# US 2025/0148501 A1 · Methods and systems to improve post opportunities > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0148501 A1 - Document ID: US20250148501A1 - Status: Published application - Kind: A1 - Application number: 18/890,540 - Filed: 2024-09-19 - Published: 2025-05-08 - Earliest claimed: 2023-11-03 - Applicants: Meta Platforms, Inc. - Inventors: Gregory Vannoni; Giacomo Lami; Hongfa Huo; Shao-Huang Pin; Shourya Mukherjee; Sophie Zheng; Hari Sivakumar; Prasad Marla; Chen Liu; Alessandra Motta; Charles Liam Goudge; Samira Allahverdiyeva; Brandon Herring; Elif Tekerlekci; Hussein Fahmy; Nina Ahmed; Swapnil Subodh Mahajan - Web page: https://patentgrep.com/patent/US20250148501A1 ## CPC - G06Q 30/0244: {Optimization} - G06N 3/0475: Generative networks - G06N 3/08: Learning methods - G06N 20/00: Machine learning - G06Q 30/0276: {Advertisement creation} - 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract The present disclosure provides systems and methods for optimizing media content. One step of the method may include receiving, via a user interface, an indication of a selected post including media content associated with a user. Another step of the method may include evaluating, via a LLM model trained on training data, a quality of the media content of the selected post. A further step may include generating, via the trained LLM model and based upon the evaluated quality, a modified post including modified media content. A change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold. Even a further step may include transmitting, via the user interface, the modified post for consideration by the user. Yet even a further step may include receiving, via the user interface, an indication of a rejection or an acceptance of the modified post. ## Claims ### Claim 1 A method comprising: receiving, via a user interface, an indication of a selected post including media content associated with a user; evaluating, via a large language model (LLM) trained on training data, a quality of the media content of the selected post, wherein the training data comprises any one or more of a profile of the user, a profile of a follower of the user, or an attribute of one or more previous posts associated with the user; generating, via the trained LLM model and based upon the evaluated quality, a modified post comprising modified media content, wherein a change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold, and wherein the first type of change is more substantive than the second type of change; transmitting, via the user interface, the modified post for consideration by the user; and receiving, via the user interface, an indication of a rejection or an acceptance of the modified post. ### Claim 2 (depends on 1) The method of claim 1, further comprising: causing to perform, based upon the indication of the rejection or acceptance, a comparative test based upon the selected post and the modified post; and retraining, based upon a result of the comparative test, the LLM model. ### Claim 3 (depends on 1) The method of claim 1, wherein the profile of the user comprises any one or more of a budget, duration, location of the user or audience, target audience, or modality of an offered service. ### Claim 4 (depends on 1) The method of claim 1, wherein the attribute of one or more previous posts comprises any one or more of a heading, wording, formatting, visual or audio enhancements, audience reach, or optimal time of audience engagement. ### Claim 5 (depends on 1) The method of claim 1, wherein the selected post is based upon an existing post on a media platform. ### Claim 6 (depends on 1) The method of claim 1, wherein the first type of change comprises one or more optimized views of the media content. ### Claim 7 (depends on 6) The method of claim 6, wherein the second type of change comprises a single optimized view of the media content. ### Claim 8 (depends on 1) The method of claim 1, further comprising: receiving, via the user interface, an indication of a request to generate a new post including media content; assessing, via the trained LLM based upon any one or more of the profile of the user, an account of the user, a historical advertiser of the user, the attribute of one or more previous posts, or guidance provided by the user, the received indication of the request; generating, based upon the assessment, the new post; and transmitting, via the user interface, the new post to the user. ### Claim 9 (depends on 1) The method of claim 1, wherein the media content comprises advertisement generation data. ### Claim 10 (depends on 9) The method of claim 9, wherein the media content of the selected post comprises any one or more of a caption, an image or a video. ### Claim 11 (depends on 2) The method of claim 2, wherein the comparative test comprises an AB test. ### Claim 12 An apparatus comprising: one or more processors; and at least one memory storing instructions, that when executed by the one or more processors, cause the apparatus to: receive, via a user interface, an indication of a selected post comprising media content associated with a user; evaluate, via a large language model (LLM) trained on training data, a quality of the media content of the selected post, wherein the training data comprises any one or more of a profile of the user, a profile of a follower of the user, or an attribute of one or more previous posts associated with the user; generate, via the trained LLM model and based upon the evaluated quality, a modified post comprising modified media content, wherein a change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold; and transmit, via the user interface, the modified post for consideration by the user. ### Claim 13 (depends on 12) The apparatus of claim 12, wherein when the one or more processors further execute the instructions, the apparatus is configured to: cause to perform, based upon an indication of a rejection or an acceptance of the modified post, a comparative test based upon the selected post and the modified post; and retrain, based upon a result of the comparative test, the LLM model. ### Claim 14 (depends on 12) The apparatus of claim 12, wherein the profile of the user comprises any one or more of a budget, duration, location of the user or audience, target audience, or modality of an offered service. ### Claim 15 (depends on 12) The apparatus of claim 12, wherein the attribute of one or more previous posts comprises any one or more of a heading, wording, formatting, visual or audio enhancements, audience reach, or optimal time of audience engagement. ### Claim 16 (depends on 12) The apparatus of claim 12, wherein the selected post is based upon an existing post on a media platform. ### Claim 17 (depends on 12) The apparatus of claim 12, wherein the first type of change comprises one or more optimized views of the media content, and wherein the second type of change comprises a single optimized view of the media content. ### Claim 18 (depends on 12) The apparatus of claim 12, wherein when the one or more processors further execute the instructions, the apparatus is configured to: receive, via the user interface, an indication of a request to generate a new post comprising media content; assess, via the trained LLM based upon any one or more of the profile of the user, the attribute of one or more previous posts, or guidance provided by the user, the received indication of the request; generate, based upon the assessment, the new post; and transmit, via the user interface, the new post to the user. ### Claim 19 A non-transitory computer-readable medium storing instructions that, when executed, cause: receiving, via a user interface, an indication of a selected post comprising media content associated with a user; evaluating, via a large language model (LLM) trained on training data, a quality of the media content of the selected post, wherein the training data comprises any one or more of a profile of the user, a profile of a follower of the user, or an attribute of one or more previous posts associated with the user; generating, via the trained LLM model and based upon the evaluated quality, a modified post comprising modified media content, wherein a change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold, and wherein the first type of change is more substantive than the second type of change; transmitting, via the user interface, the modified post for consideration by the user; and receiving, via the user interface, an indication of a rejection or an acceptance of the modified post. ### Claim 20 (depends on 19) The computer-readable medium of claim 19, wherein the media content of the selected post comprises any one or more of a caption, an image or a video.