing the grouping on a layout of the actor and one or more other actors in the group of media objects. ### Claim 13 An image capture device configured to capture one or more images, and generate media objects, the image capture device including a processor and storage, the processor performing: generating a group from the media objects based on a combination of a script of sequential events and an actor associated with one or more of the media objects in the script; segmenting the group into segments each including one or more of the media objects, based on clustering or classification; providing titling and captioning for the segments; and generating recommendations based on knowledge associations in the media objects, data, and the combination of the script and the actor, across the media objects of the group. ### Claim 14 (depends on 13) The image capture device of claim 13, wherein the recommendations comprise at least one of filter effect recommendations and annotation recommendations, and the recommendations are predictive based on data associated with a knowledge base. ### Claim 15 (depends on 14) The image capture device of claim 14, wherein the filter effect recommendations are based on the data including online feedback received from one or more other users. ### Claim 16 (depends on 14) The image capture device of claim 14, wherein the sticker recommendations are based on the data including information associated with the images. ### Claim 17 (depends on 13) The image capture device of claim 13, wherein the recommendations comprise one or more of a type, a location and a density of at least one of a sticker, a text box and an emoji. ### Claim 18 (depends on 13) The image capture device of claim 13, wherein the generating the group further comprises basing the grouping on a layout of the actor and one or more other actors in the group of media objects.