# US 12,223,555 B2 · Experience based social media platform > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,223,555 B2 - Document ID: US12223555B2 - Status: Granted - Kind: B2 - Application number: 17/741,091 - Filed: 2022-05-10 - Published: 2023-11-16 - Granted: 2025-02-11 - Earliest claimed: 2022-05-10 - Assignees: Life Enhancement Media, LLC - Applicants: Life Enhancement Media, LLC - Inventors: Stanley B. Dalton; Stanley Thomas Dalton - Web page: https://patentgrep.com/patent/US12223555B2 ## CPC - G06Q 30/0269: {based on user profile or attribute} - G06Q 10/42: Determination of affinities or common interests between users - G06Q 10/48: using social graphs - 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 An example method includes receiving, from a user device, a first challenge content for inclusion in a challenge list and identifying, based on the challenge content and selection criteria, additional challenge content suggested for inclusion in the challenge list. The method further includes displaying the additional challenge content at a user interface of the user device and updating the selection criteria based on whether the additional challenge content is selected for inclusion in the challenge list. ## Claims ### Claim 1 A method comprising: training a machine learning model to identify similar challenges, wherein predictions are based on user activity, and activity of users associated with each other, profile information; receiving, from a user device, a first challenge content for inclusion in a challenge list; identifying, via the machine learning model, based on the first challenge content and selection criteria, additional challenge content suggested for inclusion in the challenge list; displaying the additional challenge content at a user interface of the user device; updating the selection criteria based on whether the additional challenge content is selected for inclusion in the challenge list; and refining the machine learning model using feedback comprising whether the additional challenge content is selected for inclusion in the challenge list, wherein the machine learning model is refined using selections of suggested challenge content and inaction in relation to the suggested challenge content across multiple users; wherein: the first challenge content is tracked and classified using metadata, wherein the first challenge content is classified within a category; the additional challenge content is tracked and classified using the metadata, wherein the additional challenge content is classified within the category or a sub-category of the category; and the additional challenge content is identified further based on; the category of the first challenge content being related to the additional challenge content. ### Claim 2 (depends on 1) The method of claim 1, wherein the selection criteria includes one or more user preferences associated with the user device. ### Claim 3 (depends on 1) The method of claim 1, wherein the additional challenge content is selected from a database of challenge content associated with challenges publicly available to the user device. ### Claim 4 (depends on 3) The method of claim 3, wherein the selection criteria includes the additional challenge content being associated with a similar challenge list in the database of challenge content. ### Claim 5 (depends on 1) The method of claim 1, wherein identifying the additional challenge content comprises: providing the first challenge content to a machine learning model trained to identify the additional challenge content for inclusion in the challenge list based on challenge content already selected for the list. ### Claim 6 (depends on 5) The method of claim 5, wherein updating the selection criteria comprises updating the machine learning model by providing an indication of whether the additional challenge content is selected for inclusion in the challenge list to the machine learning model through a feedback loop. ### Claim 7 (depends on 1) The method of claim 1, further comprising: identifying a second additional challenge content for inclusion in the challenge list based on the first challenge content, the additional challenge content, and the updated selection criteria. ### Claim 8 (depends on 1) The method of claim 1, wherein the first challenge content comprises one or more of completion of an outdoor activity, visiting a predetermined number of tourist locations, completing an artistic activity, cooking a predetermined meal, running a marathon, visiting a predetermined number of locations of a restaurant, and reading a predetermined number of books. ### Claim 9 (depends on 1) The method of claim 1, wherein the metadata includes one or more of: geolocation information, associated costs, age restrictions, physical difficulty, contact information for an entity associated with the first challenge content or the additional challenge content, respectively, or content generated by other users who have completed the first challenge content or the additional challenge content, respectively. ### Claim 10 (depends on 3) The method of claim 3, wherein the database includes a coding system including codes associated with each classification of a set of classifications. ### Claim 11 (depends on 10) The method of claim 10, wherein: the set of classifications includes a parent category and at least one sub-category. ### Claim 12 (depends on 11) The method of claim 11, wherein: the parent category is associated with a first code, and each of the at least one sub-category is associated with a unique code; and wherein challenge content is tracked using a combination of the first code associated with the parent category and the unique code of each of the at least one subcategory.