# US 10,872,112 B2 · Automatic suggestions to share images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,872,112 B2 - Document ID: US10872112B2 - Status: Granted - Kind: B2 - Application number: 16/546,259 - Filed: 2019-08-20 - Published: 2020-02-06 - Granted: 2020-12-22 - Earliest claimed: 2017-03-31 - Assignees: Google Inc. - Applicants: Google LLC - Inventors: Jason Chang; Catherine Wah; Loren Puchalla Fiore; Teresa Ko - Web page: https://patentgrep.com/patent/US10872112B2 ## CPC - G06Q 30/02: Marketing; Price estimation or determination; Fundraising - G06F 16/51: Indexing; Data structures therefor; Storage structures - G06F 16/5838: {using colour} - G06F 18/22: Matching criteria, e.g. proximity measures - G06Q 10/42: Determination of affinities or common interests between users - G06V 20/30: in albums, collections or shared content, e.g. social network photos or video - G06V 40/172: {Classification, e.g. identification} - G06V 40/16: Human faces, e.g. facial parts, sketches or expressions - 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 - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Some implementations can include a computer-implemented method and/or system for automatic suggestions to share images containing people of importance to a user. The method can include determining, based on pixels of an image associated with a user account, one or more clusters associated with the image. The method can also include determining a share probability score for the image based on a probabilistic model and determining that the share probability score meets a threshold. The method can further include, in response to determining that the share probability score meets the threshold, providing a suggestion to a user associated with the user account to share the image. ## Claims ### Claim 1 A computer-implemented method comprising: determining a top percentile cluster associated with a user account, wherein a first likelihood that images that include one or more people that are in the top percentile cluster are shared is higher than a second likelihood that images that do not include the one or more people that are in the top percentile cluster are shared; determining that a face in a particular image corresponds to the one or more people that are in the top percentile cluster; determining a share probability score that indicates a probability the particular image will be shared based on a probabilistic model, wherein the probabilistic model is based on an account size of the user account and the top percentile cluster; determining that the share probability score meets a threshold value; and in response to determining that the share probability score meets the threshold value, providing a suggestion to a user associated with the user account to share the particular image. ### Claim 2 (depends on 1) The method of claim 1, wherein the account size includes a total number of images. ### Claim 3 (depends on 1) The method of claim 1, wherein the probabilistic model is further based on one or more of a total number of images associated with the user account, a number of clusters associated with the user account, or a number of clusters that have a name label provided by the user. ### Claim 4 (depends on 1) The method of claim 1, wherein the probabilistic model is further based on a probability that the user shares any image, a probability that a shared image includes at least one person that is in the one or more people that are in the top percentile cluster, and a probability that any image includes at least one person that is in the one or more people that are in the top percentile cluster. ### Claim 5 (depends on 1) The method of claim 1, wherein: the top percentile cluster is a person cluster with a ranking that meets a ranking percentile threshold; and the ranking is based on at least one of face quality, a number of images associated with the user account, a recency of images in a set of clusters, a count of time periods of the set of clusters, and whether a person in an image was assigned a name label by the user. ### Claim 6 (depends on 1) The method of claim 1, wherein the face is a dominant face in the particular image, and further comprising identifying that the face is the dominant face in the particular image by applying a facial recognition technique to the particular image. ### Claim 7 (depends on 1) The method of claim 1, further comprising ranking a set of person clusters, wherein determining the top percentile cluster is based on identifying a top ranked person cluster from the set of person clusters. ### Claim 8 A system comprising: one or more processors; and a memory that stores instructions that, when executed by the one or more processors cause the one or more processors to perform operations comprising: determining a top percentile cluster associated with a user account, wherein a first likelihood that images that include one or more people that are in the top percentile cluster are shared is higher than a second likelihood that images that do not include the one or more people that are in the top percentile cluster are shared; determining that a face in a particular image corresponds to the one or more people that are in the top percentile cluster; determining a share probability score that indicates a probability the particular image will be shared based on a probabilistic model, wherein the probabilistic model is based on an account size of the user account and the top percentile cluster; determining that the share probability score meets a threshold value; and in response to determining that the share probability score meets the threshold value, providing a suggestion to a user associated with the user account to share the particular image. ### Claim 9 (depends on 8) The system of claim 8, wherein the account size includes a total number of images. ### Claim 10 (depends on 8) The system of claim 8, wherein the probabilistic model is further based on one or more of a total number of images associated with the user account, a number of clusters associated with the user account, or a number of clusters that have a name label provided by the user. ### Claim 11 (depends on 8) The system of claim 8, wherein the probabilistic model is further based on a probability that the user shares any image, a probability that a shared image includes at least one person that is in the one or more people that are in the top percentile cluster, and a probability that any image includes at least one person that is in the one or more people that are in the top percentile cluster. ### Claim 12 (depends on 8) The system of claim 8, wherein: the top percentile cluster is a person cluster with a ranking that meets a ranking percentile threshold; and the ranking is based on at least one of face quality, a number of images associated with the user account, a recency of images in a set of clusters, a count of time periods of the set of clusters, and whether a person in an image was assigned a name label by the user. ### Claim 13 (depends on 8) The system of claim 8, wherein the face is a dominant face in the particular image, and further comprising identifying that the face is the dominant face in the particular image by applying a facial recognition technique to the particular image. ### Claim 14 (depends on 8) The system of claim 8, wherein the operations further comprise ranking a set of person clusters, wherein determining the top percentile cluster is based on identifying a top ranked person cluster from the set of person clusters. ### Claim 15 A non-transitory computer-storage medium encoded with a computer program, the computer program comprising instructions that, when executed by one or more computers, cause the one or more computers generate a video by performing operations comprising: determining a top percentile cluster associated with a user account, wherein a first likelihood that images that include one or more people that are in the top percentile cluster are shared is higher than a second likelihood that images that do not include the one or more people that are in the top percentile cluster are shared; determining that a face in a particular image corresponds to the one or more people that are in the top percentile cluster; determining a share probability score that indicates a probability the particular image will be shared based on a probabilistic model, wherein the probabilistic model is based on an account size of the user account and the top percentile cluster; determining that the share probability score meets a threshold value; and in response to determining that the share probability score meets the threshold value, providing a suggestion to a user associated with the user account to share the particular image. ### Claim 16 (depends on 15) The computer-storage medium of claim 15, wherein the account size includes a total number of images. ### Claim 17 (depends on 15) The computer-storage medium of claim 15, wherein the probabilistic model is further based on one or more of a total number of images associated with the user account, a number of clusters associated with the user account, or a number of clusters that have a name label provided by the user. ### Claim 18 (depends on 15) The computer-storage medium of claim 15, wherein the probabilistic model is further based on a probability that the user shares any image, a probability that a shared image includes at least one person that is in the one or more people that are in the top percentile cluster, and a probability that any image includes at least one person that is in the one or more people that are in the top percentile cluster. ### Claim 19 (depends on 15) The computer-storage medium of claim 15, wherein: the top percentile cluster is a person cluster with a ranking that meets a ranking percentile threshold; and the ranking is based on at least one of face quality, a number of images associated with the user account, a recency of images in a set of clusters, a count of time periods of the set of clusters, and whether a person in an image was assigned a name label by the user. ### Claim 20 (depends on 15) The computer-storage of claim 15, wherein the face is a dominant face in the particular image, and further comprising identifying that the face is the dominant face in the particular image by applying a facial recognition technique to the particular image.