# US 2022/0036614 A1 · Automatic generation of people groups and image-based creations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0036614 A1 - Document ID: US20220036614A1 - Status: Published application - Kind: A1 - Application number: 17/252,707 - Filed: 2020-06-25 - Published: 2022-02-03 - Earliest claimed: 2020-06-25 - Assignees: Google Inc. - Inventors: Dominick Lim; Kristi Bphl; Jason Chang; Vidya Valmikinathan; Taehee Lee; Jeremy Zhu - Web page: https://patentgrep.com/patent/US20220036614A1 ## CPC - G06V 40/173: {face re-identification, e.g. recognising unknown faces across different face tracks} - G06F 18/23: Clustering techniques - G06T 11/60: Creating or editing images; Combining images with text - G06V 20/00: Scenes; Scene-specific elements (control of digital cameras H04N23/60) - G06F 3/048: Interaction techniques based on graphical user interfaces [GUI] - G06F 16/55: Clustering; Classification - G06F 16/5866: {using information manually generated, e.g. tags, keywords, comments, manually generated location and time information} - G06F 16/75: Clustering; Classification - G06F 16/7867: {using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings} - G06F 18/23211: with adaptive number of clusters - G06Q 50/10: Services - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06T 2200/24: involving graphical user interfaces [GUIs] - G06T 2207/20221: Image fusion; Image merging - G06V 10/763: {Non-hierarchical techniques, e.g. based on statistics of modelling distributions} - G06V 20/30: in albums, collections or shared content, e.g. social network photos or video - G06V 20/47: {Detecting features for summarising video content} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Implementations described herein relate to methods, devices, and computer-readable media to generate and provide image-based creations. A computer-implemented method includes obtaining a plurality of episodes, each episode associated with a corresponding time period and including a respective set of images and person identifiers for each image. The method further includes forming a respective cluster for each episode that includes at least two person identifiers. The method further includes determining whether one or more person identifiers are included in less than a threshold number of clusters, and in response, removing the one or more person identifiers from the clusters that the one or more person identifiers that are included in. The method further includes merging identical clusters to obtain a plurality of people groups that each include two or more person identifiers and providing a user interface that includes an image-based creation based on a particular people group. ## Claims ### Claim 1 A computer-implemented method, the method comprising: obtaining a plurality of episodes, wherein each episode is associated with a corresponding time period, and includes a respective set of images and person identifiers for each image in the respective set of images; forming a respective cluster for each episode, wherein each cluster includes at least two person identifiers; determining whether one or more person identifiers are included in less than a threshold number of clusters; if it is determined that the one or more person identifiers are included in less than the threshold number of clusters, removing the one or more person identifiers from the clusters that the one or more person identifiers that are included in; after the removing, merging identical clusters to obtain a plurality of people groups, wherein each people group includes two or more person identifiers; and providing a user interface that includes an image-based creation based on a particular people group of the plurality of people groups. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein forming the respective cluster for each episode comprises: mapping all person identifiers that appear in at least one image in the set of images of the episode to the cluster; determining a number of images from the set of images of the episode that include a most frequent person identifier for the episode; and removing from the cluster person identifiers that are associated with less than a threshold number of images, the threshold number of images determined based on the number of images that include the most frequent person identifier for the episode. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising: after the merging, determining that at least one people group is associated with less than a threshold number of episodes; and in response to determination that at least one people group is associated with less than the threshold number of episodes, combining the at least one people group with one or more other people groups, wherein the one or more other people groups each include a subset of the person identifiers included in the at least one people group, and wherein providing the user interface is performed after merging the at least one people group. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein merging the identical clusters comprises associating, based on the clusters, a respective set of episodes to each of the plurality of people groups, and wherein providing the image-based creation comprises selecting a subset of images for the image-based creation from the respective sets of images included in the episodes associated with the particular people group, wherein each image in the subset of images depicts persons that correspond to at least two of the two or more person identifiers included in the particular people group. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein providing the user interface is performed in response to at least one of: detecting that an image that matches the particular group was captured recently; detecting that a current date matches a date associated with the particular people group; or detecting an event that matches the particular people group. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein providing the image-based creation comprises: selecting a subset of images for the image-based creation based on the particular people group, wherein each image in the subset of images depicts persons that correspond to at least two of the two or more person identifiers included in the particular people group; and generating the image-based creation based on the subset of images. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, wherein each image in the subset of images depicts persons that correspond to each of the two or more person identifiers included in the particular people group. ### Claim 8 (depends on 6) The computer-implemented method of claim 6, wherein each image in the subset of images depicts persons that correspond to at least two of the two or more person identifiers included in the particular people group. ### Claim 9 (depends on 6) The computer-implemented method of claim 6, wherein the subset of images includes images from at least two episodes of the plurality of episodes. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein the subset of images is selected such that the subset provides one or more of: location diversity, pose diversity, or visual diversity. ### Claim 11 A non-transitory computer-readable medium with instructions stored thereon that, when executed by a processor, cause the processor to perform operations comprising: obtaining a plurality of episodes, wherein each episode is associated with a corresponding time period, and includes a respective set of images and person identifiers for each image in the respective set of images; forming a respective cluster for each episode, wherein each cluster includes at least two person identifiers; determining whether one or more person identifiers are included in less than a threshold number of clusters; if it is determined that the one or more person identifiers are included in less than the threshold number of clusters, removing the one or more person identifiers from the clusters that the one or more person identifiers that are included in; after the removing, merging identical clusters to obtain a plurality of people groups, wherein each people group includes two or more person identifiers; and providing a user interface that includes an image-based creation based on a particular people group of the plurality of people groups. ### Claim 12 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein forming the respective cluster for each episode comprises: mapping all person identifiers that appear in at least one image in the set of images of the episode to the cluster; determining a number of images from the set of images of the episode that include a most frequent person identifier for the episode; and removing from the cluster person identifiers that are associated with less than a threshold number of images, the threshold number of images determined based on the number of images that include the most frequent person identifier for the episode. ### Claim 13 (depends on 11) The non-transitory computer-readable medium of claim 11, with further instructions stored thereon that cause the processor to perform operations comprising: after the merging, determining that at least one people group is associated with less than a threshold number of episodes; and in response to determination that at least one people group is associated with less than the threshold number of episodes, combining the at least one people group with one or more other people groups, wherein the one or more other people groups each include a subset of the person identifiers included in the at least one people group, and wherein providing the user interface is performed after merging the at least one people group. ### Claim 14 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein providing the image-based creation comprises: selecting a subset of images for the image-based creation based on the particular people group, wherein each image in the subset of images depicts persons that correspond to at least two of the two or more person identifiers included in the particular people group; and generating the image-based creation based on the subset of images. ### Claim 15 (depends on 11) The non-transitory computer-readable medium of claim 11, wherein merging the identical clusters comprises associating, based on the clusters, a respective set of episodes to each of the plurality of people groups, and wherein providing the image-based creation comprises selecting a subset of images for the image-based creation from the respective sets of images included in the episodes associated with the particular people group. ### Claim 16 A computing device comprising: a processor; and a memory coupled to the processor, with instructions stored thereon that, when executed by the processor, cause the processor to perform operations comprising: obtaining a plurality of episodes, wherein each episode is associated with a corresponding time period, and includes a respective set of images and person identifiers for each image in the respective set of images; forming a respective cluster for each episode, wherein each cluster includes at least two person identifiers; determining whether one or more person identifiers are included in less than a threshold number of clusters; if it is determined that the one or more person identifiers are included in less than the threshold number of clusters, removing the one or more person identifiers from the clusters that the one or more person identifiers that are included in; after the removing, merging identical clusters to obtain a plurality of people groups, wherein each people group includes two or more person identifiers; and providing a user interface that includes an image-based creation based on a particular people group of the plurality of people groups. ### Claim 17 (depends on 16) The computing device of claim 16, wherein the operation of forming the respective cluster for each episode comprises: mapping all person identifiers that appear in at least one image in the set of images of the episode to the cluster; determining a number of images from the set of images of the episode that include a most frequent person identifier for the episode; and removing from the cluster person identifiers that are associated with less than a threshold number of images, the threshold number of images determined based on the number of images that include the most frequent person identifier for the episode. ### Claim 18 (depends on 16) The computing device of claim 16, wherein the memory has further instructions stored thereon that, when executed by the processor, cause the processor to perform operations comprising: after the merging, determining that at least one people group is associated with less than a threshold number of episodes; and in response to determination that at least one people group is associated with less than the threshold number of episodes, combining the at least one people group with one or more other people groups, wherein the one or more other people groups each include a subset of the person identifiers included in the at least one people group, and wherein providing the user interface is performed after merging the at least one people group. ### Claim 19 (depends on 16) The computing device of claim 16, wherein the operation of providing the image-based creation comprises: selecting a subset of images for the image-based creation based on the particular people group, wherein each image in the subset of images depicts persons that correspond to at least two of the two or more person identifiers included in the particular people group; and generating the image-based creation based on the subset of images. ### Claim 20 (depends on 16) The computing device of claim 16, wherein the operation of merging the identical clusters comprises associating, based on the clusters, a respective set of episodes to each of the plurality of people groups, and wherein providing the image-based creation comprises selecting a subset of images for the image-based creation from the respective sets of images included in the episodes associated with the particular people group.