# US 12,604,051 B2 · Methods and systems for generating a multiple user profile > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,604,051 B2 - Document ID: US12604051B2 - Status: Granted - Kind: B2 - Application number: 18/472,630 - Filed: 2023-09-22 - Published: 2025-03-27 - Granted: 2026-04-14 - Earliest claimed: 2023-09-22 - Assignees: DISH Network L.L.C. - Applicants: DISH Network L.L.C. - Inventors: Levi Boscardin; Erik Nava - Web page: https://patentgrep.com/patent/US12604051B2 ## CPC - H04N 21/252: {Processing of multiple end-users' preferences to derive collaborative data} - H04N 21/25891: {being end-user preferences (retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18)} - H04N 21/4668: {for recommending content, e.g. movies} - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract The present disclosure is directed to methods and systems generating a multiple user profile. The profile system can create a multiple user profile based on metadata associate with two or more user profiles. The multiple user profile can include media content for the users to consume together based on shared attributes between the individual profiles of the users. The profile system can determine profiles that have similar attributes and send a recommendation for the users to join a group profile. ## Claims ### Claim 1 A method comprising: receiving, from a first user device associated with a first user, a request for a recommendation of a user to consume content together with the first user; determining a first set of attributes of media content consumed by the first user associated with a first user profile; determining a second set of attributes of media content consumed by a second user associated with a second user profile; assigning a first score to a type of media content consumed by the first user based on the first set of attributes; assigning a second score to the type of media content consumed by the second user based on the second set of attributes; in response to the first score being within a threshold difference of the second score, generating a multiple user profile for the first user and the second user to access media content together, wherein the multiple user profile includes the type of media content, identify at least one media content item that has at least one common attribute among the first user profile and the second user profile, and sending a notification to the first user device to recommend the first user consume content together with the second user, wherein the notification includes the at least one media content item. ### Claim 2 (depends on 1) The method of claim 1, further comprising: generating, based on at least one attribute of the multiple user profile, a recommendation of media content for the first user profile or the second user profile; and sending the recommendation to the first user profile or the second user profile. ### Claim 3 (depends on 1) The method of claim 1, further comprising: determining a third set of attributes of media content consumed by a third user associated with a third user profile; assigning a third score to the type of media content consumed by the third user based on the third set of attributes; in response to the first score being with the threshold difference of the third score, generating a recommendation for the first user to consume content with the third user; and sending, to the first user and the third user, the recommendation to consume content together. ### Claim 4 (depends on 1) The method of claim 1, further comprising: sending, from the first user profile to the second user profile, a recommendation for the second user profile to consume a media content item. ### Claim 5 (depends on 1) The method of claim 1, further comprising: receiving a request for a content item recommendation for three or more users to consume together; analyzing three or more user profiles associated with the three or more users to identify at least one media content item that has at least one common attributed among the three or more user profiles; and sending, to at least one profile of the three or more user profiles, a recommendation of the at least one media content item. ### Claim 6 (depends on 1) The method of claim 1, wherein the first score is based on a duration the first user consumes the type of media content, a frequency that the first user consumes the type of media content, or feedback from the first user regarding the type of media content. ### Claim 7 (depends on 1) The method of claim 1, wherein the multiple user profile is generated by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with previously generated multiple user profiles. ### Claim 8 A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising: receiving, from a first user device associated with a first user, a request for a recommendation of a user to consume content together with the first user; determining a first set of attributes of media content consumed by the first user associated with a first user profile; determining a second set of attributes of media content consumed by a second user associated with a second user profile; assigning a first score to a type of media content consumed by the first user based on the first set of attributes; assigning a second score to the type of media content consumed by the second user based on the second set of attributes; in response to the first score being within a threshold difference of the second score, generating a multiple user profile for the first user and the second user to access media content together, wherein the multiple user profile includes the type of media content, identify at least one media content item that has at least one common attribute among the first user profile and the second user profile, and sending a notification to the first user device to recommend the first user consume content together with the second user, wherein the notification includes the at least one media content item. ### Claim 9 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the operations further comprise: generating, based on at least one attribute of the multiple user profile, a recommendation of media content for the first user profile or the second user profile; and sending the recommendation to the first user profile or the second user profile. ### Claim 10 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the operations further comprise: determining a third set of attributes of media content consumed by a third user associated with a third user profile; assigning a third score to the type of media content consumed by the third user based on the third set of attributes; in response to the first score being with the threshold difference of the third score, generating a recommendation for the first user to consume content with the third user; and sending, to the first user and the third user, the recommendation to consume content together. ### Claim 11 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the operations further comprise: sending, from the first user profile to the second user profile, a recommendation for the second user profile to consume a media content item. ### Claim 12 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the operations further comprise: receiving a request for a content item recommendation for three or more users to consume together; analyzing three or more user profiles associated with the three or more users to identify at least one media content item that has at least one common attributed among the three or more user profiles; and sending, to at least one profile of the three or more user profiles, a recommendation of the at least one media content item. ### Claim 13 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the first score is based on a duration the first user consumes the type of media content, a frequency that the first user consumes the type of media content, or feedback from the first user regarding the type of media content. ### Claim 14 (depends on 8) The non-transitory computer-readable medium of claim 8, wherein the multiple user profile is generated by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with previously generated multiple user profiles. ### Claim 15 A system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform a process comprising: receiving, from a first user device associated with a first user, a request for a recommendation of a user to consume content together with the first user; determining a first set of attributes of media content consumed by the first user associated with a first user profile; determining a second set of attributes of media content consumed by a second user associated with a second user profile; assigning a first score to a type of media content consumed by the first user based on the first set of attributes; assigning a second score to the type of media content consumed by the second user based on the second set of attributes; in response to the first score being within a threshold difference of the second score, generating a multiple user profile for the first user and the second user to access media content together, wherein the multiple user profile includes the type of media content, identify at least one media content item that has at least one common attribute among the first user profile and the second user profile, and sending a notification to the first user device to recommend the first user consume content together with the second user, wherein the notification includes the at least one media content item. ### Claim 16 (depends on 15) The system according to claim 15, wherein the process further comprises: generating, based on at least one attribute of the multiple user profile, a recommendation of media content for the first user profile or the second user profile; and sending the recommendation to the first user profile or the second user profile. ### Claim 17 (depends on 15) The system according to claim 15, wherein the process further comprises: determining a third set of attributes of media content consumed by a third user associated with a third user profile; assigning a third score to the type of media content consumed by the third user based on the third set of attributes; in response to the first score being with the threshold difference of the third score, generating a recommendation for the first user to consume content with the third user; and sending, to the first user and the third user, the recommendation to consume content together. ### Claim 18 (depends on 15) The system according to claim 15, wherein the process further comprises: sending, from the first user profile to the second user profile, a recommendation for the second user profile to consume a media content item. ### Claim 19 (depends on 15) The system according to claim 15, wherein the process further comprises: receiving a request for a content item recommendation for three or more users to consume together; analyzing three or more user profiles associated with the three or more users to identify at least one media content item that has at least one common attributed among the three or more user profiles; and sending, to at least one profile of the three or more user profiles, a recommendation of the at least one media content item. ### Claim 20 (depends on 15) The system according to claim 15, wherein the multiple user profile is generated by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with previously generated multiple user profiles.